An underground cable visual monitoring method and system based on digital twinning

Abstract

The application discloses a kind of underground cable visualization monitoring methods and systems based on digital twinning, it is related to the field of pipe network monitoring.The method is applied to cable monitoring platform, the method comprises: according to the equipment parameters of underground cable and monitoring dataset, the first digital twin model of underground cable is built;According to the monitoring dataset, the comprehensive interference index of operating environment is calculated;Using time window-comprehensive interference index mapping function, the monitoring data acquisition time window of underground cable is calculated;According to monitoring data acquisition time window, the second digital twin model is constructed;Based on the second digital twin model, the abnormality of underground cable is predicted, and the visualization prediction result is output.The technical scheme provided in the application is implemented, and the problem that the prediction result accuracy of dynamic evolution simulation is reduced due to the construction of digital twin body using the fixed time window at present is solved.

Description

Technical Field

[0001] This application relates to the technical field of pipeline monitoring, specifically to a method and system for visual monitoring of underground cables based on digital twins. Background Technology

[0002] With the development of intelligent power systems, the need for safety monitoring of underground cables, as key facilities for power transmission, is becoming increasingly urgent.

[0003] Currently, digital twin technology is widely used in the field of cable monitoring because it can construct virtual mapping models of physical entities. Based on the physical data collected on the cable site, it combines digital twin technology to construct a cable twin model to restore the real situation on site. Then, it uses real-time monitoring data to update the model, so that the model reflects the real state of the cable and the site. Finally, it performs dynamic evolution simulation on the model, thereby realizing the analysis of anomalies on the cable site.

[0004] However, the operating environment of underground cables is complex, and various environmental factors are constantly changing. Current cable monitoring solutions based on digital twins still use monitoring data collected within a fixed time window to construct the digital twin. This leads to problems such as excessive noise introduced by the time window if it is too long, or failure to capture key monitoring data if the time window is too short, which in turn reduces the accuracy of subsequent dynamic evolution simulation predictions. Summary of the Invention

[0005] To address the issue that the accuracy of prediction results in dynamic evolution simulations is reduced due to the use of fixed time windows to construct digital twins, this application provides a method and system for visual monitoring of underground cables based on digital twins.

[0006] In a first aspect, this application provides a digital twin-based method for visual monitoring of underground cables, applied to a cable monitoring platform, the method comprising:

[0007] Based on the equipment parameters and monitoring dataset of the underground cable, a first digital twin model of the underground cable is constructed.

[0008] Based on the monitoring dataset, calculate the comprehensive interference index of the operating environment;

[0009] The monitoring data acquisition time window of the underground cable is calculated using a time window-comprehensive interference index mapping function.

[0010] Based on the monitoring data collection time window, a second digital twin model is constructed;

[0011] Based on the second digital twin model, anomaly prediction is performed on the underground cable, and the prediction results are output as a visualization.

[0012] Optionally, the monitoring dataset includes various monitoring data, and the calculation of the comprehensive interference index of the operating environment based on the monitoring dataset specifically includes:

[0013] Calculate the entropy weights of the various monitoring data and construct an entropy weight matrix;

[0014] Principal component analysis is performed on the entropy weight matrix to determine the initial influence weights of various monitoring data.

[0015] Based on the correlation coefficients between each pair of the various monitoring data, the initial influence weights of the various monitoring data are subjected to weight decay processing to obtain the target influence weights corresponding to each of the various monitoring data.

[0016] Based on the target influence weights corresponding to the various monitoring data, the comprehensive interference index of the operating environment is calculated.

[0017] Optionally, based on the target influence weights corresponding to the various monitoring data, the comprehensive interference index of the operating environment is calculated, specifically as follows:

[0018]

[0019] in, The comprehensive interference index, Let be the target influence weight, real-time monitoring value, historical mean, and variance of the i-th type of monitoring data in the monitoring dataset, respectively, and let n be the number of types of monitoring data in the monitoring dataset.

[0020] Optionally, the monitoring dataset includes historical monitoring data of the operating environment within a preset time period. The calculation of the monitoring data acquisition time window for the underground cable using a time window-comprehensive interference index mapping function specifically includes:

[0021] Calculate the comprehensive interference index of historical monitoring data of the operating environment within the preset time period, and construct a comprehensive interference index slope curve;

[0022] Calculate the mean slope of the comprehensive interference index slope curve;

[0023] Based on the average slope and the comprehensive interference index of the current operating environment, the monitoring data acquisition time window of the underground cable is corrected to obtain the target monitoring data acquisition time window.

[0024] Optionally, the step of using a time window-comprehensive interference index mapping function to calculate the monitoring data acquisition time window for the underground cable further includes:

[0025] The duration of interference in the preset time period is determined based on a preset comprehensive interference index threshold.

[0026] Determine whether the duration of the interference is greater than a preset interference duration warning threshold;

[0027] If the duration is greater than the specified duration, the monitoring data acquisition time window is adjusted based on the duration of the interference to obtain the target monitoring data acquisition time window.

[0028] Optionally, the step of constructing a second digital twin model based on the monitoring data collection time window further includes:

[0029] The monitoring data collected during the monitoring data acquisition time window is converted into a comprehensive interference index curve;

[0030] Calculate the marginal impact value of each data point in the comprehensive interference index curve;

[0031] Assess the computational resource consumption required after updating the monitoring data corresponding to multiple data points to the first digital twin model;

[0032] Based on the available computing resources and the required computing resources consumption corresponding to the multiple data points, multiple data points are eliminated in order of increasing marginal impact value to obtain multiple data points to be updated.

[0033] The monitoring data corresponding to the multiple data points to be updated are updated into the first digital twin model to obtain the second digital twin model.

[0034] Secondly, this application provides a digital twin-based underground cable visualization monitoring system. The system is a cable monitoring platform, comprising a receiving module, a processing module, and an output module, wherein:

[0035] The receiving module is used to build a first digital twin model of the underground cable based on the equipment parameters and monitoring dataset of the underground cable.

[0036] The processing module is used to calculate the comprehensive interference index of the operating environment based on the monitoring dataset; calculate the monitoring data acquisition time window of the underground cable using a time window-comprehensive interference index mapping function; and construct a second digital twin model based on the monitoring data acquisition time window.

[0037] The output module is used to perform anomaly prediction on the underground cable based on the second digital twin model and output the visualized prediction results.

[0038] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0039] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0040] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0041] This application uses digital twin modeling of the underground cable's on-site environment to obtain a first digital twin model of the current moment. Then, by calculating the comprehensive interference index of the operating environment, the combined impact of various interference factors is accurately quantified. Next, a time window-comprehensive interference index mapping function is used to calculate the monitoring data acquisition time window, thus matching the monitoring data acquisition time window with the environmental interference situation. When the comprehensive interference index is high, indicating severe environmental interference, the monitoring data acquisition time window is automatically shortened to reduce the introduction of interference factors and avoid distortion of prediction results due to excessive interference. When the comprehensive interference index is low, indicating a more stable environment, the monitoring data acquisition time window is appropriately extended to more comprehensively analyze the cable's state evolution trend and fully explore potential anomalies. Then, based on the monitoring data acquisition time window, monitoring data is collected and a second digital twin model is constructed, ensuring that the second digital twin model conforms to the actual operating environment of the cable. Finally, dynamic evolution simulation is performed based on the second digital twin model, resulting in more accurate visualized prediction results. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating a digital twin-based method for visual monitoring of underground cables, as provided in an embodiment of this application.

[0043] Figure 2 This is a schematic diagram of the structure of an underground cable visualization monitoring system based on digital twin provided in an embodiment of this application.

[0044] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0045] Explanation of reference numerals in the attached drawings: 1. Receiving module; 2. Processing module; 3. Output module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0047] Compared to overhead cables, underground cables, being buried deep underground for extended periods, make it difficult to visually monitor their operational status. Therefore, most monitoring relies on various types of sensor modules. However, with the development of digital twin technology, the visualization and monitoring of underground cables has made breakthroughs. By constructing a digital twin of the underground cable and updating the on-site monitoring data into the digital twin, and then using computer trend analysis, the development trends of potential anomalies can be displayed to maintenance personnel. Based on the displayed information, maintenance personnel can then take preventative measures to maintain the normal operation of the underground cable.

[0048] However, underground cables operate in harsh environments, constantly facing various environmental interference factors such as changes in soil moisture, electromagnetic interference fluctuations, and changes in temperature and air pressure. When constructing digital twins, the method of collecting monitoring data within a fixed time window is still used. This leads to problems such as excessive noise introduced by the time window or failure to capture key monitoring data due to the time window being too long, which in turn reduces the accuracy of subsequent dynamic evolution simulation predictions.

[0049] To address the aforementioned issues, this application provides a digital twin-based method for visual monitoring of underground cables. This method is applied to cable monitoring platforms, such as... Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0050] S101. Based on the equipment parameters and monitoring dataset of the underground cable, build the first digital twin model of the underground cable.

[0051] In the above steps, the equipment parameters of the underground cable are first obtained. These parameters include, but are not limited to, static physical attribute data such as cable type, conductor material, insulation thickness, cable length, joint location, and structural parameters. At the same time, a cable monitoring dataset is collected, which includes dynamic data collected in real time by distributed sensors. Then, the equipment parameters are input into 3D modeling software to construct a geometric model of the cable body and its ancillary facilities. The monitoring dataset is then connected to the model in real time to establish a mapping relationship between the equipment parameters, monitoring data, and the spatial location of the 3D model. Finally, a digital twin engine is used to model the cable's operating status in real time, ultimately forming a first visual digital twin model that reflects the cable's current physical structure and operating status.

[0052] S102. Calculate the comprehensive interference index of the operating environment based on the monitoring dataset.

[0053] In the above steps, the monitoring dataset contains various types of monitoring data, such as temperature, humidity, electromagnetic interference intensity, vibration frequency, partial discharge signal intensity, current and voltage values, etc. During actual cable operation, some environmental factors can affect the cable's normal operation. In this case, monitoring data related to these environmental factors are extracted from the monitoring dataset, such as ambient temperature, electromagnetic interference intensity, vibration frequency, and soil moisture. Then, the comprehensive interference index of the operating environment is calculated based on the extracted environmentally relevant monitoring data to quantify the combined impact of various interference factors. Specifically, the following formula can be used:

[0054]

[0055] in, The comprehensive interference index, Let be the target influence weight, real-time monitoring value, historical mean, and variance of the i-th type of monitoring data in the monitoring dataset, respectively, and let n be the number of types of monitoring data in the monitoring dataset.

[0056] In the above formula, first through The environmental monitoring data of type i are standardized to eliminate the differences in dimensions between different data types, transforming them into comparable relative values ​​that reflect the degree to which this type of data deviates from the historical mean; then multiplied by the influence weight of the corresponding monitoring data. This reflects the differences in the degree of interference to cables caused by different environmental factors. Finally, the comprehensive interference index S is obtained by summing the processing results of all types of monitoring data.

[0057] Regarding the influence weights of different monitoring data, this application first uses the entropy weight method to process multiple monitoring data, calculates their entropy weights based on the dispersion of each monitoring data, and then constructs an entropy weight matrix from the calculated entropy weights of multiple monitoring data. Then, principal component analysis is used to analyze the entropy weight matrix, calculate the contribution of each monitoring data in the principal component space, and determine the initial influence weights of multiple monitoring data based on the contribution. In addition, in actual working conditions, some influencing factors have mutual influence relationships. At this time, the weights of multiple monitoring data with influence relationships will be repeatedly amplified in the principal component analysis process. Therefore, the initial influence weights of multiple monitoring data need to be corrected. Specifically, the correlation coefficients between each pair of multiple monitoring data are first calculated using the Pearson correlation coefficient method. Then, based on the preset correlation coefficient threshold, strongly correlated monitoring data are identified. Finally, the weights of these strongly correlated monitoring data are attenuated by multiplying the initial weight values ​​by a preset attenuation coefficient to obtain the final target influence weights of multiple monitoring data. The scheme first uses the entropy weight method to extract the information value of the data, then uses principal component analysis to compress redundant information, and finally uses the correlation coefficient to correct the correlation bias, so that the influence weight of various monitoring data is more in line with the physical mechanism of actual interference, thereby improving the accuracy of the comprehensive interference index.

[0058] S103. The time window-comprehensive interference index mapping function is used to calculate the monitoring data acquisition time window for underground cables.

[0059] In the above steps, after determining the comprehensive interference index of the current operating environment, a high comprehensive interference index indicates strong environmental interference. In this case, the time window needs to be reduced to minimize the impact of interference factors and promptly capture rapid changes in cable status. Conversely, a low comprehensive interference index indicates weak environmental interference. In this case, the time window can be increased to analyze the cumulative effects of long-term environmental influences and deeply explore potential threats. Therefore, this application proposes a time window-comprehensive interference index mapping function to calculate the monitoring data acquisition time window that matches the current operating environment, specifically:

[0060]

[0061] Where L represents the monitoring data collection time window, These represent the minimum and maximum monitoring data acquisition time windows allowed by the fault prediction algorithm, respectively. The comprehensive interference index, This is the critical value of the comprehensive interference index.

[0062] In the above formula, when the comprehensive interference index S is greater than... If this occurs, it indicates that the current operating environment is a highly interfering environment. It is a positive correlation function; as S increases, The smaller the value, until the monitoring data acquisition time window L approaches the minimum value. This reduces the impact of interference factors and allows for timely detection of rapid changes in cable status; when the comprehensive interference index S is less than At this time It is a negative correlation function; as S decreases, The larger the window, the closer the monitoring data acquisition time window L becomes. This allows for the analysis of the cumulative effects of long-term environmental influences. Consequently, this formula enables the dynamic adjustment of the monitoring data acquisition time window based on the comprehensive interference index, achieving precise matching with the current operating environment and providing a suitable time-dimensional analysis basis for subsequent cable anomaly prediction.

[0063] In one possible implementation, since the actual environmental interference is dynamic, and the monitoring data acquisition time window calculated by the aforementioned time window-comprehensive interference index mapping function reflects the monitoring data acquisition time window that matches the current moment, in order to make the monitoring data acquisition time window conform to the changing trend of environmental interference, this application further corrects the monitoring data acquisition time window by analyzing the intensity changing trend and duration of environmental interference, so that the monitoring data acquisition time window is more in line with the dynamic changes of environmental interference, thereby improving the accuracy and reliability of subsequent cable anomaly prediction.

[0064] Specifically, the correction method for the intensity change trend of environmental interference on the monitoring data acquisition time window is as follows: First, the comprehensive interference index of the operating environment within a preset time period is converted into a comprehensive interference index slope curve. The preset time period is the period from the upper limit of the system storage time for monitoring data to the current time point. Then, the average slope of the comprehensive interference index slope curve is calculated. At this time, the difference between the current comprehensive interference index slope value and the average slope of the comprehensive interference index slope curve is analyzed to determine the correction coefficient for the intensity change trend of environmental interference. The specific calculation method can be as follows:

[0065]

[0066] Where α is the correction coefficient for the intensity change trend of environmental interference, and y is the slope value of the comprehensive interference index at the current moment. This represents the average slope of the comprehensive interference index slope curve. The standard deviation of the slope curve of the comprehensive interference index. This is an adjustment factor for the underground cable installation area.

[0067] In the above formula, when When the value is greater than or equal to 0, the interference intensity of the operating environment is on the rise, meaning that the interference of the operating environment on the underground cable is increasing. If the comprehensive interference index at the current moment deviates significantly from this value... The correction coefficient for the intensity trend of environmental interference decreases, shortening the monitoring data acquisition time window and mitigating the risk of escalating interference; when When the value is less than or equal to 0, the interference intensity of the operating environment is decreasing, meaning the interference of the operating environment on the underground cable is weakening. If the comprehensive interference index at the current moment deviates significantly... This increases the correction coefficient for the intensity change trend of environmental interference, extends the monitoring data acquisition time window, and allows for a comprehensive analysis of the cable condition evolution trend and in-depth exploration of potential anomalies. Additionally, regarding the adjustment coefficient... If the underground cable is installed in an urban area, the value will be larger to amplify the impact of the intensity change trend of environmental interference on the monitoring data acquisition time window; if the underground cable is installed in a suburban area, the value will be smaller to avoid over-adjustment.

[0068] The specific method for correcting the duration of environmental interference on the monitoring data acquisition time window is as follows: First, based on the preset comprehensive interference index threshold, the duration of interference in the operating environment within a certain period is identified. Then, it is determined whether the duration of interference exceeds the preset interference duration warning threshold. If it does, the correction coefficient for the duration of environmental interference is determined based on the longest allowable interference duration, as follows:

[0069]

[0070] Where β is the correction factor for the duration of environmental disturbance, and t is the duration of environmental disturbance. The warning threshold for the duration of interference. To allow the longest possible duration of interference, This is the attenuation coefficient.

[0071] In the above formula, when the duration of the interference exceeds As the duration of interference increases, the monitoring data acquisition time window is gradually reduced to avoid excessive accumulation of interference noise over a long window. When the interference duration is less than... If the duration of the interference is low, it indicates that the current duration of the interference has little impact on the normal operation of the cable, and in this case, there is no need to adjust the monitoring data acquisition time window.

[0072] Finally, the correction coefficients for the intensity and duration of environmental disturbances are multiplied by the pre-calculated monitoring data acquisition time window to correct the monitoring data acquisition time window, making it more closely aligned with the dynamic changes of environmental disturbances.

[0073] S104. Construct a second digital twin model based on the monitoring data collection time window.

[0074] In the above steps, based on the monitoring data collection time window, the window monitoring data within the corresponding time period is read from the monitoring dataset, and then the window monitoring data is updated to the first digital twin model to obtain the second digital twin model.

[0075] In one possible implementation, during the monitoring data update process, if the monitoring data collection time window is long, it indicates low environmental interference. However, updating a large amount of window monitoring data at this time will generate redundant data, thereby increasing the computational burden. If the monitoring data collection time window is short, it indicates high environmental interference. In this case, the window monitoring data updated in the first digital twin model may have key data masked due to interference, thus reducing the reliability of the prediction results. Therefore, before constructing the second digital twin model, this application converts the monitoring data within the monitoring data collection time window into a comprehensive interference index curve. The comprehensive interference index curve is a two-dimensional coordinate curve with time on the horizontal axis and the comprehensive interference index on the vertical axis. Each time point corresponds to multiple monitoring data at that time point. Then, the comprehensive interference index curve is converted into a marginal impact curve. The specific conversion formula is as follows:

[0076]

[0077] in, Let S(t) be the marginal impact value at time t in the marginal impact curve, S(t) be the comprehensive interference index at time t in the comprehensive interference index curve, and S be the comprehensive interference index at the current time.

[0078] In the above formula, when The greater the difference between the marginal impact value and S(t), the greater the impact of the monitoring data at time t on the comprehensive interference index after it changes to the monitoring data at the current time. The marginal impact value can be understood as a quantitative indicator of this impact. At this time, it also means that the monitoring data at time t contains more key information and needs to be updated to the first digital twin model to improve the reliability of subsequent prediction data.

[0079] Then, the computational resource consumption required to update the monitoring data corresponding to multiple data points in the marginal impact curve to the first digital twin model is evaluated. Based on the system's available computational resources and the required computational resource consumption, multiple data points are sequentially eliminated in ascending order of marginal impact value, resulting in multiple data points to be updated. The monitoring data corresponding to these multiple data points are then updated to the first digital twin model, resulting in the second digital twin model. This scheme prioritizes updating high-value monitoring data under resource constraints, avoiding the computational burden caused by redundant data in long windows and preventing interference from masking key data in short windows. This improves the reliability and computational efficiency of the model's data foundation, laying the foundation for the accuracy of subsequent anomaly prediction and optimized resource utilization.

[0080] S105. Based on the second digital twin model, perform anomaly prediction on underground cables and output visualized prediction results.

[0081] In the above steps, the second digital twin model is input into the dynamic evolution model of cable operation status to generate continuous operation status evolution data of underground cables. Then, threshold judgment is performed on the operation status evolution data, and abnormal operation status evolution data is marked. The marked operation status evolution data is then input into the visualization module, and the marked abnormal operation status evolution data is displayed in the corresponding area of ​​the cable. Finally, the visualization prediction results are output to help operation and maintenance personnel quickly identify risks, thereby ensuring the normal operation of the power system.

[0082] Reference Figure 2 This application also provides a digital twin-based underground cable visualization monitoring system. The system is a cable monitoring platform, comprising a receiving module 1, a processing module 2, and an output module 3, wherein:

[0083] Receiver module 1 is used to build the first digital twin model of the underground cable based on the equipment parameters and monitoring dataset of the underground cable;

[0084] Processing module 2 is used to calculate the comprehensive interference index of the operating environment based on the monitoring dataset; calculate the monitoring data acquisition time window of the underground cable using the time window-comprehensive interference index mapping function; and construct a second digital twin model based on the monitoring data acquisition time window.

[0085] Output module 3 is used to predict anomalies in underground cables based on the second digital twin model and output visualized prediction results.

[0086] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0087] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0088] The communication bus 302 is used to enable communication between these components.

[0089] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0090] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0091] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0092] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a digital twin-based method for visual monitoring of underground cables.

[0093] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for a digital twin-based underground cable visualization monitoring method. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0094] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0095] In the various embodiments provided in this application, it should be understood that the disclosed apparatus 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 system, 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 service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0096] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0097] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0099] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0100] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for visual monitoring of underground cables based on digital twins, characterized in that, The method, applied to a cable monitoring platform, includes: Based on the equipment parameters and monitoring dataset of the underground cable, a first digital twin model of the underground cable is constructed. Based on the monitoring dataset, the comprehensive interference index of the operating environment is calculated. The monitoring dataset includes various monitoring data, specifically including: Calculate the entropy weights of the various monitoring data and construct an entropy weight matrix; Principal component analysis is performed on the entropy weight matrix to determine the initial influence weights of various monitoring data. Based on the correlation coefficients between each pair of the various monitoring data, the initial influence weights of the various monitoring data are subjected to weight decay processing to obtain the target influence weights corresponding to each of the various monitoring data. Based on the target influence weights corresponding to the various monitoring data, the comprehensive interference index of the operating environment is calculated; The monitoring data acquisition time window for the underground cable is calculated using a time window-comprehensive interference index mapping function, which is specifically as follows: Where L represents the monitoring data collection time window, These represent the minimum and maximum allowed monitoring data acquisition time windows, respectively. The overall interference index of the operating environment. This is the critical value of the comprehensive interference index; The monitoring dataset includes historical monitoring data of the operating environment within a preset time period. The method for correcting the monitoring data collection time window based on the trend of environmental interference intensity changes is as follows: Calculate the comprehensive interference index of historical monitoring data of the operating environment within the preset time period, and construct a comprehensive interference index slope curve; Calculate the mean slope of the comprehensive interference index slope curve; Based on the difference between the average slope and the slope value of the comprehensive interference index at the current moment, the monitoring data acquisition time window of the underground cable is corrected to obtain the target monitoring data acquisition time window. The method for correcting the monitoring data acquisition time window based on the duration of environmental interference is as follows: The duration of interference in the preset time period is determined based on a preset comprehensive interference index threshold. Determine whether the duration of the interference is greater than a preset interference duration warning threshold; If it is greater than that, then as the duration of the interference increases, the monitoring data acquisition time window is gradually reduced to obtain the target monitoring data acquisition time window; Based on the target monitoring data collection time window, a second digital twin model is constructed; Based on the second digital twin model, anomaly prediction is performed on the underground cable, and the prediction results are output as a visualization.

2. The method according to claim 1, characterized in that, Based on the target influence weights corresponding to the various monitoring data, the comprehensive interference index of the operating environment is calculated, specifically as follows: in, These represent the target influence weight, real-time monitoring value, historical mean, and variance of the i-th type of monitoring data in the monitoring dataset, respectively, where n is the number of types of monitoring data in the monitoring dataset.

3. The method according to claim 1, characterized in that, The step of constructing a second digital twin model based on the target monitoring data collection time window specifically includes: The monitoring data of the target monitoring data collection time window is converted into a comprehensive interference index curve; Calculate the marginal impact value of each data point in the comprehensive interference index curve; Assess the computational resource consumption required after updating the monitoring data corresponding to multiple data points to the first digital twin model; Based on the available computing resources and the required computing resources consumption corresponding to the multiple data points, multiple data points are eliminated in order of increasing marginal impact value to obtain multiple data points to be updated. The monitoring data corresponding to the multiple data points to be updated are updated into the first digital twin model to obtain the second digital twin model.

4. A digital twin-based underground cable visualization monitoring system, the system being used to execute the digital twin-based underground cable visualization monitoring method as described in any one of claims 1 to 3, characterized in that, The system is a cable monitoring platform, which includes a receiving module, a processing module, and an output module, wherein: The receiving module is used to build a first digital twin model of the underground cable based on the equipment parameters and monitoring dataset of the underground cable. The processing module is used to calculate the comprehensive interference index of the operating environment based on the monitoring dataset. The monitoring dataset includes various monitoring data, specifically including: Calculate the entropy weights of the various monitoring data and construct an entropy weight matrix; Principal component analysis is performed on the entropy weight matrix to determine the initial influence weights of various monitoring data. Based on the correlation coefficients between each pair of the various monitoring data, the initial influence weights of the various monitoring data are subjected to weight decay processing to obtain the target influence weights corresponding to each of the various monitoring data. Based on the target influence weights corresponding to the various monitoring data, the comprehensive interference index of the operating environment is calculated; The monitoring data acquisition time window of the underground cable is calculated using a time window-comprehensive interference index mapping function. The monitoring data acquisition time window is corrected to obtain the target monitoring data acquisition time window; Based on the target monitoring data collection time window, a second digital twin model is constructed; The output module is used to perform anomaly prediction on the underground cable based on the second digital twin model and output the visualized prediction results.

5. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device (300) to perform the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 3.

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