Self-sensing monitoring method, system and equipment based on lightning arrester

Abstract

The invention discloses a self-sensing monitoring method, system and equipment based on a lightning arrester, and relates to the technical field of power equipment monitoring. The self-sensing monitoring method, system and equipment based on the lightning arrester comprises the following steps: S1, collecting lightning stroke data in real time through multi-sensing fusion, and performing data cleaning and standardization processing on the lightning stroke data; s2, adopting a three-mode sensing and three-source power supply cooperation mechanism to realize state-driven adaptive sensing and energy path switching; s3, automatically scheduling a communication path according to a communication type and link quality, dynamically analyzing and compensating transmitting power, and completing authentication encryption and multi-hop secure transmission; s4, lightning stroke characteristics are extracted, an equipment life prediction model is constructed, residual life prediction is realized, and equipment health grade division and operation strategy adjustment are carried out according to the residual life prediction model; and S5, calculating an electric leakage deviation value by reconstructing a current waveform, triggering graded early warning and linking a communication power and energy supply strategy. The problem that the operation of a power system is affected due to large leakage current monitoring errors is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment monitoring, and in particular to a self-sensing monitoring method, system and equipment based on a lightning arrester. Background Art

[0002] Lightning arresters are critical devices in power systems used to dissipate lightning current and protect equipment. Their monitoring methods directly impact system safety. Traditional mechanical counters currently suffer from high response thresholds, reliance on manual reading, and inability to remotely manage them. Wired monitoring systems are complex to wire, expensive to maintain, and difficult to adapt to complex terrain. Wireless solutions such as 2G / 4G and NB-IoT are limited by signal blind spots, latency, and coverage, making it difficult to ensure timely communication after a lightning strike. Furthermore, integration with devices such as intelligent reactors presents compatibility challenges.

[0003] For example, the patent with publication number: CN116466148A discloses a lightning arrester self-sensing monitoring unit, which is used to be installed on the lightning arrester, including a trigger sensor, an acquisition sensor, an acquisition and control module, a communication module, a battery and a casing. It is used to monitor the action of the lightning arrester, assist in judging the operating status of the lightning arrester, and is used for accident query and positioning after lightning overvoltage faults, and guide line operation and maintenance repairs.

[0004] For example, patent publication number CN119044643A discloses an airborne fixed-point wake-up arrester online monitoring system and method, which includes an arrester monitoring terminal and an arrester monitoring main system, both of which are connected and communicated via a wireless communication module. The arrester monitoring terminal includes a battery and energy extraction unit, an arrester acquisition unit, an MCU, and a wireless communication submodule, while the arrester monitoring main system includes a wireless communication main module, a monitoring master station, a protocol converter, and a substation system. This solves the technical problem of query-type communication for zinc oxide arrester self-energy extraction monitors and is suitable for online monitoring of zinc oxide arresters.

[0005] In addition, existing equipment generally has problems such as large leakage current monitoring errors, which not only makes it difficult to timely identify the aging trend of lightning arrester insulation, but also easily leads to misjudgment, omission or late reporting of the cumulative effects of lightning strikes, thereby affecting the formulation of operation and maintenance strategies and risk warning efficiency. In severe cases, it may lead to protection failure and threaten the stability and power supply security of the entire power system.

[0006] Therefore, in order to solve the above problems, there is an urgent need for a self-sensing monitoring method, system and equipment based on lightning arresters. Summary of the Invention

[0007] Technical problems solved

[0008] In view of the shortcomings of the existing technology, the present invention provides a self-sensing monitoring method, system and equipment based on lightning arresters, which solves the problem that the operation of the power system is affected by large leakage current monitoring errors.

[0009] Technical Solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a self-sensing monitoring method based on lightning arrester, comprising the following steps: S1, real-time collection of lightning strike data through multi-sensor fusion, and data cleaning and standardization of the lightning strike data; S2, adopting a three-mode perception and three-source power supply collaborative mechanism to realize state-driven adaptive perception and energy path switching; S3, automatically scheduling communication paths according to communication type and link quality, dynamically analyzing and compensating transmission power, completing authentication encryption and multi-hop secure transmission; S4, extracting lightning strike characteristics, constructing an equipment life prediction model based on a long short-term memory network, realizing remaining life prediction, and based on this, dividing the equipment health level and adjusting the operation strategy; S5, calculating the leakage deviation value by reconstructing the current waveform, triggering a graded warning and linking the communication power and energy supply strategy.

[0011] Furthermore, the specific steps of real-time collection of lightning strike data through multi-sensor fusion and data cleaning and standardization of lightning strike data are as follows: Lightning strike data include lightning strike current waveform, lightning strike current peak value, leakage current value, signal strength, lightning strike temperature, lightning strike event timestamp, terminal voltage, and battery power; among them, the lightning strike current peak value is collected in real time by a high-frequency magnetic ring sensor; the leakage current value is periodically collected by a temperature-compensated leakage current module; the spatial electric field vector is collected by a three-dimensional electric field detection array; the lightning strike temperature is collected by a built-in temperature sensor; the signal strength is collected by the RF receiving module and register interface of the LoRa chip to obtain the channel quality; the battery power and terminal voltage are collected by the analog-to-digital converter and power management unit inside the terminal microcontroller All collected data are timestamped by a local clock and cached in the internal storage of the microcontroller unit, and uploaded in batches after the communication cycle arrives; by constructing feature hash values ​​for rapid comparison, duplicate lightning strike data generated during caching and relay forwarding are automatically identified and eliminated; lightning strike data are screened using null value detection and field integrity rules, and lightning strike data with empty fields and missing key fields are deleted and corrected; the interquartile range method is used to calculate the upper and lower boundaries of the lightning strike data set to determine whether the value exceeds the reasonable physical range and eliminate outliers with no physical meaning; the Kalman filter algorithm is used to denoise and extract trends from lightning strike data to obtain stable lightning strike data; the maximum and minimum normalization algorithm is used to normalize the lightning strike data and convert the lightning strike data into a unified standard range.

[0012] Furthermore, the specific steps of adopting the three-mode perception and three-source power supply coordination mechanism to realize state-driven adaptive perception and energy path switching are as follows: the intelligent monitoring terminal adopts a three-mode perception structure, integrating a high-frequency magnetic ring sensor, a temperature-compensated leakage current monitoring module and a three-dimensional electric field detection array, and automatically controls the perception path according to the operating status: in the normal inspection state, the leakage current module is kept awake at regular intervals; when a sudden lightning strike or electric field disturbance precursor appears, the magnetic ring and electric field array are quickly started for synchronous collection to enter the event monitoring mode; the terminal power supply system adopts a photovoltaic and lithium battery dual-mode architecture, supplemented by an electromagnetic induction energy capture module, forming a three-source coordination mechanism: photovoltaic path: integrated maximum The power point tracking chip tracks the maximum power point of solar energy, serves as the main power source during the day, and intelligently charges the lithium battery; the lithium battery path: as the energy storage core, it is equipped with a charge and discharge management chip, and integrates overcharge, over-discharge, overheating protection and temperature detection modules to ensure stable operation of the equipment at night and on rainy days; the induction backup path: the electromagnetic induction coil installed at the bottom of the lightning arrester can capture transient electromagnetic energy during a lightning strike, and convert it into usable power through rectification and energy storage circuits, supplying supercapacitors to support lightning sampling and high-power communication; the system monitors data indicators such as terminal voltage, current waveform and battery power in real time, and dynamically switches the power supply path according to energy input and current load status.

[0013] Furthermore, the specific steps for automatically scheduling communication paths according to communication type and link quality are as follows: based on the collected lightning strike data, the communication scheduling paths are divided into two categories: periodic reporting channel: used for periodic uploading of operating status data such as leakage current and battery power, using low-power Class A mode; event trigger channel: when a lightning strike event and a large sudden change in current are identified, it immediately switches to Class C mode and opens a fast communication window; at the beginning of each communication cycle, the system determines whether direct communication is possible based on the link quality between itself and the gateway; if the LoRa signal strength is lower than the signal threshold, a multi-hop relay link is automatically constructed; if the LoRa signal strength is greater than the signal threshold, direct communication is performed; after the communication path is determined, the estimated transmission power required for this communication is calculated.

[0014] Furthermore, the specific steps for dynamically analyzing and compensating the transmission power are as follows: obtain the peak value of the lightning current, battery power, terminal voltage and channel quality, and set the minimum power value and the maximum power value; first, calculate the product of the peak value of the lightning current and the lightning weight to obtain the lightning factor; calculate the difference between 1 and the channel quality and multiply it by the channel weight to obtain the channel factor; calculate the product of the battery power, power weight and terminal voltage plus the inverse of the minimum term to obtain the power correction factor; add these three factors to obtain the total adjustment factor, multiply the total adjustment factor by the difference between the maximum power and the minimum power to obtain a product, and add the minimum power to this product to obtain the estimated transmission power; the system has a built-in real-time power adaptation mechanism based on channel feedback: every 5 minutes The signal strength is collected periodically and the channel quality is calculated based on it. If the bit error rate is detected to exceed the set signal threshold, the linear power compensation mechanism is immediately triggered: first, the difference between the signal threshold and the channel quality is calculated and then divided by the channel quality plus 1. The ratio is squared as the first part; the ratio of the lightning temperature plus 1 divided by the terminal voltage plus the minimum term is calculated as the second part; the difference between 1 and the power weight multiplied by the square of the battery power is calculated as the third part; the compensation weight, the first part, the second part and the third part are multiplied together to obtain the transmission power compensation value; the transmission power compensation value is added to the transmission power estimate without violating the maximum power value limit to form the final transmission power value written into the LoRa chip register.

[0015] Furthermore, the specific steps to complete authentication encryption and multi-hop secure transmission are as follows: After the final transmit power value is determined, the identity authentication request is triggered and the encrypted transmission preparation process is started: the terminal generates an authentication request with the terminal number, timestamp, random challenge code and signature summary, and sends it to the target gateway through the LoRa network to confirm the legitimacy of the identity of the communication counterpart; after receiving the authentication request, the gateway verifies the terminal identity and generates an authentication response, which includes the gateway signature. After receiving it, the terminal performs local decryption and verification to complete the two-way identity authentication and establish the trust basis for this communication; after the authentication is passed, the transmit power control interface is called to write the final transmit power value into the transmit power register of the LoRa chip If the terminal voltage, battery level and lightning temperature are found to be outside the safety range during this stage, the system will automatically reduce the estimated transmit power for protection and mark the abnormal state to prevent overload communication. After the transmit power is set, AES-128 encryption is performed on the payload portion of the data packet to be sent. After encryption is completed, communication is formally initiated through the LoRa network path. After the data is sent, the system begins to wait for the ACK confirmation signal from the gateway to determine whether the communication is successful. If the ACK is received within the set time limit, the terminal records the communication as successful. If no response is received within the time limit, the failure state is recorded and the number of failures determines whether to resend or switch power and try again.

[0016] Furthermore, the lightning strike characteristics are extracted, and an equipment life prediction model is constructed based on the long short-term memory network to realize the remaining life prediction. The specific steps of classifying the equipment health level and adjusting the operation strategy are as follows: calling the waveform feature extraction algorithm, performing multi-dimensional feature recognition on the lightning strike waveform, completing the lightning strike behavior portrait, and constructing the lightning strike feature matrix; constructing the equipment life prediction model based on the long short-term memory network, taking the lightning strike feature matrix as the input sequence, performing time series learning and trend fitting, and outputting the remaining life assessment value of the current equipment; after the life prediction is completed, the remaining life assessment value and the life threshold are compared in real time. The life threshold includes the first-level life threshold and the second-level life threshold. According to the comparison results, the current health status of the equipment is graded and evaluated: if the remaining life assessment value is greater than or equal to the first-level life threshold, the equipment status is divided into normal operation: the terminal maintains the Class A communication mode, and the equipment status is divided into normal operation: the terminal maintains the Class A communication mode, and the equipment status is divided into Class B communication mode, and the equipment status is divided into Class C communication mode, and the equipment status is divided into Class D communication mode, and the equipment status is divided into Class D communication mode, and the equipment status is divided into Class B ... Photovoltaic power supply is the main mode, lithium battery is the auxiliary mode, and the induction power supply path is closed; the platform is marked as green, the monitoring data is recorded and archived, and no alarm is triggered; the remaining life assessment value is between the first-level life threshold and the second-level life threshold, and the device status is divided into attention warning: shorten the terminal communication cycle, increase the sampling frequency, and adjust the transmission power in real time according to the channel quality; the power supply management enters the energy consumption adjustment stage, dispatches photovoltaic and battery paths on demand, and evaluates the electromagnetic induction standby status at the same time; the platform is marked as yellow, and an operation and maintenance attention reminder is automatically generated; the remaining life assessment value is less than or equal to the second-level life threshold, and the device status is divided into degradation warning: the terminal switches to Class C communication mode, and the sampling frequency is increased to the highest level; the three energy supply paths of photovoltaic, lithium battery and electromagnetic induction are started to operate in coordination; the platform marks the device as red, automatically generates an emergency maintenance work order and pushes multi-channel alarms.

[0017] Furthermore, by reconstructing the current waveform to calculate the leakage deviation value, triggering the graded warning and linking the communication power and energy supply strategy, the specific steps are as follows: the historical leakage current data is predicted and updated by continuously executing the Kalman filter, and the sampling value at each moment is smoothed and estimated to obtain a baseline leakage sequence representing the normal operation trend of the equipment; in each communication cycle, the leakage current sequence of the current period is extracted, and it is smoothed and reconstructed using the cubic spline interpolation algorithm to obtain a fitting waveform curve of the leakage current of the current period; the fitting waveform curve is compared with the baseline current sequence point by point, and the difference between the leakage current value and the baseline leakage value is calculated point by point, and the absolute value of the difference at all time points is summed up. Obtain the leakage deviation value; after obtaining the leakage deviation value, compare the leakage deviation value and the leakage threshold in real time. When the leakage deviation value exceeds the leakage threshold within three consecutive communication cycles, a warning is triggered: the device maintains Class A mode operation and shortens the terminal communication cycle; when the leakage deviation value exceeds the leakage threshold within five consecutive communication cycles, an abnormal alarm is triggered, and a maintenance work order is automatically generated and pushed to the operation and maintenance center; once the abnormal alarm is triggered, the LoRa communication strategy is linked to force switching to Class C mode and recalculate the estimated transmission power; at the same time, the life prediction model is called. If it is detected that the battery power does not reach the safety threshold, the electromagnetic induction backup power supply path is enabled to prevent communication interruption during the alarm stage.

[0018] The second aspect of the present invention provides a self-sensing monitoring system based on a lightning arrester, which is characterized by: including: a lightning strike data acquisition and preprocessing module, a terminal perception and power supply management module, a LoRa communication and power control module, a cloud analysis and life assessment module and a leakage warning and linkage control module, wherein: the lightning strike data acquisition and preprocessing module is used to collect lightning strike data in real time through multi-sensor fusion, and perform data cleaning and standardization on the lightning strike data; the terminal perception and power supply management module is used to adopt a three-mode perception and three-source power supply collaborative mechanism to realize state-driven adaptive perception and energy path switching; the LoRa communication and power control module is used to automatically schedule the communication path according to the communication type and link quality, dynamically analyze and compensate the transmission power, and complete authentication encryption and multi-hop secure transmission; the cloud analysis and life assessment module is used to extract lightning strike characteristics, construct an equipment life prediction model based on a long short-term memory network, realize remaining life prediction, and perform equipment health level classification and operation strategy adjustment based on this; the leakage warning and linkage control module is used to calculate the leakage deviation value by reconstructing the current waveform, trigger a graded warning and link the communication power and energy supply strategy.

[0019] The third aspect of the present invention provides a self-sensing monitoring device based on a lightning arrester, which is characterized by: including: a data collector, a data processor, a communication controller, and an energy supply management mechanism, wherein: the data collector is used to perform multi-source signal monitoring on the working status of the lightning arrester, and collect key characteristic parameters such as lightning current, leakage current, electric field changes, and ambient temperature; the data processor is used to filter, normalize and feature extract the lightning monitoring data obtained by the sensing acquisition module, and calculate the current health index, power regulation factor and remaining life prediction value of the equipment in combination with the historical operating status; the communication controller is used to establish a two-way authentication connection with the gateway based on the LoRa communication mode, dynamically set the transmission power according to the data processing results, realize event-driven or periodic data upload, and support multi-hop relay networking path selection; the energy supply management mechanism is used to manage multi-source power supply channels such as photovoltaic, lithium battery and electromagnetic induction, switch the optimal energy path according to the real-time load and power supply status, and provide protection strategies under overvoltage, over-discharge and high temperature conditions.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) The self-sensing monitoring method, system and equipment based on lightning arrester proposes a three-source integrated power supply architecture. Through the combination of photovoltaic tracking, lithium battery intelligent management and electromagnetic induction energy replenishment, it can dynamically switch the power supply path in different operating environments. It is not only suitable for scenes such as continuous rain and night, but also can capture kinetic energy at the moment of lightning strike for high-power communication, effectively extending the service life of the equipment from the conventional 1-2 years to more than 5 years.

[0023] (2) The self-sensing monitoring method, system and equipment based on lightning arrester realizes the coordinated monitoring of lightning current, environmental parameters and spatial field strength by integrating multi-mode sensors such as high-frequency magnetic ring, temperature-compensated leakage current module and three-dimensional electric field array, and cooperates with Kalman filtering and spline fitting algorithm to effectively remove noise and outliers, ensuring the accuracy of lightning data, which far exceeds the traditional single-point collection solution.

[0024] (3) The self-sensing monitoring method, system and equipment based on lightning arrester proposes the "leakage deviation value" indicator by comparing the real-time current waveform after spline fitting with the historical benchmark model, supports the continuous detection of small trend degradation, and automatically adjusts the warning level in combination with communication power and power supply status, forming a full-chain closed-loop intelligent warning mechanism of "perception-diagnosis-upload-response".

[0025] (4) The self-sensing monitoring method, system and equipment based on lightning arrester builds a network model for predicting equipment life based on key features such as lightning strike history sequence and leakage current trend, outputs the remaining life value, realizes hierarchical diagnosis of operating status and strategy linkage, and significantly improves the remote intelligent operation monitoring, fault prevention and operation and maintenance response capabilities of lightning arrester, realizing the transformation from "post-event disposal" to "active warning".

[0026] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the self-sensing monitoring method based on lightning arrester;

[0028] Figure 2 This is the topology diagram of the self-sensing monitoring system based on lightning arrester;

[0029] Figure 3 This is a schematic diagram of the "photovoltaic + lithium battery + inductive energy" three-source power supply architecture;

[0030] Figure 4 This is a flow chart of the dynamic transmit power adjustment algorithm;

[0031] Figure 5 This is a diagram of the fault warning model based on the life prediction model. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] See also Figure 1-Figure 5, an embodiment of the present invention provides a technical solution: a self-sensing monitoring method based on a lightning arrester, comprising the following steps: S1, real-time acquisition of lightning strike data through multi-sensor fusion, the lightning strike data including lightning strike current peak value, leakage current value, electric field vector, ambient temperature, terminal voltage, battery power and timestamp information, after acquisition is completed, data cleaning is performed on the lightning strike data, including duplicate value removal, missing value correction and physical outlier screening, and then the maximum and minimum standardization method is used to complete data normalization processing, providing a unified data basis for subsequent analysis; S2, a three-mode perception and three-source power supply collaborative mechanism is adopted, that is, under the collaborative control of a high-frequency magnetic ring sensor, a temperature-compensated leakage current module and a three-dimensional electric field detection array, a state-driven perception path dynamic switching is realized, and at the same time, according to the real-time power supply status of the three energy sources of photovoltaic, lithium battery and electromagnetic induction, the optimal energy path is dynamically selected to improve the system stability and low-power operation capability; S3, automatic scheduling of communication paths according to communication type and link quality, including periodic reporting and event triggering modes, and the system determines whether to proceed based on the RSSI values ​​of the terminal and the gateway. The system then dynamically calculates the estimated transmit power and makes compensation adjustments based on the real-time bit error rate. This is then written into the LoRa chip register to complete the transmit power configuration and initiate the identity authentication process to ensure data encryption and the security of the transmission link. S4 extracts lightning strike features, including the rising slope, duration, and peak intensity of the current waveform, constructs a multidimensional feature matrix, and inputs it into the equipment life prediction model based on the long short-term memory network. The estimated remaining equipment life is obtained through time series learning, and the current status is classified as normal, concern, or degraded based on the life percentage. The communication frequency, power supply strategy, and upload priority are adjusted accordingly. S5 calculates the leakage deviation value by reconstructing the current waveform. The Kalman filter and cubic spline interpolation method are used to compare the historical baseline current with the current waveform point by point. The sum of the absolute values ​​of the residual sequence is calculated as the leakage deviation indicator. When the deviation value continuously exceeds the threshold, the graded warning mechanism is automatically triggered, and the communication power regulator and energy supply controller are linked to ensure that the alarm data is uploaded first and the power supply path is safe and available, realizing a closed-loop linkage from abnormality perception to response control.

[0034] Specifically, the specific steps of real-time collection of lightning strike data through multi-sensor fusion and data cleaning and standardization of lightning strike data are as follows: Lightning strike data include multiple key parameters such as lightning strike current waveform, lightning strike current peak, leakage current value, signal strength, lightning strike temperature, lightning strike event timestamp, terminal voltage, battery power, etc., covering the multi-dimensional operating status of the lightning arrester during the lightning strike process; Among them, the lightning strike current peak is collected in real time by the high-frequency magnetic ring sensor, and the waveform change details are captured at a high sampling rate; the leakage current value is periodically collected by the temperature compensation leakage current module, which effectively suppresses the influence of temperature drift and improves the measurement accuracy; the spatial electric field vector is collected by the three-dimensional electric field detection array to realize the pre-identification of lightning strike precursors and disturbance signals; the lightning strike temperature is collected by the built-in temperature sensor, and the thermal shock effect is evaluated in combination with the temperature rise trend; the signal strength is collected by the RF receiving module and register interface of the LoRa chip to obtain the channel quality to assist in the calculation of communication power; the analog-to-digital converter and power management unit inside the terminal microcontroller are used to collect the signal strength. The battery power and terminal voltage are collected to provide support for power supply scheduling and status diagnosis; all collected data are timestamped by the local clock and cached in the internal storage of the microcontroller unit, and uploaded in batches after the communication cycle arrives to ensure data integrity and time consistency; in the data processing stage, by constructing feature hash values ​​for rapid comparison, duplicate lightning strike data generated during the cache and relay forwarding process are automatically identified and eliminated to avoid redundant interference; the lightning strike data are screened using null value detection and field integrity rules, and lightning strike data with empty fields and missing key fields are deleted and corrected to ensure data structure standardization; the interquartile range method is used to calculate the upper and lower boundaries of the lightning strike data set to determine whether the value exceeds the reasonable physical range, eliminate outliers with no physical meaning, and improve data credibility; the Kalman filter algorithm is used to denoise and extract trends from the lightning strike data to obtain a stable lightning strike data sequence and reduce the impact of occasional errors; finally, the maximum and minimum normalization algorithm is used to normalize the lightning strike data and convert the lightning strike data into a unified standard range.

[0035] In this implementation, multi-sensor fusion enables high-precision, comprehensive acquisition of multi-dimensional key parameters such as lightning current, leakage current, electric field disturbance, ambient temperature, and power supply status. Data integrity and continuity are ensured through the integration of local caching and timing synchronization mechanisms. During the data processing phase, the integration of feature hashing, null and outlier removal, Kalman filtering smoothing, and maximum and minimum normalization techniques effectively improves the accuracy, stability, and structural consistency of the raw lightning strike data. This provides a reliable data foundation for subsequent modules such as life prediction, power regulation, and intelligent early warning, significantly enhancing the system's real-time perception and analysis capabilities of lightning strike status.

[0036] Specifically, if Figure 3As shown in the schematic diagram of the "photovoltaic + lithium battery + inductive energy" three-source power supply architecture, the specific steps of using three-mode perception and three-source power supply coordination mechanism to achieve state-driven adaptive perception and energy path switching are as follows: The intelligent monitoring terminal adopts a three-mode perception structure, integrating a high-frequency magnetic ring sensor, a temperature-compensated leakage current monitoring module and a three-dimensional electric field detection array. The three work together to build a full-domain perception capability of the operating status of the lightning arrester; among them, the system automatically controls the perception path according to the operating status: Under the routine inspection state, the leakage current module is kept awake at regular intervals, and the insulation status of the equipment is periodically obtained in a low-power manner; when a sudden lightning strike or electric field disturbance precursor appears, the main control chip identifies the characteristic waveform and quickly starts the synchronous acquisition of the magnetic ring and electric field array to achieve millisecond-level response and enter the high-sampling rate event monitoring mode; the terminal power supply system adopts a photovoltaic and lithium battery dual-mode architecture, supplemented by an electromagnetic induction energy capture module, forming a three-source coordination mechanism to ensure the continuity and stability of equipment operation under different environmental conditions Features: In the photovoltaic path, the integrated maximum power point tracking chip tracks the maximum power point of solar energy, effectively improving the energy acquisition efficiency under daytime lighting conditions, and intelligently charges the lithium battery after the voltage stabilizes; in the lithium battery path, as the system energy storage core, it is equipped with a charge and discharge management chip, integrating overcharge, over-discharge, overheating protection and temperature detection modules, and independently supplies power at night, in rainy days or when there is insufficient light, to ensure the continuity of system operation; in the induction backup path, the electromagnetic induction coil installed at the bottom of the lightning arrester can capture transient electromagnetic energy during a lightning strike, and convert it into usable power through rectification and energy storage circuits to supply supercapacitors for supporting short-term high-power consumption tasks such as lightning data sampling and high-frequency communication; the system monitors data indicators such as terminal voltage, current waveform and battery power in real time, comprehensively evaluates external energy supply conditions and internal load requirements, and dynamically switches the power supply path according to energy input and current load status, to ensure high-reliability power supply and intelligent energy management in multiple scenarios and strong disturbance environments.

[0037] In this implementation plan, by constructing a multi-source information fusion perception system and an adaptive energy scheduling strategy, highly sensitive identification of the operating status of the lightning arrester and stable power supply guarantee are achieved. On the one hand, through the joint perception of high-frequency magnetic rings, leakage current modules and three-dimensional electric field arrays, the system can flexibly switch sampling paths under normal and emergency conditions, realizing a monitoring mode that takes into account both low power consumption and high response; on the other hand, with the coordinated cooperation of the three energy supply paths of photovoltaic, lithium battery and electromagnetic induction, the system can intelligently switch power supply plans according to environmental changes and energy consumption loads, ensuring continuous operation in extreme climates or environments with high lightning strikes. The overall mechanism effectively improves the perception accuracy, response efficiency and energy adaptability of the equipment, providing a reliable edge perception and power supply foundation for intelligent monitoring of lightning arresters.

[0038] Specifically, the steps for automatically scheduling communication paths based on communication type and link quality are as follows: Based on collected lightning strike data, the system divides communication tasks into two types of communication scheduling paths: a periodic reporting channel and an event-triggered channel. The periodic reporting channel is used to regularly upload operating status data such as leakage current and battery level. It uses the low-power Class A mode, which reduces energy consumption by setting fixed communication intervals. This is suitable for routine inspections of lightning arresters under long-term stable operation. The event-triggered channel automatically wakes up upon identifying characteristic indicators such as lightning strikes, sudden current fluctuations, or electric field disturbances, immediately switching to Class C mode and entering a fast communication window to ensure that sudden data is uploaded to the backend as quickly as possible. To improve communication stability and coverage capabilities, at the beginning of each communication cycle, the system will evaluate the current link status in real time and judge the link quality between itself and the gateway based on RSSI signal strength, bit error rate and historical communication success rate; if the LoRa signal strength is lower than the set communication signal threshold, the system will start the relay scanning mechanism, automatically build a multi-hop relay link, and upload data through relay forwarding of adjacent terminal nodes; if the LoRa signal strength is higher than the signal threshold, the system will determine that direct communication is possible, skipping the relay stage and simplifying the transmission path; after the communication path is determined, the system combines parameters such as lightning strike data, power supply status and channel quality to dynamically calculate the estimated transmission power required for this communication, and use it as the basis for subsequent communication power adjustment and authentication configuration.

[0039] In this implementation, by dynamically selecting the communication mode based on the type of lightning strike event and link quality, a differentiated transmission strategy for the arrester terminal under different operating conditions is effectively implemented. Under normal conditions, the system uses the low-power Class A mode to regularly report operating data. In emergencies such as lightning strikes, it quickly switches to Class C mode to ensure that high-priority data is uploaded immediately. At the same time, it automatically determines whether to establish a multi-hop relay link based on real-time signal strength, improving long-distance communication capabilities and environmental adaptability. This mechanism significantly enhances the flexibility, reliability, and energy efficiency of communication, providing strong support for the system's continued stable operation in complex geographical or extreme climatic conditions.

[0040] Specifically, if Figure 4As shown in the flow chart of the dynamic transmission power adjustment algorithm, the specific steps of dynamically analyzing and compensating the transmission power are as follows: obtain key operating parameters such as lightning current peak, battery power, terminal voltage and channel quality, and set the minimum power value and maximum power value as the upper and lower limits of the system power adjustment; first, calculate the product of the lightning current peak value and the lightning weight to obtain the lightning factor, which is used to characterize the impact of lightning intensity on communication power requirements; then calculate 1 minus the difference between the channel quality and multiply it by the product of the channel weight to obtain the channel factor, which is used to reflect the requirements of the current link quality for signal penetration capability; among them, through the linear regression method, the historical lightning The current peak, signal strength and battery power are used as input features to construct a training sample matrix, and the normalized lightning strike weight, channel quality weight and power weight are obtained by fitting. The value range of the three is [0,1]. Then the product of the battery power, power weight and terminal voltage plus the inverse of the minimum term is calculated to obtain the power correction factor, which is used to measure the current power supply capacity to adjust the transmission power. After adding these three factors to obtain the total adjustment factor, it is multiplied by the difference between the maximum power and the minimum power to obtain a product. This product is added to the minimum power to obtain the basic transmission power estimate. The system has an embedded real-time power adaptive mechanism based on channel feedback. The system periodically collects signal strength every 5 minutes and calculates channel quality based on this, forming a dynamically updated communication status baseline. If the bit error rate exceeds the set signal threshold, the system immediately triggers the linear power compensation mechanism to improve communication reliability. First, the difference between the signal threshold and the channel quality is calculated, divided by the channel quality plus 1, and this ratio is squared as the first part. The second part is the ratio of the lightning temperature plus 1 divided by the terminal voltage plus the minimum term, which is used to assess the power consumption risk under high-temperature lightning strikes. The third part is the difference between 1 and the power weight multiplied by the square of the battery power, which reflects the need to suppress energy consumption under low power conditions. Finally, the compensation weight, the first part, the second part and the third part are multiplied together to obtain the transmission power compensation value; among them, the Bayesian estimation method is used to construct the posterior probability distribution of the adjustment coefficient with the historical RSSI value, communication success rate, compensated transmission power and whether ACK is received as input features, and the maximum posterior probability point is extracted. After normalization, the compensation weight is obtained, and the value range of the compensation weight is [0,1]. This compensation value is superimposed with the transmission power estimate without violating the maximum power limit to form the final transmission power value and written into the LoRa chip transmission power register for this round of communication scheduling and data upload process.

[0041] The specific calculation method of the transmit power estimate is:

[0042]

[0043] Where P represents the estimated transmit power, P maxIndicates the preset maximum transmit power, P min Indicates the preset minimum transmit power, I indicates the peak lightning current, B indicates the battery level, S indicates the channel quality, V indicates the terminal voltage, ε indicates the minimum term, α indicates the lightning weight, β indicates the channel weight, and γ indicates the battery weight.

[0044] The specific calculation method of the transmit power compensation value is:

[0045]

[0046] Where, P 补 represents the transmit power compensation value, k represents the compensation weight, S 阈 Indicates the set signal threshold, S indicates the channel quality, T indicates the lightning temperature, V indicates the terminal voltage, ε indicates the minimum term, B indicates the battery power, and γ indicates the power weight.

[0047] This implementation achieves refined estimation and real-time adaptive adjustment of transmit power by introducing multi-dimensional data factors such as lightning strike intensity, channel quality, battery charge, voltage level, and ambient temperature. Basic power calculation combines operating status with device power supply capabilities to ensure minimum energy consumption matching for communications. A compensation mechanism dynamically adjusts power based on bit error rate feedback and signal strength deviation, ensuring highly reliable communications even under complex operating conditions such as link deterioration, lightning strikes, high temperatures, or low battery levels. This mechanism effectively improves the system's overall performance in terms of communication distance, energy efficiency, and anti-interference capabilities, enhancing the robustness and stability of communications for terminal devices in remote, high-risk areas.

[0048] Specifically, the specific steps to complete authentication encryption and multi-hop secure transmission are as follows: When the final transmission power value is determined, the system immediately triggers an identity authentication request and starts the encryption transmission preparation process to ensure the integrity and credibility of the communication link; the terminal generates an authentication request with the terminal number, timestamp, random challenge code and signature summary, and sends it to the target gateway through the LoRa network. This process uses an asymmetric encryption mechanism to encapsulate the authentication information to prevent man-in-the-middle attacks and ensure the immutability of the identity information; after receiving the authentication request, the gateway uses the locally stored key and trust list to verify the terminal identity and generate an authentication response, which contains the gateway signature and timestamp verification value. After receiving it, the terminal performs local decryption and signature verification through the built-in security module to verify the consistency of the gateway identity, complete the two-way identity authentication, and formally establish the trust basis for this communication; after the authentication is passed, the transmission power control interface is called to write the final transmission power value into the transmission power register of the LoRa chip to ensure that the parameter is accurately executed by the hardware communication layer; at the same time, the system synchronously checks the status indicators such as terminal voltage, battery power and lightning temperature. If If any parameter does not meet the safe operating range, the system will protectively reduce the transmit power value to prevent abnormal device operation due to transient high power consumption or thermal shock, and mark the abnormality in the system status table for subsequent analysis and tracing. After the transmit power is set, the system performs AES-128 encryption on the payload portion of the outgoing data packet to ensure that the data content is not intercepted or maliciously tampered with during wireless transmission. After encryption is complete, the terminal officially initiates communication through the LoRa network path at the set transmit power. The system automatically selects a direct path or a multi-hop relay path to complete data forwarding based on signal quality. After the data is sent, the system enters the listening state, waiting for an ACK confirmation signal from the target gateway. This signal serves as the basis for determining the success of the communication. If an ACK response is received within the set maximum delay time, the terminal records the communication as successful and updates the link quality indicator. If no response is received after the timeout, it records the failure status. At the same time, based on the number of failures, link status, and remaining battery power, it decides whether to resend or switch the transmit power level to retry, ensuring a balance between communication stability and energy efficiency.

[0049] In this implementation plan, by introducing two-way authentication, AES-128 encryption, state-aware transmission power control, and ACK feedback mechanism, comprehensive protection of the communication link in terms of security, reliability, and robustness is achieved. It not only ensures the legitimacy of the identity verification between the terminal and the gateway, but also dynamically reduces the power when abnormal voltage, power, or temperature is detected to prevent equipment overload. At the same time, it supports automatic selection of direct or multi-hop paths and real-time judgment of the communication success status and optimization of the retry strategy, which greatly improves the data transmission stability and anti-interference ability in complex environments, and provides strong communication security support for remote intelligent monitoring of lightning arresters. It is also suitable for access to a variety of edge devices including intelligent reactors, providing strong communication security support for remote intelligent monitoring of lightning arresters.

[0050] Specifically, if Figure 5As shown in the figure of the fault warning model based on the life prediction model, the lightning strike characteristics are extracted, and the equipment life prediction model is constructed based on the long short-term memory network to realize the remaining life prediction, and the specific steps of equipment health level classification and operation strategy adjustment are as follows: call the waveform feature extraction algorithm to perform multi-dimensional feature recognition on the lightning strike waveform, including key indicators such as peak current value, rising edge slope, duration and energy density, complete the lightning strike behavior portrait, and construct the lightning strike feature matrix to ensure that the input sequence has complete time series feature expression capabilities; construct the equipment life prediction model based on the long short-term memory network, through the lightning strike feature sequence of multiple historical cycles Input, conduct time series learning and trend fitting, extract the changing rules of equipment performance with the evolution of lightning load, and output the remaining life assessment value of the current equipment. The assessment result is in "days" and has the ability to roll over. After the life prediction is completed, the system will compare the remaining life assessment value with the life threshold set by the platform in real time. The life threshold includes the first-level life threshold and the second-level life threshold. According to the comparison results, the current health status of the equipment is graded and assessed: when the remaining life assessment value is greater than or equal to the first-level life threshold, the system will classify the equipment status as normal operation, the terminal maintains Class A communication mode, and adopts photovoltaic power supply as the main power supply and lithium battery as the secondary power supply. The system operates in an auxiliary mode and turns off the electromagnetic induction power path to reduce system power consumption. The platform marks the device in green state and archives the monitoring data. No warning is triggered in the current cycle. When the remaining life assessment value is between the first-level life threshold and the second-level life threshold, the system divides the device status into a focus warning. The terminal actively shortens the communication cycle and increases the lightning strike and leakage current sampling frequency. At the same time, it dynamically adjusts the transmission power according to the channel quality to ensure stable communication. The power supply management module enters the energy consumption adjustment stage, dynamically allocates energy to the photovoltaic and lithium battery paths, and enables the electromagnetic induction path status assessment mechanism to quickly respond when energy is insufficient. The platform marks the device as yellow and automatically generates an operation and maintenance attention reminder to the management center. If the remaining life assessment value is less than or equal to the secondary life threshold, the system classifies the device status as a degradation warning, and the terminal switches to Class C communication mode, maintains a high sampling frequency and enables low-latency communication. At this time, the system fully activates the three energy supply paths of photovoltaic, lithium battery and electromagnetic induction, and operates in coordination to ensure power supply continuity. The platform marks the device as red and immediately generates an emergency maintenance work order, which is simultaneously alerted to relevant operation and maintenance personnel through multiple channels such as SMS and APP push, realizing a closed-loop control process from life prediction to warning response.

[0051] In this implementation, a time series modeling method based on long-short-term memory networks (LSTMs) was introduced to achieve dynamic assessment of the arrester's operating status and accurate prediction of its future lifespan. The system constructs a feature matrix using multidimensional lightning strike characteristics as input, outputting a quantifiable remaining lifespan indicator. Combined with pre-defined multi-level lifespan thresholds, it categorizes equipment health levels and enables adaptive adjustments to communication modes, sampling frequencies, and power supply strategies. This mechanism significantly enhances the intelligent judgment and proactive response capabilities of the arrester monitoring system, providing strong support for early warning, scientific operation and maintenance, and extended equipment service life.

[0052] Specifically, the specific steps of calculating the leakage deviation value by reconstructing the current waveform, triggering the graded warning and linking the communication power and energy supply strategy are as follows: first, the historical leakage current data is modeled, and the system continuously executes the prediction and update steps of the Kalman filter to smooth the estimation of the sampling value at each moment, so as to dynamically track the normal current fluctuation trend of the equipment in the non-fault state, thereby obtaining a leakage sequence representing the healthy operation benchmark of the equipment, which serves as a reference standard for subsequent abnormal judgment; in each communication cycle, the leakage current sequence of the current period is extracted, and the cubic spline interpolation algorithm is used to smoothly reconstruct it to obtain the fitting waveform curve of the leakage current in the current period. The curve has continuity and high-order derivative smoothness in the time domain, which improves the accuracy of comparison with the benchmark sequence; then the fitting waveform curve is compared with the benchmark current sequence point by point, and the difference between the current measured leakage current value and the benchmark leakage value is calculated point by point, and the absolute value of the difference at all time points is summed up, and finally the leakage deviation value used to quantify the degree of state deviation is obtained; After obtaining the leakage deviation value, the system compares it with the set leakage threshold in real time. When the leakage deviation value exceeds the leakage threshold for three consecutive communication cycles, the system triggers a warning and the device maintains Class A mode operation, but appropriately shortens the communication cycle to increase the data refresh frequency. When the leakage deviation value exceeds the leakage threshold for five consecutive communication cycles, the system upgrades the alarm level to an abnormal alarm, and the platform automatically generates a maintenance work order and pushes it to the operation and maintenance center, realizing closed-loop management from monitoring to response. Once the abnormal alarm is triggered, the system immediately links the LoRa communication strategy, forcing the terminal to switch to Class C mode to obtain higher communication priority and response speed, and recalculates the estimated transmission power required for this round of communication to ensure signal penetration. At the same time, the system calls the life prediction model to evaluate the current remaining life status and detect the battery power indicator. If it is lower than the safety threshold, the electromagnetic induction backup power supply path is immediately enabled to prevent the system from disconnecting or losing data due to insufficient energy during high-frequency communication and alarm data transmission.

[0053] The specific calculation formula for the leakage deviation value is:

[0054]

[0055] Where, L represents the leakage deviation value, I 实测t Represents the leakage current value at the tth time point, I 基准t It represents the baseline leakage value at the tth time point, and n represents the number of sampling points in the current communication cycle.

[0056] In this implementation plan, by building a historical benchmark current model and comparing it with the real-time fitting curve, the abnormal change trend of the leakage current is accurately identified, and a multi-level early warning response strategy is triggered with the deviation value as the core, which significantly improves the system's early perception of lightning arrester insulation degradation. Combined with communication mode switching, transmission power recalculation, and power supply path linkage control, the system can achieve rapid communication guarantee and energy compensation response when an abnormality occurs, forming a closed-loop process of "monitoring-judgment-response", providing a stable and reliable decision-making basis for intelligent prevention and control of lightning arrester operation risks and maintenance scheduling. In this process, it can also be compatible with the access and collaborative operation of related devices such as intelligent reactors, enhancing the scalability and adaptability of the overall platform.

[0057] The second aspect of the present invention provides a self-sensing monitoring system based on a lightning arrester, which is characterized by: including: a lightning strike data acquisition and preprocessing module, a terminal perception and power supply management module, a LoRa communication and power control module, a cloud analysis and life assessment module and a leakage warning and linkage control module, wherein: the lightning strike data acquisition and preprocessing module is used to collect lightning strike data in real time through multi-sensor fusion, and perform data cleaning and standardization on the lightning strike data; the terminal perception and power supply management module is used to adopt a three-mode perception and three-source power supply collaborative mechanism to realize state-driven adaptive perception and energy path switching; the LoRa communication and power control module is used to automatically schedule the communication path according to the communication type and link quality, dynamically analyze and compensate the transmission power, and complete authentication encryption and multi-hop secure transmission; the cloud analysis and life assessment module is used to extract lightning strike characteristics, construct an equipment life prediction model based on a long short-term memory network, realize remaining life prediction, and perform equipment health level classification and operation strategy adjustment based on this; the leakage warning and linkage control module is used to calculate the leakage deviation value by reconstructing the current waveform, trigger a graded warning and link the communication power and energy supply strategy.

[0058] In this implementation plan, by building five functional modules covering data collection, intelligent perception, communication control, cloud analysis, and linkage warning, full-cycle, multi-dimensional, self-perception monitoring of the arrester's operating status is achieved. The system integrates high-precision lightning data collection and preprocessing capabilities, and has a state-driven perception and power switching mechanism. It can dynamically schedule communication paths and adjust transmission power based on link quality. At the same time, it relies on long-term and short-term memory network models to achieve life prediction and health assessment, and combines leakage deviation identification to trigger multi-level warning and response control, effectively improving the intelligence, precision, and reliability of the arrester's online monitoring, and meeting the long-term, low-power, and high-security operation and maintenance requirements in complex environments.

[0059] The third aspect of the present invention provides a self-sensing monitoring device based on a lightning arrester, which is characterized by: including: a data collector, a data processor, a communication controller, and an energy supply management mechanism, wherein: the data collector is used to perform multi-source signal monitoring on the working status of the lightning arrester, and collect key characteristic parameters such as lightning current, leakage current, electric field changes, and ambient temperature; the data processor is used to filter, normalize and feature extract the lightning monitoring data obtained by the sensing acquisition module, and calculate the current health index, power regulation factor and remaining life prediction value of the equipment in combination with the historical operating status; the communication controller is used to establish a two-way authentication connection with the gateway based on the LoRa communication mode, dynamically set the transmission power according to the data processing results, realize event-driven or periodic data upload, and support multi-hop relay networking path selection; the energy supply management mechanism is used to manage multi-source power supply channels such as photovoltaic, lithium battery and electromagnetic induction, switch the optimal energy path according to the real-time load and power supply status, and provide protection strategies under overvoltage, over-discharge and high temperature conditions.

[0060] This implementation integrates data acquisition, intelligent processing, communication control, and multi-source power supply, resulting in a highly integrated and intelligent system. The device can accurately collect multi-dimensional parameters such as lightning current, leakage current, electric field, and temperature, and uses filtering and feature extraction algorithms to calculate state quantities and predict lifespan. Dynamic power regulation and two-way authentication communication via the LoRa network in related devices, including intelligent reactors, ensure secure and efficient data transmission. A three-source collaborative power supply mechanism, comprised of photovoltaics, lithium batteries, and electromagnetic induction, supports adaptive energy management in complex environments, effectively ensuring long-term, stable, and low-power operation of the device in the field, significantly enhancing the practicality and reliability of intelligent monitoring of lightning arresters.

[0061] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0062] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A self-sensing monitoring method based on a lightning arrester, characterized in that: The following steps are involved: S1, collects lightning strike data in real time through multi-sensor fusion, and performs data cleaning and standardization on the lightning strike data; S2, adopts a three-mode sensing and three-source power supply coordination mechanism to achieve state-driven adaptive sensing and energy path switching; S3 automatically schedules communication paths based on communication type and link quality, dynamically analyzes and compensates for transmit power, and completes authentication, encryption, and multi-hop secure transmission. S4, extracts lightning strike characteristics and builds an equipment life prediction model based on long-short-term memory networks to achieve remaining life prediction, and then classifies equipment health levels and adjusts operation strategies accordingly; S5, calculates the leakage deviation value by reconstructing the current waveform, triggers a graded warning and links the communication power and energy supply strategy.

2. The self-sensing monitoring method based on lightning arrester according to claim 1 is characterized in that: The specific steps of collecting lightning strike data in real time through multi-sensor fusion and performing data cleaning and standardization on the lightning strike data are as follows: Lightning strike data includes lightning strike current waveform, lightning strike current peak, leakage current value, signal strength, lightning strike temperature, lightning strike event timestamp, terminal voltage, and battery power. The lightning strike current peak is collected in real time by a high-frequency magnetic ring sensor; the leakage current value is periodically collected by a temperature-compensated leakage current module; the spatial electric field vector is collected by a three-dimensional electric field detection array; the lightning strike temperature is collected by a built-in temperature sensor; the signal strength is collected by the RF receiving module and register interface of the LoRa chip to obtain the channel quality; the battery power and terminal voltage are collected by the analog-to-digital converter and power management unit inside the terminal microcontroller; all collected data are timestamped by a local clock and cached in the microcontroller for internal storage, and uploaded in batches after the communication cycle arrives. By constructing feature hash values ​​for rapid comparison, duplicate lightning strike data generated during caching and relay forwarding are automatically identified and eliminated; null value detection and field integrity rules are used to screen lightning strike data, and lightning strike data with empty fields or missing key fields are deleted and corrected; the interquartile range method is used to calculate the upper and lower boundaries of the lightning strike data set to determine whether the values ​​exceed the reasonable physical range and eliminate outliers with no physical meaning; the Kalman filter algorithm is used to denoise and extract trends from lightning strike data to obtain stable lightning strike data; the maximum and minimum normalization algorithm is used to normalize the lightning strike data and convert the lightning strike data into a unified standard range.

3. The self-sensing monitoring method based on lightning arrester according to claim 1 is characterized in that: The specific steps for implementing state-driven adaptive sensing and energy path switching using the three-mode sensing and three-source power supply coordination mechanism are as follows: The intelligent monitoring terminal adopts a three-mode sensing structure, integrating a high-frequency magnetic ring sensor, a temperature-compensated leakage current monitoring module, and a three-dimensional electric field detection array. It automatically controls the sensing path according to the operating status: in the normal inspection state, the leakage current module is kept awake at regular intervals; when a sudden lightning strike or electric field disturbance precursor occurs, the magnetic ring and electric field array are quickly activated for synchronous data collection, entering the event monitoring mode. The terminal power supply system adopts a dual-mode architecture of photovoltaic and lithium-ion batteries, supplemented by an electromagnetic induction energy capture module, forming a three-source synergy mechanism: The photovoltaic path: An integrated maximum power point tracking chip tracks the maximum power point of solar energy, serves as the main power source during the day, and intelligently charges the lithium-ion battery; the lithium-ion battery path: As the energy storage core, it is equipped with a charge and discharge management chip, integrated with overcharge, over-discharge, overheating protection and temperature detection modules, to ensure stable operation of the equipment at night and on rainy days; the induction backup path: The electromagnetic induction coil installed at the bottom of the lightning arrester can capture transient electromagnetic energy during a lightning strike, and convert it into usable power through rectification and energy storage circuits, supplying supercapacitors to support lightning sampling and high-power communication. The system monitors terminal voltage, current waveform, battery charge and other data indicators in real time, and dynamically switches the power supply path based on energy input and current load status.

4. The self-sensing monitoring method based on lightning arrester according to claim 1 is characterized in that: The specific steps of automatically scheduling the communication path according to the communication type and link quality are as follows: Based on the collected lightning strike data, two communication scheduling paths are divided into: a periodic reporting channel: used to regularly upload operating status data such as leakage current and battery power, using the low-power Class A mode; an event-triggered channel: when a lightning strike event or a large current mutation is identified, it immediately switches to Class C mode and opens a fast communication window; At the beginning of each communication cycle, the system determines whether direct communication is possible based on the link quality between itself and the gateway; If the LoRa signal strength is lower than the signal threshold, a multi-hop relay link will be automatically established; If the LoRa signal strength is greater than the signal threshold, direct communication is performed; after the communication path is determined, the estimated transmission power required for this communication is calculated.

5. The self-sensing monitoring method based on lightning arrester according to claim 1 is characterized in that: The specific steps of dynamically analyzing and compensating the transmit power are as follows: Obtain the lightning current peak, battery charge, terminal voltage, and channel quality, and set the minimum and maximum power values. First, calculate the lightning current peak multiplied by the lightning weight to obtain the lightning factor. Calculate the difference between 1 and the channel quality and multiply it by the channel weight to obtain the channel factor. Calculate the battery charge, charge weight, terminal voltage, and the reciprocal of the minimum term to obtain the charge correction factor. Add these three factors to get the total adjustment factor. Multiply the total adjustment factor by the difference between the maximum power and the minimum power to get a product. Add this product to the minimum power to get the estimated transmit power. The system has a built-in real-time power adaptation mechanism based on channel feedback. Signal strength is collected periodically every five minutes and used to calculate channel quality. If the bit error rate exceeds the set signal threshold, a linear power compensation mechanism is immediately triggered. The difference between the signal threshold and the channel quality is calculated, divided by the channel quality plus 1, and the square of this ratio is used as the first part. Calculate the ratio of the lightning temperature plus 1 divided by the terminal voltage plus the minimum term as the second part; Calculate 1 minus the power weight multiplied by the square of the battery power to get a difference, which is used as the third part; multiply the compensation weight, the first part, the second part, and the third part to get the transmission power compensation value; this transmission power compensation value is added to the transmission power estimate without violating the maximum power value limit to form the final transmission power value and write it into the LoRa chip register.

6. The self-sensing monitoring method based on lightning arrester according to claim 1, characterized in that: The specific steps to complete authentication encryption and multi-hop secure transmission are as follows: When the final transmit power value is determined, an identity authentication request is triggered and the encrypted transmission preparation process is started: the terminal generates an authentication request with the terminal number, timestamp, random challenge code and signature summary, and sends it to the target gateway through the LoRa network to confirm the legitimacy of the identity of the communication peer; After receiving the authentication request, the gateway verifies the terminal's identity and generates an authentication response, which includes the gateway's signature. The terminal then decrypts and verifies the response locally, completing two-way identity authentication and establishing a trust basis for this communication. After authentication is passed, the transmit power control interface is called to write the final transmit power value into the transmit power register of the LoRa chip; If the terminal voltage, battery level, and lightning temperature are found to be outside the safe range during this phase, the system will automatically reduce the estimated transmit power as a protective measure and mark the abnormal state to prevent overloaded communications. After the transmit power is set, AES-128 encryption is performed on the payload portion of the data packet to be sent. After encryption is complete, communication is formally initiated through the LoRa network path. After the data is sent, the system starts waiting for the ACK confirmation signal from the gateway to determine whether the communication is successful. If an ACK is received within the set time limit, the terminal records the communication as successful; if no response is received within the time limit, the terminal records the failure status and decides whether to resend or switch power and try again based on the number of failures.

7. The self-sensing monitoring method based on lightning arrester according to claim 1 is characterized in that: The specific steps for extracting lightning strike characteristics, building an equipment life prediction model based on a long short-term memory network, realizing remaining life prediction, and classifying equipment health levels and adjusting operation strategies based on this are as follows: The waveform feature extraction algorithm is used to perform multi-dimensional feature recognition on the lightning waveform, complete the lightning behavior profile, and construct the lightning feature matrix. The equipment life prediction model is constructed based on the long-short-term memory network, using the lightning feature matrix as the input sequence, performing time series learning and trend fitting, and outputting the remaining life assessment value of the current equipment. After the lifespan prediction is completed, the remaining lifespan assessment value is compared with the lifespan threshold in real time. The lifespan thresholds include the first-level lifespan threshold and the second-level lifespan threshold. Based on the comparison results, the current health status of the device is graded and assessed. If the remaining lifespan assessment value is greater than or equal to the first-level lifespan threshold, the device status is classified as normal operation. The terminal maintains Class A communication mode, with photovoltaic power supply as the main power source and lithium battery as the auxiliary power source, and the inductive power supply path is disabled. The platform is marked as green, records the monitoring data for archiving, and does not trigger an alarm; If the remaining life assessment value is between the first-level and second-level life thresholds, the device status is classified as a concern and warning: the terminal communication cycle is shortened, the sampling frequency is increased, and the transmit power is adjusted in real time based on the channel quality. Power supply management enters the energy consumption regulation stage, scheduling photovoltaic and battery paths on demand, and evaluating the electromagnetic induction backup status. The platform marks the status as yellow and automatically generates an operation and maintenance concern reminder. If the remaining life assessment value is less than or equal to the secondary life threshold, the device status is classified as degradation warning: the terminal switches to Class C communication mode and increases the sampling frequency to the highest level; The three energy supply paths of photovoltaic, lithium battery and electromagnetic induction are started to operate in coordination; the platform marks the equipment in red status, automatically generates emergency maintenance work orders and pushes multi-channel alarms.

8. The self-sensing monitoring method based on lightning arrester according to claim 1, characterized in that: The specific steps of calculating the leakage deviation value by reconstructing the current waveform, triggering the graded warning and linking the communication power and energy supply strategy are as follows: By continuously performing Kalman filtering prediction and updating on historical leakage current data, the sampled values ​​at each moment are smoothed and estimated to obtain a baseline leakage current sequence that represents the normal operating trend of the equipment. In each communication cycle, the leakage current sequence of the current period is extracted and reconstructed smoothly using the cubic spline interpolation algorithm to obtain the fitting waveform curve of the leakage current of the current period. The fitting waveform curve is compared with the reference current sequence point by point, and the difference between the leakage current value and the reference leakage value is calculated point by point. The absolute value of the difference at all time points is summed to obtain the leakage deviation value. After obtaining the leakage deviation value, the system compares it with the leakage threshold in real time. If the leakage deviation value exceeds the leakage threshold within three consecutive communication cycles, a warning is triggered: the device maintains Class A operation and shortens the terminal communication cycle. If the leakage deviation value exceeds the leakage threshold within five consecutive communication cycles, an abnormal alarm is triggered, and a maintenance work order is automatically generated and pushed to the operation and maintenance center. Once an abnormal alarm is triggered, the LoRa communication strategy is linked to force switching to Class C mode and recalculate the estimated transmit power. At the same time, the life prediction model is called. If it is detected that the battery power does not reach the safety threshold, the electromagnetic induction backup power supply path is enabled to prevent communication interruption during the alarm phase.

9. The self-sensing monitoring system based on lightning arrester is characterized by: include: Lightning strike data collection and preprocessing module, terminal perception and power supply management module, LoRa communication and power control module, cloud analysis and life assessment module, and leakage warning and linkage control module, including: The lightning strike data collection and preprocessing module is used to collect lightning strike data in real time through multi-sensor fusion, and perform data cleaning and standardization on the lightning strike data; The terminal perception and power supply management module is used to adopt a three-mode perception and three-source power supply coordination mechanism to achieve state-driven adaptive perception and energy path switching; The LoRa communication and power control module is used to automatically schedule the communication path according to the communication type and link quality, dynamically analyze and compensate the transmission power, and complete authentication encryption and multi-hop secure transmission; The cloud-based analysis and lifespan assessment module is used to extract lightning strike characteristics, build an equipment lifespan prediction model based on a long-short-term memory network, predict remaining lifespan, and classify equipment health levels and adjust operating strategies accordingly. The leakage warning and linkage control module is used to calculate the leakage deviation value by reconstructing the current waveform, triggering the graded warning and linking the communication power and energy supply strategy.

10. Self-sensing monitoring equipment based on lightning arrester, characterized by: include: Data collector, data processor, communication controller, energy supply management mechanism, including: The data collector is used to monitor the working status of the arrester using multiple signals and collect key characteristic parameters such as lightning current, leakage current, electric field changes, and ambient temperature; The data processor is used to filter, normalize and extract features from the lightning monitoring data acquired by the sensing and acquisition module, and calculate the current health index, power regulation factor and remaining life prediction value of the equipment in combination with the historical operating status; The communication controller is used to establish a two-way authentication connection with the gateway based on the LoRa communication mode, dynamically set the transmission power according to the data processing results, realize event-driven or periodic data upload, and support multi-hop relay networking path selection; The energy supply management mechanism is used to manage multiple power supply channels such as photovoltaic, lithium battery and electromagnetic induction, switch the optimal energy path according to the real-time load and power supply status, and provide protection strategies under overvoltage, over-discharge and high temperature conditions.

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