An intelligent sectional management method and system for power cables

Abstract

The present invention discloses an intelligent sectional management method and system for power cables, belonging to the field of power supply management. The method includes: setting a first time-series sectional influence coefficient and a second time-series sectional influence coefficient, and establishing a dynamic decision-making node; obtaining the cable layout information of the power cable to be monitored; configuring a retrospective time window and establishing the retrospective basic parameters of the power cable to be monitored; performing an adaptation analysis of balancing task requirements and balancing climate impacts according to the retrospective basic parameters, and generating a first clustering constraint based on the adaptation analysis result; establishing a second clustering constraint according to the cable layout information, establishing a dynamic sectionalization strategy according to the first clustering constraint, the second clustering constraint, and the dynamic decision-making node, allocating an edge joint warning network, and performing sectional management based on the edge joint warning network. A sectionalization strategy is dynamically generated based on multi-dimensional data, realizing precise and intelligent early warning and management of power cables, and improving the early warning response speed and accuracy.

Description

Technical Field

[0001] The present invention relates to the field of power supply management, and particularly to an intelligent segmented management method and system for power cables. Background Art

[0002] The safe and reliable operation of power cables is directly related to the stability of power supply. With the continuous expansion and complexity of the power grid scale, traditional power cable management methods have been difficult to meet the requirements of modern power grids. Currently, power cable management systems adopt fixed-cycle inspections or simple remote monitoring methods. However, this management method cannot adjust the segmentation strategy in real time according to task requirements and environmental changes. Specifically, the existing power cable management adopts a unified management strategy, which is difficult to conduct differential management for the characteristics and risk levels of different cable segments. When facing a complex and changeable operating environment, it is unable to adjust the monitoring frequency and focus in a timely manner, resulting in over-monitoring of some sections and under-monitoring of other sections. In addition, the existing power cable management often considers too single influencing factors, mainly focusing on parameters such as temperature or load, while ignoring the impact of comprehensive factors such as climate change and geographical location characteristics on the cable state. Therefore, the power cable management in the prior art cannot adjust the segmentation strategy in real time according to task requirements and environmental changes, resulting in low warning efficiency and accuracy. Summary of the Invention

[0003] This application provides an intelligent segmented management method and system for power cables, aiming to solve the technical problem that the power cable management in the prior art cannot adjust the segmentation strategy in real time according to task requirements and environmental changes, resulting in low warning efficiency and accuracy.

[0004] In view of the above problems, this application provides an intelligent segmented management method and system for power cables.

[0005] In the first aspect disclosed by the present application, a method for intelligent segmented management of power cables is provided. The method includes: obtaining a pre-execution task of the power cable to be monitored, establishing a task cycle identifier based on the pre-execution task, and setting a first time-series segmentation influence coefficient based on the task cycle identifier; performing position climate prediction of the power cable to be monitored, and establishing a second time-series segmentation influence coefficient based on the position climate prediction result; inputting the first time-series segmentation influence coefficient and the second time-series segmentation influence coefficient into a dynamic decision model to establish a dynamic decision node; establishing an equilibrium task demand and an equilibrium climate impact between nodes with the dynamic decision node; obtaining the cable layout information of the power cable to be monitored, where the cable layout information includes cable layout structure information and cable layout fixed environment information; configuring a retrospective time window, performing retrospective monitoring of the power cable to be monitored with the retrospective time window, and establishing retrospective basic parameters of the power cable to be monitored; performing an adaptation analysis of the equilibrium task demand and the equilibrium climate impact according to the retrospective basic parameters, and generating a first clustering constraint based on the adaptation analysis result; establishing a second clustering constraint according to the cable layout information, establishing a dynamic segmentation strategy according to the first clustering constraint, the second clustering constraint, and the dynamic decision node, allocating an edge joint warning network based on the dynamic segmentation strategy, and performing segmented management based on the edge joint warning network.

[0006] In another aspect disclosed by the present application, a system for intelligent segmented management of power cables is provided. The system includes: a first influence coefficient module for obtaining a pre-execution task of the power cable to be monitored, establishing a task cycle identifier based on the pre-execution task, and setting a first time-series segmentation influence coefficient based on the task cycle identifier; a second influence coefficient module for performing position climate prediction of the power cable to be monitored and establishing a second time-series segmentation influence coefficient based on the position climate prediction result; a decision node generation module for inputting the first time-series segmentation influence coefficient and the second time-series segmentation influence coefficient into a dynamic decision model to establish a dynamic decision node; a node equilibrium analysis module for establishing an equilibrium task demand and an equilibrium climate impact between nodes with the dynamic decision node; a cable information acquisition module for obtaining the cable layout information of the power cable to be monitored, where the cable layout information includes cable layout structure information and cable layout fixed environment information; a retrospective parameter establishment module for configuring a retrospective time window, performing retrospective monitoring of the power cable to be monitored with the retrospective time window, and establishing retrospective basic parameters of the power cable to be monitored; a clustering constraint generation module for the first clustering constraint module to perform an adaptation analysis of the equilibrium task demand and the equilibrium climate impact according to the retrospective basic parameters and generate a first clustering constraint based on the adaptation analysis result; a segmented management execution module for establishing a second clustering constraint according to the cable layout information, establishing a dynamic segmentation strategy according to the first clustering constraint, the second clustering constraint, and the dynamic decision node, allocating an edge joint warning network based on the dynamic segmentation strategy, and performing segmented management based on the edge joint warning network.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By obtaining the pre-execution tasks of the power cable to be monitored, establishing a task cycle identifier, and setting the first time-segment influence coefficient, basic data for subsequent segmented management is provided, ensuring that the management strategy can be adjusted according to specific task requirements; performing location climate prediction on the power cable to be monitored and establishing the second time-segment influence coefficient based on the prediction results, considering the impact of environmental factors on the cable state and enhancing the adaptability of the management strategy; inputting the first time-segment influence coefficient and the second time-segment influence coefficient into a dynamic decision-making model to establish dynamic decision nodes, achieving comprehensive consideration of task requirements and environmental factors and laying a foundation for formulating an intelligent management strategy; establishing an equilibrium task demand and equilibrium climate impact between nodes with the dynamic decision nodes to ensure a balance between tasks and environmental factors among different decision nodes and improving the coordination of the overall management strategy; obtaining the cable layout information of the power cable to be monitored, including structural information and fixed environment information, taking into account the physical characteristics of the cable and further improving the decision-making basis; configuring a retrospective time window to perform retrospective monitoring of the power cable to be monitored, establishing retrospective basic parameters, and providing a reference basis for the current decision through historical data analysis; performing an adaptation analysis of the equilibrium task demand and equilibrium climate impact based on the retrospective basic parameters to generate the first clustering constraint, realizing the combination of historical data and current requirements and improving the accuracy of the decision; establishing the second clustering constraint according to the cable layout information to further refine the management strategy and make it more suitable for the actual cable layout situation; establishing a dynamic segmentation strategy according to the first clustering constraint, the second clustering constraint, and the dynamic decision nodes, allocating an edge joint warning network, and performing segmented management based on this, realizing the technical solution of efficient and accurate segmented management, solving the technical problem in the prior art that the segmented strategy of power cable management cannot be adjusted in real time according to task requirements and environmental changes, resulting in low warning efficiency and accuracy, and achieving the technical effect of dynamically generating a segmented strategy based on multi-dimensional data, realizing precise and intelligent warning and management of power cables, and improving the warning response speed and accuracy.

[0009] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically illustrates the specific embodiments of this application. Brief Description of the Drawings

[0010] Figure 1 It is a schematic flowchart of a method for intelligent segmented management of power cables provided by an embodiment of this application;

[0011] Figure 2This application provides a structural schematic diagram of an intelligent segmented management system for power cables in an embodiment of the present application.

[0012] Explanation of reference numerals: The first influence coefficient module 11, the second influence coefficient module 12, the decision node generation module 13, the node balance analysis module 14, the cable information acquisition module 15, the backtracking parameter establishment module 16, the clustering constraint generation module 17, and the segmented management execution module 18. Specific implementation manners

[0013] The general idea of the technical solution provided by this application is as follows:

[0014] This application provides an intelligent segmented management method and system for power cables. Through the comprehensive analysis of multi-dimensional data and the application of a dynamic decision-making model, the real-time optimization of power cable management strategies and accurate segmented early warning are realized.

[0015] First, obtain the pre-execution task information and location climate prediction data of the power cable to be monitored, and establish the first time-sequence segmentation influence coefficient and the second time-sequence segmentation influence coefficient to lay a foundation for dynamic decision-making. Subsequently, construct decision nodes through a dynamic decision-making model and establish a balanced task demand and climate influence relationship between the nodes. Then, introduce cable layout information and historical monitoring data, and generate clustering constraints through backtracking analysis and adaptation analysis to further refine the management strategy. After that, based on the obtained factors, establish a dynamic segmentation strategy and execute accurate segmented management through an edge joint early warning network, thereby improving the response speed and accuracy of power cable early warning.

[0016] After introducing the basic principle of this application, the various non-limiting implementation manners of this application will be specifically introduced below in conjunction with the accompanying drawings of the specification.

[0017] Embodiment 1, as Figure 1 shown, this application provides an intelligent segmented management method for power cables, and the method includes:

[0018] S1: Obtain the pre-execution task of the power cable to be monitored, establish a task cycle identifier based on the pre-execution task, and set the first time-sequence segmentation influence coefficient based on the task cycle identifier.

[0019] Specifically, first, obtain the pre-execution tasks of the power cable to be monitored in a future period of time. The pre-execution tasks can be routine inspections, repairs, etc. The purpose of obtaining the pre-execution tasks is to understand the working status and task arrangements of the power cable. Then, based on the obtained pre-execution tasks, establish the corresponding task cycle identifier. The task cycle identifier reflects the periodic characteristics of the pre-execution tasks, such as daily, weekly, monthly, etc. Through the task cycle identifier, the pre-execution tasks can be classified and divided according to the cycle. After establishing the task cycle identifier, set the first time-segment influence coefficient based on the task cycle identifier. The first time-segment influence coefficient quantifies the influence degree of different task cycles on the segmented management of the power cable. By setting this coefficient, the influence of different task cycles on the segmented management is quantified, providing a basis for subsequent dynamic decision-making.

[0020] By obtaining the pre-execution tasks, establishing the task cycle identifier, and setting the first time-segment influence coefficient, it provides basic data and quantified influence factors for the subsequent intelligent segmented management of the power cable, obtains the task characteristics of the power cable on different time scales, and converts them into quantitative indicators that can be used for segmented management decision-making.

[0021] S2: Perform the location climate prediction of the power cable to be monitored, and establish the second time-segment influence coefficient based on the location climate prediction result.

[0022] Specifically, by analyzing the historical climate data of the area where the power cable is located and using the climate prediction model, obtain the climate change trend and the possibility of extreme weather events occurring in the area in a future period of time. Among them, the location climate prediction result includes the change conditions of multiple climate elements such as temperature, humidity, precipitation, and wind speed. According to the predicted values of these climate elements, it is possible to judge the climate environment in which the power cable is located in different time periods and evaluate the influence degree of the climate conditions on the cable. For example, by analyzing the historical climate data of the past 10 years and using the ARIMA time series model, it is predicted that the average temperature in a certain area will be 3°C higher than the historical average in the next month, the precipitation will decrease by 20% compared with the historical average, and there is a 30% probability of high-temperature weather with a daily maximum temperature exceeding 35°C in the next month. Based on the predicted values of these climate elements, thus judge the climate environment in which the power cable is located in different time periods and evaluate the influence degree of the climate conditions on the cable.

[0023] Then, based on the location climate prediction results, a second time-series segmented impact coefficient is established. The second time-series segmented impact coefficient quantifies the impact degree of different climate conditions on the segmented management of power cables. For example, extreme weather such as high temperature and heavy precipitation will accelerate the aging of the cables and increase the risk of faults. Therefore, the corresponding second time-series segmented impact coefficient will be relatively high. Under mild climate conditions, the operating environment of the cables is relatively stable, and the corresponding impact coefficient is relatively low. For example, according to the location climate prediction results, the probability of high temperature weather with a daily maximum temperature exceeding 35°C in the next month is 30%. Since high temperature weather will accelerate the aging of cable insulation, the "probability of high temperature weather in the next month" is used as an impact factor and multiplied by a weight coefficient to obtain the contribution value to the second time-series segmented impact coefficient. Similarly, the "amplitude of the average temperature increase in the next month compared with the historical same period" is used as another impact factor and multiplied by another weight coefficient to obtain the corresponding contribution value. By adding up the contribution values of all impact factors, the second time-series segmented impact coefficient can be obtained.

[0024] By establishing the second time-series segmented impact coefficient, the impact of climate factors on the segmented management of power cables is quantified, providing a more comprehensive basis for subsequent dynamic decision-making, realizing the inclusion of the climate environment factors where the power cables are located in the consideration scope of segmented management, and improving the accuracy and adaptability of segmented management.

[0025] S3: Input the first time-series segmented impact coefficient and the second time-series segmented impact coefficient into the dynamic decision-making model to establish dynamic decision-making nodes.

[0026] Specifically, the dynamic decision-making model is a computer model that can dynamically adjust the decision-making scheme according to the real-time input data. The dynamic decision-making model is based on artificial intelligence algorithms such as reinforcement learning and decision trees. Through learning historical data and analyzing the current state, it autonomously generates and optimizes the decision-making scheme. The input data of the dynamic decision-making model includes the first time-series segmented impact coefficient and the second time-series segmented impact coefficient, and the output result is the dynamic decision-making node. The first time-series segmented impact coefficient reflects the working state and task characteristics of the power cables in different task cycles, and the second time-series segmented impact coefficient reflects the change trend of the climate environment where the power cables are located and the possibility of extreme weather events. By inputting these two impact coefficients into the dynamic decision-making model, the dynamic decision-making model comprehensively considers the impacts of the task cycle and the climate environment and establishes dynamic decision-making nodes. Each dynamic decision-making node represents the optimal segmented management scheme of the power cables in a specific time period. For example, the segmented management scheme of a certain dynamic decision-making node: in the next week, key monitoring will be carried out on sections A and B of the power cables, with 2 inspections per day, and the detection indicators include conductor temperature and tension; routine monitoring will be carried out on sections C and D, with 1 inspection per week, and the detection indicators include conductor temperature and insulation resistance.

[0027] Since the decision nodes in the dynamic decision-making model are dynamically generated based on the influence coefficients input in real time, the content of the dynamic decision nodes will change dynamically over time. For example, if the influence coefficient of the second time series segment changes within the next week (such as the meteorological department issues a high temperature warning), the dynamic decision-making model will automatically adjust the decision nodes, increase the monitoring frequency of section A and section B, or add the degree of conductor surface contamination as a detection index. Through dynamic adjustment, the section management plan can adapt to the changes in task requirements and climate environment in real time, improving the accuracy and timeliness of management.

[0028] By inputting the influence coefficient of the first time series segment and the influence coefficient of the second time series segment into the dynamic decision-making model, dynamic decision nodes are established. Each dynamic decision node represents the optimal section management plan for the power cable within a certain time period, which can comprehensively consider the influence of the task cycle and climate environment, and dynamically adjust according to the data input in real time, so as to achieve the precision and self-adaptability of power cable management, and provide a decision-making basis for the subsequent implementation of section management.

[0029] S4: Establish the balanced task requirements and balanced climate impacts among the nodes based on the dynamic decision nodes.

[0030] Specifically, since each dynamic decision node represents the optimal section management plan for the power cable within a certain time period, there are differences in task requirements and climate impacts among different decision nodes. For example, during the period with intensive tasks, the dynamic decision nodes allocate more inspection and maintenance tasks; during the period with frequent extreme weather, the dynamic decision nodes allocate more monitoring and protection measures. Such differences among nodes will lead to unbalanced section management, with excessive management tasks in some time periods and insufficient management tasks in other time periods.

[0031] Therefore, based on the dynamic decision nodes, the balanced task requirements and balanced climate impacts among the nodes are established. Balanced task requirements mean that, under the condition of unchanged overall task requirements, the task requirements of different decision nodes are adjusted and balanced so that the management task volume in each time period is as balanced as possible. For example, some tasks during the period with intensive tasks are transferred to the period with fewer tasks, or some preventive maintenance tasks are arranged during the period with fewer tasks to balance the task distribution throughout the management cycle. Balanced climate impacts mean that, under the condition of unchanged overall climate impacts, the climate impacts of different decision nodes are adjusted and balanced so that the adaptability of the management plan to climate change in each time period is as consistent as possible. For example, for the period with frequent extreme weather, the monitoring frequency and protection measures in adjacent time periods are appropriately increased to improve the climate adaptability throughout the management cycle; for the period with relatively stable climate conditions, the monitoring and protection requirements in adjacent time periods are appropriately simplified to balance the management cost and management effect.

[0032] In the process of establishing balanced task requirements and balanced climate impacts, multiple factors need to be comprehensively considered, such as the overall task volume, climate change trends, management costs, etc. For example, optimization algorithms (such as integer programming, dynamic programming, etc.) can be used to solve the optimal balanced solution, or heuristic algorithms (such as simulated annealing, genetic algorithms, etc.) can be used to quickly search for approximate optimal solutions. The purpose of establishing balanced task requirements and balanced climate impacts is to enable the power cable to be relatively balanced and consistent in management throughout the entire management cycle, avoid drastic fluctuations in management tasks and management effects, improve the integrity and stability of segmented management, achieve further optimization and adjustment of dynamic decision-making nodes, make the segmented management plan smoother and more coherent, and better meet the long-term management needs of power cables.

[0033] By establishing balanced task requirements and balanced climate impacts among dynamic decision-making nodes, optimizing the time distribution and climate adaptability of the segmented management plan, the integrity and stability of management are improved, thus achieving precise and long-term management.

[0034] S5: Obtain the cable layout information of the power cable to be monitored, where the cable layout information includes cable layout structure information and cable layout and fixation environment information.

[0035] Specifically, by consulting historical materials such as engineering design drawings and construction records, extracting the structural parameters and environmental conditions of the cable layout, the cable layout information of the power cable to be monitored is obtained, including cable layout structure information and cable layout and fixation environment information. The cable layout structure information refers to the layout method and structural characteristics of the power cable in space, including the cable layout path (such as along the street, through the forest area, etc.), layout form (such as overhead, underground, etc.), cable model and specifications (such as conductor cross-sectional area, insulation level, etc.), pole type and spacing, etc., which reflects the physical structure characteristics of the power cable and is an important factor affecting the cable's stress, heat dissipation, and insulation performance. By obtaining the cable layout structure information, the spatial distribution and structural characteristics of the power cable to be monitored are mastered, providing basic data for subsequent segmented management. The cable layout and fixation environment information refers to the surrounding environment and fixation conditions where the power cable is located, including the geological conditions (such as soil quality, terrain, etc.) passed by the cable, the distribution of surrounding buildings and trees, the type and status of cable support and fixation devices (such as electric poles, guy wires, etc.), which reflects the characteristics of the environment where the power cable is located. By obtaining the cable layout and fixation environment information, the characteristics and potential risks of the environment where the power cable to be monitored is located are mastered, providing an environmental basis for subsequent segmented management.

[0036] By obtaining the cable layout information of the power cable to be monitored, including the cable layout structure information and the cable layout and fixing environment information, the spatial distribution characteristics of the power cable and the characteristics of the environment where it is located are grasped, reflecting the influencing factors of the power cable in terms of stress, heat dissipation, insulation, corrosion, etc., providing an important basis for accurate segmented management.

[0037] S6: Configure a retrospective time window, and perform retrospective monitoring of the power cable to be monitored with the retrospective time window to establish the retrospective basic parameters of the power cable to be monitored.

[0038] Specifically, the retrospective time window refers to a period of time before the current moment, which is used for retrospective analysis of the historical monitoring data of the power cable. The length of the retrospective time window is set according to actual needs. For example, it ranges from several months to one year. By configuring the retrospective time window, the time range of the historical monitoring data of the power cable to be monitored can be determined, facilitating its analysis and mining.

[0039] After determining the retrospective time window, perform retrospective monitoring of the power cable to be monitored with the retrospective time window. Retrospective monitoring refers to the process of collecting, sorting, and analyzing the historical monitoring data of the power cable to be monitored within the retrospective time window. Among them, the historical monitoring data includes electrical parameters of the cable (such as current, voltage, power, etc.), environmental parameters (such as temperature, humidity, wind speed, etc.), mechanical parameters (such as tension, vibration, inclination, etc.) and other multi-dimensional data. Through retrospective monitoring, comprehensively understand the operating status and performance of the power cable to be monitored in the past period of time, providing a historical basis for subsequent segmented management.

[0040] After completing the retrospective monitoring, based on the retrospective monitoring data, establish the retrospective basic parameters of the power cable to be monitored. The retrospective basic parameters refer to the key indicators extracted by analyzing and mining the retrospective monitoring data, reflecting the historical operating characteristics of the power cable, including the average current, maximum current, average temperature, highest temperature, average wind speed, maximum wind speed, conductor vibration frequency, tower inclination angle, etc. By establishing the retrospective basic parameters, quantitatively describe the operating status and performance level of the power cable within the retrospective time window, providing a quantitative basis for subsequent segmented management decisions. For example, by statistically analyzing the current data within the retrospective time window, parameters such as the average current and maximum current of the cable are obtained, reflecting the load level and overload risk of the cable; by analyzing the temperature data within the retrospective time window, parameters such as the average temperature and highest temperature of the cable are obtained, reflecting the heat dissipation status and over-temperature risk of the cable; by performing spectral analysis on the vibration data within the retrospective time window, parameters such as the main frequency and amplitude of the conductor vibration are obtained, reflecting the mechanical vibration characteristics and fatigue risk of the cable.

[0041] By configuring the backtracking time window, the power cable to be monitored is backtracked and monitored, and the backtracking basic parameters are established based on the backtracking monitoring data. The historical operation big data of the power cable is fully utilized, and the key indicators reflecting the characteristics of the cable are extracted through data mining, providing a comprehensive and quantitative basis for subsequent segmented management decisions.

[0042] S7: Perform an adaptation analysis of balancing task requirements and climate impacts according to the backtracking basic parameters, and generate the first clustering constraint based on the results of the adaptation analysis.

[0043] Specifically, the adaptation analysis refers to matching and correlating the backtracking basic parameters with the balancing task requirements and climate impacts, and evaluating the adaptation degree and correlation between the two. Through the adaptation analysis, the internal relationship between the operation characteristics of the power cable in the historical period and the current management tasks and environmental conditions is revealed, providing a basis for optimizing the segmented management plan. When performing the adaptation analysis, the backtracking basic parameters are compared and correlated with the balancing task requirements and climate impacts in multiple dimensions. For example, the average current parameter of the cable is compared with the load prediction value in the task requirements to evaluate the matching degree between the power supply capacity of the cable and the load demand; the highest temperature parameter of the cable is compared with the high temperature warning value in the climate impact to evaluate the adaptation degree of the heat dissipation capacity of the cable to the ambient temperature; the fault history of the cable is correlated with the task requirements and climate impacts to evaluate the cable fault risk under different tasks and climate conditions. Through the adaptation analysis, an adaptation degree matrix or adaptation degree level between the backtracking basic parameters and the balancing task requirements and climate impacts is obtained. The adaptation degree matrix is a multi-dimensional matrix, where each element represents the adaptation degree between a certain backtracking basic parameter and a certain task requirement or climate impact factor, and the value range is [0, 1] or [-1, 1], etc. The adaptation degree level is the level label obtained by discretizing the adaptation degree matrix, such as "highly adapted", "moderately adapted", "lowly adapted", etc.

[0044] Subsequently, based on the adaptation degree matrix or adaptation degree level obtained from the adaptation analysis, a first clustering constraint is generated. The first clustering constraint refers to the parameter similarity constraint conditions that need to be satisfied when segmenting and clustering power cables. Specifically, for the backtracking basic parameters with a high adaptation degree and the equilibrium task requirements / equilibrium climate impacts, they should be clustered into the same cable segment; while for the parameters and factors with a low adaptation degree, they should be clustered into different cable segments. In this way, it is ensured that the parameter similarity within each cable segment is high, while the parameter differences between segments are large, which helps to achieve refined and differentiated segment management. For example, if the adaptation analysis finds that the average current parameter of cable A has a very high adaptation degree with the load prediction value of a certain heavy-load line, while the adaptation degree of the average current parameter of cable B with this load prediction value is very low, then a first clustering constraint is generated: cable A should be preferentially clustered into the same management segment as this heavy-load line, while cable B should not be clustered with this heavy-load line.

[0045] Through the adaptation analysis, the backtracking basic parameters are matched and associated with the equilibrium task requirements and equilibrium climate impacts in multiple dimensions, and a first clustering constraint is generated based on the adaptation analysis results, establishing a connection between the historical operating characteristics of the cable and the current management requirements, providing a parameter similarity constraint for subsequent segment clustering, which helps to achieve refined and differentiated segment management and improve the pertinence and effectiveness of cable management.

[0046] S8: Establish a second clustering constraint according to the cable layout information, establish a dynamic segmentation strategy based on the first clustering constraint, the second clustering constraint, and the dynamic decision nodes, allocate the edge joint warning network based on the dynamic segmentation strategy, and perform segment management based on the edge joint warning network.

[0047] Specifically, the second clustering constraint refers to the cable layout similarity constraint conditions that need to be satisfied when segmenting and clustering power cables. The cable layout information includes the cable layout structure information and the cable layout and fixing environment information, reflecting the distribution characteristics of the power cable in space and the characteristics of the environment where it is located. For cables with similar layout structures and similar layout environments, they usually have similar physical properties and external influencing factors, so they should be clustered into the same management segment. For example, for cables with the same wire cross-sectional area, the same wire material, and the same tower type, their physical parameters such as conductivity, mechanical strength, and thermal expansion and contraction characteristics are very close, so a second clustering constraint can be established: requiring these cables to be clustered into the same management segment. Another example is that for cables with similar terrain conditions, similar surrounding building distributions, and similar environmental temperatures, the external physical impacts (such as wind load, ice load, solar radiation, etc.) they receive are usually very similar, so a second clustering constraint can also be established: requiring these cables to be clustered into the same management segment as much as possible.

[0048] After establishing the first clustering constraint and the second clustering constraint, they are integrated with the established dynamic decision nodes to establish a dynamic segmentation strategy. The dynamic segmentation strategy refers to a strategy for dynamically segmenting power cables based on multiple clustering constraint conditions and decision nodes, which stipulates how to adopt segmentation schemes at different time nodes, different task requirements, and different environmental conditions, as well as how to dynamically adjust and optimize between different segments. Among them, the establishment of the dynamic segmentation strategy adopts artificial intelligence algorithms such as multi-objective optimization, decision tree learning, and reinforcement learning. By modeling and optimizing multiple constraint conditions and decision variables such as the first clustering constraint, the second clustering constraint, and dynamic decision nodes, a comprehensive and dynamically changing segmentation strategy is obtained, which can adaptively adjust the segmentation scheme according to multiple factors such as the historical characteristics, current requirements, and future predictions of the cable, balance various management objectives, and achieve the dynamic optimization of segmentation management.

[0049] After that, based on the dynamic segmentation strategy, the edge joint warning network is further allocated. The edge joint warning network is a distributed and collaborative cable monitoring and warning system, which consists of multiple monitoring terminals, communication nodes, data processing units, etc. distributed on different cable segments. By mapping the dynamic segmentation strategy onto the edge joint warning network, differential monitoring and warning of different cable segments are realized, improving the accuracy of monitoring and the timeliness of warning. For example, according to the dynamic segmentation strategy, important cable segments and high-risk cable segments are allocated to high-performance monitoring terminals and high-speed communication nodes in the edge joint warning network to achieve key monitoring and rapid warning of these cables; while ordinary cable segments and low-risk cable segments are allocated to conventional monitoring terminals and low-speed communication nodes in the edge joint warning network to achieve daily monitoring and regular warning of these cables. After that, based on the allocated edge joint warning network, segmentation management is executed. Segmentation management refers to implementing differential and refined management measures for different cable segments according to the dynamic segmentation strategy and the edge joint warning network, including real-time monitoring, status assessment, risk warning, maintenance decision-making, etc. By continuously collecting the monitoring data of each cable segment through the edge joint warning network and analyzing and making decisions according to the dynamic segmentation strategy, the all-round, full-cycle, and whole-process management of the cable can be realized, improving the safety, reliability, and economy of cable operation.

[0050] By establishing the second clustering constraint according to the cable layout information, then integrating the first clustering constraint, the second clustering constraint, and the dynamic decision nodes to establish a dynamic segmentation strategy, allocating the edge joint warning network based on the dynamic segmentation strategy, and executing segmentation management according to the edge joint warning network, the intelligent upgrade of power cables from static segmentation to dynamic management is fully realized, and the efficient, accurate, and collaborative segmentation management of the entire cable life cycle is achieved.

[0051] Further, the embodiments of the present application further include:

[0052] Obtain the cable material information of the power cable to be monitored, conduct a material backtracking impact evaluation of the cable based on the cable material information, and establish an aging backtracking score;

[0053] Conduct a seasonal climate change evaluation of the area where the power cable to be monitored is located, establish a climate cycle, obtain the occurrence frequency of extreme weather in the area, and construct a seasonal climate change score and an extreme weather event frequency score respectively according to the climate cycle and the occurrence frequency;

[0054] Establish a comprehensive score based on the aging backtracking score, the seasonal climate change score, and the extreme weather event frequency score, determine the length of the backtracking time window based on the comprehensive score, and complete the configuration of the backtracking time window.

[0055] In a feasible implementation manner, first obtain the cable material information of the power cable to be monitored. The cable material information includes conductor materials (such as copper, aluminum, etc.), insulation materials (such as cross-linked polyethylene, silicone rubber, etc.), sheath materials (such as polyvinyl chloride, polyethylene, etc.), etc. The physical and chemical properties of different materials are different, so their aging processes and influencing factors are also different. Based on the obtained cable material information, conduct a material backtracking impact evaluation of the cable. The material backtracking impact evaluation refers to analyzing the aging laws and influencing factors of different materials during long-term operation, and evaluating the degree of influence of material aging on the cable performance. For example, for copper conductors, the evaluation indicators include conductivity, tensile strength, elongation, etc., and the influencing factors include temperature, humidity, vibration, etc.; for cross-linked polyethylene insulation, the evaluation indicators include insulation resistance, dielectric loss, breakdown strength, etc., and the influencing factors include temperature, electric field strength, partial discharge, etc. Through the material backtracking impact evaluation, a quantified aging backtracking score is obtained, which reflects the aging degree and performance attenuation of the cable material during long-term operation. The higher the score, the more serious the aging of the cable material, the greater the impact on the cable performance, and the longer the backtracking time window is required for monitoring and analysis.

[0056] Then, conduct a seasonal climate change assessment on the area where the power cable to be monitored is located. The climate conditions vary greatly in different regions, so it is necessary to specifically analyze the impact of climate factors on the cable. First, establish a climate cycle, which reflects the periodic change law of the regional climate on an annual scale, such as the alternation of the four seasons, the coincidence of rainfall and heat, etc. According to the meteorological data of this region over the years, methods such as Fourier analysis and wavelet analysis are used to extract the characteristics of the climate cycle. Secondly, obtain the occurrence frequency of extreme weather events in the region. Extreme weather events such as typhoons, heavy rains, and ice disasters will pose a serious threat to power cables, so they need to be focused on. By counting the number of extreme weather events in historical meteorological data, calculate their occurrence frequency. Then, based on the climate cycle and the occurrence frequency of extreme weather events, construct a seasonal climate change score and an extreme weather event frequency score respectively. The seasonal climate change score reflects the degree of influence of the periodic characteristics of the regional climate on the cable performance. Using methods such as fuzzy comprehensive evaluation and analytic hierarchy process, the climate cycle characteristics are associated with the cable performance impact to form a seasonal climate change score. The extreme weather event frequency score reflects the threat degree of extreme weather events to the cable performance. Map the event frequency into a fixed score interval. The higher the frequency, the higher the extreme weather event frequency score.

[0057] Subsequently, based on the aging backtracking score, the seasonal climate change score, and the extreme weather event frequency score, establish a comprehensive score. The comprehensive score reflects the comprehensive impact of multiple factors such as cable materials and regional climate conditions on the cable performance, and is the direct basis for determining the length of the backtracking time window. For example, using methods such as weighted average and geometric average, fuse the three scoring indicators into a comprehensive score. Then, based on the comprehensive score, determine the length of the backtracking time window. Among them, the higher the comprehensive score value, the more serious the aging of the cable material and the more severe the regional climate environment. Therefore, a longer backtracking time window is required for comprehensive historical data analysis; conversely, if the comprehensive score value is lower, appropriately shorten the length of the backtracking time window.

[0058] Through multiple dimensions such as cable material evaluation and regional climate evaluation, quantitatively evaluate the key factors affecting cable performance, and determine the length of the backtracking time window based on the comprehensive score, which more comprehensively and objectively reflects the actual operating state of the cable, helps to select the optimal backtracking time window, and improves the pertinence and effectiveness of cable monitoring and analysis.

[0059] Furthermore, the embodiments of this application also include:

[0060] Configure the comprehensive scoring formula as follows:

[0061] ;

[0062] Wherein, is the comprehensive score, Characterize any one scoring feature, is the feature corresponding weight, Characterize the feature scoring function, where:

[0063] ;

[0064] ;

[0065] ;

[0066] Characterize the aging backtracking score, and are the linear coefficients, Characterize the cable aging speed feature obtained based on cable material information, Characterize the seasonal climate change score, Characterize the amplitude coefficient, Characterize the period, is the input variable of seasonal climate change, Characterize the extreme weather event frequency score, and are the adjustment coefficients, is the occurrence frequency;

[0067] ;

[0068] is the length of the backtracking time window, and are the shortest and longest optional values of the time window respectively.

[0069] In a preferred embodiment, a specific comprehensive score calculation method is given. By configuring the comprehensive score formula, the aging backtracking score, seasonal climate change score, and extreme weather event frequency score are quantitatively integrated to obtain a comprehensive score that comprehensively reflects the cable state, and the length of the backtracking time window is determined based on this comprehensive score.

[0070] Specifically, the comprehensive score formula adopts the form of weighted summation to obtain the comprehensive score. The comprehensive score formula is: ; where, is the comprehensive score, represents any one scoring index, and the value range is from 1 to 3, corresponding to the aging backtracking score, seasonal climate change score, and extreme weather event frequency score respectively; represents the th weight coefficient of the scoring index, reflecting the importance of this index in the comprehensive score; Represents the scoring function for the th scoring metric, which is used to map the original metric value into a unified scoring range. For the scoring functions of three scoring metrics, the specific function forms are given. The scoring function for the aging backtracking score is: , where represents the cable aging speed characteristic obtained based on the cable material information, and are linear coefficients, which are used to adjust the slope and intercept of the linear function, reflecting the positive correlation between the cable aging speed characteristic and the aging backtracking score. The scoring function for the seasonal climate change score is: , where is the input variable of the seasonal climate change, is the amplitude coefficient, is the period, which describes the periodic characteristic of the seasonal climate change. The scoring function for the extreme weather event frequency score is: , where is the occurrence frequency of the extreme weather event, and are adjustment coefficients, which describe the non-linear relationship between the extreme weather event frequency and its impact degree.

[0071] After obtaining the comprehensive score , the length of the backtracking time window is determined according to the size, that is, , where and are respectively the minimum and maximum values of the time window length, reflecting the negative correlation between the comprehensive score value and the backtracking time window length, that is, the higher the comprehensive score, the worse the cable state, and the longer the backtracking time window is required; on the contrary, the lower the comprehensive score value, the better the cable state, and the backtracking time window can be appropriately shortened. By adjusting and , the value range of the backtracking time window length can be set according to actual needs.

[0072] By configuring the comprehensive scoring formula, multiple cable state scoring metrics are quantitatively integrated, and multiple influencing factors such as cable material aging, regional climate change, and extreme weather events are comprehensively considered, which can objectively and accurately evaluate the overall state of the cable, and accordingly determine the optimal backtracking time window length, providing an important basis for subsequent cable segment management.

[0073] Furthermore, the embodiments of the present application further include:

[0074] Performing similarity clustering on the segmentation results through the dynamic segmentation strategy to generate a segmentation similarity clustering result;

[0075] A baseline early warning network is established based on the segmented similarity clustering results, and incremental learning based on the similarity clustering results is performed through the baseline early warning network to build multiple early warning networks mapped to the dynamic segmentation strategy.

[0076] The combined influence of each early warning network is established based on the dynamic segmentation strategy, and the edge combined early warning network allocation is completed through the combined influence.

[0077] In a feasible implementation manner, through segmented similarity clustering, incremental learning of the baseline early warning network, and analysis of the combined influence of the early warning network, the accurate mapping and efficient deployment of the early warning network and the dynamic segmentation strategy are realized.

[0078] First, the power cable is segmented using the dynamic segmentation strategy, and the segmentation results are subjected to similarity clustering. Similarity clustering means dividing cable segments with similar characteristics into the same clustering cluster, so that the similarity within the cluster is the highest and the difference between clusters is the largest. For example, clustering algorithms such as k-means and DBSCAN are used, or clustering methods based on deep learning, such as autoencoders and convolutional neural networks, are used. By performing similarity clustering on the segmentation results, several segmented similarity clustering clusters are obtained. The cable segments within each clustering cluster have similar electrical characteristics, mechanical characteristics, environmental characteristics, etc., and similar early warning strategies and early warning thresholds can be adopted. Through the similarity-based segmented clustering method, the complexity of the design and deployment of the early warning network can be greatly reduced, and the accuracy and real-time performance of the early warning decision-making can be improved. Then, based on the segmented similarity clustering results, a baseline early warning network is established. The baseline early warning network is a standardized early warning model for a certain type of cable segment, including key monitoring indicators, early warning thresholds, decision rules, etc. for this type of segment. According to methods such as expert experience and historical data analysis, a baseline early warning network is designed for each segmented clustering cluster. After that, the baseline early warning network is used to perform incremental learning on the cable segments within each clustering cluster. According to the newly collected monitoring data, the parameters and structure of the early warning network are continuously updated and optimized to enable it to adapt to the changing trend of the cable state. Specifically, the cable segment data within each clustering cluster is input into the corresponding baseline early warning network, and through machine learning algorithms such as backpropagation and gradient descent, the parameters such as the weights and thresholds of the network are fine-tuned and optimized, so as to obtain personalized early warning networks for different cable segments, and multiple early warning networks are obtained. Through the incremental learning of the baseline early warning network, while ensuring the consistency and interpretability of the early warning decision-making, the individual characteristics and dynamic change information of each cable segment are fully utilized to improve the adaptability and accuracy of the early warning network. At the same time, the incremental learning method based on the similarity clustering results reduces the training complexity and computational cost of the early warning network, enabling the early warning network to be quickly and flexibly deployed to the edge side to achieve distributed real-time early warning.

[0079] After obtaining multiple warning networks for different cable segments, further analyze the combined effects among the warning networks to achieve collaborative optimization and overall deployment of the edge-side warning networks. The analysis of the combined effects of warning networks mainly includes two aspects: one is the logical and dependency relationships among different warning networks, and the other is the degree of influence of different warning networks on the overall state of the cable. For the logical and dependency relationships among warning networks, based on the segmentation logic and decision-making process in the dynamic segmentation strategy, construct a directed acyclic graph of the warning networks. Among them, each node represents a warning network, and the directed edges between nodes represent the dependency relationships and sequence of warning decisions. By analyzing the topological structure and critical path of the directed acyclic graph of the warning networks, optimize the execution process of warning decisions to improve the speed and accuracy of warning responses. For the degree of influence of warning networks on the overall state of the cable, use methods such as sensitivity analysis and feature importance ranking to quantify the influence weights of each warning network on the key performance indicators of the cable (such as failure rate, availability, etc.). By calculating the influence weights of warning networks, determine the priority and resource allocation strategies for the deployment of different warning networks on the edge side, and achieve optimized configuration and dynamic scheduling of warning networks. Subsequently, comprehensively consider the logical dependency relationships and influence weights of warning networks to obtain the deployment plan of the edge joint warning network, clarify the deployment locations, execution sequences, resource allocations, etc. of each warning network on the edge side, and achieve precise mapping and collaborative optimization between the warning network and the dynamic segmentation strategy. Based on the edge joint warning network, distributed, real-time, and collaborative cable status monitoring and warning can be realized, greatly improving the intelligent level and management and control efficiency of cable operation and maintenance.

[0080] Through the precise mapping and efficient deployment of the warning network and the dynamic segmentation strategy, the similarity and dynamic characteristics of cable segmentation are fully utilized, improving the accuracy, real-time performance, and scalability of warning decisions, and providing important technical support for intelligent cable operation and maintenance.

[0081] Furthermore, the embodiments of this application also include:

[0082] Perform segmented monitoring of the power cable to be monitored through the edge joint warning network, and generate independent segmented warning results;

[0083] Activate the joint analysis layer of the edge joint warning network, perform edge joint analysis with the joint analysis layer, and establish joint segmented warning results;

[0084] Verify and identify the independent segmented warning results and joint segmented warning results, and report verification warning results based on the verification and identification results.

[0085] In a preferred embodiment, first, the edge joint early warning network that has been deployed in place is utilized to conduct segmented monitoring on the power cable to be monitored. Through Internet of Things devices such as sensors and intelligent terminals deployed on each cable segment, multi-source heterogeneous monitoring data such as the current, voltage, temperature, and vibration of the cable are collected in real time, and this data is transmitted to the early warning network node on the edge side for local processing and analysis. Within each early warning network node, feature extraction, pattern recognition, and anomaly detection are performed on the local monitoring data to generate a local early warning result for this cable segment, that is, an independent segmented early warning result, which reflects the operating status and potential risks of a single cable segment, including various types of early warning information such as local overload, local overheating, and partial discharge of the cable. By processing and analyzing a large amount of segmented monitoring data in parallel on the edge side, the real-time performance and scalability of the early warning analysis are improved, and the computing and storage pressure on the central cloud platform are reduced. At the same time, since each early warning network node only processes local monitoring data, the individual characteristics of the cable segment and environmental factors can be fully utilized to improve the pertinence and accuracy of the early warning analysis.

[0086] To further improve the comprehensiveness and consistency of the early warning analysis, a joint analysis layer is introduced into the edge joint early warning network. The joint analysis layer realizes the global state assessment and risk prediction of the entire cable by connecting and coordinating each early warning network node. Specifically, the joint analysis layer organizes each early warning network node into a hierarchical and ordered analysis process in the form of a directed acyclic graph. Each node represents an early warning network node, and the directed edges between the nodes represent data transmission and control dependency relationships, thereby realizing collaborative analysis and result fusion between the early warning network nodes. When an independent segmented early warning result is generated by a certain early warning network node, this result is transmitted to the next node in the directed acyclic graph for further analysis. The next node conducts correlation analysis on the received local early warning result and the local monitoring data to generate higher-level early warning information. Through such recursive propagation and analysis, until reaching the root node of the directed acyclic graph, the global early warning result of the entire cable can be obtained, that is, the joint segmented early warning result. The joint segmented early warning result synthesizes the local early warning information of each cable segment and considers the correlation effects and transmission effects between the segments. Therefore, it can more comprehensively and accurately reflect the health status and risk level of the entire cable. Based on the joint segmented early warning result, the systematic risks and cascading failures of the cable can be discovered in a timely manner, providing a more reliable basis for cable operation and maintenance decisions.

[0087] Subsequently, the independent segment warning results and the combined segment warning results are verified and identified. The purpose of verification and identification is to exclude uncertain factors such as false alarms and missed alarms in the warning results through cross-verification and comprehensive analysis of multi-source data, and improve the confidence and interpretability of the warning results. For example, using prior knowledge such as physical models and electrical laws to conduct rationality checks and consistency verification on the independent segment warning results and the combined segment warning results, and exclude warning information that violates physical constraints or logical contradictions; using reference information such as historical operation data and simulation test data to conduct comparative analysis and threshold verification on the independent segment warning results and the combined segment warning results, and identify abnormal deviations and unreasonable fluctuations in the warning results; using artificial intelligence methods such as expert experience and decision rules to conduct semantic understanding and scenario analysis on the independent segment warning results and the combined segment warning results, and identify semantic errors and context contradictions in the warning results. By comprehensively diagnosing the quality and evaluating the credibility of the independent segment warning results and the combined segment warning results from multiple dimensions, the verified warning results are obtained, providing more complete and reliable decision support information for cable management.

[0088] Furthermore, the embodiments of the present application further include:

[0089] Obtain the warning identification evaluation identifier of the edge joint warning network;

[0090] Establish the construction adaptation feedback of the dynamic decision node through the warning identification evaluation identifier;

[0091] Execute the adaptive compensation of the dynamic decision model based on the construction adaptation feedback.

[0092] In a feasible implementation, first, obtain the early warning recognition evaluation identifier of the edge joint early warning network. The early warning recognition evaluation identifier is a set of indicators for evaluating and scoring the early warning performance and recognition quality of the early warning network, reflecting the key performances such as effectiveness, accuracy, and reliability of the early warning network in actual applications. For example, during the actual operation stage of the early warning network, through manual review and post-event analysis of the early warning results, indicators such as the accuracy rate, missed alarm rate, and false alarm rate of the early warning network are statistically analyzed to evaluate the early warning network and obtain the early warning recognition evaluation identifier. Then, using the early warning recognition evaluation identifier, establish the construction adaptation feedback of the dynamic decision node. The construction adaptation feedback is a mechanism that correlates and maps the evaluation results of the early warning network with the construction process of the decision model. By identifying the advantages and disadvantages of the early warning network, dynamically adjust the structure and parameters of the decision model to achieve the collaborative optimization of the two. Specifically, first, classify and aggregate the early warning recognition evaluation identifier according to different cable segments and decision nodes to form an adaptation evaluation vector for the decision-making process; then, analyze the correlation and sensitivity between the adaptation evaluation vector and the construction parameters of the decision node to identify the key decision factors that have the greatest impact on the early warning performance; afterwards, according to the value and change trend of the adaptation evaluation vector, dynamically adjust the construction parameters of the decision node, such as the segment boundary, feature selection, decision threshold, etc., to make it adapt to the actual performance of the early warning network. By establishing the construction adaptation feedback of the dynamic decision node, realize the two-way interaction and co-evolution between the decision model and the early warning network. On the one hand, the evaluation results of the early warning network can provide data support and improvement directions for the optimization of the decision model, helping the decision model better adapt to the actual cable environment and monitoring requirements; on the other hand, the dynamic adjustment of the decision model can also provide guidance and constraints for the training and update of the early warning network, helping the early warning network identify and locate cable risks more accurately and efficiently.

[0093] Subsequently, based on the construction adaptation feedback, perform adaptive compensation on the dynamic decision model to improve the adaptability, robustness, and interpretability of the decision model. For example, based on the key decision factors identified by the construction adaptation feedback, introduce corresponding compensation terms or correction factors into the decision model, and dynamically adjust the parameter values and threshold judgments in the decision-making process to make the decision results closer to the actual cable state. By performing adaptive compensation on the dynamic decision model, realize the dynamic alignment and continuous optimization of the decision-making process and the early warning results, effectively reduce the deviation and misjudgment between the decision model and the actual cable state, improve the accuracy and reliability of the decision-making scheme, and at the same time continuously enhance the generalization ability and adaptation ability of the decision model, enabling it to better cope with the complex and changing cable environment and monitoring requirements.

[0094] Furthermore, the embodiments of the present application further include:

[0095] Configure the early warning time sequence period based on the recognition result of the edge joint early warning network;

[0096] Verify the response effect according to the warning time sequence period, and establish a progressive response feedback factor based on the verification result of the response effect;

[0097] Optimize the feedback of warning identification through the progressive response feedback factor.

[0098] In a preferred embodiment, the warning time sequence period refers to the time interval or frequency for warning analysis and decision-making response for specific cable segments within a continuous time range. A reasonable warning time sequence period can ensure the timeliness and accuracy of warnings while avoiding the waste of computing resources and the cost of human intervention caused by overly frequent warning decisions. For example, according to the edge joint warning network, for important backbone cables, configure a warning period at the hourly level to achieve 24-hour uninterrupted monitoring; for secondary branch cables, configure a daily warning period for warning analysis once a day; for low-risk auxiliary cables, configure a weekly or monthly warning period for regular spot checks and inspections. Then, based on the configured warning time sequence period, track and verify the response effect of the warning decision to obtain the response effect verification. The response effect verification refers to evaluating the impact and improvement degree of the warning decision on the actual cable operation state after each warning time sequence period ends, so as to judge the effectiveness and timeliness of the warning decision. For example, compare the key performance indicators of the cable before and after the warning decision, such as failure rate, availability, power quality, etc., to evaluate the improvement effect of the warning decision on the cable health state; analyze the implementation situation of the warning decision and the input of human and material resources to evaluate the operability and economy of the warning decision; collect the feedback opinions of cable operation and maintenance personnel and users to evaluate the interpretability and user experience of the warning decision. Through multi-dimensional response effect verification, obtain quantitative effect evaluation indicators, such as the percentage reduction of the failure rate, the percentage increase of availability, the percentage savings of human cost, etc. Based on these indicators, establish a progressive response feedback factor to measure the change trend of the response effect of the warning decision over time. Then, based on the change trend of the progressive response feedback factor, adaptively adjust the length of the warning time sequence period, extend the warning period when the response effect improves, and shorten the warning period when the response effect decreases.

[0099] Through the feedback optimization of warning identification, realize the dynamic alignment and continuous improvement of warning decisions and actual response effects, effectively improve the adaptability and robustness of the warning network, enable it to quickly respond to changes in cable states, and continuously learn and evolve to achieve precise operation and maintenance of cable segment management.

[0100] To sum up, the power cable intelligent segment management method provided by the embodiment of the present application has the following technical effects:

[0101] Obtain the pre-execution tasks of the power cable to be monitored, establish a task cycle identifier based on the pre-execution tasks, and set the first time-segment influence coefficient based on the task cycle identifier, providing basic data for the management strategy to ensure that the strategy can be adjusted according to specific task requirements and improve the pertinence of management. Execute the location climate prediction of the power cable to be monitored, establish the second time-segment influence coefficient based on the location climate prediction results, consider the influence of environmental factors on the cable status, and enhance the environmental adaptability of the management strategy. Input the first time-segment influence coefficient and the second time-segment influence coefficient into the dynamic decision-making model to establish dynamic decision nodes, realizing the comprehensive consideration of task requirements and environmental factors, and laying a foundation for formulating an intelligent management strategy. Establish the balanced task requirements and balanced climate impacts between nodes based on the dynamic decision nodes, ensuring the balance of tasks and environmental factors among different decision nodes and improving the coordination of the overall management strategy. Obtain the cable layout information of the power cable to be monitored, where the cable layout information includes cable layout structure information and cable layout and fixing environment information, taking into account the physical characteristics of the cable to further improve the decision-making basis. Configure a retrospective time window to perform retrospective monitoring of the power cable to be monitored with the retrospective time window, establish the retrospective basic parameters of the power cable to be monitored, and provide a reference basis for the current decision through historical data analysis, enhancing the reliability of the decision. Conduct an adaptation analysis of the balanced task requirements and balanced climate impacts based on the retrospective basic parameters, generate the first clustering constraint based on the adaptation analysis results, realize the combination of historical data and current requirements, and improve the accuracy of the decision. Establish the second clustering constraint according to the cable layout information, establish a dynamic segmentation strategy based on the first clustering constraint, the second clustering constraint, and the dynamic decision nodes, allocate the edge joint warning network based on the dynamic segmentation strategy, and perform segmented management based on the edge joint warning network. Considering the above-mentioned obtained factors, dynamically generate a segmented strategy based on multi-dimensional data to achieve precise and intelligent warning and management of power cables, and improve the warning response speed and accuracy.

[0102] Embodiment 2, based on the same inventive concept as the power cable intelligent segmented management method in the foregoing embodiment, as Figure 2 shown, the embodiment of the present application provides a power cable intelligent segmented management system, which includes:

[0103] The first influence coefficient module 11 is used to obtain the pre-execution tasks of the power cable to be monitored, establish a task cycle identifier based on the pre-execution tasks, and set the first time-segment influence coefficient based on the task cycle identifier;

[0104] The second influence coefficient module 12 is used to execute the location climate prediction of the power cable to be monitored and establish the second time-segment influence coefficient based on the location climate prediction results;

[0105] A decision node generation module 13, configured to input the first time-series segmented influence coefficient and the second time-series segmented influence coefficient into a dynamic decision-making model to establish a dynamic decision node;

[0106] A node equilibrium analysis module 14, configured to establish an equilibrium task requirement and an equilibrium climate impact among nodes based on the dynamic decision node;

[0107] A cable information acquisition module 15, configured to acquire cable layout information of a power cable to be monitored, where the cable layout information includes cable layout structure information and cable layout fixed environment information;

[0108] A backtracking parameter establishment module 16, configured to configure a backtracking time window, perform backtracking monitoring of the power cable to be monitored based on the backtracking time window, and establish backtracking basic parameters of the power cable to be monitored;

[0109] A clustering constraint generation module 17, where a first clustering constraint module performs an adaptation analysis of the equilibrium task requirement and the equilibrium climate impact according to the backtracking basic parameters, and generates a first clustering constraint based on the adaptation analysis result;

[0110] A segmented management execution module 18, configured to establish a second clustering constraint according to the cable layout information, establish a dynamic segmentation strategy according to the first clustering constraint, the second clustering constraint, and the dynamic decision node, allocate an edge joint warning network based on the dynamic segmentation strategy, and perform segmented management based on the edge joint warning network.

[0111] Further, the backtracking parameter establishment module 16 includes the following execution steps:

[0112] Acquire cable material information of the power cable to be monitored, perform an evaluation of the material backtracking impact of the cable based on the cable material information, and establish an aging backtracking score;

[0113] Perform a seasonal climate change evaluation of the area where the power cable to be monitored is located, establish a climate cycle, obtain the occurrence frequency of extreme weather in the area, and respectively construct a seasonal climate change score and an extreme weather event frequency score according to the climate cycle and the occurrence frequency;

[0114] Establish a comprehensive score according to the aging backtracking score, the seasonal climate change score, and the extreme weather event frequency score, and determine the length of the backtracking time window based on the comprehensive score to complete the configuration of the backtracking time window.

[0115] Further, the backtracking parameter establishment module 16 further includes the following execution steps:

[0116] Configure a comprehensive score formula as follows:

[0117] ;

[0118] Among them, is the comprehensive score, characterizes any scoring feature, is the weight corresponding to feature ; characterizes the scoring function of feature , where:

[0119] ;

[0120] ;

[0121] ;

[0122] characterizes the aging backtracking score, and are the linear coefficients, characterizes the cable aging speed feature obtained based on cable material information, characterizes the seasonal climate change score, characterizes the amplitude coefficient, characterizes the period, is the input variable of the seasonal climate change, characterizes the extreme weather event frequency score, and are the adjustment coefficients, is the occurrence frequency;

[0123] ;

[0124] is the length of the backtracking time window, and are respectively the shortest and longest selectable values of the time window.

[0125] Furthermore, the segmented management execution module 18 includes the following execution steps:

[0126] Perform similar clustering on the segmentation results through the dynamic segmentation strategy to generate a segmented similar clustering result;

[0127] Establish a benchmark warning network according to the segmented similar clustering result, and perform incremental learning based on the similar clustering result through the benchmark warning network to build multiple warning networks mapped to the dynamic segmentation strategy;

[0128] Establish the joint influence of each warning network based on the dynamic segmentation strategy, and complete the edge joint warning network allocation through the joint influence.

[0129] Further, the embodiment of the present application further includes an early warning result verification module, and this module includes the following execution steps:

[0130] Perform segmented monitoring on the power cable to be monitored through the edge joint early warning network, and generate independent segmented early warning results;

[0131] Activate the joint analysis layer of the edge joint early warning network, perform edge joint analysis with the joint analysis layer, and establish joint segmented early warning results;

[0132] Verify and identify the independent segmented early warning results and the joint segmented early warning results, and report the verified early warning results based on the verification and identification results.

[0133] Further, the embodiment of the present application further includes a decision model optimization module, and this module includes the following execution steps:

[0134] Obtain the early warning identification evaluation identifier of the edge joint early warning network;

[0135] Establish a construction adaptation feedback of the dynamic decision node through the early warning identification evaluation identifier;

[0136] Perform adaptive compensation of the dynamic decision model based on the construction adaptation feedback.

[0137] Further, the embodiment of the present application further includes an early warning feedback optimization module, and this module includes the following execution steps:

[0138] Configure the early warning time sequence period based on the identification result of the edge joint early warning network;

[0139] Perform response effect verification with the early warning time sequence period, and establish a progressive response feedback factor based on the response effect verification result;

[0140] Perform feedback optimization of early warning identification through the progressive response feedback factor.

[0141] Any step of the method described above can be stored as computer instructions or programs in an unrestricted computer memory, and can be called and recognized by an unrestricted computer processor to implement any method in the embodiment of the present application, and no redundant restrictions are made here.

[0142] Further, the first or second described above may not only represent an order relationship, but may also represent a certain specific concept, and / or refer to the selection of multiple elements individually or in whole. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. In this way, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and deformations.

Claims

1. A method for intelligent segmentation management of power cables, characterized in that: The method comprises: Acquire a pre-execution task of the power cable to be monitored, establish a task cycle identifier with the pre-execution task, and set a first timing segmentation influence coefficient based on the task cycle identifier, wherein the first timing segmentation influence coefficient is used to quantify the influence of different task cycles on the segmentation management of the power cable; Performing location climate prediction for the power cable to be monitored, establishing a second time-series segmentation influence coefficient based on the location climate prediction result, and the second time-series segmentation influence coefficient is used to quantify the influence of different climate conditions on the segmentation management of the power cable; Inputting the first time segmentation influence coefficient and the second time segmentation influence coefficient into a dynamic decision model to establish dynamic decision nodes, each of which represents an optimal segmentation management solution for power cables in a specific time period; Establishing balanced task requirements and balanced climate impacts among nodes using the dynamic decision nodes; Acquire cable laying information of the power cable to be monitored, wherein the cable laying information includes cable laying structure information and cable laying fixing environment information; Configure a retrospective time window, perform retrospective monitoring of the power cable to be monitored with the retrospective time window, and establish retrospective basic parameters of the power cable to be monitored, wherein the retrospective basic parameters are key indicators reflecting the historical operation characteristics of the power cable established by configuring the retrospective time window, performing retrospective monitoring on the power cable, and analyzing and mining historical monitoring data; Performing an adaptation analysis of the balanced task requirements and the balanced climate impact according to the backtracking basic parameters, and generating a first clustering constraint based on the adaptation analysis result, wherein the first clustering constraint refers to a parameter similarity constraint condition that needs to be satisfied when segmenting and clustering the power cables; Establishing a second clustering constraint according to the cable layout information, wherein the second clustering constraint refers to a cable layout similarity constraint condition that needs to be satisfied when segmenting and clustering the power cables; Establishing a dynamic segmentation strategy according to the first clustering constraint, the second clustering constraint, and the dynamic decision node, allocating an edge joint warning network based on the dynamic segmentation strategy, and performing segmentation management based on the edge joint warning network; The dynamic decision model is a computer model that can dynamically adjust the decision plan based on real-time input data. The dynamic decision model is based on artificial intelligence algorithms and autonomously generates and optimizes decision plans by learning historical data and analyzing current status.

2. A method for intelligent segmentation management of power cables according to claim 1, characterized in that: The configuration backtracking time window further includes: Obtain cable material information of the power cable to be monitored, perform a material retrospective impact assessment of the cable based on the cable material information, and establish an aging retrospective score; Conducting a seasonal climate change evaluation on the power cables to be monitored, establishing a climate cycle, obtaining the frequency of occurrence of extreme weather in the region, and constructing a seasonal climate change score and an extreme weather event frequency score respectively according to the climate cycle and the frequency of occurrence; A comprehensive score is established based on the aging backtracking score, seasonal climate change score, and extreme weather event frequency score, and the length of the backtracking time window is determined by the comprehensive score to complete the backtracking time window configuration.

3. A method for intelligent segmentation management of power cables according to claim 2, characterized in that: The method of establishing a comprehensive score based on the aging backtracking score, the seasonal climate change score, and the extreme weather event frequency score, and determining the length of the backtracking time window based on the comprehensive score to complete the backtracking time window configuration, further includes: Configure the comprehensive scoring formula as follows: ； in, For the comprehensive rating, Represent any scoring feature, Features The corresponding weights, Characterization characteristics The scoring function is: ； ； ； Characterizes aging retrospective scoring, and is the linear coefficient, Characterize the cable aging speed characteristics based on cable material information, Characterize seasonal climate change scores, Characterize the amplitude coefficient, Characterization cycle, is the input variable of seasonal climate change, The frequency score of extreme weather events was collected. and is the adjustment coefficient, is the frequency of occurrence; ； is the length of the lookback time window, and They are the shortest and longest optional values ​​of the time window respectively.

4. The method for intelligent segmentation management of power cables according to claim 1, characterized in that: The allocating edge joint warning network based on the dynamic segmentation strategy and performing segmentation management based on the edge joint warning network also includes: Perform similarity clustering of segmentation results by using the dynamic segmentation strategy to generate segmentation similarity clustering results; Establishing a benchmark early warning network according to the segmented similarity clustering results, and performing incremental learning based on the similarity clustering results through the benchmark early warning network to build multiple early warning networks mapped with dynamic segmentation strategies; The joint influence of each early warning network is established based on the dynamic segmentation strategy, and the edge joint early warning network allocation is completed through the joint influence.

5. A method for intelligent segmentation management of power cables according to claim 4, characterized in that: The method further comprises: Performing segmented monitoring of the power cables to be monitored through the edge joint early warning network to generate independent segmented early warning results; Activate the joint analysis layer of the edge joint warning network, perform edge joint analysis with the joint analysis layer, and establish a joint segmented warning result; The independent segmented warning results and the combined segmented warning results are verified and identified, and a verification warning result is reported based on the verification and identification result.

6. A method for intelligent segmentation management of power cables according to claim 1, characterized in that: The method further comprises: Obtain the warning identification and evaluation mark of the edge joint warning network; Establishing adaptive feedback of dynamic decision nodes through the early warning identification and evaluation identification; An adaptive compensation of the dynamic decision model is performed based on the constructed adaptation feedback.

7. The method for intelligent segmentation management of power cables according to claim 1, characterized in that: The method further comprises: Configuring a warning timing cycle based on the recognition result of the edge joint warning network; Performing response effect verification in the warning time sequence cycle, and establishing a progressive response feedback factor based on the response effect verification result; Feedback optimization of early warning identification is performed through the progressive response feedback factor.

8. An intelligent segmentation management system for power cables, characterized in that: A method for intelligent segmentation management of power cables according to any one of claims 1 to 7, the system comprising: A first influence coefficient module, the first influence coefficient module is used to obtain a pre-execution task of the power cable to be monitored, establish a task cycle identifier with the pre-execution task, and set a first time-series segment influence coefficient based on the task cycle identifier; A second influence coefficient module, the second influence coefficient module is used to perform location climate prediction of the power cable to be monitored, and establish a second time series segmented influence coefficient based on the location climate prediction result; A decision node generation module, wherein the decision node generation module is used to input the first time segment influence coefficient and the second time segment influence coefficient into a dynamic decision model to establish a dynamic decision node; A node balance analysis module, the node balance analysis module is used to establish balanced task requirements and balanced climate impacts between nodes using the dynamic decision nodes; A cable information acquisition module, the cable information acquisition module is used to obtain cable laying information of the power cable to be monitored, the cable laying information includes cable laying structure information and cable laying fixing environment information; A backtracking parameter establishment module, the backtracking parameter establishment module is used to configure a backtracking time window, perform backtracking monitoring of the power cable to be monitored with the backtracking time window, and establish backtracking basic parameters of the power cable to be monitored; A cluster constraint generation module, wherein the cluster constraint generation module is used for the first cluster constraint module to perform an adaptation analysis of the equilibrium task requirements and the equilibrium climate impact according to the backtracking basic parameters, and to generate a first cluster constraint based on the adaptation analysis result; A segmentation management execution module is used to establish a second clustering constraint according to the cable laying information, establish a dynamic segmentation strategy according to the first clustering constraint, the second clustering constraint, and the dynamic decision node, allocate an edge joint warning network based on the dynamic segmentation strategy, and perform segmentation management based on the edge joint warning network.

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