A substation optical cable panoramic management and control method and system based on three-dimensional modeling

Abstract

This application discloses a method and system for panoramic management and control of substation optical cables based on 3D modeling in the field of 3D modeling application technology for power transmission and transformation engineering. The system includes an optical cable fault location efficiency judgment module, an optical cable fault location demand classification module, and an optical cable area scaling and control module. This application locates faults in each optical cable using a panoramic model of the substation's optical cables, and quantitatively analyzes the efficiency of scaling the panoramic model to determine the qualification of optical cable fault location efficiency. It also classifies the fault location demand for each optical cable area. Finally, by statistically analyzing the qualification of optical cable fault location efficiency based on the panoramic model meeting stable characteristic conditions within a time window, it performs optical cable area scaling judgment and control optimization, solving the problem in existing technologies where large scaling spans lead to reduced efficiency after optical cable fault location. This achieves the technical effect of improving the efficiency of optical cable fault location.

Description

Technical Field

[0001] This application relates to the field of 3D modeling application technology for power transmission and transformation engineering, and is particularly applicable to a panoramic management and control method and system for optical cables in substations based on 3D modeling. Background Technology

[0002] For smart substations that meet the requirements of power automation communication networks and systems, panoramic modeling of the substation is typically required, and optical fiber cables are a crucial component of cable lines. The basic data sources for data acquisition and modeling include Building Information Modeling (BIM), laser scanning, and Geographic Information System (GIS) data. BIM uses computer-aided design (CAD) drawings and BIM modeling software such as Revit and Bentley to digitally model the substation's main equipment, trenches, cable trays, and optical fiber routes. Laser scanning acquires point cloud data of the substation scene, which is then used to generate high-precision 3D models using modeling software such as ContextCapture and CloudCompare. GIS data is correlated with the geographical environment outside the substation area and the route alignment to ensure the panoramic control system has spatial coordinate positioning capabilities. Optical fiber cables are modeled in the 3D model according to their actual cabling paths. Nodes such as joints, splices, and cabinets are abstracted as topological points, and a BIM-GIS fusion approach is used to bind the optical fiber cables to geographic coordinates and equipment attributes, ensuring accurate positioning later. Data storage and topology management primarily store optical cable resource information such as model, length, fiber core number, and splice relationship in structured databases like Oracle and PostgreSQL+PostGIS. It also stores 3D model data in formats readable by 3D engines, such as glTF, IFC, and 3D Tiles. Simultaneously, it utilizes graph databases like Neo4j or dedicated network management modules to maintain the logical relationships between optical cables, fiber cores, ports, and devices, enabling path tracing, such as quickly locating affected protection channels or devices when a fiber is broken. 3D visualization is implemented using 3D engines like WebGL / Three.js or Unity / Unreal as the visualization front end, enabling panoramic roaming, layered display, topology linkage, and alarm location. Monitoring and operation and maintenance integration includes real-time monitoring and operation and maintenance management. Real-time monitoring links the DTS distributed fiber optic temperature monitoring system and the OTDR fiber optic fault detection system with the 3D model. When a fiber break / attenuation anomaly occurs, the system automatically marks the alarm location in the 3D model. Operation and maintenance management allows personnel to retrieve the corresponding optical cable's 3D information and topology by scanning QR codes or RFID tags on-site via mobile terminals or AR glasses.

[0003] Patent document CN114494630A discloses a method and system for intelligent safety management and control of substation infrastructure based on precise positioning technology, which forms a visualized 3D model based on a 3D basic model and information database. Patent document CN119672233A discloses a method and system for safety management and control of substations based on a 3D reconstruction model, which uses 3D point cloud data to monitor and warn of substation equipment. However, in these solutions, the 3D model is mainly used as a visualization background and spatial coordinate reference, and there is a lack of consideration for the efficiency of internal interaction of the model. Because the model itself may become a performance bottleneck, they are all difficult to monitor and optimize the model interaction performance, thus making it difficult to improve the efficiency of optical cable operation and maintenance. Due to the large number of optical cables and the complex trenches in substations, the 3D model can easily become cluttered, making it difficult to quickly locate the target optical cable. This may be due to differences in spatial scale. The 3D model needs to display the overall view of the substation area while also being detailed down to the fiber core level of each optical cable, resulting in a large range of scaling levels. This reduces the efficiency of browsing the panoramic 3D model of the substation's optical cables, and consequently, the efficiency of checking after locating optical cable faults. Typical scaling performance optimizations target rendering performance and visual fidelity. For example, patent document CN119963723A discloses a method, device, storage medium, and electronic device for processing 3D rendering models. Although it also focuses on underlying performance, it cannot solve the problems of interaction stuttering and low positioning efficiency caused by large scaling spans of complex 3D models in the specific business scenario of substation optical cable operation and maintenance, thereby improving the overall system performance. Summary of the Invention

[0004] In view of this, and in response to the problems of the prior art, this application provides a method and system for panoramic management and control of optical cables in substations based on 3D modeling, which aims to solve the technical problem that the efficiency of viewing optical cable fault location is reduced due to the large scaling span in the prior art.

[0005] To achieve the above objectives, this application provides a method for panoramic management and control of optical cables in substations based on 3D modeling, comprising the following steps: S1 uses a panoramic model of the substation optical cable that visualizes the optical cable and fiber core relationship of the specified substation in three dimensions to locate the faults of each optical cable. At the same time, it performs a quantitative analysis of the efficiency when scaling the panoramic model of the substation optical cable, then judges the qualification of the optical cable fault location efficiency and visually marks the scaled area. S2 performs fault location requirement classification for each optical cable area in the panoramic model of substation optical cable; S3 uses the statistical analysis results of the optical cable panoramic model to determine the qualification of optical cable fault location efficiency within a time window that meets the stable characteristic conditions, and then performs optical cable area scaling judgment and control optimization.

[0006] Preferably, the quantitative analysis includes the step of obtaining a scaling efficiency measure; This step first obtains the perceived utility data before and after scaling, and then normalizes the data to obtain the perceived utility value before scaling and the perceived utility value after scaling. The perceived utility data includes information density and perceived screen error. This step then acquires resource data and performs curve quantization to obtain the corresponding resource mapping quantity. The resource data includes loading response latency time, network data transmission volume, newly added video memory, and historical switching frequency. The resource mapping quantity includes loading response latency time mapping quantity, network data transmission volume mapping quantity, newly added video memory mapping quantity, and historical switching frequency. This step then queries the resource weights corresponding to the resource data and performs a weighted fusion with each resource mapping quantity to obtain the corresponding resource quantification value. The resource weights include loading response latency weight, network data transmission volume weight, newly added video memory weight, and historical switching frequency weight. Finally, this step performs differential quantification on the perceived utility value after scaling and the perceived utility value before scaling. The result is then compared with the resource quantification value and the extracted numerical stability constant to obtain a scaling efficiency measure.

[0007] Preferably, the step of determining the pass / failability of the optical cable fault location efficiency and visually marking the zoomed area is as follows: If the scaling efficiency measurement value is not less than the preset scaling efficiency judgment value, the scaling efficiency is determined to meet the preset standard. The scaling efficiency deviation between the scaling efficiency measurement value and the scaling efficiency judgment value is obtained, and the scaling efficiency deviation is compared with the preset scaling efficiency grade value. If the scaling efficiency deviation is greater than the scaling efficiency grade value, the corresponding scaling area is marked as a smooth loading area. If the scaling efficiency deviation is not greater than the scaling efficiency grade value, the corresponding scaling area is marked as a pre-adjustment area. If the scaling efficiency measurement value is less than the scaling efficiency judgment value, it is determined that the scaling efficiency does not meet the preset standard, and the corresponding scaling area is marked as a scaling optimization area.

[0008] Preferably, the final step of determining the pass / failability of optical cable fault location efficiency and visually marking the zoomed area may further include the following steps: If the same optical cable area is scaled more than once and the marking results are different, the optical cable area is marked as a composite marking area. The composite marking area is continuously monitored within a time window by a preset number of optical cable panoramic models. If the composite marking area is marked as a scaling optimization area, the composite marking area is marked as a scaling optimization area. If the composite marking area is continuously marked as a pre-adjustment area, the composite marking area is marked as a pre-adjustment area. If the composite marking area is continuously marked as a smooth loading area, the composite marking area is marked as a smooth loading area. If the marking results of the composite marking regions are different, the marking times of the smooth loading region, the pre-adjustment region, and the scaling optimization region are counted separately to obtain the corresponding number of times for the smooth loading region, the pre-adjustment region, and the scaling optimization region. The obtained data is judged as follows: if the number of times for the scaling optimization region is the largest, scaling efficiency control measures are implemented for the composite marking region; if the number of times for the pre-adjustment region is the largest, pre-adjustment measures for the optical cable region are implemented; if the number of times for the smooth loading region is the largest, the efficiency of the next scaling of the substation optical cable panoramic model is monitored; if there are equal marking times among the number of times for the smooth loading region, the pre-adjustment region, and the scaling optimization region, the corresponding execution measures are determined based on the priority order of the scaling optimization region, the pre-adjustment region, and the smooth loading region.

[0009] Preferably, the fault location requirement classification includes the following steps: Each optical cable region is initially marked with an importance rating to obtain a corresponding initial importance level, which is used to characterize the importance of the optical cable region. The impact of optical cable fault location is quantified based on the initial importance level of each optical cable area and the reference fault data queried. The reference fault data includes the number of fault points and the frequency of faults. The impact of locating the optical cable fault area is compared with a pre-set optical cable fault location demand level comparison table to obtain the optical cable fault location demand level for the corresponding optical cable area, which is used to characterize the demand for optical cable fault location inspection.

[0010] Preferably, the analysis results include the scaling efficiency pass rate and the percentage of marked areas; The steps for obtaining the scaling efficiency qualification ratio are as follows: Within a preset time window of the optical cable panoramic model, the total number of scaling times and the number of times the scaling efficiency is qualified for the substation optical cable panoramic model are counted, as well as the volume of the smoothly loading area, the volume of the scaling optimized area, the volume of the area to be adjusted, and the total volume of the scaling viewing area. The number of times the scaling efficiency is qualified is the number of times the scaling efficiency measurement value is not less than the scaling efficiency judgment value within the time window of the optical cable panoramic model, and the number of times the scaling efficiency is not standard is the number of times the scaling efficiency measurement value is less than the scaling efficiency judgment value. The scaling efficiency qualification ratio is obtained by proportionally quantifying the scaling efficiency qualification ratio to the total number of scaling times. The steps for obtaining the percentage of the marked region are as follows: the volume of the smooth loading region, the volume of the scaling-optimized region, and the volume of the pre-adjustment region are respectively quantified by the total scaling and viewing region volume to obtain the corresponding percentage of the marked region. The percentage of the marked region includes the percentage of the smooth loading region, the percentage of the scaling-optimized region, and the percentage of the pre-adjustment region.

[0011] Preferably, the optical cable area scaling judgment and control optimization includes the following steps: If the scaling efficiency qualification rate is higher than or equal to the pre-set scaling efficiency qualification rate threshold, then continue to monitor the scaling efficiency of the next substation optical cable panoramic model within the time window. If the scaling efficiency qualification ratio is lower than the pre-set scaling efficiency qualification ratio threshold, scaling efficiency control measures are implemented in the scaling optimization area, and optical cable positioning optimization adjustment and area volume ratio analysis are performed simultaneously. The optical cable positioning optimization adjustment is used to determine the optimization order of each scaling optimization area, and the area volume ratio analysis is used to determine whether to take pre-control measures for the optical cable area in the pre-adjustment area.

[0012] Preferably, the optical cable positioning optimization adjustment includes the following steps: sorting the optical cable fault positioning requirement levels of each scaling optimization area, and performing scaling efficiency control measures on each scaling optimization area in sequence. If the optical cable fault positioning requirement levels are the same, the optical cable positioning optimization order is determined by comparing the number of times the scaling efficiency of the scaling optimization area is qualified within a preset number of optical cable panoramic model time windows. If the number of times the scaling efficiency is qualified is the same, the optical cable positioning optimization order is determined by comparing the volume of the scaling optimization area. The area volume ratio analysis includes the following steps: quantifying the difference between the proportion of the loading smooth area and the proportion of the pre-adjustment area to obtain the corresponding marked difference amount. If the marked difference amount is greater than the initially set difference boundary value, the pre-adjustment optical cable area measures are not performed on the pre-adjustment area. If the marked difference amount is not greater than the difference boundary value, the pre-adjustment optical cable area measures are performed on the pre-adjustment area.

[0013] Preferably, the scaling efficiency control measures include the following steps: quantifying the scaling optimization amount based on the volume of the scaling optimization region and the corresponding number of times the scaling optimization region is marked, projecting the scaling optimization impact amount, and optimizing and compensating the scaling optimization impact amount with the loading resource limit parameters, the loading resource limit parameters including the upper limit of video memory usage, cache size, and maximum number of concurrent requests; the pre-adjustment of the optical cable area measures include the following steps: quantizing the pre-adjustment value based on the volume of the pre-adjustment region and the corresponding number of times the pre-adjustment region is marked, mapping the pre-adjustment range, performing pre-response caching on all pre-adjustment regions according to the pre-adjustment range, and comparing the pre-adjustment value with the pre-designed optical cable fault location requirement level upgrade boundary value. If the pre-adjustment value is not less than the optical cable fault location requirement level upgrade boundary value, the optical cable fault location requirement level of the corresponding pre-adjustment region is increased by one level; otherwise, no additional processing is performed.

[0014] Based on the same inventive concept, this application also provides a substation optical cable panoramic management and control system based on three-dimensional modeling, used to implement the aforementioned substation optical cable panoramic management and control method based on three-dimensional modeling, including an optical cable fault location efficiency determination module, an optical cable fault location demand classification module, and an optical cable area scaling and control module. The optical cable fault location efficiency determination module is configured to execute step S1, the optical cable fault location demand classification module is configured to execute step S2, and the optical cable area scaling and control module is configured to execute step S3.

[0015] The beneficial effects of this application are as follows: the substation optical cable panoramic model visualizes the relationship between optical cables and fiber cores in three dimensions, which realizes the intuitive presentation of complex optical cable and fiber core topology through three-dimensional visualization, reduces the operation and maintenance understanding cost, and the real-time monitoring model can locate faults in each optical cable and improve the alarm response speed. This solution's substation fiber optic cable panoramic management method and system, based on 3D modeling, performs real-time quantitative analysis of the scaling efficiency of the substation fiber optic cable panoramic model and determines the efficiency qualification of fiber optic cable fault location and inspection. This allows for more accurate monitoring of the scaling status of the substation fiber optic cable panoramic model, providing a data foundation for scaling performance control. Next, the fault location requirements of each fiber optic cable area in the substation fiber optic cable panoramic model are categorized. This not only helps to initialize the importance of fault location in each fiber optic cable area within the substation fiber optic cable panoramic model but also prioritizes the modeling accuracy and loading speed of key fiber optic cable areas. Finally, by statistically analyzing the qualification of fiber optic cable fault location efficiency that meets the stability characteristics of the fiber optic cable panoramic model within a time window, it helps to continuously improve scaling efficiency and fault location efficiency. Simultaneously, the optimization of fiber optic cable area scaling judgment and control not only improves the efficiency of fiber optic cable fault location and inspection but also effectively solves the problem of reduced efficiency after fiber optic cable fault location and inspection due to large scaling spans in existing technologies. Visual markers for zoomed areas provide a more intuitive view of the loading performance of each region. When the zoom efficiency measurement meets the preset standard, areas with high zoom efficiency are marked as smooth and maintained as is, based on the zoom efficiency deviation. Areas with medium zoom efficiency are marked as requiring adjustment, as these areas may experience performance risks under high loads, facilitating subsequent optimization. When the zoom efficiency does not meet the preset standard, it is marked as requiring zoom optimization, clearly identifying the problem areas that need optimization. Through the aforementioned hierarchical processing and visualization, not only is the smoothness of the zooming process improved, but the efficiency of fiber optic cable fault location is also enhanced. Statistical analysis of the visualization results allows for continued monitoring of the zoom efficiency within the next substation fiber optic cable panoramic model time window when the impact on fiber optic cable fault location is minor, ensuring continuous monitoring of substation fiber optic cable fault location. When the impact on fiber optic cable fault location is significant, zoom efficiency control measures are implemented in the zoom optimization area, and fiber optic cable location optimization adjustments are performed simultaneously to clarify the optimization order. This facilitates hierarchical optimization, addressing the most critical issues first, and analyzing the region volume ratio to intervene in potential problems in advance, thereby taking pre-control measures to help avoid large-scale lag in the 3D model later. In summary, this application ensures the smoothness and real-time performance of scaling operations through quantitative analysis of scaling efficiency metrics and dynamic control of marked smooth loading regions and optimized regions, providing an optimization engine for the efficient operation of the fiber core automatic search algorithm.This engine goes beyond passively optimizing performance; it also possesses proactive, business-demand-based intelligent scheduling capabilities. Its demand-level fusion strategy works in conjunction with the fiber optic cable fault location demand-level module. When allocating computing resources for tasks such as preloading and LOD adjustment, it prioritizes the performance of high-importance fiber optic cable areas, prioritizing or quickly loading and rendering areas with a greater impact from faults. This not only speeds up the search process but also improves it more intelligently, prioritizing critical tasks and enhancing overall operational efficiency. This effectively overcomes performance bottlenecks arising from high-frequency, deep interactions with complex 3D models. Attached Figure Description

[0016] To illustrate the objectives and technical solutions of this application, the present invention provides the following accompanying drawings: Figure 1 This is a schematic diagram of the overall process of an embodiment of the substation optical cable panoramic management and control method based on 3D modeling of this application; Figure 2 This is a flowchart illustrating the qualification determination of optical cable fault location efficiency in one embodiment of the substation optical cable panoramic management and control method based on 3D modeling of this application. Figure 3 This is a schematic diagram of the process for analyzing the regional volume proportion in one embodiment of the substation optical cable panoramic management method based on 3D modeling in this application. Figure 4 This is a schematic diagram illustrating the composition of an embodiment of the substation optical cable panoramic control system based on 3D modeling according to this application. Detailed Implementation

[0017] To make the objectives and technical solutions of this application clearer, the application will be described in detail below with reference to the accompanying drawings and embodiments.

[0018] In the embodiments of this application, words such as "exemplarily" and "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as an "example" in this application should not be construed as being better or more advantageous than other embodiments or design options. Specifically, the use of the word "example" is intended to present the concept in a concrete manner. Furthermore, in the embodiments of this application, the meaning expressed by "and / or" can be both, or it can be either one or the other.

[0019] In the embodiments of this application, "of", "corresponding (relevant)" and "corresponding" can sometimes be used interchangeably. It should be noted that when their differences are not emphasized, their meanings are consistent.

[0020] Figure 1 This is a flowchart illustrating a panoramic management and control method for substation optical cables based on 3D modeling, provided in an embodiment of this application. (Refer to...) Figure 1 The specific steps are as follows: By creating a 3D visualization of the optical cables and fiber cores of a designated substation, a panoramic model of the substation optical cables is used to locate faults in each cable. At the same time, the efficiency of scaling the panoramic model of the substation optical cables is quantitatively analyzed to determine the qualification of the optical cable fault location efficiency, thereby more accurately monitoring the scaling of the panoramic model of the substation optical cables.

[0021] The fault location requirement is classified for each optical cable area in the panoramic model of the substation optical cable to initialize the importance of fault location for each optical cable area in the specified substation.

[0022] By analyzing the statistical results of the optical cable panoramic model meeting the stability characteristics within a time window, the efficiency of optical cable fault location is improved through optical cable area scaling judgment and control optimization.

[0023] like Figure 2 The diagram shows a flowchart illustrating the qualification determination of optical cable fault location efficiency provided in this application embodiment. The specific logic is as follows: The scaling efficiency measurement value is compared with a preset scaling efficiency judgment value. If the scaling efficiency measurement value is not less than the preset scaling efficiency judgment value, the scaling efficiency deviation between the scaling efficiency measurement value and the scaling efficiency judgment value is obtained. The scaling efficiency deviation is then compared with a preset scaling efficiency grading value to visualize and mark the scaling area. Specifically, if the scaling efficiency deviation is greater than the scaling efficiency grading value, the corresponding scaling area is marked as a smooth loading area. If the scaling efficiency deviation is not greater than the scaling efficiency grading value, the corresponding scaling area is marked as a pre-adjustment area. The scaling efficiency measurement value is used to quantify the scaling effect during the process of locating optical cable faults in the panoramic model of substation optical cables. If the scaling efficiency measurement value is less than the scaling efficiency judgment value, it indicates that the scaling efficiency does not meet the preset standard, and the corresponding scaling area is marked as a scaling optimization area. Through the above process, not only is the visualization effect of the 3D model enriched, but the accuracy and efficiency of substation optical cable fault location are also ensured.

[0024] The evaluation of the efficiency of optical cable fault location includes two specific scenarios: In the first scenario, if the scaling efficiency measurement value is not less than the preset scaling efficiency judgment value, it indicates that the scaling efficiency meets the preset standard. The scaling efficiency deviation between the scaling efficiency measurement value and the scaling efficiency judgment value is then obtained, and compared with the preset scaling efficiency grading value for visual labeling of the scaling area. Specifically: if the scaling efficiency deviation is greater than the scaling efficiency grading value, it indicates that the scaling efficiency of the corresponding scaling area is high, and the corresponding scaling area is marked as a smooth loading area. If the scaling efficiency deviation is not greater than the scaling efficiency grading value, it indicates that the scaling efficiency level of the corresponding scaling area is medium, and the corresponding scaling area is marked as a pre-adjustment area. The scaling efficiency measurement value is used to quantify the scaling effect during the process of locating optical cable faults in the panoramic model of optical cables in substations.

[0025] It should be added that the scaling efficiency deviation is the ratio of the difference between the scaling efficiency measurement value and the scaling efficiency judgment value to the scaling efficiency judgment value; at the same time, the scaling efficiency grading value is extracted from the preset database and is preset and stored in the preset database by preset staff.

[0026] In the second case, if the scaling efficiency measurement value is less than the scaling efficiency judgment value, it means that the scaling efficiency does not meet the preset standard, and the corresponding scaling area is marked as the scaling optimization area.

[0027] In this embodiment, "scaling efficiency" is transformed into a calculable metric, avoiding the problems of relying on human experience or subjective judgment in the past. By classifying the loading smooth area, the pre-adjustment area, and the scaling optimization area, a hierarchical scaling performance evaluation system is established. This hierarchical mechanism can not only detect current performance deficiencies but also identify "medium performance areas," providing early warnings of potential risks. It has a forward-looking and preventative nature. Through dynamic judgment and area marking, the system can promptly detect scaling efficiency problems and take different optimization or monitoring measures according to the classification. This ensures that the optical cable panoramic model maintains high scaling efficiency and smooth interaction during operation and maintenance, ultimately improving the speed and accuracy of optical cable fault location. Through quantitative evaluation, hierarchical marking, and visualization, not only is a scientific judgment of scaling efficiency achieved, but user experience is also improved, potential problems are detected in advance, and subsequent optimization is supported, thereby effectively ensuring the accuracy and efficiency of substation optical cable fault location.

[0028] The qualification assessment of optical cable fault location efficiency also includes: If the same optical cable area is scaled more than once and the marking results are different, it means that the stability characteristic condition is not met, and the optical cable area is marked as a composite marking area; where the stability characteristic condition means that the marking results of the optical cable panoramic model are the same within the time window.

[0029] Within a time window, a preset number of fiber optic panoramic models continuously monitor the composite marked areas. If all composite marked areas are marked as scaling optimization areas, then the composite marked areas are marked as scaling optimization areas. If the composite marked areas are continuously marked as areas to be adjusted, then the composite marked areas are marked as areas to be adjusted. If the composite marked areas are continuously marked as areas with smooth loading, then the composite marked areas are marked as areas with smooth loading. The preset number is pre-set data that can be extracted from a preset database.

[0030] If the marking results of the composite marking regions are different, the number of markings for the smooth loading region, the pre-adjustment region, and the scaling optimization region are counted separately to obtain the corresponding number of times for the smooth loading region, the pre-adjustment region, and the scaling optimization region.

[0031] The obtained data is evaluated as follows: if the number of times the scaling optimization area is the largest, the scaling efficiency control scheme is implemented for the composite marked area; if the number of times the pre-adjustment area is the largest, the pre-adjustment measures for the optical cable area are implemented; if the number of times the loading smooth area is the largest, the efficiency of the next scaling of the substation optical cable panoramic model is monitored; if there are equal marks among the number of times the loading smooth area, the number of times the pre-adjustment area, and the number of times the scaling optimization area is selected, the corresponding execution measures are determined based on the priority order of the set scaling optimization area, pre-adjustment area, and loading smooth area.

[0032] In this embodiment, the concept of composite marking is used to comprehensively consider multiple results, avoiding erroneous judgments caused by the randomness or fluctuation of a single result, thus improving the stability and reliability of the judgment. By continuously monitoring the composite-marked region within a time window, short-term performance fluctuations or occasional anomalies can be filtered out. Only when a certain region consistently performs consistently over a period of time will it be identified as a certain type of region (optimized / adjusted / smooth), making the judgment more robust and reducing false alarms and misjudgments. Furthermore, when the number of markings is equal, a preset priority sorting is introduced (scaling optimization region > pre-adjustment region > loading smooth region) to ensure that the system provides clear execution measures. When the scaling optimization region has the most markings, a scaling efficiency control scheme is executed, which helps to focus on high-risk regions and proactively optimize performance bottlenecks. When the pre-adjustment region has the most markings, pre-control measures are executed, enabling early intervention and preventing potential problems from evolving into serious problems. When the loading smooth region has the most markings, monitoring continues to avoid unnecessary resource waste and maintain a balance between performance and efficiency.

[0033] Furthermore, the specific process for obtaining the scaling efficiency metric is as follows: It should be noted that, in order to more accurately measure scaling efficiency, the existing original expression has been optimized and supplemented to obtain a specific measure of scaling efficiency for 3D model scaling. Specifically, the original expression is as follows:

[0034] In the formula, ZE represents the scaling efficiency measure, ΔU represents the difference between the perceived utility value before and after scaling, and ΔC represents the resource quantification value. This form is usually used to measure how much useful information / perceptual improvement can be obtained by investing one unit of resources. This is a natural choice when making trade-offs in engineering. At the same time, in visual interaction systems, there is also a similar approach of "perceptual gain / resource overhead" (taking the improvement of perceptual availability or error rate as a gain and normalizing time / bandwidth / memory, etc. as costs). However, directly using the original expression, ΔU is not easy to define and normalize, and ΔC is composed of various resources such as time, bandwidth, and memory with different units. Therefore, it is necessary to include the visual perception / task-related ROI in the utility definition and normalize different resources with a unified scale, which helps to obtain a more reliable scaling efficiency measure.

[0035] To obtain ΔU, the perceptual utility data before and after scaling is acquired to perform perceptual utility normalization before and after scaling, resulting in perceptual utility values ​​before and after scaling. The perceptual utility data includes information density and perceptual screen error.

[0036] It should be added that information density represents the amount of effective information received by the user in a unit display space such as screen area or 3D scene window. The higher the information density, the more information the user needs to process, but too high a density may lead to cognitive load. It is obtained by calculating the number of elements such as nodes, edges, objects, and text in the display area and comparing them with the corresponding real area size. Perceived screen error represents the deviation between the information perceived by the user during observation or operation and the objective real value. It is obtained by calculating the difference between the perceived value obtained by the user through the interface and the actual real value and taking the absolute value.

[0037] Specifically, by substituting the information density before and after scaling and the perceived screen error into the expression of the perception function U, the corresponding perceived utility value before scaling and the perceived utility value after scaling are obtained. The difference between the perceived utility value before scaling and the perceived utility value after scaling is then calculated to obtain ΔU. Furthermore, the specific expression of the perception function U is as follows: ; Where ID represents information density (key feature quantity per unit screen pixel), IDmax represents normalization constant, which is usually set in advance by professional technicians based on empirical rules, and pSSE represents perceived screen error. This represents the normalization upper limit of pSSE, which is usually set in advance by professional technicians based on empirical rules. ω ID Represents the information density weight, ω P The weight represents the perceived screen error weight, and the sum of the information density weight and the perceived screen error weight is 1. This weight is preset by the technicians. k represents the error sensitivity parameter, which is determined through offline calibration and A / B testing. This formula combines "information gain" and "error reduction" into a single perceived benefit (normalized to 0-1).

[0038] Secondly, by modeling both useful information (ID) and visual / perceptual quality (pSSE) simultaneously through the perception function U, it can cover the two core objectives of interactive systems: displaying more useful information and ensuring display quality / low distortion. The weights and flexibility allow for task-specific optimization (e.g., inspection prioritizes ID, while browsing prioritizes pSSE).

[0039] At the same time, resource data is acquired for curve quantization to obtain the corresponding resource mapping quantity. The resource data includes loading response latency time, network data transmission volume, newly added video memory, and historical switching frequency. The resource mapping quantity includes loading response latency time mapping quantity, network data transmission volume mapping quantity, newly added video memory mapping quantity, and historical switching frequency.

[0040] Specifically, by substituting the loading response delay time, the amount of network data transmitted, and the amount of newly added video memory into the nonlinear cost mapping function, the outputs gT(ΔT), gB(ΔB), and gM(ΔM) represent the loading response delay time mapping amount, the network data transmission amount mapping amount, and the newly added video memory mapping amount, respectively.

[0041] In addition, the historical switching frequency P OSC Obtained through the following expression: ; In the formula, The sensitivity parameter, t, is pre-set and stored in a pre-defined database by designated professionals. ω Indicates a time window. Indicates time window The number of LOD switches that occur within the time limit.

[0042] Next, the resource weights corresponding to the resource data are queried and weighted together with the mapping quantities of each resource to obtain the corresponding resource quantification value. The resource weights include the loading response latency weight, the network data transmission volume weight, the weight of newly added video memory, and the weight of historical switching frequency.

[0043] Specifically, resource weights are extracted from a resource weight matching table set in a preset database. This table reflects the mapping relationship between resource data and corresponding resource weights. Real-time loading response latency, network data transmission volume, newly added video memory, and historical switching frequency are input into the resource weight matching table. The corresponding loading response latency weight, network data transmission volume weight, newly added video memory weight, and historical switching frequency weight are output. These weights represent the degree of influence of loading response latency, network data transmission volume, newly added video memory, and historical switching frequency on the scaling effect measurement value. These weights are usually obtained after training with resource weight training data. The resource weight training data includes resource data within a historical time period and resource weights set by professional technicians based on empirical rules. The sum of the loading response latency weight, network data transmission volume weight, newly added video memory weight, and historical switching frequency weight is 1.

[0044] It should be added that the specific method for obtaining the resource quantification value C is as follows:

[0045] Where α represents the loading response latency weight, β represents the network data transmission volume weight, γ represents the added video memory weight, δ represents the historical switching frequency weight, ΔT represents the additional response latency caused by switching / loading, ΔB represents the additional network bandwidth / transmission volume, ΔM represents the additional memory / video memory usage, Mcap represents the video memory volume, aT represents the coefficient controlling the "sensitivity of latency ΔT to cost contribution", which determines the initial growth slope of the logarithmic function, and bB represents the coefficient controlling the "speed at which the marginal effect of bandwidth ΔB disappears", which determines how quickly the saturation function approaches its upper limit. Both aT and bB are queried from a preset database and are usually preset by professional technicians. This expression realizes the unified mapping of different resources according to their respective impact on user experience.

[0046] Finally, the scaling efficiency measure is obtained by performing differential quantification on the perceived utility value after scaling and the perceived utility value before scaling, and by comparing the resource quantification value with the extracted numerical stability constant.

[0047] Therefore, the final expression for the scaling efficiency metric is as follows: ; Where ϵ represents the numerical stability constant, which is usually set by designated technical personnel and pre-stored in a pre-defined database, and U old U represents the perceived utility value before scaling. new This represents the perceived utility value after scaling.

[0048] In this embodiment, the final expression of the scaling efficiency measure obtained by reasoning and evolving the original expression more accurately quantifies the scaling efficiency of the 3D model of the substation optical cable panorama. The scaling efficiency measure clearly weighs the improvement in user-perceived utility against the perceived cost of achieving this improvement. When the scaling efficiency measure increases, it means that a larger perceived benefit is obtained with a smaller perceived cost, which helps guide the system to make more informed resource allocation decisions and take more appropriate optimization measures, thereby improving the efficiency of optical cable fault location in the 3D panoramic model of the optical cable of a specified substation.

[0049] The specific process for fault location requirement classification is as follows: First, based on the importance of each optical cable area and with the initial marking of importance by pre-selected professional technicians, the corresponding initial importance level is obtained. The initial importance level indicates that the importance of the optical cable area increases step by step with the initial importance level.

[0050] Next, the impact of optical cable fault location is quantified based on the initial importance level of each optical cable area and the reference fault data queried. The reference fault data includes the number of fault points and the frequency of faults.

[0051] Specifically, the reference fault data and initial importance level are normalized. The reference fault data are obtained by querying and statistically analyzing historical data. After averaging the normalized reference fault data and initial importance level, the corresponding optical cable fault area location impact is obtained.

[0052] Finally, the impact of locating the optical cable fault area is compared with the pre-set optical cable fault location demand level comparison table to obtain the optical cable fault location demand level for the corresponding optical cable area. The optical cable fault location demand level increases progressively as the demand for optical cable fault location and inspection increases.

[0053] It should be added that the impact of optical cable fault location is input into the pre-trained optical cable fault location requirement level comparison table, and the corresponding optical cable fault location requirement level is obtained by projection. The optical cable fault location requirement level comparison table is used to fit the mapping relationship between the impact of optical cable fault location and the optical cable fault location requirement level. The optical cable fault location requirement level comparison table is trained based on the requirement level training data, which includes the impact of optical cable fault location in historical time periods, as well as the optical cable fault location requirement level set by professional technicians based on experience rules.

[0054] In this embodiment, the initial importance marking by professional technicians fully leverages human experience and knowledge of substation operation patterns and key areas. Combined with historical fault data on the number and frequency of fault points, the importance of optical cable areas is objectively corrected, avoiding the biases of relying solely on experience or data. This dual-basis grading method ensures more accurate positioning of demand levels, better reflecting actual operating conditions. Furthermore, the "optical cable fault area positioning impact" is used as a unified quantitative indicator, directly reflecting the degree of influence of different optical cable areas during fault location. By comparing with a preset "demand level comparison table," a shift from qualitative description to quantitative calculation is achieved, making the grading results repeatable and verifiable. Areas with high demand levels can be quickly identified and located when faults occur, thus shortening fault investigation time. The system can dynamically adjust scaling strategies and visualization loading priorities according to the demand levels of different areas, ensuring priority handling of critical faults and ultimately improving overall fault location efficiency and maintenance accuracy.

[0055] The analysis results show that the optical cable panoramic model meets the stability characteristics conditions within a time window, and the results include the scaling efficiency qualification rate and the proportion of marked areas.

[0056] The scaling efficiency pass rate is obtained as follows: Within the preset time window of the optical cable panoramic model, the total number of scaling times and the number of times the scaling efficiency is qualified for the substation optical cable panoramic model are counted, as well as the volume of the smoothly loading area, the volume of the scaling optimized area, the volume of the area to be adjusted, and the total volume of the scaling viewing area. The number of times the scaling efficiency is qualified indicates the number of times the scaling efficiency measurement value is not less than the scaling efficiency judgment value within the time window of the optical cable panoramic model, and the number of times the scaling efficiency is not standard indicates the number of times the scaling efficiency measurement value is less than the scaling efficiency judgment value. The scaling efficiency pass rate is quantified by the ratio of the number of times the scaling efficiency is qualified to the total number of scaling times to obtain the corresponding scaling efficiency pass rate.

[0057] The method for obtaining the percentage of the marked area is as follows: the volume of the smooth loading area, the volume of the scaling-optimized area, and the volume of the pre-adjustment area are respectively compared with the total scaling and viewing area volume to obtain the corresponding percentage of the marked area. The percentage of the marked area includes the percentage of the smooth loading area, the percentage of the scaling-optimized area, and the percentage of the pre-adjustment area.

[0058] In this embodiment, a clear scaling efficiency pass rate indicator is formed by dividing the number of times scaling efficiency is qualified by the total number of scaling times. This transforms the previously difficult-to-measure scaling interaction experience into a quantifiable numerical result, making the scaling process "visual and quantifiable," avoiding subjective judgment. Furthermore, by statistically analyzing the proportion of the volume of the smooth loading area, the pre-adjustment area, and the scaling optimization area to the total scaling viewing area, the spatial distribution of performance bottlenecks can be more clearly displayed, making it easier to pinpoint specific performance problem areas and optimize them accordingly. It also provides spatial management data and supports local optimization and differentiated management. By performing statistics within a time window, the system can monitor performance changes in real time and dynamically, avoiding incorrect judgments due to instantaneous fluctuations or single anomalies, making the results more stable and reliable, helping to filter out occasional performance anomalies, and ensuring the scientific nature of the judgment. When scaling efficiency decreases or the proportion of the scaling optimization area increases, the system can trigger optimization measures in advance, reducing the probability of fiber optic cable fault location being affected by 3D model stuttering.

[0059] The specific process for determining and optimizing fiber optic cable area scaling is as follows: On the one hand, if the qualified scaling efficiency ratio is higher than the pre-set qualified scaling efficiency ratio threshold, it means that the impact on optical cable fault location is small, and the scaling efficiency of the next substation optical cable panoramic model within the time window will continue to be monitored; the qualified scaling efficiency ratio threshold is pre-set by professional technicians based on experience rules and stored in a preset database in advance.

[0060] On the other hand, if the qualified scaling efficiency ratio is lower than the pre-set qualified scaling efficiency ratio threshold, it indicates that it has a significant impact on optical cable fault location. In this case, a scaling efficiency control scheme is implemented in the scaling optimization area, and optical cable positioning optimization adjustment and area volume ratio analysis are carried out simultaneously. The optical cable positioning optimization adjustment is used to determine the optimization order of each scaling optimization area, and the area volume ratio analysis is used to determine whether to take pre-control measures for the optical cable area in the area to be adjusted.

[0061] In this embodiment, the qualified scaling efficiency ratio is used as the core indicator and compared with a preset threshold to achieve objective judgment of performance status, thereby realizing automatic identification of performance risks and improving the intelligence level of optical cable fault location. When performance degrades, the control scheme is directly executed on the scaling optimization area, prioritizing the resolution of critical problem areas. This helps to quickly improve the scaling efficiency of performance bottleneck areas and ensure the smoothness and accuracy of critical optical cable fault location. Through optical cable location optimization and adjustment, the priority and optimization order of each scaling optimization area are determined, avoiding resource waste or excessive system pressure caused by one-time comprehensive optimization. This achieves a gradual and priority-based optimization strategy, improving fault location efficiency and system stability. Furthermore, while performing optimization, by analyzing the volume ratio of each area, it is determined whether to intervene in the area to be adjusted in advance. Measures can be taken before potential risks expand, achieving proactive defense and enhancing the system's foresight and robustness.

[0062] The specific steps for optimizing and adjusting the optical cable positioning are as follows: sort the optical cable fault positioning requirement levels of each scaling optimization area, and execute the scaling efficiency control scheme for each scaling optimization area in sequence. If the optical cable fault positioning requirement levels are the same, the number of times the scaling efficiency of the scaling optimization area is qualified within a preset number of optical cable panoramic model time windows is compared to determine the optical cable positioning optimization order. If the number of times the scaling efficiency is qualified is the same, the volume of the scaling optimization area is compared to determine the optical cable positioning optimization order.

[0063] It should be added that the specific content of the scaling efficiency control scheme is as follows: The scaling optimization amount is obtained by quantifying the volume of the scaling optimization region and the corresponding number of times the scaling optimization region is marked. The scaling optimization impact amount is obtained by projection. The scaling optimization impact amount is then optimized and compensated with the loading resource limit parameters. This means that the scaling optimization impact amount is multiplied by the loading resource limit parameters, which include the upper limit of video memory usage, the amount of cache, and the maximum number of concurrent requests.

[0064] Specifically, after normalizing the volume of the scaling optimization region and the corresponding number of times the scaling optimization region is marked, the scaling optimization quantity is obtained by weighted fusion with the volume analysis quantity and the number of times analysis quantity preset in the preset database.

[0065] It should be noted that the scaling optimization amount is input into the pre-trained scaling optimization projection sequence, and the corresponding scaling optimization influence amount is obtained by projection. The scaling optimization projection sequence is used to fit the mapping relationship between the scaling optimization amount and the scaling optimization influence amount. The scaling optimization projection sequence is trained based on the scaling optimization training data, which includes the scaling optimization amount in the historical time period, as well as the scaling optimization influence amount set by professional technicians based on empirical rules.

[0066] In this embodiment, the system first prioritizes critical fiber optic cable fault location requirements, ensuring that critical areas are optimized first. When the requirements are the same, the number of times scaling efficiency passes is compared, prioritizing areas with low efficiency and large fluctuations. If they are still the same, the system sorts by area volume, prioritizing areas with larger volumes. This ensures a clear optimization order, avoids resource dispersion, and ensures that limited optimization measures are used for the most urgent, critical, and impactful areas first. Furthermore, during optimization, resource limitations such as memory usage limits, cache size, and maximum concurrent requests are considered. An optimization compensation mechanism prevents system crashes, memory overflows, or response delays caused by over-optimization or concentrated optimization, balancing optimization with system capacity and ensuring that optimization measures improve performance without compromising overall stability.

[0067] like Figure 3 The diagram shows a flowchart of the regional volume ratio analysis provided in this embodiment of the invention. The specific logic is as follows: The scaling efficiency qualified ratio is compared with a pre-set scaling efficiency qualified ratio threshold. If the scaling efficiency qualified ratio is higher than the pre-set scaling efficiency qualified ratio threshold, the scaling efficiency of the next substation optical cable panoramic model within the time window continues to be monitored. If the scaling efficiency qualified ratio is lower than the pre-set scaling efficiency qualified ratio threshold, the scaling efficiency control scheme is implemented in the scaling optimization area, and the optical cable positioning optimization adjustment is performed simultaneously. At the same time, the difference between the loading smooth area ratio and the pre-adjustment area ratio is quantified to obtain the corresponding marked difference amount. If the marked difference amount is greater than the initially set difference boundary value, the pre-adjustment optical cable area measures are not implemented in the pre-adjustment area; otherwise, the pre-adjustment optical cable area measures are implemented in the pre-adjustment area. Through the above process, not only is the robustness and dynamic adaptability of the system improved, but also the efficiency of optical cable fault location is guaranteed.

[0068] The region volume proportion analysis process is as follows: The difference between the proportion of the smoothly loading region and the proportion of the region to be adjusted is quantified. This involves calculating the difference between the proportion of the smoothly loading region and the proportion of the region to be adjusted, obtaining the corresponding marked difference, and then making corresponding judgments. There are two main judgment results, as detailed below: The first determination result is that if the marked difference is greater than the initially set difference boundary value, the pre-adjustment of the optical cable area will not be implemented in the area to be adjusted. The difference boundary value is obtained from the preset database and is usually set in advance by professional technicians based on experience rules and stored in the preset database.

[0069] The second determination result is that if the marked difference is not greater than the difference boundary value, then the pre-adjustment measures for the optical cable area will be implemented in the area to be adjusted.

[0070] It needs to be explained that the pre-adjustment measures for optical cable areas are as follows: the pre-adjustment value is obtained by quantifying the volume of the pre-adjustment area and the number of times the corresponding pre-adjustment area is marked, thereby mapping the corresponding pre-adjustment range. The pre-adjustment area is buffered in advance according to the pre-adjustment range, that is, the pre-adjustment range is buffered in advance according to the pre-adjustment range. At the same time, the pre-adjustment value is compared with the pre-designed optical cable fault location requirement level upgrade boundary value. If the pre-adjustment value is not less than the optical cable fault location requirement level upgrade boundary value, the optical cable fault location requirement level of the corresponding pre-adjustment area is increased by one level; otherwise, no additional processing is performed.

[0071] Specifically, after normalizing the volume of the pre-adjustment area and the corresponding number of times the pre-adjustment area is marked, the pre-adjustment value is obtained by weighted fusion with the volume pre-adjustment influence amount and the number pre-adjustment influence amount preset in the preset database.

[0072] It should be noted that the pre-adjustment value is input into the pre-trained pre-adjustment projection sequence, and the corresponding pre-adjustment amplitude is projected. The pre-adjustment projection sequence is used to fit the mapping relationship between the pre-adjustment value and the pre-adjustment amplitude. The pre-adjustment projection sequence is trained based on the pre-adjustment training data, which includes the pre-adjustment values ​​in the historical time period and the pre-adjustment amplitude set by professional technicians based on empirical rules.

[0073] In this embodiment, the difference between the proportions of the smooth loading region and the scaling optimization region is calculated to obtain a marked difference amount. Instead of relying on manual experience, the size of the difference threshold is used to determine whether pre-adjustment of the pre-adjustment region is necessary, improving the objectivity, repeatability, and scientific rigor of the judgment and avoiding subjective or arbitrary decisions. By judging various combinations of difference amounts and boundary values, different performance states can be distinguished, achieving hierarchical and differentiated dynamic responses, avoiding blind optimization, and ensuring efficient utilization of system resources. Simultaneously, the pre-adjustment value is obtained through the volume of the pre-adjustment region and the number of times it is marked, and mapped to the pre-adjustment amplitude. This allows pre-adjustment to dynamically adjust cache and response resources based on region size and problem frequency, resulting in more precise optimization measures and avoiding resource waste from over-adjustment or performance risks from under-adjustment. Furthermore, from determining the proportion difference to deciding whether to execute pre-adjustment, then quantifying the pre-adjustment amplitude, and finally judging the level upgrade, a complete pre-control closed loop is formed. This allows the system to intervene before performance problems are fully exposed, reducing the risk of overall scaling efficiency decline. This not only improves the system's robustness and adaptability but also ensures that the optical cable fault location process remains smooth and efficient.

[0074] Based on the same inventive concept, this application also provides an embodiment of a substation optical cable panoramic management and control system based on 3D modeling. For example... Figure 4 The diagram shows the structure of a substation optical cable panoramic management and control system based on 3D modeling. The system includes: an optical cable fault location efficiency determination module, an optical cable fault location requirement classification module, and an optical cable area scaling and control module.

[0075] The optical cable fault location efficiency judgment module is used to locate faults in each optical cable by creating a three-dimensional panoramic model of the optical cable and fiber core relationship of a designated substation. At the same time, it performs quantitative analysis on the efficiency when scaling the panoramic model of the substation optical cable to determine the qualification of the optical cable fault location efficiency, thereby more accurately monitoring the scaling of the panoramic model of the substation optical cable.

[0076] The optical cable fault location requirement classification module is used to classify the fault location requirements of each optical cable area in the panoramic model of optical cables in a substation, so as to initialize the importance of fault location in each optical cable area in a specified substation.

[0077] The optical cable area scaling and control module is used to analyze the evaluation results of the optical cable panoramic model within a time window to determine and optimize the optical cable area scaling in order to improve the efficiency of optical cable fault location.

[0078] In this embodiment, the complex relationships between optical cables and fiber cores are presented in an intuitive 3D model, greatly simplifying the identification and location of fault points. This is clearer than traditional 2D drawings or text descriptions. The system can not only locate faults but also quantify and analyze the efficiency during scaling and make qualification judgments. This means that the system can objectively evaluate the smoothness of operation, ensuring that there will be no delays or misjudgments due to system lag in critical fault location stages, thereby directly improving the efficiency and accuracy of optical cable fault location. By classifying the fault location needs of each optical cable area, the system can identify which areas are crucial to substation operation. This allows the system to prioritize the performance of critical areas in subsequent resource allocation and optimization, avoiding inefficient "one-size-fits-all" optimization and ensuring that core business is not affected. At the same time, based on quantified efficiency and demand classification, the system can intelligently determine which areas need optimization and execute corresponding control schemes, enabling the system to dynamically adjust resources and continuously improve the overall efficiency of optical cable fault location.

[0079] It should be noted that the inventive concept of this solution can be applied not only to physical equipment such as optical cable systems and secondary circuit devices, ODFs and switches in the three-dimensional operation and maintenance of intelligent substations, but also to other three-dimensional modeling objects such as cables and accessories, cable support and fixing hardware, and monitoring equipment in intelligent substations.

[0080] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0081] It should be understood that, in the embodiments of this application, the execution order of the above steps should be determined by their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0082] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0083] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0084] In the embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0085] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for panoramic management and control of optical cables in substations based on 3D modeling, characterized in that, Includes the following steps: S1 uses a panoramic model of the substation optical cable that visualizes the optical cable and fiber core relationship of the specified substation in three dimensions to locate the faults of each optical cable. At the same time, it performs a quantitative analysis of the efficiency when scaling the panoramic model of the substation optical cable, then judges the qualification of the optical cable fault location efficiency and visually marks the scaled area. S2 performs fault location requirement classification for each optical cable area in the panoramic model of substation optical cable; S3 uses the statistical analysis results of the optical cable panoramic model to determine the qualification of optical cable fault location efficiency within a time window that meets the stable characteristic conditions, and then performs optical cable area scaling judgment and control optimization.

2. A method for panoramic management and control of optical cables in substations based on 3D modeling, characterized in that: The quantitative analysis includes the step of obtaining a scaling efficiency measure; This step first obtains the perceived utility data before and after scaling, and then normalizes the data to obtain the perceived utility value before scaling and the perceived utility value after scaling. The perceived utility data includes information density and perceived screen error. This step then acquires resource data and performs curve quantization to obtain the corresponding resource mapping quantity. The resource data includes loading response latency time, network data transmission volume, newly added video memory, and historical switching frequency. The resource mapping quantity includes loading response latency time mapping quantity, network data transmission volume mapping quantity, newly added video memory mapping quantity, and historical switching frequency. This step then queries the resource weights corresponding to the resource data and performs a weighted fusion with each resource mapping quantity to obtain the corresponding resource quantification value. The resource weights include loading response latency weight, network data transmission volume weight, newly added video memory weight, and historical switching frequency weight. Finally, this step performs differential quantification on the perceived utility value after scaling and the perceived utility value before scaling. The result is then compared with the resource quantification value and the extracted numerical stability constant to obtain a scaling efficiency measure.

3. The substation optical cable panoramic management and control method based on three-dimensional modeling according to claim 2, characterized in that: The steps for determining the pass / failability of optical cable fault location efficiency and visually marking the zoomed area are as follows: If the scaling efficiency measurement value is not less than the preset scaling efficiency judgment value, the scaling efficiency is determined to meet the preset standard. The scaling efficiency deviation between the scaling efficiency measurement value and the scaling efficiency judgment value is obtained, and the scaling efficiency deviation is compared with the preset scaling efficiency grade value. If the scaling efficiency deviation is greater than the scaling efficiency grade value, the corresponding scaling area is marked as a smooth loading area. If the scaling efficiency deviation is not greater than the scaling efficiency grade value, the corresponding scaling area is marked as a pre-adjustment area. If the scaling efficiency measurement value is less than the scaling efficiency judgment value, it is determined that the scaling efficiency does not meet the preset standard, and the corresponding scaling area is marked as a scaling optimization area.

4. The substation optical cable panoramic management and control method based on three-dimensional modeling according to claim 3, characterized in that, The final step of determining the pass / failability of optical cable fault location efficiency and visually marking the zoomed area may also include the following steps: If the same optical cable area is scaled more than once and the marking results are different, the optical cable area is marked as a composite marking area. The composite marking area is continuously monitored within a time window by a preset number of optical cable panoramic models. If the composite marking area is marked as a scaling optimization area, the composite marking area is marked as a scaling optimization area. If the composite marking area is continuously marked as a pre-adjustment area, the composite marking area is marked as a pre-adjustment area. If the composite marking area is continuously marked as a smooth loading area, the composite marking area is marked as a smooth loading area. If the marking results of the composite marking regions are different, the number of markings for the smooth loading region, the pre-adjustment region, and the scaling optimization region are counted separately to obtain the corresponding number of markings for the smooth loading region, the pre-adjustment region, and the scaling optimization region. The obtained data is evaluated as follows: if the number of times the scaling optimization area is the largest, scaling efficiency control measures are implemented for the composite marked area; if the number of times the pre-adjustment area is the largest, pre-adjustment measures for the optical cable area are implemented; if the number of times the loading smooth area is the largest, the efficiency of the next scaling of the substation optical cable panoramic model is monitored; if there are equal number of marked areas among the loading smooth area, the pre-adjustment area, and the scaling optimization area, the corresponding execution measures are determined based on the priority order of the set scaling optimization area, the pre-adjustment area, and the loading smooth area.

5. The substation optical cable panoramic management and control method based on three-dimensional modeling according to claim 4, characterized in that, The fault location requirement classification includes the following steps: Each optical cable region is initially marked with an importance rating to obtain a corresponding initial importance level, which is used to characterize the importance of the optical cable region. The impact of optical cable fault location is quantified based on the initial importance level of each optical cable area and the reference fault data queried. The reference fault data includes the number of fault points and the frequency of faults. The impact of locating the optical cable fault area is compared with a pre-set optical cable fault location demand level comparison table to obtain the optical cable fault location demand level for the corresponding optical cable area, which is used to characterize the demand for optical cable fault location inspection.

6. The substation optical cable panoramic management and control method based on three-dimensional modeling according to claim 5, characterized in that: The analysis results include the scaling efficiency pass rate and the percentage of marked areas; The steps for obtaining the qualified scaling efficiency ratio are as follows: within the preset time window of the optical cable panoramic model, the total number of scaling times and the number of qualified scaling efficiency of the substation optical cable panoramic model are counted, as well as the volume of the smoothly loading area, the volume of the scaling optimized area, the volume of the pre-adjustment area, and the total volume of the scaling viewing area. The number of qualified scaling efficiency is the number of times the scaling efficiency measurement value is not less than the scaling efficiency judgment value within the time window of the optical cable panoramic model, and the number of times the scaling efficiency is not standard is the number of times the scaling efficiency measurement value is less than the scaling efficiency judgment value. The scaling efficiency pass rate is proportionally quantified to the total number of scaling times to obtain the corresponding scaling efficiency pass rate. The steps for obtaining the percentage of the marked region are as follows: the volume of the smooth loading region, the volume of the scaling-optimized region, and the volume of the pre-adjustment region are respectively quantified by the total scaling and viewing region volume to obtain the corresponding percentage of the marked region. The percentage of the marked region includes the percentage of the smooth loading region, the percentage of the scaling-optimized region, and the percentage of the pre-adjustment region.

7. The substation optical cable panoramic management and control method based on three-dimensional modeling according to claim 6, characterized in that, The optical cable area scaling judgment and control optimization includes the following steps: If the scaling efficiency qualification rate is higher than or equal to the pre-set scaling efficiency qualification rate threshold, then continue to monitor the scaling efficiency of the next substation optical cable panoramic model within the time window. If the scaling efficiency qualification ratio is lower than the pre-set scaling efficiency qualification ratio threshold, scaling efficiency control measures are implemented in the scaling optimization area, and optical cable positioning optimization adjustment and area volume ratio analysis are performed simultaneously. The optical cable positioning optimization adjustment is used to determine the optimization order of each scaling optimization area, and the area volume ratio analysis is used to determine whether to take pre-control measures for the optical cable area in the pre-adjustment area.

8. The substation optical cable panoramic management and control method based on three-dimensional modeling according to claim 7, characterized in that, The optical cable positioning optimization and adjustment includes the following steps: sorting the optical cable fault positioning requirement levels of each scaling optimization area, and performing scaling efficiency control measures on each scaling optimization area in sequence. If the optical cable fault positioning requirement levels are the same, the number of times the scaling efficiency of the scaling optimization area is qualified within a preset number of optical cable panoramic model time windows is compared to determine the optical cable positioning optimization order. If the number of times the scaling efficiency is qualified is the same, the volume of the scaling optimization area is compared to determine the optical cable positioning optimization order. The area volume ratio analysis includes the following steps: quantifying the difference between the proportion of the smoothly loaded area and the proportion of the pre-adjustment area to obtain the corresponding marked difference amount. If the marked difference amount is greater than the initially set difference boundary value, the pre-adjustment optical cable area measures are not implemented in the pre-adjustment area. If the marked difference amount is not greater than the difference boundary value, the pre-adjustment optical cable area measures are implemented in the pre-adjustment area.

9. The substation optical cable panoramic management and control method based on three-dimensional modeling according to claim 4, characterized in that: The scaling efficiency control measures include the following steps: quantifying the scaling optimization amount based on the volume of the scaling optimization region and the corresponding number of times the scaling optimization region is marked, projecting the scaling optimization impact amount, and optimizing and compensating the scaling optimization impact amount with the loading resource limit parameters, including the upper limit of video memory usage, cache size, and maximum number of concurrent requests; the pre-adjustment measures for the optical cable area include the following steps: quantizing the pre-adjustment value based on the volume of the pre-adjustment region and the corresponding number of times the pre-adjustment region is marked, mapping the pre-adjustment range, performing pre-response caching on all pre-adjustment regions according to the pre-adjustment range, and comparing the pre-adjustment value with the pre-designed optical cable fault location requirement level upgrade boundary value. If the pre-adjustment value is not less than the optical cable fault location requirement level upgrade boundary value, the optical cable fault location requirement level of the corresponding pre-adjustment region is increased by one level; otherwise, no additional processing is performed.

10. A substation optical cable panoramic management and control system based on three-dimensional modeling, used to implement the substation optical cable panoramic management and control method based on three-dimensional modeling as described in any one of claims 1 to 9, characterized in that: The system includes an optical cable fault location efficiency determination module, an optical cable fault location demand classification module, and an optical cable area scaling control module. The optical cable fault location efficiency determination module is configured to execute step S1 in claim 1, the optical cable fault location demand classification module is configured to execute step S2 in claim 1, and the optical cable area scaling control module is configured to execute step S3 in claim 1.

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