Cable full life cycle health state prediction analysis method and system

Abstract

This invention discloses a method and system for predicting and analyzing the health status of cables throughout their entire life cycle, relating to the field of cable condition monitoring technology. The invention includes power supply management, comprehensive health analysis, and adaptive sampling communication modules. The power supply management module collects electrical parameter flows, generates instantaneous power density sequences, combines historical and real-time data, detects charging and discharging states, and outputs parameters such as power outage type and power supply reliability. The comprehensive health analysis module dynamically calculates the sampling power consumption budget, predicts and outputs energy curves and risk levels through graph-time series fusion, and determines the recommended sampling level after correction. The adaptive sampling communication module maps a hierarchical strategy and dynamically executes sampling and communication scheduling. The system solves the problem of adaptive sampling strategy matching in multi-node high-voltage distribution network monitoring, achieving accurate monitoring and strategy adaptation under differentiated aging, and improving the efficiency and reliability of cable health management.

Description

Technical Field

[0001] This invention relates to the field of cable condition monitoring technology, and more specifically, to a method and system for predicting and analyzing the health status of cables throughout their entire life cycle. Background Technology

[0002] In existing technologies, monitoring the health status of cables mainly relies on manual inspections or periodic testing with handheld devices. This approach has several shortcomings: due to the inability to monitor continuously online, maintenance personnel cannot obtain complete status data throughout the cable's entire life cycle in a timely manner, making it impossible to accurately predict the aging trend and remaining service life of the cable. Often, hidden dangers are only discovered after a serious fault or power outage occurs, affecting the reliability of the power grid. At the same time, some existing online monitoring devices have limited functions, such as monitoring only single parameters like partial discharge or temperature, failing to integrate and analyze multiple status parameters, making it difficult to assess the cable's health status in a timely and comprehensive manner.

[0003] Furthermore, online monitoring equipment is constrained by its power supply method: these devices are typically installed at outdoor cable joints and are mostly powered by batteries or current transformers. Existing monitoring systems mostly use fixed-frequency sampling and data uploading strategies, without dynamically adjusting the sampling frequency and power consumption based on cable aging or load changes. On the one hand, too low a sampling frequency may miss early fault data, reducing diagnostic accuracy; on the other hand, too high a sampling frequency will accelerate battery energy consumption, especially when the load current is low and the current transformer is insufficient, leading to power shortages or even power outages and disconnections for the monitoring device.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for predicting and analyzing the health status of cables throughout their entire life cycle, in order to address the insufficient matching of adaptive sampling strategies under the differentiated aging of cables in real-time monitoring scenarios of multi-node high-voltage distribution networks.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] The cable lifecycle health status prediction and analysis system includes the following modules:

[0008] The power supply management module is used to collect cable electrical parameter flow and aging basic data of the cable monitoring device nodes, and perform derivative calculations on the cable electrical parameter flow to generate an instantaneous power density sequence. Based on the instantaneous power density sequence and the cable electrical parameter flow, the module detects the charging and discharging status of the cable monitoring device nodes and outputs power outage type identifier, power supply reliability parameters including mutual inductance power remaining ratio and battery state of charge percentage, cable aging level coefficient, and the upper limit of actual supportable sampling power consumption.

[0009] The health comprehensive analysis module is used to dynamically calculate the upper limit of the sampling power consumption budget based on the power outage type identifier, cable aging level coefficient, and power supply reliability parameters. It also performs time synchronization based on cable status data to generate a health initial vector. Based on the health initial vector and the node topology of the cable monitoring device as a graph structure, it predicts and outputs the remaining available energy curve, uncertainty curve, and preliminary risk level through graph time series fusion. Based on the uncertainty curve correction, the preliminary risk level is confidence-corrected to obtain the final risk level, and the upper limit of the sampling power consumption budget and the recommended sampling level are determined.

[0010] The adaptive sampling communication module receives the final risk level and recommended sampling level, maps them to a hierarchical sampling strategy, and dynamically executes the sampling and communication strategies of each cable monitoring device node.

[0011] As a further aspect of the present invention: the power supply management module performs the following steps: collecting bus current, bus voltage, backup battery current and battery terminal voltage at a fixed sampling frequency to generate cable electrical parameter flow; multiplying the bus voltage and current corresponding to each sampling point to calculate the instantaneous power of the bus; performing a first derivative operation on the instantaneous power of the bus to generate an instantaneous power density sequence; and determining the power outage type as deep discharge, normal discharge or charging operation state based on the instantaneous power density sequence and the cable electrical parameter flow and their corresponding preset thresholds.

[0012] As a further aspect of the present invention: the determination criteria for the power outage type include: detecting the charging and discharging state of the cable monitoring device node based on the obtained instantaneous power density sequence, cable electrical parameters and their corresponding preset battery continuous discharge threshold and preset battery static current threshold, preset power density positive fluctuation upper limit threshold and preset power density negative fluctuation lower limit threshold, including:

[0013] When the instantaneous power density sequence of the monitoring device node corresponding to N consecutive sampling points is less than the preset negative power density fluctuation lower limit threshold and the backup battery discharge current is less than the preset battery continuous discharge threshold, it is determined to be in a deep discharge power failure state and a deep discharge power failure flag is output.

[0014] When the instantaneous power density sequence is between the lower threshold of negative power density fluctuation and the upper threshold of preset positive power density fluctuation, and the absolute value of the backup battery discharge current is less than or equal to the preset battery static current threshold, the corresponding monitoring device node is in normal discharge and outputs a normal discharge power failure flag.

[0015] When the instantaneous power density sequence is greater than the preset upper limit threshold for positive power density fluctuation and the backup battery discharge current is greater than the preset battery continuous discharge threshold, the corresponding cable monitoring device node is significantly charging and outputs a charging operation indicator.

[0016] As a further aspect of this invention: the power supply reliability parameters, cable aging level coefficient, and the upper limit of actual supportable sampling power consumption are generated in the following way: The instantaneous power density sequence is integrated to obtain the energy integral value within a certain period; this energy integral value is compared with the rated mutual inductance power supply capacity to calculate the mutual inductance power supply remaining ratio; based on the ampere-hour integration method, the battery state-of-charge percentage is derived by integrating the backup battery current and combining it with the battery's rated capacity; according to the following formula: In the formula, This represents the state of charge of the battery at time t. It is the initial moment The state of charge of the battery. This represents the battery's rated capacity, measured in ampere-hours (Ahs). It is the battery current at time t, in amperes, during charging. It is a positive value during discharge and a negative value during discharge. Indicates from the initial time The integral of the battery current up to the current time t, that is, the change in the amount of charge in the battery during this time.

[0017] Based on the pre-stored and provided cable service life and historical fault data within the system, the ratio of the cable service life to the preset life reference value is calculated to obtain the basic aging ratio;

[0018] The offset is set based on the number of historical faults. The basic aging ratio is added to the offset to generate a quantitative value, which is recorded as the cable aging level coefficient. The product of the mutual inductance power remaining ratio, the battery state of charge percentage and the cable aging level coefficient is multiplied by the device preset power consumption to calculate the upper limit of the actual sampling power consumption that can be supported.

[0019] As a further aspect of the present invention: the upper limit of the sampling power consumption budget in the health comprehensive analysis module includes: under the deep discharge power failure flag, when the cable aging level coefficient is less than or equal to the low aging risk threshold, it is marked as a low aging tag, and its corresponding first compression ratio is configured.

[0020] When the cable aging level coefficient is greater than the low aging risk threshold but less than the high aging threshold, it is marked as a medium aging label and its corresponding second compression ratio is configured.

[0021] When the cable aging level coefficient is greater than or equal to the high aging threshold, it is marked as a high aging label and its corresponding third compression ratio is configured; and the first compression ratio is greater than the second compression ratio is greater than the third compression ratio; and based on the above classification by aging label, the different configured compression ratios are multiplied by the actual supportable upper limit of sampling power consumption to obtain the maximum allowable sampling power consumption, i.e., the upper limit of sampling power consumption budget, under the current node deep discharge mode.

[0022] As a further aspect of the present invention: the health comprehensive analysis module employs a graph time-series fusion prediction algorithm, including: graph convolutional network processing to construct a relationship graph with cable monitoring device nodes as graph nodes and cable connection topology as edges; long short-term memory network processing of multi-source sensor data of each cable monitoring device node; fusing recurrent neural network and graph neural network to output the remaining available energy curve and preliminary risk level; calculating the confidence weight of each time step based on the uncertainty curve; and using a weighted average formula to perform bias correction on the preliminary risk level to generate the final risk level; wherein, the remaining available energy curve is used to reflect the possible remaining energy changes of the cable monitoring device node's own power supply in the future period.

[0023] As a further aspect of the present invention: the rules for determining the recommended sampling level in the health comprehensive analysis module include: when the final risk level is greater than or equal to the high risk level threshold or the aging label is a high aging label, high-priority monitoring is triggered, that is, the core parameters should be sampled at high frequency according to the upper limit of the sampling power consumption budget.

[0024] When the final risk level is greater than or equal to the medium risk threshold and less than the high risk level threshold, or when the aging label is a medium aging label, medium priority monitoring is triggered, i.e., the sampling of core and non-core parameters is balanced according to the upper limit of the sampling power consumption budget.

[0025] When the final risk level is less than the low risk threshold and the aging label is a low aging label, low priority monitoring is triggered, that is, the sampling frequency of non-core parameters is reduced according to the upper limit of the sampling power consumption budget.

[0026] When the aging label is a normal aging label, the normal power supply scenario adaptation is triggered and the final risk level is determined directly, and the sampling frequency is still based on the fixed sampling frequency.

[0027] As a further aspect of the present invention, it also includes a lifespan degradation assessment function: based on the remaining available energy curve and the cable aging level coefficient, a judgment result of high lifespan degradation risk, power supply warning or aging warning is generated; when the remaining available energy curve shows that the energy will drop below the energy warning threshold for maintaining basic monitoring in the future period of time, and the cable aging level coefficient is still greater than or equal to the high aging threshold, the cable monitoring device node is determined to be at high lifespan degradation risk.

[0028] If the remaining available energy drops below the energy warning threshold and the cable aging level coefficient is still less than or equal to the low aging threshold, then it is determined to be an energy supply warning.

[0029] If only the aging level coefficient is higher than the energy warning threshold, and the cable aging level coefficient is still greater than or equal to the high aging threshold, then it is judged as an aging warning.

[0030] For cable monitoring device nodes with high lifespan degradation risk, maintenance work orders that include joint inspection suggestions and sampling strategy upgrades are automatically triggered.

[0031] As a further aspect of the present invention: when the adaptive sampling communication module executes the sampling strategy of each cable monitoring device node, it includes: when the recommended sampling level corresponding to the cable monitoring device node is high-priority monitoring, it enters the first-level strategy mode and uses an event-triggered mechanism to upload key data in real time; when the recommended sampling level corresponding to the cable monitoring device node is medium-priority monitoring, it enters the second-level strategy mode and uses data compression and batch upload strategies to reduce communication energy consumption.

[0032] When the recommended sampling level for the cable monitoring device node is low-priority monitoring, it enters a three-level strategy mode oriented towards energy saving.

[0033] The method for predicting and analyzing the health status of cables throughout their entire life cycle includes the following steps: Step 1: Collect the bus current, bus voltage, backup battery current, and battery terminal voltage of the cable monitoring device nodes to generate cable electrical parameter flow; calculate the first derivative of the instantaneous power of the bus to generate an instantaneous power density sequence; based on the instantaneous power density sequence and a preset threshold, determine the power outage type as deep discharge, normal discharge, or charging operation status;

[0034] Step 2: Obtain cable service life and historical fault data, calculate the ratio of service life to preset life reference value to obtain the basic aging ratio; set an offset based on the number of historical faults, and add the basic aging ratio to the offset to generate the cable aging level coefficient.

[0035] Step 3: Construct a graph with cable monitoring device nodes as nodes and cable connection topology as edges; use a graph time series fusion prediction algorithm to process cable status data and output the remaining available energy curve, uncertainty curve, and preliminary risk level; calculate confidence weights based on the uncertainty curve, and perform bias correction on the preliminary risk level to generate the final risk level;

[0036] Step 4: When the power outage type is identified as deep discharge, compress the upper limit of the sampling power consumption budget according to the cable aging level coefficient; and combine the final risk level and aging level coefficient to allocate a recommended sampling level, map the recommended sampling level to a hierarchical sampling strategy, and dynamically adjust the sampling and communication operations of each cable monitoring device node according to the upper limit of the sampling power consumption budget based on the hierarchical sampling strategy.

[0037] The beneficial effects of this invention are:

[0038] (1) This invention achieves dynamic monitoring and adaptive sampling of the cable's health status throughout its entire lifecycle by constructing a collaborative architecture of a power supply management module, a health comprehensive analysis module, and an adaptive sampling communication module. The power supply management module collects cable electrical parameters in real time and generates an instantaneous power density sequence to accurately identify the power outage type and power supply status. The health comprehensive analysis module generates risk levels based on a graph time-series fusion prediction algorithm, combined with cable topology and multi-source data, and dynamically matches the sampling power consumption budget. The adaptive sampling communication module flexibly adjusts the sampling frequency and communication strategy according to the risk level and power supply status. The collaboration of these three modules solves the contradiction between excessive energy consumption leading to power outages and insufficient sampling causing missed hazards in the traditional fixed sampling mode, improving the accuracy of fault early warning while ensuring monitoring continuity.

[0039] (2) This invention achieves full-chain management from condition monitoring to life prediction by introducing the correlation analysis between the cable aging level coefficient and the remaining usable energy curve. On the one hand, based on the aging level coefficient and the power supply status, the sampling power consumption is dynamically allocated, such as using a differentiated compression ratio according to the aging tag during deep discharge. When the power supply is tight, the monitoring of core parameters is prioritized to extend the equipment's lifespan. On the other hand, by coupling the remaining usable energy curve with the aging level, results such as high lifespan decay risk and power supply warning are generated and linked to maintenance work orders, promoting the transformation of the maintenance mode from post-fault repair to predictive maintenance, which significantly improves the reliability and maintenance efficiency of power grid cable operation. Attached Figure Description

[0040] The invention will now be further described with reference to the accompanying drawings.

[0041] Figure 1 This is a schematic diagram of the system framework of the cable full life cycle health status prediction and analysis system of the present invention;

[0042] Figure 2 This is a schematic diagram of the power supply management module in the cable life cycle health status prediction and analysis system of the present invention;

[0043] Figure 3 This is a schematic diagram illustrating the implementation process of the cable full life cycle health status prediction and analysis method of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1

[0046] Please see Figure 1 As shown, the present invention is a cable full life cycle health status prediction and analysis system, including the following modules: power supply management module, health comprehensive analysis module, and adaptive sampling communication module;

[0047] The power supply management module is used to acquire cable electrical parameter flow and aging basic data, and perform first derivative calculation on the cable electrical parameter flow to generate instantaneous power density sequence; based on the instantaneous power density sequence and the cable electrical parameter flow, the charging and discharging status of the cable monitoring device node is detected to obtain the cable power failure type identifier, and output the mutual inductance power remaining ratio and power supply reliability parameters including the battery state of charge percentage and the upper limit of actual supportable sampling power consumption in real time.

[0048] In this embodiment, the power supply management module is mainly responsible for power supply and electrical parameter calculation of the cable monitoring device nodes. Its circuit principle is as follows: Figure 2 As shown; Figure 2 The power management module shown has a snap-on current transformer (CT1). CT1 senses the AC signal in the cable busbar, filters it through inductor L2 (I-type inductor), and then rectifies it into a DC signal through rectifier bridge DB1. The DC signal is then amplified and stabilized by charging chip U1, outputting approximately 12V to charge battery BT1. The entire power management module is installed as a cable monitoring device node at the cable joint. Multiple cable monitoring device nodes can be deployed along the line. Each cable monitoring device node forms a distributed monitoring network with the backend platform through a wireless network, enabling continuous status perception and risk prediction throughout the cable's entire life cycle.

[0049] While providing power supply, this module collects and analyzes key electrical parameters and historical data to determine the power supply status and basic cable conditions, providing a foundation for subsequent analysis. The implementation steps are as follows:

[0050] The system collects electrical parameters reflecting cable power supply and battery status to form a time series of electrical parameters, i.e., cable electrical parameter flow. Specifically, this includes the bus current, i.e., the magnitude of the cable load current induced by CT1, reflecting the load current intensity through this node; the bus voltage, i.e., the DC bus voltage after rectification and filtering by the mutual inductor energy extraction circuit, indicating the current power supply capacity output level of the mutual inductor; the backup battery current, i.e., the charging and discharging current of battery BT1, with positive values ​​usually representing charging current and negative values ​​representing discharging current, i.e., the battery discharging to supply power to the system; and the battery terminal voltage, the voltage across BT1, which reflects the battery's charge level.

[0051] It should be noted that these electrical parameters can be obtained through the ADC sampling circuit and battery management unit in the power supply management module, and the sampling frequency is preferably 1s to 10s. The continuous recording can be adjusted according to the actual response requirements to obtain the cable electrical parameters that change over time.

[0052] Based on the collected cable electrical parameters, the instantaneous power of the bus is approximately calculated by multiplying the bus voltage and current at each sampling point. The first derivative of the instantaneous power is then calculated to obtain the instantaneous power density sequence. By taking the first derivative of relevant parameters, the transient characteristics of parameter abrupt changes can be highlighted. For example, when the cable bus current suddenly drops, the first derivative of the instantaneous power of the bus shows a significant negative peak; when the backup battery switches from charging to discharging, the rate of change of battery current changes drastically. This instantaneous power change sequence can sensitively capture transient events such as power outages and sudden load changes, providing a basis for identifying power supply anomalies.

[0053] Integrating the instantaneous power density sequence yields the energy integral value over a certain period. This energy integral value is then compared to the rated capacity of the mutual inductance power supply to calculate the remaining capacity ratio. Simultaneously, the state of charge (SCC) is obtained from the battery terminal voltage and standby battery current collected by the battery management unit. Based on the ampere-hour integration method, the SCC is derived by integrating the standby battery current and combining it with the battery's rated capacity, and the output is... The battery's state of charge percentage reflects the remaining battery capacity; specifically, the calculation of the battery's state of charge percentage is based on the following formula: In the formula, This represents the state of charge of the battery at time t. It is the initial moment The state of charge (SOC) of a battery is typically determined after battery initialization or calibration, such as when a new battery is fully charged. Can be set to , The rated capacity of a battery is expressed in ampere-hours (AH). This value can be obtained from the battery's specifications and reflects the total amount of charge the battery can store. It is the battery current at time t, in amperes, during charging. It is a positive value during discharge and a negative value during discharge. Indicates from the initial time The integral of the battery current up to the current time t, that is, the change in the amount of charge in the battery during this time.

[0054] Furthermore, the system pre-stores and provides cable service life and historical fault data as auxiliary inputs for assessing the current state. The cable service life is divided by a preset cable life reference value to obtain a basic aging ratio. An offset is then set based on the historical fault data, and the basic aging ratio is added to the set offset to obtain the cable aging level coefficient. For example, if a cable has been in operation for 15 years and has experienced two faults in the past 3 years, the basic aging ratio based on a 30-year lifespan is 15 divided by 30, which equals 0.5. The two faults may correspond to an additional 0.1, resulting in a cable aging level coefficient of 0.6. The value range can be 0 to 1 or 0 to 100%.

[0055] Based on the obtained instantaneous power density sequence, cable electrical parameters and their corresponding preset battery continuous discharge threshold, preset battery static current threshold, preset upper limit threshold for positive power density fluctuation, and preset lower limit threshold for negative power density fluctuation, the charging and discharging status of the cable monitoring device nodes is detected, including:

[0056] When the following conditions are met simultaneously for N consecutive sampling points (N≥3), the corresponding monitoring device node is in a deep discharge power failure state and outputs a deep discharge power failure flag; the conditions are that the instantaneous power density sequence is less than the preset power density negative fluctuation lower limit threshold and the backup battery discharge current is less than the preset battery continuous discharge threshold.

[0057] When the instantaneous power density sequence is between the lower threshold of negative power density fluctuation and the upper threshold of preset positive power density fluctuation, and the absolute value of the backup battery discharge current is less than or equal to the preset battery static current threshold, the corresponding monitoring device node is in normal discharge, i.e. there is no significant discharge or charging, and outputs a normal discharge power-off indicator.

[0058] When the instantaneous power density sequence is greater than the preset upper limit threshold for positive power density fluctuation and the backup battery discharge current is greater than the preset battery continuous discharge threshold, the corresponding cable monitoring device node is significantly charging and outputs a charging operation indicator.

[0059] It should be noted that the aforementioned preset battery continuous discharge threshold is set to a negative value based on the backup battery's discharge capacity and safety boundary, such as the lower limit of continuous discharge current in the battery specification. For example, if the continuous discharge current is 5A, then the preset battery continuous discharge threshold is -5A, used to identify the battery's deep discharge state. The preset power density positive fluctuation upper limit threshold is calculated based on the maximum positive fluctuation value of the instantaneous power density sequence under normal operating conditions, such as the power increase caused by short-term load fluctuations, used to define that the power fluctuation is still within the normal range. The preset power density negative fluctuation lower limit threshold is based on historical normal operating data, calculated based on the minimum negative abrupt change value of the instantaneous power density sequence, such as the negative extreme value of the instantaneous power density sequence during 1000 hours of normal operation, and its absolute value is taken as the preset power density negative fluctuation lower limit threshold, used to identify abnormal decreases in power supply.

[0060] The calculated mutual inductance power remaining ratio and battery state of charge percentage are used as power supply reliability parameters and output together with the power outage type identifier and cable aging level coefficient. At the same time, the product of the mutual inductance power remaining ratio, battery state of charge percentage and cable aging level coefficient is multiplied by the device's preset power consumption to calculate the actual upper limit of the sampling power consumption that can be supported, providing a basis for subsequent module control sampling strategy. The device's preset power consumption can be obtained through the basic configuration information of the corresponding cable monitoring device node.

[0061] The health comprehensive analysis module is used to dynamically adjust the sampling power consumption based on the power supply reliability parameters and cable aging level coefficient after receiving the discharge type identifier, and to collect multi-channel synchronous data of each cable status to obtain the initial health vector. The module then uses the cable joint branch topology to perform time-series fusion prediction to model the initial health vector, outputs the remaining available energy curve, uncertainty curve, and preliminary risk level of the cable joint monitoring node within the variable prediction window, calculates the confidence weight based on the uncertainty curve, performs bias correction on the preliminary risk level, and generates the final risk level.

[0062] The system receives core data output from the power supply management module, including: power failure type identifier (deep discharge, normal discharge, charging operation), power supply reliability parameters (mutual inductance power remaining ratio and battery state of charge percentage), actual supportable sampling power consumption limit, cable aging level coefficient, and cable electrical parameters including real-time load current, etc.

[0063] Furthermore, the multi-source sensor data of each cable monitoring device node are synchronously acquired and preprocessed to form an initial health vector for analysis. Since each cable monitoring device node is equipped with multiple sensors such as those for partial discharge, temperature, current, and humidity, and uploads data periodically via a 4G module, the data from different sources are first synchronized by timestamp to align the sensor readings and ensure that the parameters collected at the same monitoring time point together form the health status vector of the cable monitoring device node. For cable monitoring device nodes distributed in multiple locations, the data of each cable monitoring device node are processed in parallel to obtain their respective sets of initial health vectors.

[0064] Based on the above core data and initial health vector set, graph time series fusion prediction is performed to generate a preliminary risk level, including: using cable monitoring device nodes as graph nodes, edges representing the connection relationship or mutual influence relationship between cable monitoring device nodes; typically, high-voltage cable lines are segmented, with each two adjacent joints connected by cable conductors, and there is coupling in electrical and thermal behavior;

[0065] A graph-time series fusion prediction algorithm is adopted, using the core data and initial health vector sets of each cable monitoring device node as prediction inputs. This algorithm can be understood as a fusion model that combines time series models such as recurrent neural networks (RNN) and LSTM to capture the time dependence of single nodes with graph neural networks (GNN) or spatial weight matrices to capture the spatial correlation between nodes. In implementation, a prediction model can be established for each cable monitoring device node, and information from neighboring nodes can be introduced through graph convolution and other methods to achieve information propagation in the topology. To flexibly adapt to different scenario requirements, the prediction algorithm adopts a variable prediction window, that is, dynamically adjusting the prediction time span according to load fluctuations and the required prediction accuracy. For example, when the load and status are stable, a longer prediction window, such as the next 24 hours, can be selected to plan long-term trends; when the status changes drastically or there is great uncertainty, a shorter window, such as the next 2 hours, is used to improve prediction accuracy. Through the above model calculations, the remaining available energy curve of each cable monitoring device node within the prediction window is output, representing the possible remaining energy changes of the monitoring device's own power supply in the future. This energy change is obtained from the current energy state provided by the power supply management module, namely the mutual inductance power remaining ratio and the battery state of charge percentage. The uncertainty curve reflects the prediction error range, such as the standard deviation of each time step and the preliminary risk level, including levels 0-5. Specifically, the graph-time series fusion prediction algorithm integrates LSTM and GCN: the input layer receives a 6-dimensional health initial vector; two LSTM layers (32 neurons per layer) process the multi-source sensing data of each cable monitoring device node over 24 hours, outputting the energy trend of a single node; one GCN layer (3×3 convolution kernel) fuses the features of adjacent nodes using the cable topology as the adjacency matrix; the fusion layer weights and fuses spatiotemporal features through an attention mechanism; the output layer outputs the energy curve for the next 48 hours, the preliminary risk level of 0-5, and the uncertainty curve.

[0066] Furthermore, the preliminary risk level is corrected based on the uncertainty curve to obtain the final risk level, including:

[0067] Based on the standard deviation σ(t) of each time step in the uncertainty curve, according to the confidence weight The calculation involves using a weighted average formula to correct the initial risk level. This involves multiplying the result of subtracting the confidence weight from 1 by the baseline low-risk value, and then adding the product of the confidence weight and the initial risk level to the result of the multiplication. The sum obtained is the final risk level. The baseline low-risk value is generally a low risk level, such as level 0, which is used to make the risk assessment more biased towards the safe side when the prediction is uncertain.

[0068] The corrected final risk level is smoothed to avoid unfounded level jumps, and continuous high-risk sections are marked as the basis for subsequent sampling level determination.

[0069] For each cable monitoring device node, based on its corresponding power outage type identifier, the upper limit of the sampling power consumption budget is dynamically calculated to ensure that the power consumption is adapted to the power supply status, including:

[0070] When a cable monitoring device node is detected to have a deep discharge type, it indicates that the node is primarily relying on battery discharge for power supply, resulting in a power shortage. The deep discharge power consumption compression coefficient is dynamically adjusted based on the corresponding cable aging level coefficient and its corresponding aging risk threshold. The aging risk threshold includes a low aging risk threshold and a high aging risk threshold, set based on a comprehensive consideration of cable aging patterns, historical fault data, and the operating environment. In actual operation, cable aging is affected by various factors, such as service life, load conditions, ambient temperature, and humidity. By collecting and analyzing a large amount of historical cable data, the correlation between different aging levels and the probability of fault occurrence can be identified. Cables with longer service lives and more historical faults have relatively higher aging risks.

[0071] When the cable aging level coefficient is less than or equal to the low aging risk threshold, it is marked as low aging. Since the cable is in relatively healthy condition, power consumption can be moderately reduced, and its corresponding first compression ratio can be configured to be 50%.

[0072] When the cable aging level coefficient is greater than the low aging risk threshold but less than the high aging threshold, it is marked as medium aging, and its corresponding second compression ratio can be configured to be 40%.

[0073] When the cable aging level coefficient is greater than or equal to the high aging threshold, it is marked as a high aging label, and its corresponding third compression ratio can be configured to be 30%; the first compression ratio, the second compression ratio, and the third compression ratio are the deep discharge power consumption ratio factors preset by the system and satisfy the condition that the first compression ratio > the second compression ratio > the third compression ratio.

[0074] The maximum sampling power consumption allowed in the current node's deep discharge mode is obtained by multiplying the deep discharge power consumption compression coefficient obtained above with the actual supportable sampling power consumption upper limit.

[0075] When a cable monitoring device node is detected to have a power failure type of normal discharge, it is marked as a normal aging tag. The power supply system is in a steady state and the upper limit of the aging load sampling power consumption output by the power supply management module is directly used. That is, the upper limit of the sampling power consumption budget is equal to the upper limit of the actual supportable sampling power consumption.

[0076] Furthermore, combining the final risk level and aging label, recommended sampling levels are assigned based on preset risk level thresholds. These preset risk level thresholds include low-risk, medium-risk, and high-risk thresholds. For example, in a risk level range of 0-5, the low-risk threshold is 1, the medium-risk threshold is 2 or 3, and the high-risk threshold is 4 or 5. These values ​​are obtained by statistically analyzing the failure probabilities under different risk levels based on historical failure data.

[0077] When the final risk level is greater than or equal to the high risk level threshold or the aging label is a high aging label, high-priority monitoring is triggered, which means that core parameters such as partial discharge and bus current should be sampled at high frequency (e.g., once per second) according to the upper limit of the sampling power consumption budget.

[0078] When the final risk level is greater than or equal to the medium risk threshold and less than the high risk threshold, or when the aging label is a medium aging label, medium priority monitoring is triggered, i.e., the sampling of core and non-core parameters is balanced according to the upper limit of the sampling power consumption budget, such as core parameters 5 times per second and non-core parameters 10 times per second.

[0079] When the final risk level is less than the low risk threshold and the aging label is a low aging label, low priority monitoring is triggered, that is, the sampling frequency of non-core parameters is reduced according to the upper limit of the sampling power consumption budget, such as 10 times for core parameters and 30 times for non-core parameters.

[0080] When the aging tag is a normal aging tag, the normal power supply scenario adaptation is triggered and the final risk level is determined directly. If the risk level is high, the monitoring is high priority; if the risk level is medium, the monitoring is medium priority. The sampling frequency refers to the preset strategy, such as the core parameter 5 times per second.

[0081] The adaptive sampling and communication module utilizes the final risk level and recommended sampling level provided by the health comprehensive analysis module to dynamically adjust the data acquisition and communication strategies for each cable monitoring device node, so as to achieve the unity of refined monitoring and efficient energy utilization.

[0082] The system receives core data from the comprehensive health analysis module in real time, which serves as the basis for strategy adjustments. This includes decision instructions, such as recommended sampling levels and sampling power consumption budget limits; status parameters include final risk level, aging label, remaining available energy curve, and cable electrical parameters.

[0083] The recommended sampling level output from the comprehensive health analysis module is directly mapped to a preset tiered sampling strategy; when the recommended sampling level is high-priority monitoring, it is mapped to a first-level strategy, with the highest sampling frequency and the most comprehensive collection of parameters.

[0084] When the recommended sampling level is medium priority monitoring, it is mapped to a secondary strategy, with a medium sampling frequency and core parameters plus some non-core parameters.

[0085] When the recommended sampling level is low priority monitoring, it is mapped to a level three strategy, with the lowest sampling frequency, collecting only key indicators;

[0086] When the recommended sampling level is normal power supply scenario adaptation mapping is normal strategy, the sampling frequency refers to the preset benchmark.

[0087] In addition, the module introduces new discriminant factors such as risk change trends into its strategy judgment: for example, when a risk level is detected to be rising rapidly even if it has not exceeded the previous level, the monitoring strategy can be upgraded to a higher level in advance to avoid delayed response; similarly, if key indicators such as partial discharge remain abnormal but the comprehensive risk assessment has not yet reached the threshold, the module can temporarily increase the sampling density of the corresponding parameters to ensure that potential hazards are not overlooked. Through the above-mentioned fine-grained rule expansion, differentiated sampling strategies can be matched for nodes with different risk levels and aging degrees, improving the accuracy of adaptive sampling in covering diverse cable aging conditions;

[0088] Based on the determined hierarchical sampling strategy and sampling power consumption budget, the module issues real-time acquisition and communication scheduling commands to coordinate and control each sensing unit and communication unit, dynamically executing the optimal sampling and communication scheme to achieve a balance between precise monitoring and efficient energy utilization. Under high-priority monitoring, the module drives the node to enter a first-level strategy mode: acquiring core parameters such as partial discharge and current at the highest possible frequency, while appropriately increasing the acquisition frequency of auxiliary parameters such as temperature and humidity; shortening the data upload cycle, and using event triggering to instantly report key data when necessary, ensuring that the backend can obtain detailed first-line status in a timely manner.

[0089] During medium-priority monitoring, the module-driven node enters a secondary strategy mode to balance sampling tasks: maintaining a high sampling frequency for core parameters while periodically collecting some non-core parameters, typically every 5 seconds for core parameters and every 10 seconds for general parameters, using batch uploading or data compression uploading to reduce communication energy consumption. Under low-priority monitoring, the module enters a tertiary strategy mode with energy conservation as the guiding principle: significantly reducing most sampling activities except for key indicators, such as extending the sampling period for core indicators to 10 seconds or longer, extending the sampling interval for non-core indicators to 30 seconds or even collecting data on demand, and temporarily storing data locally for periodic reporting, thereby minimizing the duty cycle of the wireless communication module. Throughout the process, the module strictly adheres to the sampling power consumption budget limit provided by the power management module for all sampling and transmission operations, ensuring that the actual sampling frequency and communication frequency are within the current energy supply capacity. When a switch in the power supply status of a cable monitoring device node is detected, such as from normal power supply to battery discharge or an update of the health status assessment result, the corresponding sampling communication strategy is immediately switched according to the new recommended sampling level, ensuring the real-time and continuous nature of strategy adjustments.

[0090] In response to situations where sampling energy or communication resources are limited, the module enhances the elastic adjustment capability of power consumption strategy allocation and intelligently optimizes the allocation of sampling resources to adapt to actual deployment conditions. First, based on the current power supply margin provided by the power supply management module, such as the remaining ratio of mutual inductor power and the percentage of battery state of charge, the power consumption requirements of the current sampling scheme are compared to assess the pressure of the sampling task on the power supply. When a node is detected to be in a deep discharge state, relying solely on battery power and facing power shortages, the module will compress sampling power consumption while maintaining basic monitoring. This involves dynamically adjusting the power allocation ratio based on the aging level of the node's cable monitoring device. For example, for healthy, low-aging nodes, 50% of the current actual supportable sampling power consumption limit may be used for sampling, while for nodes with high aging risk, sampling power consumption will be strictly controlled to around 30% of the supportable limit to extend device lifespan. Finally, the module calculates the upper limit of the sampling power consumption budget under deep discharge mode based on the corresponding ratio, and accordingly reduces or suspends the collection of some non-critical parameters. Secondly, the module optimizes task scheduling to achieve on-demand allocation of sampling resources. For example, when multiple cable monitoring device nodes simultaneously require high-frequency monitoring, the sampling and transmission timing of each node is staggered to prevent communication conflicts or instantaneous power consumption from affecting the overall monitoring effect. Throughout the adaptation process, the module continuously balances monitoring accuracy and energy consumption constraints, using intelligent strategies to ensure effective monitoring of key health parameters with the lowest necessary power consumption even under harsh conditions such as low battery power and insufficient electromagnetic energy extraction, demonstrating high environmental adaptability and deployment practicality.

[0091] Furthermore, the lifespan analysis and operation-maintenance linkage based on the remaining available energy curve and aging level coefficient include: while executing the sampling communication strategy, the module uses the remaining available energy curve and cable aging level coefficient output by the health comprehensive analysis module to assess lifespan degradation; wherein, the remaining available energy curve is used to reflect the possible remaining energy changes of the cable monitoring device node's own power supply in the future period, such as the energy change trend in the next 6 months, and the cable aging level coefficient reflects the aging degree of the cable itself, such as a quantitative value in the range of 0-1; specifically, when the remaining available energy curve shows that the energy will drop below the energy warning threshold for maintaining basic monitoring in the future period, such as in the next 3 months, and the cable aging level coefficient is still greater than or equal to the high aging threshold, the cable monitoring device node is determined to have a high lifespan degradation risk; if only the remaining available energy drops below the energy warning threshold and the cable aging level coefficient is still less than or equal to the low aging threshold, the cable is considered healthy, and a power supply warning is determined; if only the aging level coefficient is higher than the energy warning threshold and the cable aging level coefficient is still greater than or equal to the high aging threshold, an aging warning is determined; wherein the energy warning threshold is configured by statistically analyzing historical monitoring device power outage data and cable fault data;

[0092] Based on the above judgment results, corresponding maintenance work orders are generated: For nodes with high lifespan degradation risk, the work order includes joint inspection suggestions such as insulation testing, joint replacement, and temporary adjustment instructions for sampling strategies, such as upgrading to the first-level strategy of intensive monitoring, and simultaneously increasing the priority of power supply guarantee; For power supply warning nodes, the work order suggests prioritizing the replacement of the monitoring device battery or optimizing the mutual inductor power extraction structure, and temporarily reducing the sampling frequency of non-core parameters to extend the battery life; For aging warning nodes, the work order suggests strengthening the sampling accuracy of aging-related parameters such as partial discharge and temperature, such as increasing the partial discharge pulse acquisition duration, without needing to adjust the power supply strategy;

[0093] The module executes the above sampling communication strategy adjustment process in a cyclical manner according to a preset period or event trigger, forming an online closed-loop optimization mechanism; that is, it monitors new power supply status and health status information in real time, continuously feeds the latest data back to the decision-making link, and works with the power supply management module and the health comprehensive analysis module to update the final risk level and recommended sampling level, thereby driving the adaptive adjustment of the sampling strategy in the next round.

[0094] In this embodiment, the system includes power supply management, comprehensive health analysis, and adaptive sampling communication modules. The power supply management module collects electrical parameter streams, generates instantaneous power density sequences, combines historical and real-time data, detects charging and discharging states, and outputs parameters such as power outage type and power supply reliability. The comprehensive health analysis module dynamically calculates the sampling power consumption budget, predicts and outputs energy curves and risk levels through graph-time series fusion, and determines the recommended sampling level after correction. The adaptive sampling communication module maps a hierarchical strategy and dynamically executes sampling and communication scheduling. The system solves the problem of adaptive sampling strategy matching in multi-node high-voltage distribution network monitoring, achieves accurate monitoring and strategy adaptation under differentiated aging, and improves the efficiency and reliability of cable health management.

[0095] like Figure 3 As shown, the method for predicting and analyzing the health status of a cable throughout its entire life cycle includes the following steps: Step 1: Collect the bus current, bus voltage, backup battery current, and battery terminal voltage of the cable monitoring device nodes to generate cable electrical parameter flow; calculate the first derivative of the instantaneous power of the bus to generate an instantaneous power density sequence; based on the instantaneous power density sequence and a preset threshold, determine the power outage type as deep discharge, normal discharge, or charging operation status;

[0096] Step 2: Obtain cable service life and historical fault data, calculate the ratio of service life to preset life reference value to obtain the basic aging ratio; set an offset based on the number of historical faults, and add the basic aging ratio to the offset to generate the cable aging level coefficient.

[0097] Step 3: Construct a graph with cable monitoring device nodes as nodes and cable connection topology as edges; use a graph time series fusion prediction algorithm to process cable status data and output the remaining available energy curve, uncertainty curve, and preliminary risk level; calculate confidence weights based on the uncertainty curve, and perform bias correction on the preliminary risk level to generate the final risk level;

[0098] Step 4: When the power outage type is identified as deep discharge, compress the upper limit of the sampling power consumption budget according to the cable aging level coefficient; and combine the final risk level and aging level coefficient to allocate a recommended sampling level, map the recommended sampling level to a hierarchical sampling strategy, and dynamically adjust the sampling and communication operations of each cable monitoring device node according to the upper limit of the sampling power consumption budget based on the hierarchical sampling strategy.

[0099] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0101] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0103] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0104] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cable full life cycle health status prediction and analysis system, characterized in that, Includes the following modules: The power supply management module is used to collect cable electrical parameter flow and aging basic data from the cable monitoring device nodes, and to generate an instantaneous power density sequence by performing derivative calculations on the cable electrical parameter flow. By comparing the changing trend of the instantaneous power density sequence and the bus voltage and current relationship in the cable electrical parameter flow, it determines the charging and discharging status of the cable monitoring device nodes and outputs a power outage type identifier. It calculates the mutual inductance power remaining ratio and battery state of charge percentage by combining historical data fitting and real-time values, and records them as power supply reliability parameters. It also implements the output cable aging level coefficient and the upper limit of actual supportable sampling power consumption. Specifically, the instantaneous power density sequence is integrated to obtain the energy integral value within a certain period. The energy integral value is compared with the rated mutual inductance power capacity to calculate the mutual inductance power remaining ratio. The product of the mutual inductance power remaining ratio, battery state of charge percentage, and cable aging level coefficient is multiplied by the device's preset power consumption to calculate the upper limit of actual supportable sampling power consumption. The comprehensive health analysis module dynamically calculates the upper limit of the sampling power consumption budget based on the power outage type identifier, cable aging level coefficient, and power supply reliability parameters. It then performs time synchronization based on cable status data to generate a health initial vector. Using this initial vector as a graph structure with the cable monitoring device node topology, it performs graph time-series fusion prediction to output the remaining available energy curve, uncertainty curve, and preliminary risk level. Based on the uncertainty curve correction, it performs confidence correction on the preliminary risk level to obtain the final risk level, determining the upper limit of the sampling power consumption budget and the recommended sampling level. The uncertainty curve reflects the prediction error range, and the remaining available energy curve reflects the potential changes in the remaining energy of the cable monitoring device node's power supply over a future period. The adaptive sampling communication module receives the final risk level and recommended sampling level, maps them to a hierarchical sampling strategy, and dynamically executes the sampling and communication strategies of each cable monitoring device node.

2. The cable lifecycle health status prediction and analysis system according to claim 1, characterized in that, The power supply management module performs the following steps: It collects bus current, bus voltage, backup battery current, and battery terminal voltage at a fixed sampling frequency to generate cable electrical parameter flow; it calculates the instantaneous power of the bus by multiplying the bus voltage and current corresponding to each sampling point; it performs a first derivative operation on the instantaneous power of the bus to generate an instantaneous power density sequence; and based on the instantaneous power density sequence and the cable electrical parameter flow and their corresponding preset thresholds, it determines the power outage type as deep discharge, normal discharge, or charging operation.

3. The cable full life cycle health status prediction and analysis system according to claim 2, characterized in that, The criteria for determining the power outage type include: detecting the charging and discharging status of the cable monitoring device nodes based on the obtained instantaneous power density sequence, cable electrical parameters and their corresponding preset battery continuous discharge threshold, preset battery static current threshold, preset upper limit threshold for positive power density fluctuation, and preset lower limit threshold for negative power density fluctuation, including: When the instantaneous power density sequence of the monitoring device node corresponding to N consecutive sampling points is less than the preset negative power density fluctuation lower limit threshold and the backup battery discharge current is less than the preset battery continuous discharge threshold, it is determined to be in a deep discharge power failure state and a deep discharge power failure flag is output. When the instantaneous power density sequence is between the lower threshold of negative power density fluctuation and the upper threshold of preset positive power density fluctuation, and the absolute value of the backup battery discharge current is less than or equal to the preset battery static current threshold, the corresponding monitoring device node is in normal discharge and outputs a normal discharge power failure flag. When the instantaneous power density sequence is greater than the preset upper limit threshold for positive power density fluctuation and the backup battery discharge current is greater than the preset battery continuous discharge threshold, the corresponding cable monitoring device node is significantly charging and outputs a charging operation indicator.

4. The cable lifecycle health status prediction and analysis system according to claim 1, characterized in that, Power supply reliability parameters, cable aging level coefficient, and the upper limit of actual supportable sampling power consumption are generated as follows: derived based on the ampere-hour integration method by integrating the backup battery current and combining it with the battery's rated capacity, outputting the battery's state of charge percentage; according to the following formula: In the formula, This represents the state of charge of the battery at time t. It is the initial moment The state of charge of the battery. The rated capacity of a battery is expressed in ampere-hours (AH). It is the battery current at time t, in amperes, during charging. It is a positive value during discharge and a negative value during discharge. Indicates from the initial time The integral of the battery current up to the current time t, that is, the change in the amount of charge in the battery during this time. Based on the pre-stored and provided cable service life and historical fault data within the system, the ratio of the cable service life to the preset life reference value is calculated to obtain the basic aging ratio; The offset is set based on the number of historical faults, and the basic aging ratio is added to the offset to generate a quantitative value, which is recorded as the cable aging level coefficient.

5. The cable lifecycle health status prediction and analysis system according to claim 1, characterized in that, The health comprehensive analysis module dynamically calculates the upper limit of the sampling power consumption budget, including: under the deep discharge power failure flag, when the cable aging level coefficient is less than or equal to the low aging risk threshold, it is marked as a low aging tag and its corresponding first compression ratio is configured; When the cable aging level coefficient is greater than the low aging risk threshold but less than the high aging threshold, it is marked as a medium aging label and its corresponding second compression ratio is configured. When the cable aging level coefficient is greater than or equal to the high aging threshold, it is marked as a high aging label and its corresponding third compression ratio is configured; and the first compression ratio is greater than the second compression ratio is greater than the third compression ratio; and the maximum allowable sampling power consumption under the current node deep discharge mode is obtained by multiplying the different compression ratios configured according to the aging label by the actual support sampling power consumption limit.

6. The cable full life cycle health status prediction and analysis system according to claim 1, characterized in that, The comprehensive health analysis module employs a graph-time series fusion prediction algorithm, which includes: a fusion architecture of graph convolutional networks and long short-term memory networks. The graph convolutional network processes the relationship graph constructed with cable monitoring device nodes as nodes and cable connection topology as edges; the long short-term memory network processes the multi-source sensor data of each cable monitoring device node; the recurrent neural network and the graph neural network are fused to output the remaining available energy curve and preliminary risk level; the confidence weights for each time step are calculated based on the uncertainty curve; and a weighted average formula is used to bias-correct the preliminary risk level to generate the final risk level.

7. The cable full life cycle health status prediction and analysis system according to claim 1, characterized in that, The rules for determining the recommended sampling level in the comprehensive health analysis module include: when the final risk level is greater than or equal to the high risk level threshold or the aging label is a high aging label, high-priority monitoring is triggered, that is, the core parameters should be sampled at high frequency according to the upper limit of the sampling power consumption budget. When the final risk level is greater than or equal to the medium risk threshold and less than the high risk level threshold, or when the aging label is a medium aging label, medium priority monitoring is triggered, i.e., the sampling of core and non-core parameters is balanced according to the upper limit of the sampling power consumption budget. When the final risk level is less than the low risk threshold and the aging label is a low aging label, low priority monitoring is triggered, that is, the sampling frequency of non-core parameters is reduced according to the upper limit of the sampling power consumption budget. When the aging label is a normal aging label, the normal power supply scenario adaptation is triggered and the final risk level is determined directly, and the sampling frequency is still based on the fixed sampling frequency.

8. The cable lifecycle health status prediction and analysis system according to claim 6, characterized in that, It also includes a lifespan degradation assessment function: based on the remaining available energy curve and the cable aging level coefficient, it generates a judgment result for high lifespan degradation risk, power supply warning or aging warning; When the remaining available energy curve shows that the energy will drop below the energy warning threshold for maintaining basic monitoring in the future, and the cable aging level coefficient is still greater than or equal to the high aging threshold, the cable monitoring device node is determined to be at high risk of life degradation. If the remaining available energy drops below the energy warning threshold and the cable aging level coefficient is still less than or equal to the low aging threshold, then it is determined to be an energy supply warning. If only the aging level coefficient is higher than the energy warning threshold, and the cable aging level coefficient is still greater than or equal to the high aging threshold, then it is judged as an aging warning. For cable monitoring device nodes with high lifespan degradation risk, maintenance work orders that include joint inspection suggestions and sampling strategy upgrades are automatically triggered.

9. The cable full life cycle health status prediction and analysis system according to claim 1, characterized in that, When the adaptive sampling communication module executes the sampling strategy of each cable monitoring device node, it includes: when the recommended sampling level of the cable monitoring device node is high-priority monitoring, it enters the first-level strategy mode and uses an event-triggered mechanism to upload key data in real time; when the recommended sampling level of the cable monitoring device node is medium-priority monitoring, it enters the second-level strategy mode and uses data compression and batch upload strategies to reduce communication energy consumption. When the recommended sampling level for the cable monitoring device node is low-priority monitoring, it enters a three-level strategy mode oriented towards energy saving.

10. A method for predicting and analyzing the health status of cables throughout their entire life cycle, used to implement the cable life cycle health status prediction and analysis system according to any one of claims 1-9, characterized in that, Includes the following steps: Step 1: Collect bus current, bus voltage, backup battery current, and battery terminal voltage from the cable monitoring device nodes to generate cable electrical parameter flow; calculate the first derivative of the instantaneous power of the bus to generate an instantaneous power density sequence; based on the instantaneous power density sequence and a preset threshold, determine the power outage type as deep discharge, normal discharge, or charging operation. Step 2: Obtain cable service life and historical fault data, and calculate the ratio of service life to preset life reference value to obtain the basic aging ratio; The offset is set based on the number of historical failures, and the basic aging ratio is added to the offset to generate the cable aging level coefficient. Step 3: Construct a relationship graph using the cable monitoring device nodes as graph nodes and the cable connection topology as edges; A graph-time series fusion prediction algorithm is used to process cable status data and output the remaining available energy curve, uncertainty curve, and preliminary risk level. Confidence weights are calculated based on the uncertainty curve, and bias corrections are applied to the preliminary risk level to generate the final risk level. The uncertainty curve reflects the prediction error range, and the remaining available energy curve reflects the possible changes in the remaining energy of the cable monitoring device node's own power supply over a future period. Step 4: When the power outage type is identified as deep discharge, compress the upper limit of the sampling power consumption budget according to the cable aging level coefficient. Combined with the final risk level and aging level coefficient, a recommended sampling level is assigned, and the recommended sampling level is mapped to a hierarchical sampling strategy. Based on the hierarchical sampling strategy, the sampling and communication operations of each cable monitoring device node are dynamically adjusted according to the upper limit of the sampling power consumption budget.

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