Residual-current circuit breaker remote monitoring system integrated with Internet of Things

Abstract

The invention relates to the technical field of earth leakage protection monitoring, and discloses an earth leakage circuit breaker remote monitoring system integrated with the Internet of Things. An electric leakage characteristic modeling module of the system obtains real-time monitoring data, extracts current waveform characteristic parameters, identifies an abnormal waveform mode and a first occurrence time sequence, and generates an original electric leakage characteristic template; the network path generation module performs topological sorting on the monitoring nodes based on the template, establishes an equipment connection path and constructs an Internet of Things topological path; a path anomaly analysis module extracts a node sequence and screens abnormal transmission paths to obtain an abnormal path set; the risk level judgment module collects terminal node risk tags and obtains a risk level tag group through matching; and the topological structure output module counts associated nodes, divides abnormal paths, establishes a mapping relation and generates a remote monitoring topological table. The system improves the accuracy and efficiency of electric leakage monitoring, and is suitable for a complex power network environment.

Description

Technical Field

[0001] The present invention relates to the technical field of leakage protection monitoring, and in particular to a remote monitoring system for leakage circuit breakers integrated with the Internet of Things. Background Art

[0002] With the continuous development and intelligent upgrade of power systems, the need to monitor the operating status of leakage circuit breakers, key equipment for ensuring power safety, has become increasingly prominent. Traditional leakage circuit breaker monitoring methods rely heavily on local manual inspections or simple alarms from a single node, making it difficult to achieve comprehensive coverage and real-time monitoring of large-scale, distributed power networks. In actual applications, due to the widespread distribution of power network nodes and the complex environment, leakage faults are often sudden and hidden. Relying solely on local monitoring makes it difficult to quickly capture fault characteristics, which can easily lead to delayed troubleshooting and even cause safety accidents such as electrical fires and electric shocks. Existing monitoring systems often use isolated monitoring nodes, lacking effective analysis of the relationships between nodes and making it difficult to construct a complete leakage fault propagation path. When a leakage fault occurs in a specific area, the system cannot accurately trace the fault's source and impact range, forcing maintenance personnel to check multiple nodes one by one, increasing maintenance costs and prolonging troubleshooting time. Furthermore, because different monitoring nodes may use different communication protocols and data formats, data compatibility is poor, making efficient information integration and sharing difficult, further limiting the intelligent level of leakage fault monitoring. With the increasing popularity of IoT technology, although some monitoring systems have begun to attempt to introduce IoT technology to achieve data transmission, there are still obvious deficiencies in the accurate modeling of leakage characteristics, the dynamic generation of topological paths, and the scientific determination of risk levels. Most systems can only achieve simple status data collection and upload, lacking in-depth analysis of leakage waveform characteristics, making it difficult to distinguish between normal fluctuations and abnormal leakage patterns, resulting in frequent false alarms and missed alarms. Moreover, in the construction of topological paths, fixed connection methods are often used, and the path structure cannot be dynamically adjusted according to real-time leakage characteristics, affecting the pertinence and efficiency of data transmission. The existence of these problems makes it difficult for existing leakage circuit breaker monitoring systems to meet the high-precision and high-efficiency requirements of modern power networks for safety monitoring. An intelligent monitoring solution integrating IoT technology is urgently needed to solve the above bottlenecks. Summary of the Invention

[0003] The purpose of the present invention is to provide a remote monitoring system for leakage circuit breakers integrated with the Internet of Things to solve the problems raised in the above background technology.

[0004] To achieve the above objectives, the present invention provides a remote monitoring system for leakage circuit breakers integrated with the Internet of Things, the system comprising: The leakage characteristic modeling module obtains real-time monitoring data of the circuit breaker, extracts characteristic parameters of the current waveform, identifies abnormal waveform patterns and the timing of their first occurrence, maps and generates characteristic parameter sequences, and constructs the original leakage characteristic template; A network path generation module, which performs topological sorting on the monitoring nodes based on the original leakage feature template, establishes a device connection path from the main node to the terminal node based on the sorting result, collects the communication addresses of all associated nodes in the path and the transmission protocols of adjacent nodes, and constructs an IoT topological path; The path anomaly analysis module extracts the circuit breaker node sequence according to the IoT topology path, compares the public nodes, counts the alarm frequency of the terminal nodes, filters the abnormal transmission paths, and obtains the abnormal path set; The risk level determination module collects risk tags associated with the terminal nodes in the abnormal path set, sorts them by trigger frequency, and matches the path endpoint risk identifiers to obtain a circuit breaker risk level tag group; The topology output module counts the monitoring area nodes corresponding to each label according to the circuit breaker risk level label group, divides the abnormal paths into corresponding nodes, establishes a mapping relationship structure between nodes and abnormal paths, and generates a remote monitoring topology table.

[0005] Preferably, the specific analysis process of the path anomaly analysis module includes: Set a monitoring period, and calculate the risk coefficient by comparing the number of high-risk paths within the period to the total number of transmission paths. If the risk coefficient exceeds the preset risk threshold, a level 1 alarm signal is generated. If the risk factor does not exceed the preset risk threshold, the path response delay value is calculated by subtracting the median of the preset delay range to obtain the absolute value to obtain the delay deviation value, and the ratio of the path stability coefficient to the preset stability threshold is marked as the stability detection value; The delay evaluation value is obtained by averaging the delay deviation values ​​of all paths within the monitoring period, and the stability evaluation value is obtained by averaging the stability detection values ​​of all paths within the monitoring period. The comprehensive risk value is obtained by numerically calculating the risk coefficient, delay assessment value and stability assessment value. If the comprehensive risk value exceeds the preset comprehensive threshold, a secondary alarm signal is generated. If the comprehensive risk value does not exceed the preset comprehensive threshold, a normal operation signal is generated.

[0006] Preferably, the leakage characteristic modeling module includes: The waveform parameter extraction submodule obtains real-time monitoring data from circuit breakers, performs multi-scale decomposition on the current waveform, extracts a set of spectral features from each waveform, records the timestamp position of each feature in the waveform, compares the first appearance of an abnormal pattern in the feature sequence with the waveform period, and classifies it by circuit breaker device to obtain the time series distribution of abnormal features. The feature segment reconstruction submodule extracts the data segments corresponding to the abnormal pattern in the original waveform based on the abnormal feature time series distribution results, intercepts the data based on the time interval of the abnormal pattern in the waveform, constructs a feature segment set based on the position of the intercepted data of each abnormal pattern, and reorganizes the data in combination with the device identifier to obtain the abnormal feature segment set; The feature template generation submodule counts the trigger frequency values ​​of all abnormal modes based on the abnormal feature segment set, arranges the abnormal modes in the original time sequence in the monitoring data, performs time alignment processing on the feature segment set, and integrates the feature segments of multiple abnormal modes in the same device in sequence according to the first triggering time.

[0007] Preferably, the network path generation module includes: The node hierarchy sorting submodule sorts all nodes according to their hierarchy priority values ​​based on the original leakage feature template and the network hierarchy identifier of the monitoring node, topologically rearranges the nodes in the order from the core layer to the access layer, and establishes a node sequence index table; The path deviation detection submodule obtains the set of adjacent device addresses in the node sequence according to the node sequence index value, records the communication direction and transmission delay parameters of each pair of adjacent nodes, and calculates the topological deviation strength value of the IoT path; The node relationship extraction submodule collects the node addresses and communication protocol types in all connection relationships based on the IoT path data, and constructs a node relationship mapping table based on the device connection relationships.

[0008] Preferably, the risk level determination module includes: The risk label collection submodule collects the risk classification label set corresponding to each terminal node based on the terminal nodes in the abnormal path set, performs index mapping between the abnormal path and the terminal risk label, and generates a path terminal risk label group; The label frequency statistics submodule performs a trigger frequency statistics operation on all risk labels based on the path endpoint risk label group, records the number of times each label appears in the abnormal path set, and sorts them according to the frequency to obtain a sorted risk label sequence; The risk level matching submodule performs priority matching on the label sets corresponding to the terminal nodes in the abnormal path according to the sorted risk label sequence, screens the path risk level in each path that matches the highest-order label item in the sorted sequence, and integrates the risk level identifiers of all abnormal paths.

[0009] Preferably, the topology structure output module includes: The monitoring node extraction submodule collects the regional monitoring nodes corresponding to each label according to the circuit breaker risk level label group, records the abnormal path number and the number of devices associated with each node, and establishes a mapping index between the risk label and the monitoring node; The path collection submodule divides the corresponding abnormal paths into each monitoring node according to the risk level label based on the mapping index value, establishes a bidirectional association structure between the path number and the node address, and extracts the path list managed by each node; The topology generation submodule integrates the monitoring nodes and the subordinate abnormal path numbers according to the node path management list, outputs the node address, associated risk level and total path amount, and generates a remote monitoring topology table.

[0010] Preferably, the system further includes a response delay calibration module: Collect the time interval from when the circuit breaker receives the instruction to when it executes the action, and mark it as the response time benchmark value; Obtain the maximum and minimum values ​​of all response times within the monitoring period and calculate the response fluctuation coefficient; Obtain the transmission delay compensation value through device communication quality analysis, and generate the delay calibration parameter by combining the response time benchmark value and the response fluctuation coefficient; When the response fluctuation coefficient exceeds a preset fluctuation threshold, a path optimization instruction is sent to the network path generation module.

[0011] Preferably, the device communication quality analysis includes: The signal strength and data packet loss rate between monitoring nodes are collected. If the signal strength is lower than the preset strength threshold or the packet loss rate is higher than the preset packet loss threshold, the link is determined to be a low-quality channel; The duration ratio of low-quality channels within the monitoring period is calculated and marked as the channel degradation coefficient. The channel degradation coefficient is compared with a preset channel threshold to generate a transmission delay compensation value and update it to the delay calibration parameter.

[0012] Preferably, the system further includes an impedance test trigger module: When the risk level determination module outputs a high risk label, a multi-band impedance test is initiated; Inject a sweep frequency test signal into the circuit breaker associated with the high-risk node; Collect the attenuation characteristics of the test signal in the node network; The deviation between the measured attenuation characteristics and the theoretical attenuation model is compared to generate the impedance anomaly coefficient.

[0013] Preferably, the theoretical attenuation model construction includes: The theoretical signal attenuation curve is calculated based on the node impedance parameters in the IoT topology path, and the theoretical signal attenuation threshold range is corrected by adding the environmental interference factor; When the measured attenuation characteristic exceeds the threshold range, the node is marked as an impedance anomaly point.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This IoT-integrated remote monitoring system for leakage circuit breakers utilizes multiple modules to form a comprehensive leakage monitoring system, demonstrating numerous advantages in practical applications. The leakage signature modeling module deeply mines real-time circuit breaker monitoring data, extracting current waveform characteristic parameters and identifying abnormal waveform patterns and their first occurrence timing. The resulting raw leakage signature template provides a precise foundation for subsequent fault analysis, helping to more accurately distinguish between normal operation and leakage faults, and reducing misjudgments due to inaccurate signature judgments. The network path generation module topologically sorts monitoring nodes based on the original leakage signature template, establishing device connection paths from the master node to the terminal node. It also collects relevant communication addresses and transmission protocols to construct the IoT topology path. This dynamically generated topology path can be adjusted based on real-time leakage signatures, making data transmission more targeted and avoiding the blindness of traditional fixed-path data transmission. This improves information transmission efficiency and enhances the system's adaptability to complex network environments, ensuring smooth data exchange between nodes. The path anomaly analysis module extracts the circuit breaker node sequence based on the IoT topology path, and screens abnormal transmission paths by comparing public nodes and counting the alarm frequencies of terminal nodes. The resulting abnormal path set can clearly reflect the possible propagation paths of leakage faults, helping to quickly identify the key nodes where the fault occurs, providing clear clues for fault tracing, facilitating maintenance personnel to conduct more targeted inspections, and shortening the time to locate the fault. The risk level determination module collects associated risk tags from the terminal nodes in the abnormal path set, sorts them by trigger frequency, and then matches them with the path endpoint risk identifiers. The resulting circuit breaker risk level tag group intuitively presents the risk level of different nodes. This allows monitoring personnel to rationally allocate resources based on risk level, prioritizing fault handling of high-risk nodes, optimizing the fault handling sequence, and improving overall fault handling efficiency. The topology output module counts the monitoring area nodes corresponding to each label according to the risk level label group, divides the abnormal paths into corresponding nodes and establishes a mapping relationship structure. The generated remote monitoring topology table provides a clear visual interface for monitoring personnel, which can fully grasp the distribution and correlation of leakage faults in the entire power network, help to grasp the monitoring situation as a whole, realize the global management of leakage faults, and improve the safety monitoring level of the entire power network. Through the organic combination of various modules, the system realizes the intelligent processing of the entire process from leakage feature capture, path construction, anomaly analysis to risk judgment and topology presentation, effectively integrating the Internet of Things technology with leakage monitoring needs, making the monitoring of leakage faults more accurate and efficient, and able to better adapt to the complex and changeable operating environment of modern power networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a working principle diagram of the remote monitoring system for leakage circuit breaker integrated with the Internet of Things according to the present invention; Figure 2 Flowchart for generating alarm signals for the path anomaly analysis module; Figure 3 This is the result diagram of path anomaly analysis; Figure 4 It is the network topology heat map; Figure 5 Flowchart of feature extraction for leakage feature modeling module; Figure 6 Diagram of the process of modeling leakage characteristics; Figure 7 It is the characteristic parameter analysis diagram; Figure 8 Flowchart generated for the response delay calibration module parameters. DETAILED DESCRIPTION

[0016] 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.

[0017] See also Figure 1 The present invention provides a remote monitoring system for leakage circuit breakers integrated with the Internet of Things, the system comprising: The leakage characteristic modeling module collects real-time circuit breaker monitoring data and uses multi-scale decomposition technology to extract the spectral feature set of the current waveform. It records the timestamp location of the first occurrence of the abnormal pattern and constructs a time-series distribution of the abnormal characteristics, including the device identification. The network path generation module establishes the device connection path from the main node to the terminal node based on a topological sorting algorithm, collecting the communication address and transmission protocol of each node to form the IoT topology path. The path anomaly analysis module compares common nodes with statistical alarm frequency to screen out a set of abnormal transmission paths. The risk level determination module sorts the terminal nodes according to the trigger frequency of the risk label and generates a level label group by matching the risk identification of the path endpoint. The topology output module finally divides the abnormal paths into corresponding monitoring nodes according to risk level, establishes node-path mapping relationships, and generates a remote monitoring topology table.

[0018] Example 1: See Figure 2 The detailed workflow of the Path Anomaly Analysis module is shown below. This module dynamically evaluates IoT topology paths through a periodic monitoring mechanism and employs a multi-level early warning strategy to accurately identify abnormal transmission paths. During system initialization, a monitoring cycle of 5 minutes is preset. This time window, after multiple debugging sessions, has been determined to balance real-time performance with system load. When the monitoring cycle begins, the data acquisition unit reads the status records of all currently active transmission paths from the distributed database, including basic information such as path number, node sequence, and communication quality indicators.

[0019] The risk factor calculation process first screens out path records with high-risk characteristics. Based on a preset evaluation rule base, the system labels paths as high-risk if they meet any of the following conditions: the path contains three or more consecutive alarm nodes, the path terminal node has triggered an emergency alarm within the last hour, or the path's average transmission delay exceeds twice the threshold. The statistics module calculates the ratio of the number of high-risk paths to the total number of paths in real time. This ratio, as a risk factor, directly reflects the overall security status of the network. When the risk factor exceeds the preset threshold of 0.35, the warning generation unit immediately triggers a level 1 alarm signal, which is broadcast to all associated subsystems via the message queue.

[0020] If the risk factor does not reach the threshold, the system proceeds to the second-level assessment process. The latency assessment subsystem first extracts response latency data for each path from the path quality database and calculates the absolute deviation between the actual latency of each path and the median of the preset ideal latency range. This preset ideal latency range is dynamically adjusted based on the network topology, with a range of 50-80ms for core layer paths and 100-150ms for access layer paths. The system then calculates the arithmetic average of the latency deviations for all paths to generate an assessment metric reflecting the overall latency status of the network.

[0021] The stability assessment subsystem simultaneously processes path stability data. The system maintains a sliding window recording the stability coefficient of each path for the last 20 communications. This coefficient is calculated by weighting parameters such as packet arrival rate, bit error rate, and retransmission rate. The assessment process compares the current stability coefficient with a preset baseline value to generate a standardized stability test value. The system then averages the stability test values ​​for all paths and outputs an evaluation index representing the overall stability of the network.

[0022] The comprehensive risk assessment phase utilizes a three-tiered weighted algorithm to process three core indicators. The risk factor, with a 50% weight, reflects the overall network security situation; the latency assessment, with a 30% weight, reflects the degree of transmission efficiency anomalies; and the stability assessment, with a 20% weight, characterizes fluctuations in communication quality. The system normalizes these three indicators and then weights them together to generate a comprehensive risk value ranging from 0 to 1. This value is compared with a preset threshold of 0.6, triggering the corresponding action: exceeding the threshold generates a level 2 alarm signal, indicating potential network risks; below the threshold, outputs a normal operation signal, maintaining the current monitoring status.

[0023] The alarm signal processing unit employs differentiated response strategies. When a Level 1 alarm is triggered, the system automatically performs the following actions: suspend all non-critical data transmission tasks, immediately activate a backup communication path, and send a top-priority notification to operations and maintenance personnel. When a Level 2 alarm is triggered, the system implements less severe measures: limiting data transmission for applications with bandwidth utilization exceeding 70%, increasing the monitoring frequency of relevant paths, and generating pending inspection tasks for inclusion in the operations and maintenance queue. During normal operation, the system maintains regular monitoring mode and regularly updates the path quality assessment database.

[0024] A dynamic parameter adjustment mechanism continuously optimizes assessment parameters. The system records the correlation between each risk assessment result and actual failures. Using machine learning algorithms, it automatically adjusts the following parameters: risk factor calculation weights, trigger thresholds for each level of alarm, and baseline values ​​for the ideal delay range. The parameter adjustment process is gradual, with each update not exceeding 10% of the original value, ensuring the stability of the system's assessment criteria.

[0025] The data visualization component displays analysis results in real time. The monitoring interface uses a heat map to display the risk level of each path, with color grading indicating the degree of risk. Trend charts are also provided to show historical changes in risk factors, latency assessment values, and stability assessment values. Operations and maintenance personnel can use the interactive interface to view detailed assessment data for any path, including changes in various indicators over the last 10 cycles.

[0026] The abnormal path diagnosis function automatically activates upon an alarm trigger. The system performs in-depth testing on paths marked as abnormal, including checking the operational status of path node devices, testing the physical connectivity of transmission lines, and verifying the integrity of communication protocols. The test results generate a detailed diagnostic report, noting possible root causes such as hardware failures, excessive line interference, and protocol configuration errors. This diagnostic report, combined with risk assessment data, forms a complete path anomaly analysis, providing a reference for subsequent maintenance decisions.

[0027] The system maintenance module ensures the reliability of the analysis process. Database maintenance tasks are performed daily at dawn, including index rebuilding, data compression, and historical record archiving. The evaluation algorithm is integrity-checked weekly to verify the accuracy of each metric's calculations. The evaluation criteria in the rule base are updated monthly to adapt to changes in network topology and the impact of device upgrades. Detailed log information is maintained for all maintenance operations to support subsequent audit trails.

[0028] See Figure 3 , detailing the results of the path anomaly analysis module. The bar chart on the left shows the distribution of paths of different risk levels, with high-risk paths (red) accounting for 15%, medium-risk paths (orange) accounting for 25%, low-risk paths (yellow) accounting for 30%, and normal paths (green) accounting for 30%. The line chart in the middle shows the risk factor trend over the last 10 monitoring cycles, peaking at 0.42 in the fifth cycle, triggering a level 1 alarm. The pie chart on the right shows the distribution of different alarm types, with communication delay alarms accounting for 45%, stability issue alarms accounting for 30%, and node failure alarms accounting for 25%. These data intuitively reflect the overall network security status and anomaly distribution, providing an important basis for operation and maintenance decisions.

[0029] See Figure 4 , shows the IoT topology heat map output by the network path generation module. Nodes in the map represent circuit breaker devices, and lines represent communication paths. Node color reflects the device risk level (red for high risk, green for normal), and line thickness indicates communication quality (thick lines for high quality, thin lines for low quality). The heat map background color indicates path transmission delay: red areas indicate delays exceeding 120ms, while green areas indicate delays below 60ms. The map clearly shows three major anomaly clusters: a high-risk device cluster in the upper left corner, a communication bottleneck in the middle, and a low-quality path cluster in the lower right corner. Topological analysis shows that high-risk devices are primarily located at the network's edge, while communication quality in the core area is generally good.

[0030] Example 2: See Figure 5The complete workflow of the leakage signature modeling module is shown below. This module uses multi-stage processing to convert raw current waveform data into structured signature templates, providing standardized input data for subsequent analysis. During system initialization, a current sensor array with a 10kHz sampling rate is configured to ensure the capture of high-frequency components in the waveform. The analog signals collected by the sensors are converted by a 24-bit ADC into a digitized waveform data stream that enters the preprocessing stage.

[0031] The waveform parameter extraction process uses a multi-resolution analysis method to process the current waveform. The system establishes a three-level decomposition architecture, corresponding to the feature extraction requirements of different time scales. The first level processes millisecond-level waveform fluctuations and uses a sliding window Fourier transform to extract the fundamental and harmonic components; the second level analyzes second-level trend changes and uses a discrete wavelet transform to calculate the energy distribution characteristics; the third level processes minute-level long-term evolution and identifies slow drift patterns through a moving average algorithm. Each decomposition level outputs a corresponding set of spectral features, including 12-dimensional parameters such as amplitude, phase, and harmonic distortion rate. The timestamp recording unit accurately marks the position of each feature point in the original waveform with microsecond accuracy.

[0032] A dynamic threshold detection mechanism is established for abnormal pattern recognition. The system maintains a circular buffer storing the last 30 minutes of waveform feature data as a reference for normal operating conditions. Feature parameters collected in real time are compared with the baseline data, and an abnormality flag is triggered when a deviation exceeding three standard deviations occurs. The timing analysis unit records the first occurrence of each abnormal pattern and calculates its relative time difference from the start of the waveform cycle. The device classification processor stores the abnormal feature distribution results in the corresponding device archive based on the circuit breaker's unique identification code.

[0033] During the feature segment reconstruction phase, adaptive windowing technology is used to process abnormal data. The system dynamically adjusts the capture window size based on the duration of the abnormal pattern. For transient pulse anomalies, a fixed 20ms capture window is set; for sustained oscillation anomalies, the window length is automatically extended to 1.5 times the anomaly duration. During the data capture process, a Hanning window function is used for edge smoothing to reduce spectral leakage. Each captured segment is accompanied by complete metadata, including device ID, anomaly type code, and timestamp index.

[0034] The feature template generation process achieves spatiotemporal alignment of multi-source data. The frequency statistics unit maintains a device-based, categorized counter to record the number of times each anomaly pattern is triggered. The timing alignment processor uses a dynamic time warping algorithm to address the uneven distribution of different anomaly patterns along the timeline. For multiple anomaly feature fragments detected by the same device, the system arranges and combines them in the order of their first triggering moment. The template integration engine packages the processed feature data into standard format data blocks, including feature vectors, time series indexes, and associated metadata.

[0035] Data quality control mechanisms are implemented throughout the entire processing flow. Input data validation checks the integrity and continuity of waveform samples, discarding outliers with missing data. Real-time validation is implemented during feature extraction, triggering data re-acquisition when parameter values ​​outside the physically possible range are detected. Cross-validation is performed during the outlier segment reconstruction phase to ensure that the captured data window fully encompasses the target feature pattern. After template generation, format compliance is checked before being written to the feature database.

[0036] The distributed storage architecture supports large-scale data processing. The system uses sharding technology to partition the feature database into multiple logical units based on device type. Each shard deploys an independent processing node, enabling parallel computation for feature extraction and template generation. The data synchronization service maintains consistency across shards, ensuring that global queries retrieve complete feature information. The storage engine implements data compression and index optimization, balancing access speed and storage space consumption.

[0037] A dynamic update mechanism maintains the currency of feature templates. The system employs a dual update strategy: scheduled updates execute a full feature reconstruction every 30 minutes; event-triggered updates initiate a partial reconstruction immediately upon detecting a new abnormal pattern. The template version control system records changes to each update and supports the tracing and rollback of historical versions. Aging data is automatically archived, and expired feature templates are transferred to a secondary storage area.

[0038] The visual monitoring interface provides observability of the processing process. The operation and maintenance console displays spectrum graphs at each level of waveform decomposition in real time, with the locations of abnormal feature points marked with different colors. The feature distribution heat map shows the aggregation of various abnormal patterns on the timeline. The template structure viewer presents the organizational relationship of feature data in a tree diagram, supporting drilling down to view data content at any level of detail.

[0039] Abnormal diagnosis assistance features enhance the system's practicality. The feature comparison tool allows for similarity analysis between real-time waveform data and historical templates. The pattern tracking function plots the evolution of specific abnormal features over time. The correlation analysis engine discovers potential relationships between different feature parameters, creating an interactive relationship network diagram.

[0040] The system maintenance module ensures the stability of the processing flow. Daily storage system health checks are performed to verify data integrity and access performance. Weekly feature extraction algorithms are calibrated to ensure accuracy, and decomposition parameters are adjusted to accommodate changes in device characteristics due to aging. Monthly template structure effectiveness is evaluated, and data organization is optimized to improve query efficiency. All maintenance operations are recorded in detailed logs, providing comprehensive documentation of system operations.

[0041] A fault-tolerant mechanism addresses various anomalies. In the event of a network outage, local caching is enabled to continue data collection, and backlogged tasks are processed synchronously after the connection is restored. In the event of a hardware failure, processing automatically switches to a backup processing node to ensure uninterrupted feature extraction. Data anomalies trigger a self-repair process, restoring damaged information through redundancy checks. The system resource monitoring module dynamically adjusts the processing load to prevent performance degradation caused by overload.

[0042] like Figure 6 , showing the three-level decomposition processing flow of the leakage feature modeling module. The upper waveform diagram shows the original current waveform and the location of its abnormal feature points, with three main abnormal areas marked. The three middle sub-graphs correspond to the feature extraction results at the millisecond, second, and minute levels: millisecond-level analysis shows high-frequency harmonic distortion characteristics; second-level analysis shows energy distribution changes; minute-level analysis shows a slow drift trend. The lower heat map shows the distribution density of various abnormal modes on the time axis, among which pulse-type anomalies are mainly concentrated in the first half, and oscillation-type anomalies are more evenly distributed. The statistical data at the bottom of the chart show that the system detected a total of 42 abnormal feature points, which are evenly distributed on 50 devices.

[0043] like Figure 7 , and analyzed in detail the key parameters extracted by the leakage feature modeling module. The upper scatter matrix shows the correlation between the four core feature parameters: the harmonic distortion rate is negatively correlated with the fundamental amplitude, and the energy entropy has no obvious correlation with the pulse frequency. The middle box plot compares the distribution of characteristic parameters of equipment with different risk levels. The harmonic distortion rate of high-risk equipment is significantly higher than that of other equipment. The lower parallel coordinate graph shows the combination relationship of multi-dimensional feature parameters, among which high-risk equipment is mainly distributed in areas with high harmonic distortion rate, high energy entropy and high pulse frequency. Chart analysis shows that harmonic distortion rate is the most significant feature to distinguish high-risk equipment and can be used as a key indicator for early warning.

[0044] Example 3: Collaborative Workflow between the Network Path Generation Module and the Risk Level Determination Module. This implementation constructs the IoT topology through multi-dimensional data processing and establishes a precise risk assessment system. During the system initialization phase, network configuration parameters are loaded, including the node hierarchy definition table, the communication protocol whitelist, and the transmission delay baseline. After the core processing engine is started, it first performs a node hierarchy sorting process, reading the network hierarchy identification field of each monitoring device. This field contains the device's location information within the network architecture, such as core layer devices marked as CL-1 and access layer devices marked as AL-3.

[0045] The hierarchical sorting algorithm uses an improved topology traversal method to establish a node priority queue. The processing process starts from the core layer device and expands outward layer by layer according to the network traffic forwarding direction, assigning a unique sequence index value to each node. The index calculation takes into account factors such as device type, processing power, and number of connections to form a weighted node sequence index table. Based on this index table, the path generation unit scans the physical connection relationship between adjacent devices and records the communication direction attributes of each pair of nodes, including uplink, downlink, or peer-to-peer connection. The transmission delay monitor collects path delay data in real time and calculates the path stability index after sliding average filtering: in, Indicates the path offset strength value, is the number of sampling times, It is The measured delay value, is the delay benchmark value, is the maximum allowable delay threshold. The result of this formula is used to evaluate the transmission quality stability of a path. A larger value indicates more significant path fluctuations.

[0046] The node relationship mapping process uses a graph database to store network topology information. The system creates an edge record for each connection, containing the source node address, destination node address, communication protocol type, and transmission characteristic parameters. A dynamic update mechanism regularly scans for network status changes and triggers topology reconstruction when new devices are detected or connections are disconnected. The path optimizer analyzes the connectivity of the current topology, identifies potential redundant paths and single points of failure, and recommends structural adjustments.

[0047] After the risk level determination module is activated, it first extracts a list of terminal nodes from the abnormal path set. The risk tag collector queries the device attribute database to obtain the risk classification tag associated with each terminal node. Tag definitions follow a grading standard, classifying risks into three categories: electrical safety, data security, and operational safety, each with several subcategories. The frequency statistics unit establishes a time-window-based counting mechanism to count the number of times each tag has appeared in the last 24 hours. The sorting processor performs multi-condition sorting on the tag set, primarily based on trigger frequency and secondarily considering the risk category priority.

[0048] The risk level matching process accurately associates paths with labels. The system creates a risk assessment profile for each anomalous path, recording all nodes and their risk attributes. The matching algorithm begins at the endpoint and traces the risk propagation path backwards, identifying the source of the risk with the greatest impact. Based on the matching results, the level marker assigns a comprehensive risk level identifier to the path, using a five-level scale from R1 (lowest) to R5 (highest). The risk association analyzer identifies risk transmission relationships between different paths and constructs a global risk propagation network diagram.

[0049] The data persistence layer adopts a hybrid storage strategy. Topology data is stored in a graph database, supporting complex relationship queries; node attribute information is stored in a document database, facilitating field expansion; and real-time monitoring data is stored in a time series database, optimizing time series access performance. The storage engine implements automatic sharding and load balancing to ensure system responsiveness when processing large-scale data.

[0050] The visualization subsystem provides multi-dimensional monitoring views. The topology map displays network nodes and connections in a force-directed graph format, using colors and shapes to distinguish device types and risk levels. The path tracing view shows the detailed structure and risk distribution of a selected path. The latency heat map visually displays the communication quality status of each network area. The risk trend map plots the temporal distribution of various risk events.

[0051] The system maintenance mechanism consists of daily inspections and dynamic tuning. Database integrity checks and storage space monitoring are performed daily, while topology optimization and risk rule base updates are performed weekly. The performance tuning module automatically adjusts the number of processing threads and data cache size based on operational load. When an anomaly is detected, the fault recovery system automatically initiates a backup process and notifies operations personnel.

[0052] The security control module implements strict access management. User permissions are broken down to the data field level, controlling the scope of access to topology information and risk data by different roles. Operational audit logs record all configuration changes and data processing actions, enabling retrospective review. Data transmission utilizes end-to-end encryption to prevent information leaks caused by network eavesdropping.

[0053] A fault-tolerant mechanism ensures system reliability. Local caching is enabled to continue data collection during network outages, and data synchronization is performed once the connection is restored. Communication paths are automatically rerouted in the event of node failures to maintain monitoring continuity. A retry mechanism is triggered in the event of data processing anomalies, and after the retry limit is exceeded, the system is redirected to a manual processing queue. A resource monitor dynamically adjusts system load to prevent performance degradation caused by overload.

[0054] The version control system manages the evolution of algorithms and rules. Each update creates a new version branch, preserving the ability to roll back to previous versions. Change impact assessment tools analyze the potential chain reactions of modifications and assist in decision-making regarding update timing. The test and verification environment simulates real-world network conditions to verify the stability and accuracy of new versions before deployment.

[0055] This implementation, through the aforementioned process, transforms raw network topology data into structured risk assessment results, forming a complete IoT security monitoring solution. The system design considers the complexity of actual deployment environments, balancing processing accuracy and execution efficiency to adapt to the application needs of networks of varying scales.

[0056] Example 4: See Figure 8 This paper focuses on the collaborative working mechanism between the topology output module and the response delay calibration module, and illustrates the system implementation process through specific application scenarios. This monitoring system was deployed on a regional power distribution network, comprising 12 substation nodes and 56 terminal circuit breakers, forming a three-layer IoT topology. During system runtime, the system first loads the circuit breaker risk level tag group, which contains key fields such as device number, risk level, and geographic location.

[0057] The monitoring node extraction process reads regional configuration information from the central database. The system identifies three monitoring zones within the region, each of which deploys a separate monitoring server. The data association engine performs the following operations: scans the device location codes in the risk level tag group to match them to the corresponding monitoring zone; queries the number of devices currently managed by each monitoring node; calculates the load balancing index and determines the node to which the new device belongs. Table 1 shows the monitoring node allocation for some devices in this region.

[0058] Table 1: Equipment monitoring node allocation table.

[0059] Path aggregation processing is carried out based on the above allocation results. The system creates an independent management list for each monitoring node, recording all abnormal path information for which it is responsible. The path number is represented in the form of a composite key, including the source node ID, the destination node ID and the timestamp. The bidirectional index structure maintains the mapping relationship between the path number and the device address, and supports two query modes: querying the device from the path and querying the path from the device. When a high-risk device CB-5120 is added, the system automatically assigns it to the lightest-loaded southern node and updates the relevant index.

[0060] The topology generation process uses an incremental update strategy. The monitoring interface displays the current topology table in real time, including core metrics such as node addresses, risk level distribution, and path load. Data synchronization services ensure that all monitoring nodes have the latest topology view. When three new abnormal paths are added to the East District node, the system immediately triggers the following actions: recalculating the total number of paths for that node; assessing changes in risk level distribution; and pushing update messages to relevant operation and maintenance terminals.

[0061] The response delay calibration module is activated in the circuit breaker operation test scenario. The system sends an opening command to the selected device, the CB-2057. A high-precision timer records the delay from command issuance to full contact opening. The test is repeated 10 times to obtain a baseline response time dataset. The data analysis unit calculates the device's delay fluctuation range and establishes a baseline curve for normal performance. If the response delay of a monitored operation exceeds the baseline value by 15%, the system marks the operation as abnormal.

[0062] Communication quality analysis was conducted on a specific transmission link. The communication link between node 192.168.1.10 and device CB-1024 experienced signal strength fluctuations. The system collected the following parameters: signal strength samples per minute, number of packet losses, and number of retransmission requests. The quality evaluator identified a persistent signal strength below -78dBm during the afternoon hours and calculated the channel degradation factor to be 0.38. Based on this information, the compensation value generator outputs a transmission delay compensation parameter to adjust the delay determination threshold for this link.

[0063] Path optimization instructions are triggered under specific conditions. When the average latency of the paths managed by the West Zone node exceeded 300ms, the system detected a persistently high response fluctuation coefficient. The optimization engine analyzed the current topology and discovered redundant paths that bypassed the core layer. The system automatically generated an optimization plan: changing the CB-2057's communication path to connect directly to the regional aggregation node, bypassing the core layer. After implementing this plan, monitoring data showed that the device's response latency was reduced to 65% of its original level.

[0064] A dynamic load balancing mechanism manages pressure on monitoring nodes. When the number of paths managed by the South Zone node reaches the preset upper limit of 20, the system initiates a rebalancing process. An evaluator analyzes each node's current load, processing capacity, and network bandwidth and temporarily migrates four CB-3098 paths to the East Zone node. This migration process maintains business continuity and ensures that monitoring data is not lost. The load balancing status panel displays resource utilization by node in real time, assisting operations and maintenance personnel in making decisions.

[0065] Data persistence utilizes a tiered storage strategy. Real-time monitoring data is stored in an in-memory database, supporting high-frequency read and write operations. Historical topology records are stored in a time series database, managed by time partitions. Device configuration information is stored in a document database, facilitating the expansion of attribute fields. The backup service regularly compresses and archives critical data and saves it to a distributed file system.

[0066] The exception handling process includes a multi-level response mechanism. When monitoring node 192.168.2.15 is detected as unresponsive, the system performs the following actions: marking the node as unavailable, temporarily transferring its managed devices to another node, attempting to automatically restart the node service, and notifying the operations team for intervention. After recovery, a data consistency check is performed to ensure that all operations recorded during the transfer are fully synchronized.

[0067] The visual interface provides an interactive monitoring experience. The topology map uses color-coded area ranges to dynamically display path traffic status. The device details panel displays a complete risk level history and recent operation logs. The path tracking view allows you to click on any path to view detailed quality metrics. The latency trend chart compares performance data before and after compensation.

[0068] Version control manages configuration change history. Every topology adjustment generates a change log, including the modification details, the operator, and the impact assessment. A rollback function allows restoration to any historical version, ensuring system configuration security. The change approval process requires double confirmation for major modifications.

[0069] This implementation demonstrates the system's operational mode in a real-world environment through the detailed processing flow described above. From device monitoring and allocation to path optimization and adjustment, each link is seamlessly integrated to form a closed-loop management system. The system design fully considers the complexity of field environments, ensuring both accuracy and efficiency, and adapting to the monitoring needs of IoT systems of varying scales. With rich monitoring views and detailed data logging, operations and maintenance personnel can fully understand network status and make timely decisions.

[0070] Example 5: The complete workflow of the impedance test trigger module. The system automatically initiates a multi-band impedance analysis program upon detecting a high-risk node. When the risk level determination module outputs a risk label of R4 or above, the central controller sends a test preparation instruction to the area where the target circuit breaker is located. The test signal generator uses a programmable logic device to generate a swept frequency signal from 10 Hz to 1 MHz. The frequency interval is dynamically adjusted based on the device type, using logarithmic spacing for oil-immersed circuit breakers and linear spacing for vacuum circuit breakers.

[0071] The signal injection stage uses optocoupler technology to achieve electrical isolation. The test signal is amplified to an appropriate level and connected to the circuit breaker control circuit via a dedicated test interface. The injection process strictly adheres to equipment safety specifications, ensuring that the signal amplitude does not exceed 30% of the rated operating voltage. Synchronous acquisition units are deployed at key monitoring points in the target node network and use high-precision ADC circuits to record signal waveforms. The sampling duration at each test frequency point is inversely proportional to the frequency, with more cycles of data collected at low frequencies to improve the signal-to-noise ratio.

[0072] The signal attenuation analysis process establishes a multi-dimensional evaluation system. The time domain analysis module calculates the amplitude ratio and phase difference between the received and transmitted signals, extracting the attenuation characteristics of the fundamental and harmonic components. The frequency domain processing unit performs a Fourier transform on the acquired signal, generating amplitude-frequency response curves and phase-frequency response curves. The feature extractor identifies key parameter points from the curves, including resonant frequency, decibel bandwidth, and group delay fluctuation. These parameters constitute the measured attenuation feature dataset for subsequent comparative analysis.

[0073] The theoretical attenuation model is constructed based on the physical characteristics of the equipment. The system database stores the equivalent circuit parameters of various circuit breakers, including basic data such as coil inductance, contact resistance, and ground capacitance. The model generator retrieves the corresponding parameters based on the target equipment model and calculates the signal transfer function under ideal conditions. The environmental compensation module incorporates real-time data collected by temperature and humidity sensors to refine the theoretical model. The resulting theoretical attenuation curve includes upper and lower thresholds to define an acceptable range of fluctuation.

[0074] Impedance anomaly detection utilizes a frequency-by-frequency comparison strategy. The analysis engine compares measured data with the upper and lower bounds of the theoretical model, marking frequencies exceeding the threshold. The anomaly evaluator calculates the integral of the deviation and generates an impedance anomaly coefficient ranging from 0 to 1. When this coefficient exceeds 0.15, the system determines that an impedance anomaly exists at the target node. The diagnostic reasoning module, based on the anomaly frequency distribution characteristics, makes a preliminary assessment of the possible fault type, such as contact oxidation, insulation degradation, or mechanical deformation.

[0075] Multiple safety measures are implemented during the test process. An overcurrent protection circuit monitors the injected current in real time and immediately cuts off the test signal if an abnormality occurs. An electromagnetic shielding device reduces the impact of external interference on measurement results. A test interval controller prevents equipment overheating caused by continuous high-frequency testing. An authorization verification mechanism ensures that only authorized personnel can initiate manual test commands.

[0076] The result processing system manages abnormal data by classification. Confirmed impedance anomalies are recorded in the equipment health file and updated in the historical anomaly pattern database. The early warning generator sends different levels of alert notifications based on the severity of the anomaly, immediately triggering on-site maintenance procedures for sudden and severe anomalies. The trend analyzer tracks the changing trends of multiple test results for the same equipment, identifying potential, slowly developing faults.

[0077] The visual interface provides a rich array of test analysis views. The signal spectrum plot compares the measured curve with the theoretical boundary, with abnormal frequency bands highlighted in red. The impedance parameter table displays detailed measurement data for each frequency point. A historical comparison chart overlays the test results for the same device over time. The diagnostic suggestion panel lists possible fault causes and key inspection points.

[0078] System maintenance mechanisms ensure test reliability. Regular calibration procedures verify the accuracy of signal generators and acquisition units and adjust deviation parameters. A test case library stores characteristic data of typical failure modes to aid diagnostic analysis. A software update module keeps signal processing algorithms up to date with the latest research advances. Hardware diagnostics automatically monitor test circuit connection status and component performance.

[0079] 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.

[0080] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A remote monitoring system for leakage circuit breakers integrated with the Internet of Things, characterized in that: include: The leakage characteristic modeling module obtains real-time monitoring data of the circuit breaker, extracts characteristic parameters of the current waveform, identifies abnormal waveform patterns and the timing of their first occurrence, maps and generates characteristic parameter sequences, and constructs the original leakage characteristic template; A network path generation module, which performs topological sorting on the monitoring nodes based on the original leakage feature template, establishes a device connection path from the main node to the terminal node based on the sorting result, collects the communication addresses of all associated nodes in the path and the transmission protocols of adjacent nodes, and constructs an IoT topological path; The path anomaly analysis module extracts the circuit breaker node sequence based on the IoT topology path, compares the public nodes, counts the alarm frequency of the terminal nodes, filters the abnormal transmission paths, and obtains the abnormal path set; The risk level determination module collects risk tags associated with the terminal nodes in the abnormal path set, sorts them by trigger frequency, and matches the path endpoint risk identifiers to obtain a circuit breaker risk level tag group; The topology output module counts the monitoring area nodes corresponding to each label according to the circuit breaker risk level label group, divides the abnormal paths into corresponding nodes, establishes a mapping relationship structure between nodes and abnormal paths, and generates a remote monitoring topology table.

2. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 1, characterized in that: The specific analysis process of the path anomaly analysis module includes: Set a monitoring period, and calculate the risk coefficient by comparing the number of high-risk paths within the period to the total number of transmission paths. If the risk coefficient exceeds the preset risk threshold, a level 1 alarm signal is generated. If the risk factor does not exceed the preset risk threshold, the path response delay value is calculated by subtracting the median of the preset delay range to obtain the absolute value to obtain the delay deviation value, and the ratio of the path stability coefficient to the preset stability threshold is marked as the stability detection value; The delay evaluation value is obtained by averaging the delay deviation values ​​of all paths within the monitoring period, and the stability evaluation value is obtained by averaging the stability detection values ​​of all paths within the monitoring period. The comprehensive risk value is obtained by numerically calculating the risk coefficient, delay assessment value and stability assessment value. If the comprehensive risk value exceeds the preset comprehensive threshold, a secondary alarm signal is generated. If the comprehensive risk value does not exceed the preset comprehensive threshold, a normal operation signal is generated.

3. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 1, characterized in that: The leakage characteristic modeling module includes: The waveform parameter extraction submodule obtains real-time monitoring data from circuit breakers, performs multi-scale decomposition on the current waveform, extracts a set of spectral features from each waveform, records the timestamp position of each feature in the waveform, compares the first appearance of an abnormal pattern in the feature sequence with the waveform period, and classifies it by circuit breaker device to obtain the time series distribution of abnormal features. The feature segment reconstruction submodule extracts the data segments corresponding to the abnormal pattern in the original waveform based on the abnormal feature time series distribution results, intercepts the data based on the time interval of the abnormal pattern in the waveform, constructs a feature segment set based on the position of the intercepted data of each abnormal pattern, and reorganizes the data in combination with the device identifier to obtain the abnormal feature segment set; The feature template generation submodule counts the trigger frequency values ​​of all abnormal modes based on the abnormal feature segment set, arranges the abnormal modes in the original time sequence in the monitoring data, performs time alignment processing on the feature segment set, and integrates the feature segments of multiple abnormal modes in the same device in sequence according to the first triggering time.

4. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 1, characterized in that: The network path generation module includes: The node hierarchy sorting submodule sorts all nodes according to their hierarchy priority values ​​based on the original leakage feature template and the network hierarchy identifier of the monitoring node, topologically rearranges the nodes in the order from the core layer to the access layer, and establishes a node sequence index table; The path deviation detection submodule obtains the set of adjacent device addresses in the node sequence according to the node sequence index value, records the communication direction and transmission delay parameters of each pair of adjacent nodes, and calculates the topological deviation strength value of the IoT path; The node relationship extraction submodule collects the node addresses and communication protocol types in all connection relationships based on the IoT path data, and constructs a node relationship mapping table based on the device connection relationships.

5. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 4, characterized in that: The risk level determination module includes: The risk label collection submodule collects the risk classification label set corresponding to each terminal node based on the terminal nodes in the abnormal path set, performs index mapping between the abnormal path and the terminal risk label, and generates a path terminal risk label group; The label frequency statistics submodule performs a trigger frequency statistics operation on all risk labels based on the path endpoint risk label group, records the number of times each label appears in the abnormal path set, and sorts them according to the frequency to obtain a sorted risk label sequence; The risk level matching submodule performs priority matching on the label sets corresponding to the terminal nodes in the abnormal path according to the sorted risk label sequence, screens the path risk level in each path that matches the highest-order label item in the sorted sequence, and integrates the risk level identifiers of all abnormal paths.

6. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 1, characterized in that: The topology structure output module includes: The monitoring node extraction submodule collects the regional monitoring nodes corresponding to each label according to the circuit breaker risk level label group, records the abnormal path number and the number of devices associated with each node, and establishes a mapping index between the risk label and the monitoring node; The path collection submodule divides the corresponding abnormal paths into each monitoring node according to the risk level label based on the mapping index value, establishes a bidirectional association structure between the path number and the node address, and extracts the path list managed by each node; The topology generation submodule integrates the monitoring nodes and the subordinate abnormal path numbers according to the node path management list, outputs the node address, associated risk level and total path amount, and generates a remote monitoring topology table.

7. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 1, characterized in that: The system also includes a response delay calibration module: Collect the time interval from when the circuit breaker receives the instruction to when it executes the action, and mark it as the response time benchmark value; Obtain the maximum and minimum values ​​of all response times within the monitoring period and calculate the response fluctuation coefficient; Obtain the transmission delay compensation value through device communication quality analysis, and generate the delay calibration parameter by combining the response time benchmark value and the response fluctuation coefficient; When the response fluctuation coefficient exceeds a preset fluctuation threshold, a path optimization instruction is sent to the network path generation module.

8. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 7, characterized in that: The device communication quality analysis includes: The signal strength and data packet loss rate between monitoring nodes are collected. If the signal strength is lower than the preset strength threshold or the packet loss rate is higher than the preset packet loss threshold, the link is determined to be a low-quality channel; The duration ratio of low-quality channels within the monitoring period is calculated and marked as the channel degradation coefficient. The channel degradation coefficient is compared with a preset channel threshold to generate a transmission delay compensation value and update it to the delay calibration parameter.

9. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 1, characterized in that: The system also includes an impedance test trigger module: When the risk level determination module outputs a high risk label, a multi-band impedance test is initiated; Inject a sweep frequency test signal into the circuit breaker associated with the high-risk node; Collect the attenuation characteristics of the test signal in the node network; The deviation between the measured attenuation characteristics and the theoretical attenuation model is compared to generate the impedance anomaly coefficient.

10. The remote monitoring system for leakage circuit breaker integrated with Internet of Things according to claim 9, characterized in that: The theoretical attenuation model construction includes: The theoretical signal attenuation curve is calculated based on the node impedance parameters in the IoT topology path, and the theoretical signal attenuation threshold range is corrected by adding the environmental interference factor; When the measured attenuation characteristic exceeds the threshold range, the node is marked as an impedance anomaly point.

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