Electricity testing grounding ring system integrating micro sensing and cloud operation and maintenance

Abstract

The invention relates to the technical field of electric power safety equipment, in particular to an electricity testing grounding ring system integrating micro sensing and cloud operation and maintenance, and the system comprises a data collection module which obtains the operation data collected by a micro sensor; the data transmission and intelligent analysis module is based on the low-power-consumption wide-area Internet of Things, performs compression by using a self-adaptive multi-frequency-band data compression algorithm, transmits the compressed data to a cloud server, constructs a historical data training model, and judges the abnormal type of the grounding ring; the exception response and strategy generation module is used for performing multi-dimensional exception level response of the semantic label, generating a target safety control instruction and constructing a self-iterative control strategy library; the instruction verification and control module verifies an instruction through block chain node consensus, carries out operation traceability in combination with an instruction timestamp chain, carries out operation according to a security control strategy, and carries out feedback optimization on the strategy through a self-calibration mechanism. Therefore, the problems that in the prior art, the data collection capacity is insufficient, the abnormal response mechanism is rigid, and the maintenance efficiency is remarkably low are solved.

Description

Technical Field

[0001] This invention relates to the field of power safety equipment technology, specifically to a voltage detection grounding ring system that integrates micro-sensing and cloud-based operation and maintenance. Background Technology

[0002] In power systems, voltage-detecting grounding rings are critical safety devices for transmission and distribution line maintenance. They are primarily used to ensure reliable grounding after a power outage, preventing electric shock accidents caused by induced current, residual charge, or accidental energization. Traditional voltage-detecting grounding rings typically consist of a metal conductive ring, grounding clamp, insulated operating rod, and voltage detector head. Their core function is to manually complete the voltage detection-grounding process: before work begins, maintenance personnel use the voltage detector head to check if the line is indeed voltage-free. After confirming safety, they connect the line to the grounding grid using the grounding clamp, releasing any induced charge and providing a safety barrier for subsequent maintenance. This device is simple in structure and low in cost, widely used in the daily maintenance of medium- and low-voltage distribution networks and some high-voltage lines, and is one of the essential infrastructures ensuring the personal safety of power workers.

[0003] Existing technologies for grounding ring applications suffer from several shortcomings. Their data acquisition capabilities are insufficient; relying solely on a single voltage detection element fails to capture critical operational data such as grounding ring temperature and contact resistance. Data acquisition necessitates manual on-site inspections, which is time-consuming, labor-intensive, and prone to human error. Data transmission and analysis lack efficient solutions. The absence of targeted data compression algorithms leads to significant data redundancy and high transmission power consumption. Furthermore, anomaly detection relies on fixed threshold alarms or manual experience, failing to accurately identify fault types based on the characteristics of the power industry, thus prolonging troubleshooting cycles. The anomaly response mechanism is rigid, providing only single alarm prompts without multi-dimensional anomaly level classification or dynamic risk assessment support. Manual ad-hoc handling strategies are required, resulting in delayed fault response. Moreover, the strategy library lacks self-iterative capabilities, leading to recurring issues and increased repetitive maintenance workload. The command execution and optimization processes have significant weaknesses. The lack of reliable command verification mechanisms and operational traceability methods makes manual operation prone to misjudgments. The absence of a self-calibration feedback mechanism prevents optimization of control logic based on actual operational results, leading to redundant maintenance processes, high error rates, and significantly low overall maintenance efficiency. Summary of the Invention

[0004] This application provides a voltage detection grounding ring system that integrates micro-sensing and cloud-based operation and maintenance to solve problems such as insufficient data acquisition capabilities, rigid abnormal response mechanisms, and significantly low maintenance efficiency in the prior art.

[0005] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application.

[0006] In a first aspect, embodiments of the present invention provide a grounding ring detection system integrating micro-sensing and cloud-based operation and maintenance, comprising: a data acquisition module, a data transmission and intelligent analysis module, an anomaly response and strategy generation module, and an instruction verification and control module; wherein, the data acquisition module is used to acquire first state data of the grounding ring based on micro-sensors; the data transmission and intelligent analysis module is used to compress and divide the first state data using an adaptive frequency-division data compression algorithm based on low-power wide-area IoT to obtain second state data, and transmit it to a cloud server; the data transmission and intelligent analysis module includes: a cloud-based intelligent analysis and anomaly determination unit, used to construct a historical data training model by combining a dynamic rule base specific to the power industry and historical data, the historical data training model being used to determine the anomaly level of the grounding ring; the... The data transmission and intelligent analysis module is further used to train a model based on the second state data and historical data to determine the current grounding loop's anomaly level; the anomaly response and strategy generation module is used to semantically label the current grounding loop's anomaly level, generating a target security control strategy and a target security control instruction, which are also used to construct an iterative control strategy library; the instruction verification and control module is used to verify the target security control instruction through consensus among multiple blockchain nodes based on the control strategy library. After successful verification, the module performs operation tracing by combining the timestamp chain of the target security control instruction to obtain the corresponding target security control strategy, performs maintenance operations on the grounding loop according to the target security control strategy, and optimizes the control strategy library through a self-calibration mechanism.

[0007] Furthermore, the data acquisition module includes: a miniature electrical parameter acquisition unit and an environmental status sensing unit. The miniature electrical parameter acquisition unit integrates a miniature voltage sensor and a current sensor to acquire electrical operation data during the operation of the grounding ring in real time. The electrical operation data includes voltage values, current values, and leakage parameters. The environmental status sensing unit integrates a temperature and humidity sensor and a gas sensor to acquire environmental impact data of the environment where the grounding ring is located in real time. The environmental impact data includes temperature, humidity, and the concentration of corrosive gases in the surrounding environment. The status data includes electrical operation data and environmental impact data.

[0008] Furthermore, the data transmission and intelligent analysis module includes: a multi-dimensional feature data extraction unit, an adaptive frequency band data compression unit, a low-power wide-area IoT transmission unit, and a cloud-based intelligent analysis and anomaly determination unit. Specifically, the multi-dimensional feature data extraction unit extracts key operational characteristic parameters of the grounding loop, such as current, voltage, temperature, and grounding resistance, using a sliding window statistical algorithm. The adaptive frequency band data compression unit compresses and divides the data according to the frequency band characteristics of different types of first-state data using an adaptive frequency band data compression algorithm to obtain second-state data. The low-power wide-area IoT transmission unit stably transmits the second-state data to the cloud server via a low-power wide-area IoT network. The cloud-based intelligent analysis and anomaly determination unit constructs a historical data training model using a dynamic rule base specific to the power industry and historical data, and uses this historical data training model to determine the anomaly level of the grounding loop.

[0009] Furthermore, the adaptive frequency band data compression unit is also used to compress and divide the first state data of the grounding ring according to a preset scaling function, bandwidth limit weight, information importance weight, historical average code rate and maximum code rate allowed by the network link, to obtain the second state data.

[0010] Furthermore, the anomaly response and strategy generation module includes: an anomaly semantic tagging unit, a risk assessment matrix unit, a safety control strategy generation unit, and a strategy library self-iteration unit. The anomaly semantic tagging unit matches a unique semantic tag to each anomaly level based on the grounding ring's anomaly level. These tags include: impact range, severity, and frequency of occurrence. The risk assessment matrix unit dynamically updates the weight coefficients of the risk assessment matrix based on power industry safety standards, anomaly level tags, and real-time collected grounding ring status data, and generates target safety control instructions. The safety control strategy generation unit generates target safety control strategies based on the anomaly level, the weight coefficients of the risk assessment matrix, and the target safety control instructions. The strategy library self-iteration unit stores all generated target safety control strategies and records the electrical operation data of the grounding ring after the target safety control strategies are executed. By comparing the expected and actual effects of the target safety control strategies, it optimizes the strategy parameters and continuously iterates and upgrades the control strategy library.

[0011] Furthermore, the instruction verification and control module includes: a dynamic parameter adjustment unit, an interactive verification and traceability unit, a safety execution unit, and a feedback optimization unit. The dynamic parameter adjustment unit dynamically fine-tunes the target safety control instruction based on the real-time status data of the grounding ring, ensuring that the target safety control instruction matches the current real-time status data of the grounding ring. The interactive verification and traceability unit performs multiple interactive verifications of the target safety control instruction through a blockchain node consensus mechanism to prevent misoperation or malicious instructions, and records the entire operation sequence through a timestamp chain for tamper-proof traceability throughout the process. After the target safety control instruction passes verification, the safety execution unit matches the corresponding target safety control strategy and drives the physical execution mechanism to complete the actual operation on the grounding ring according to the predetermined target safety control strategy. The feedback optimization unit compares the electrical operation data of the grounding ring after operation with the expected effect, forming feedback to drive the self-calibration and continuous optimization of the control strategy library. Secondly, embodiments of the present invention provide a method for a grounding ring detection system integrating micro-sensing and cloud-based operation and maintenance, comprising: collecting first state data of the grounding ring based on micro-sensors; compressing and dividing the first state data using an adaptive frequency-segmentation data compression algorithm based on low-power wide-area IoT to obtain second state data, and transmitting it to a cloud server; constructing a historical data training model by combining a dynamic rule base specific to the power industry and historical data, wherein the historical data training model is used to determine the anomaly level of the grounding ring; determining the current anomaly level of the grounding ring based on the second state data and the historical data training model; performing semantic tagging on the current anomaly level of the grounding ring to generate a target safety control strategy and a target safety control instruction, wherein the target safety control strategy and the target safety control instruction are also used to construct an iterative control strategy library; verifying the target safety control instruction through consensus among multiple blockchain nodes based on the control strategy library; after verification, performing operation tracing by combining the timestamp chain of the target safety control instruction to obtain the corresponding target safety control strategy; performing maintenance operations on the grounding ring according to the target safety control strategy; and optimizing the control strategy library through a self-calibration mechanism.

[0012] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for an integrated micro-sensing and cloud-based operation and maintenance voltage detection grounding ring system.

[0013] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program or instructions stored thereon, wherein when the computer program or instructions are executed, a method for implementing an integrated micro-sensor and cloud-based operation and maintenance voltage detection grounding ring system is provided.

[0014] Fifthly, embodiments of the present invention provide a computer program product, including a computer program or instructions, which, when executed, implement the method for a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance.

[0015] The beneficial effects of the embodiments of the present invention are as follows:

[0016] This invention relates to the field of power safety equipment technology, specifically to a grounding ring detection system integrating micro-sensing and cloud-based operation and maintenance. The system includes: a data acquisition module that acquires operational data collected by micro-sensors; a data transmission and intelligent analysis module that, based on low-power wide-area IoT, compresses data using an adaptive frequency-segmentation data compression algorithm, transmits the data to a cloud server, builds a historical data training model, and determines the grounding ring anomaly type; an anomaly response and strategy generation module that performs multi-dimensional anomaly level responses with semantic tags, generates target safety control commands, and builds an iterative control strategy library; and a command verification and control module that verifies commands through blockchain node consensus, performs operation traceability using a command timestamp chain, operates according to the safety control strategy, and optimizes the strategy through a self-calibration mechanism. This solves the problems of insufficient data acquisition capabilities, rigid anomaly response mechanisms, and significantly low maintenance efficiency in existing technologies.

[0017] Other features and advantages of this application will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the above-described techniques of this application.

[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to an embodiment of this application;

[0021] Figure 2 This is a schematic diagram of a data acquisition module provided according to an embodiment of this application;

[0022] Figure 3This is a schematic diagram of a data transmission and intelligent analysis module provided according to an embodiment of this application;

[0023] Figure 4 This is a schematic diagram of an exception response and strategy generation module provided according to an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of an instruction verification and control module provided according to an embodiment of this application;

[0025] Figure 6 This is a basic structural diagram of the back side of a grounding detection ring according to an embodiment of this application;

[0026] Figure 7 This is a basic structural diagram of the side of a grounding detection ring according to an embodiment of this application;

[0027] Figure 8 This is a basic structural diagram of the front side of a grounding detection ring according to an embodiment of this application;

[0028] Figure 9 This is a structural diagram of a grounding detection ring provided according to an embodiment of this application;

[0029] Figure 10 This is a flowchart illustrating a method for providing a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to an embodiment of this application;

[0030] Figure 11 This is a schematic diagram of a method for a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to an embodiment of this application;

[0031] Figure 12 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] The following description, with reference to the accompanying drawings, illustrates an embodiment of a grounding ring detection system integrating micro-sensing and cloud-based operation and maintenance. Addressing the issue of significantly low maintenance efficiency mentioned in the background section, this application provides a grounding ring detection system integrating micro-sensing and cloud-based operation and maintenance. In this system, a data acquisition module provides accurate and real-time grounding ring operation data, forming the foundation for all subsequent intelligent analysis and control. The data transmission and intelligent analysis module, leveraging low-power wide-area IoT and adaptive frequency-band data compression algorithms, ensures both high data transmission efficiency and low energy consumption. It also combines a power industry-specific dynamic rule base and historical data training models to quickly and accurately determine the anomaly level of the grounding ring. An anomaly response and strategy generation module... Based on the anomaly detection results, a multi-dimensional anomaly level response with semantic tags is implemented. A scientific target safety control strategy is generated using a dynamically updated risk assessment matrix. Simultaneously, an iterative control strategy library is constructed to continuously improve the system's ability to cope with different anomaly scenarios. The command verification and control module ensures the security and credibility of control commands through blockchain node consensus verification. Combined with the command timestamp chain, the entire operation is traceable. Furthermore, parameters are dynamically adjusted according to the grounding ring status, and optimization strategies are fed back through a self-calibration mechanism, enabling intelligent operation and maintenance of the entire grounding ring process. This significantly improves the operational safety, maintenance efficiency, and management reliability of the power system's grounding links. Therefore, it solves the problems of insufficient data acquisition capabilities, rigid anomaly response mechanisms, and significantly low maintenance efficiency in existing technologies.

[0034] Figure 1 This is a schematic diagram of a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance, provided as an embodiment of this application.

[0035] This application provides a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance. The system 10 includes:

[0036] The system includes a data acquisition module 100, a data transmission and intelligent analysis module 200, an anomaly response and strategy generation module 300, and an instruction verification and control module 400.

[0037] The data acquisition module 100 is used to acquire operational data collected by micro sensors; the data transmission and intelligent analysis module 200, based on low-power wide-area IoT, uses an adaptive frequency band data compression algorithm to compress the data and transmit it to a cloud server. Combined with a dynamic rule base specific to the power industry and historical data, it constructs a historical data training model to determine the anomaly level of the grounding loop; the anomaly response and strategy generation module 300 is used to perform multi-dimensional anomaly level responses with semantic tags according to the anomaly type, generate target safety control strategies, generate target safety control instructions based on a dynamically updated risk assessment matrix and real-time data, and construct an iterative control strategy library; the instruction verification and control module 400 is used to dynamically adjust parameters according to the grounding loop status, verify instructions through blockchain node consensus, and after verification, perform operation traceability by combining the instruction timestamp chain, operate the grounding loop according to the safety control strategy, and optimize the strategy through a self-calibration mechanism.

[0038] It is understood that in this embodiment, the data acquisition module provides the system with accurate and real-time grounding ring operation data, forming the foundation for all subsequent intelligent analysis and control. The data transmission and intelligent analysis module, leveraging low-power wide-area IoT and adaptive frequency-band data compression algorithms, ensures both high-efficiency and low-energy-consumption data transmission, and, combined with a power industry-specific dynamic rule base and historical data training model, enables rapid and accurate judgment of the grounding ring's anomaly level. The anomaly response and strategy generation module, based on the anomaly judgment results, performs multi-dimensional anomaly level responses with semantic tags, generates scientific target safety control strategies based on a dynamically updated risk assessment matrix, and constructs an iterative control strategy library to continuously improve the system's ability to cope with different anomaly scenarios. The command verification and control module ensures the security and credibility of control commands through blockchain node consensus verification, achieves full traceability of operations by combining command timestamp chains, and can dynamically adjust parameters according to the grounding ring status and provide feedback optimization strategies through a self-calibration mechanism, enabling intelligent operation and maintenance of the entire grounding ring process, significantly improving the operational safety, maintenance efficiency, and management reliability of the power system's grounding links. This solves the problems of insufficient data acquisition capabilities, rigid anomaly response mechanisms, and significantly low maintenance efficiency in existing technologies.

[0039] In this embodiment of the application, the data acquisition module 100 includes: Figure 2 As shown, there is a miniature electrical parameter acquisition unit and an environmental status sensing unit.

[0040] Among them, the miniature electrical parameter acquisition unit is used to integrate miniature voltage and current sensors to collect voltage, current and leakage parameters of the grounding ring in real time during operation; the environmental state sensing unit is used to integrate temperature and humidity sensors and gas sensors to capture environmental impact data such as temperature, humidity and surrounding corrosive gas concentration of the grounding ring (the above data is the first state data).

[0041] It is understood that this application embodiment, by integrating miniature electrical parameter and environmental status sensing units, achieves comprehensive monitoring of the grounding ring's operating status. The integration of miniature voltage and current sensors can capture electrical operating data in real time, accurately reflecting the grounding ring's electrical performance and potential faults such as short circuits and leakage. The integration of temperature, humidity, and gas sensors can simultaneously collect environmental parameters, quantitatively assessing the impact of environmental factors such as high temperature and humidity, and corrosive gas erosion on the aging of the grounding ring. This provides data support for equipment health status assessment and can trigger early warnings based on real-time data, preventing faults caused by electrical anomalies or environmental degradation in advance, significantly improving the reliability and safety of grounding ring operation. At the same time, it provides a multi-dimensional dynamic data foundation for cloud-based operation and maintenance, enabling intelligent and precise operation and maintenance decisions, extending equipment lifespan, and reducing maintenance costs.

[0042] For example, on a 10 kV transmission line tower in a coastal industrial area, a grounding ring system precisely installed by a live-line working robot is operating stably. Its miniature electrical parameter acquisition unit, integrated with miniature voltage and current sensors, captures the operating grounding voltage and loop current of the grounding ring in real time. When it detects a sudden increase in leakage current from 0.3A to 2.1A (far exceeding the 500mA safety threshold), it immediately marks an anomaly. Simultaneously, the environmental condition sensing unit's temperature and humidity sensors show a current humidity of 89% and a temperature of 32℃, while the gas sensor detects a rise in sulfur dioxide concentration to 0.08 mg / m³, confirming the accelerated corrosion risk of high humidity and corrosive gases on carbon steel grounding materials. This data is simultaneously uploaded to the cloud-based operation and maintenance platform via a wireless module. The system automatically triggers an alert after comparing historical data using algorithms, pushing a work order to the mobile device of maintenance personnel. The alert indicates that corrosion may have caused increased grounding resistance and excessive circulating current, guiding them to prioritize checking the corrosion status of the grounding ring's contact area with the soil, thus preventing equipment insulation breakdown or large-scale power outages due to grounding failure.

[0043] In this embodiment of the application, the data transmission and intelligent analysis module 200 includes: as follows Figure 3 As shown, it includes a multi-dimensional feature data extraction unit, an adaptive frequency band data compression unit, a low-power wide-area IoT transmission unit, and a cloud-based intelligent analysis and anomaly detection unit.

[0044] The multi-dimensional feature data extraction unit accurately extracts key operational characteristic parameters of the grounding ring, such as current, voltage, temperature, and grounding resistance, from the raw operational data acquired by the data acquisition module using a sliding window statistical algorithm, providing a standardized data foundation for subsequent analysis. The adaptive frequency band data compression unit compresses data according to the frequency band characteristics of different types of feature data using an adaptive frequency band data compression algorithm, minimizing data volume while ensuring data accuracy and adapting to low-power transmission requirements. The low-power wide-area IoT transmission unit transmits the compressed data stably to the cloud server via low-power wide-area IoT, while controlling transmission energy consumption to adapt to the long-term outdoor operation scenario of the grounding ring. The cloud-based intelligent analysis and anomaly judgment unit constructs a historical data training model using a dynamic rule base and historical data specific to the power industry, and accurately judges the anomaly type of the grounding ring through calculations based on the historical data training model.

[0045] It should be noted that the sliding window statistical algorithm formula (Formula Group 1) is as follows:

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053] in, for The arithmetic mean of the data within the time window; The length of the window; The current moment; For summation index variables; For the time series The specific values ​​of each sampling point; for The root mean square value of the data within the time window; For the first The square of the data from each sampling point; The variance of the data within the window; Standard deviation; The maximum value of the data within the window; The sampled value at the current moment; The minimum value of the data within the window; The slope of the data change within the window; This represents the time interval between two adjacent sampling points.

[0054] It is understood that the embodiments of this application employ a sliding window statistical algorithm through a multi-dimensional feature data extraction unit to accurately extract features from the raw operating data obtained from the data acquisition module, extracting key operating characteristic parameters of the grounding ring such as current, voltage, temperature, and grounding resistance, providing a standardized and high-value data foundation for subsequent intelligent analysis; the adaptive frequency band data compression unit uses an adaptive frequency band data compression algorithm to perform targeted compression based on the frequency band characteristics of different types of feature data, maximizing the reduction of data volume while ensuring data accuracy, effectively adapting to low-power transmission requirements, and reducing energy redundancy during transmission; low power consumption The wide-area IoT transmission unit transmits compressed data stably to the cloud server via low-power wide-area IoT technology. It also adapts to long-term outdoor operation scenarios of the grounding ring through a transmission energy consumption control mechanism, ensuring timely and reliable data upload and providing continuous data flow support for cloud analysis. The cloud-based intelligent analysis and anomaly detection unit builds a historical data training model using a dynamic rule base specific to the power industry and historical data. Relying on the efficient computation of this model, it accurately determines the anomaly type of the grounding ring, enabling rapid identification and early warning of potential faults. This improves the initiative and targeting of operation and maintenance, reducing the risk of equipment damage or power outages due to delayed anomaly detection.

[0055] For example, on a 10 kV transmission line tower in a mountainous area, a voltage detection grounding ring system installed by a live-line working robot is operating stably. When the grounding ring experiences electrochemical corrosion at the metal connection due to soil moisture, its miniature sensing module collects raw operating data in real time. The multi-dimensional feature data extraction unit immediately uses a sliding window statistical algorithm to accurately extract key parameters such as the grounding resistance surging from the normal 3Ω to 22Ω, the temperature being 11°C higher than the ambient temperature, and the zero-sequence current exhibiting intermittent fluctuations. The adaptive frequency band data compression unit, targeting the low-frequency stability of voltage signals and the high-frequency fluctuation characteristics of temperature data, employs a differentiated compression strategy, reducing the data volume by 70% while ensuring an error of less than 2%, perfectly adapting to low-power transmission requirements. Subsequently, the low-power wide-area IoT transmission unit uses LoRa technology to stably transmit the compressed data to the cloud server, with its standby power consumption controlled at the microampere level, meeting the requirements for long-term maintenance-free outdoor operation of the grounding ring. The cloud-based intelligent analysis and anomaly judgment unit calls the "sudden rise in grounding resistance + abnormal temperature" association model in the dynamic rule base of the power industry, and combines it with the algorithm trained by historical corrosion fault data to accurately determine the "poor contact of grounding down conductor" fault within 3 seconds. At the same time, it pushes the location information and maintenance suggestions to the operation and maintenance terminal, enabling staff to arrive at the site within 15 minutes to complete the handling, avoiding the problem of fault diagnosis taking more than 2 hours in traditional inspections.

[0056] In this embodiment of the application, the adaptive frequency band data compression algorithm formula (Formula Group 2) is as follows:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] in, This refers to the low-frequency component of the signal; The degree of coarsness in wavelet decomposition; This indicates the specific position of the coefficient on the time axis; The original sensor signal; This refers to the time sampling point number; It is a wavelet function; For time-frequency analysis coordinate system; These are the high-frequency components of the signal; It is a scaling function; For the first after threshold processing The detail factor of the layer; To preserve the sign of the coefficients; For the first Layer detail factor; For use in the first Adaptive threshold for layer detail coefficients; For the first Adaptive weighting factor for the layer; For conditional judgment; This is a location index that controls the time position. Weights based on the importance of information; For the first The energy of the band coefficient within the current time window; The total energy across all frequency bands; Bandwidth-limited weights; For transmission of the first Historical average bitrate of frequency band data; The maximum bit rate allowed by the network link; These are the top-level approximation coefficients after quantization; It is a rounding function; These are the top-level approximation coefficients; The basic unit used in numerical quantization.

[0063] It is understood that the embodiments of this application dynamically identify different frequency characteristics of the operating data of micro sensors, divide frequency bands and adjust compression strategies accordingly, and significantly reduce data redundancy and transmission volume while retaining key information about the grounding loop status. This not only adapts to the narrowband transmission limitations of low-power wide-area IoT, reducing device communication power consumption and extending terminal battery life, but also ensures the integrity and analysis effectiveness of data received in the cloud. This provides efficient and accurate data support for subsequent anomaly diagnosis based on dynamic rule bases and historical models, balancing the low power consumption requirements of IoT terminals with the high data quality requirements of intelligent analysis.

[0064] For example, the grounding loop operation data collected by miniature sensors (such as transient current, steady-state voltage, contact resistance, ambient temperature and humidity, and other multi-modal signals) are first divided into time windows and frequency features extracted. Based on data fluctuation characteristics, they are divided into high-frequency transient segments (such as surge current at the moment of a fault, frequency > 100Hz), mid-frequency state segments (such as periodic voltage fluctuations, frequency 1-100Hz), and low-frequency environmental segments (such as slow changes in temperature and humidity, frequency < 1Hz). For the high-frequency segment, improved wavelet packet decomposition is used to retain abrupt change features while removing redundant high-frequency noise, achieving a compression ratio of 20:1. For the mid-frequency segment, adaptive differential coding is used, based on preceding data... The algorithm predicts the current value and transmits only the residual, dynamically adjusting the prediction step size using a sliding window. For low-frequency bands, it employs a combination of run-length encoding and Huffman encoding for compression, significantly simplifying continuously repeating environmental parameters (such as stable temperature and humidity). The adaptive frequency-band data compression algorithm monitors the entropy value of each segment in real time. When an abnormal increase in the contact resistance of the grounding ring is detected (triggering an increase in entropy in the high-frequency band), it automatically improves the compression accuracy of that segment while retaining more than 90% of the original features, and simultaneously reduces the sampling frequency of the low-frequency band to balance power consumption. Finally, the data packets compressed by multi-band fusion are transmitted to the cloud via LPWAN for adaptive compression of critical data with high fidelity and routine data with low redundancy.

[0065] In this embodiment of the application, the exception response and policy generation module 300 includes, as follows: Figure 4 As shown, there are anomaly semantic tagging unit, risk assessment matrix unit, security control strategy generation unit, and strategy library self-iteration unit.

[0066] The system comprises several components: an anomaly semantic tagging unit assigns a unique semantic tag to each anomaly based on the determined anomaly level of the grounding loop, quantifying the level in terms of impact scope, severity, and frequency of occurrence; a risk assessment matrix unit dynamically updates the weight coefficients of the risk assessment matrix based on power industry safety standards and real-time collected operational data, providing a risk priority basis for subsequent strategy generation; a safety control strategy generation unit uses the anomaly level and risk priority as input, retrieves basic control schemes from the power operation and maintenance specification library, adapts and adjusts them according to real-time operating conditions, and generates the target safety control strategy; and a strategy library self-iteration unit stores all generated safety control strategies and records grounding loop status feedback data after strategy execution, automatically optimizes strategy parameters by comparing the expected and actual effects of the strategies, and continuously iterates and upgrades the control strategy library.

[0067] It is understood that the embodiments of this application use an anomaly semantic tagging unit to perform exclusive semantic tag matching for each type of grounding ring anomaly and quantify the level of impact, severity, and frequency of occurrence, making the anomaly situation clearer and providing an accurate initial judgment basis for subsequent processing; the risk assessment matrix unit dynamically updates the weight coefficients based on power industry safety standards and real-time operating data, ensuring that the risk priority judgment is consistent with the actual working conditions and avoiding assessment deviations caused by fixed weights; the safety control strategy generation unit takes the anomaly level and risk priority as input, retrieves the basic scheme of the specification library and adapts it in combination with real-time operating conditions, and can generate targeted and implementable target safety control strategies to ensure the effectiveness of operation and maintenance; the strategy library self-iteration unit continuously upgrades the control strategy library by storing strategies, recording post-execution status feedback data, and comparing expected and actual effects to optimize parameters, making the system's subsequent strategies for dealing with anomalies more in line with long-term operating needs, and improving the overall accuracy of the grounding ring anomaly response, the adaptability of the strategy, and the level of intelligence in long-term operation and maintenance.

[0068] For example, in the grounding ring system of a 10kV distribution ring main unit in the core area of ​​a city, the temperature of the B-phase grounding ring suddenly rose to 92℃ on a certain day (exceeding the 70℃ warning threshold). The abnormal semantic labeling unit quickly matched the label "conductor poor contact type high temperature abnormality", quantified its impact range as "from the ring main unit outgoing line to three downstream low-voltage distribution areas", the degree of harm as "high risk (may cause clamp melting or single-phase grounding fault)", and the frequency of occurrence as "the third time in the past 7 days (high frequency)". The risk assessment matrix unit, based on the baseline weight (0.5) of "abnormal contact point temperature" in the "Distribution Network Equipment Operation Regulations", combined with real-time data - ambient humidity 85% (aggravating oxidation) and load current 1500A (20% above the rated value), dynamically increased the humidity weight to 0.2 and the load weight to 0.3, and finally calculated a comprehensive risk value of 9.1 (out of 10), which was judged as "Level 1 Emergency Risk". The safety control strategy generation unit takes "Level 1 Risk + High Temperature Anomaly Due to Poor Contact" as input and retrieves the basic strategy "Immediately Power Off and Replace Line Clamps" from the power operation and maintenance specification library. However, considering that this ring main unit is a regional power supply hub (without backup power), it is adapted and adjusted to "Complete partial discharge detection and accurately locate the fault point within 1 hour, and simultaneously perform live grinding on the oxide layer of the line clamp surface. If the temperature does not drop below 70℃ within 30 minutes after grinding, initiate an emergency power outage." After the strategy is executed, the operation and maintenance personnel operate according to the adjusted plan and find that the contact resistance at the line clamp crimping point exceeds the standard by 3 times due to oxidation. After grinding and applying conductive paste, the temperature drops back to 62℃ within 25 minutes. The strategy library self-iteration unit records the entire process data, compares the expected (temperature drop below 70℃ within 30 minutes) with the actual effect (achieved the target ahead of schedule), determines that the strategy is effective, optimizes the "Live Grinding Priority" parameter in the "High Temperature Anomaly Due to Poor Contact" strategy, and adds "Oxide Layer Treatment" to the basic control scheme for similar anomalies, performing continuous self-upgrading of the strategy library.

[0069] In this embodiment of the application, the instruction verification and control module 400 includes, as follows: Figure 5 As shown, there are a dynamic parameter adjustment unit, an interactive verification and traceability unit, a secure execution unit, and a feedback optimization unit.

[0070] The dynamic parameter adjustment unit dynamically fine-tunes the operating parameters based on the real-time status of the grounding ring to ensure that the command accurately matches the current equipment operating condition. The interactive verification and traceability unit performs multiple interactive verifications of the control command through the blockchain node consensus mechanism to prevent misoperation or malicious commands, and records the operation sequence completely through the timestamp chain for full-process tamper-proof traceability. After the command is verified, the safety execution unit drives the physical execution mechanism to complete the actual operation of the grounding ring according to the established safety strategy. The feedback optimization unit collects the system response data after the operation, compares it with the expected effect, forms feedback, and drives the self-calibration and continuous optimization of the control strategy library.

[0071] It should be noted that the multi-interaction verification of control commands through the blockchain node consensus mechanism involves the command initiator first binding its identity with a digital signature and generating a signed original command; after receiving the command, the node performs basic verification of syntax validity, signature validity, and permission matching. If these verifications are successful, the consensus phase begins—the master node broadcasts a real-time state snapshot proposal with an additional grounding ring, and the slave nodes cross-verify and vote based on operational adaptability, security policy compliance, and historical behavior analysis. If the threshold is exceeded and all nodes agree, consensus is reached.

[0072] It is understood that the embodiments of this application use a dynamic parameter adjustment unit to fine-tune the operating parameters based on the real-time status of the grounding ring, ensuring that the control commands are accurately adapted to the current operating conditions of the equipment and avoiding operational deviations caused by fixed parameters. The interactive verification and traceability unit uses a blockchain node consensus mechanism to achieve multiple verifications of control commands, effectively preventing the risks of misoperation and malicious commands. At the same time, it uses a timestamp chain to complete the immutable record of the entire operation sequence, providing a reliable basis for subsequent problem tracing. After the command is verified, the safety execution unit strictly drives the physical execution mechanism to complete the operation according to the established safety strategy, ensuring the safety and standardization of the actual action of the grounding ring. The feedback optimization unit collects the system response data after the operation and compares it with the expected effect to form a feedback-driven control strategy library for self-calibration, helping the system to continuously optimize and intelligently iterate. Overall, it significantly improves the operational reliability, operational safety, and long-term adaptability of the voltage detection grounding ring system, and reduces operation and maintenance risks and costs.

[0073] For example, at a 10kV double-line maintenance site, after the operator sends a grounding operation command to the voltage detection grounding ring system via a tablet computer, the system immediately initiates multi-unit collaborative operation: the miniature sensor built into the grounding ring first collects data on the conductor temperature (38℃) and contact resistance (1.2Ω) under high-temperature conditions; the dynamic parameter adjustment unit then fine-tunes the mechanical engagement force and voltage detection threshold, increasing the initial engagement pressure from 50N to 55N to ensure the operation is adapted to the current working conditions; the interactive verification and traceability unit simultaneously pushes the command to three blockchain nodes: the ground operation terminal, the regional substation, and the cloud-based operation and maintenance platform. The command is verified through the PBFT consensus mechanism. After the operation is verified, an operation record with a timestamp is generated and uploaded to the blockchain, forming an immutable trajectory of "instruction initiation - node verification - authorized execution". The safety execution unit then drives the robot's end effector to complete the precise engagement of the grounding ring and the wire according to the safety strategy of "verify power first, then ground". The entire process takes less than 2 minutes. The feedback optimization unit collects the grounding resistance (0.8Ω) and equipment vibration data after the operation in real time. After comparing it with the expected value (≤1Ω, vibration amplitude <0.5mm), the deviation data is fed back to the control strategy library to automatically update the parameter adjustment algorithm under high temperature environment, providing more accurate control logic for subsequent similar operations.

[0074] This application proposes a grounding ring detection system integrating micro-sensing and cloud-based operation and maintenance. The data acquisition module provides accurate and real-time grounding ring operation data, forming the foundation for all subsequent intelligent analysis and control. The data transmission and intelligent analysis module, leveraging low-power wide-area IoT and adaptive frequency-segmented data compression algorithms, ensures both high-efficiency and low-energy data transmission. It also combines a power industry-specific dynamic rule base and historical data training models to quickly and accurately determine the anomaly level of the grounding ring. The anomaly response and strategy generation module responds to anomalies based on semantic tags across multiple dimensions, generating scientific target safety control strategies using a dynamically updated risk assessment matrix. It also constructs an iterative control strategy library to continuously improve the system's ability to handle different anomaly scenarios. The command verification and control module ensures the security and reliability of control commands through blockchain node consensus verification. Combined with a command timestamp chain, it achieves full traceability of the operation and can dynamically adjust parameters based on the grounding ring status, feeding back optimization strategies through a self-calibration mechanism. This enables intelligent operation and maintenance of the entire grounding ring process, significantly improving the operational safety, maintenance efficiency, and management reliability of the power system's grounding links. This solves the problems of insufficient data acquisition capabilities, rigid anomaly response mechanisms, and significantly low maintenance efficiency in existing technologies.

[0075] The following will illustrate a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance through a specific embodiment, including:

[0076] Against the backdrop of a city's intelligent power distribution network transformation, a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance has emerged, representing a paradigm of the digital and intelligent transformation of traditional power safety tools. The system's core is comprised of four modules: data acquisition, transmission and intelligent analysis, anomaly response and strategy generation, and command verification and control. Its physical carrier is a meticulously designed and material-innovative physical device. The system's practical application begins with its ingenious basic structural design. Unlike traditional voltage detection grounding rings, the new voltage detection grounding ring features a revolutionary structural improvement, employing a design with right-angle connecting components, such as... Figure 6As shown. This design is not arbitrary; it greatly facilitates quick and precise installation on complex 10kV distribution network towers. Field test data shows that this design reduces the average installation time from approximately 15 minutes using traditional methods to less than 5 minutes, improving efficiency by over 66%. Whether installed vertically or horizontally, the right-angle connection component provides robust mechanical support. Its base torque resistance, simulated and measured, reaches no less than 50 N·m, far exceeding the 30 N·m of traditional press-fit structures, ensuring the physical reliability of grounding operations. During routine grounding work, construction personnel can easily and reliably connect the grounding clamp to this specially designed ring. Its mechanical strength and high conductivity ensure smooth current discharge, and the contact resistance is stably controlled below 20 μΩ, fundamentally eliminating the risk of overheating, melting, or even electric shock due to poor contact. In the ungrounded state, this ring is not a simple exposed metal point. Its design takes environmental tolerance into account. More importantly, its structure avoids the long-term accumulation of water and dust. In a simulated sandstorm environment, compared with the traditional structure, the amount of dust accumulation at its internal key connection points is reduced by 75%, effectively preventing various accidents caused by environmental factors such as insulation degradation or freezing jamming. This provides a stable and reliable physical platform for subsequent intelligent functions.

[0077] The long-term stable operation of this physical platform relies heavily on its surface protection technology, which is precisely the focus of the project's research on the selection and application of intelligent insulation materials. The city, located on a plateau, experiences intense ultraviolet radiation, large diurnal temperature variations (up to 70°C annually), and frequent acid rain (pH as low as 4.3), sandstorms, and other severe weather conditions, posing a serious challenge to exposed electrical equipment. To address this, the research team selected and applied a high-performance composite silicone rubber material. This material achieves a tracking resistance rating of TMA4.5 and a hydrophobicity rating of HC1. After accelerated aging tests (equivalent to 25 years outdoors), its tear strength retention rate remains above 85%, and its breakdown field strength remains above 30kV / mm. Its key electrical insulation indicators, such as volume resistivity and breakdown electric field strength, far exceed national standards. More importantly, this material is endowed with "intelligent" characteristics. It contains embedded nanoscale conductive particles to monitor its own aging state, serving as an extension of data acquisition and capable of sensing and transmitting abnormal signals with changes in volume resistivity exceeding three orders of magnitude. In specific application scenarios, this intelligent insulating material is precisely coated onto the non-connected exposed parts of the grounding ring, such as... Figure 6This forms a robust and durable insulating protective layer. The design team devised a unique mold and infusion process to ensure a seamless fit between the insulating layer and the metal ring, with the interfacial air gap ratio controlled below 0.05%. This avoids partial discharge caused by air gaps (partial discharge amount <5pC) and prevents corrosion caused by moisture penetration along the interface, thus providing the first and crucial safety barrier for the entire system at both the physical and electrical levels.

[0078] After ensuring the stability of the basic structure and the reliability of insulation protection, the design focus shifted to the component most frequently used by users—the voltage testing connector. To improve operational efficiency, the design team deeply integrated load-bearing mechanics with an arc-shaped design concept. Through finite element analysis software simulations of stress conditions under various operating circumstances, they discovered that an arc-shaped structure conforming to fluid mechanics and lever principles not only evenly distributes mechanical stresses from the conductor's own weight, wind load (simulating a wind speed of 35 m / s), and ice load (simulating 15 mm of icing), but also disperses the maximum stress point by 60% compared to a right-angle design, avoiding metal fatigue caused by stress concentration, and cleverly utilizes gravity. Specifically, as... Figure 7 and Figure 8 As shown, this arc-shaped connector requires only a simple hanging motion during installation. Its own gravity and arc-shaped guide structure allow it to automatically slide down and lock into the predetermined position, achieving "one-click installation." Disassembly is equally simple, requiring only a reverse pull to easily remove it. The entire process requires no complex tools or cumbersome twisting actions, reducing the operating force to less than 10N, greatly improving the efficiency of on-site maintenance and reducing labor intensity. Crucially, this tightly fitting arc design allows the connector to form maximum metal-to-metal contact with the exposed surface of the grounding ring after installation. Figure 9 As shown, the effective contact area reaches 150% of that of traditional planar contact, minimizing the air gap between them. In high-voltage electric fields, air gaps are the main culprits of induced ionization and partial discharge. This design optimization increases the partial discharge initiation voltage under operating overvoltage by approximately 40%, from the original 28kV to over 39kV, directly reducing the discharge risk caused by poor contact or structural mismatch, and improving the safety of the equipment under energized operation.

[0079] However, true intelligence doesn't stop at excellent physical design. All the aforementioned ingenious mechanical structures and materials science become the perfect carriers for intelligent systems. The miniature sensors of the data acquisition module are seamlessly integrated into the grounding ring body and the insulating material. These sensors include, but are not limited to, micro-current sensors (range 0-1A, accuracy ±1%), temperature sensors (range -40℃ to +125℃, accuracy ±0.5℃), humidity sensors, and micro-strain sensors for monitoring the health of the insulating material itself. They continuously collect real-time operating data of the grounding ring 24 / 7, such as leakage current, contact point temperature, ambient humidity, and mechanical vibration frequency. The sampling frequency can be configured between 1Hz and 1kHz as needed. This data serves as the "nerve endings" for the system to perceive external conditions. Next, the data transmission and intelligent analysis module begins to function. Considering the wide distribution of power distribution terminals and their typically limited power supply capabilities, the system uses a low-power wide-area IoT as the transmission medium. Its sleep current is as low as 1.5μA, and a single 10000mAh lithium battery can support its continuous operation for over 3 years. To transmit as much effective data as possible within limited bandwidth and power consumption, the system incorporates an adaptive frequency-band data compression algorithm, achieving an average compression ratio of 5:1. Under the same channel conditions, the data transmission success rate is increased from 90% to over 99.5%. The compressed data is stably transmitted to the cloud server. In the cloud, a big data platform combining a dynamic rule base specific to the power industry (such as safety regulations, local climate history, and equipment lifespan curves) and historical operational data begins operation. Through machine learning algorithms, the platform constructs a benchmark model of the normal operating state of the grounding ring. When the real-time incoming data deviates significantly from the benchmark model (e.g., the contact point temperature rises abnormally by more than 15°C within 10 minutes, or the effective value of the leakage current exceeds the 50mA threshold for five consecutive cycles), the system can quickly perform comparative analysis to determine the type of anomaly, such as "abnormally increased contact resistance," "insulation material aging due to moisture," or "loose bolt connections." The accuracy rate for identifying typical faults reached 95.7% in historical data backtesting.

[0080] Once the anomaly type is determined, the anomaly response and strategy generation module immediately activates. This module doesn't simply provide a cold, impersonal code; instead, it semantically tags the anomaly, such as "City XX Line #18 Pole B Phase - Moderate Risk - Contact Surface Overheating - Recommended Repair within 72 Hours." This multi-dimensional (risk level, anomaly type, processing timeframe) tagged response significantly improves the readability and operability of the information. Simultaneously, the module invokes a dynamically updated risk assessment matrix. This matrix comprehensively considers real-time load current, weather forecasts (such as whether heavy rain is expected), and historical records of similar fault handling, ultimately generating a target safety control strategy. For example, if the system determines a "moderate overheating risk" (temperature between 80℃ and 100℃) and predicts rainfall within the next 6 hours, the generated strategy might be "Recommend temporarily reducing the line's load current from 280A to 220A within 4 hours via the SCADA system, and automatically generating a maintenance work order to be pushed to the mobile maintenance app." All these strategies and decision-making processes are recorded and integrated into an iterative control strategy library. The system automatically optimizes and evaluates the strategy library quarterly, resulting in a continuous improvement in strategy matching accuracy at a rate of approximately 2% per month. This allows the system to become increasingly "intelligent" and precise in its response to strategies over time. Finally, the crucial command verification and control module ensures the security and reliability of the execution process. For control commands requiring remote automatic execution (such as notifying the upstream circuit breaker to prepare for current limiting), the system will not act rashly. The command first enters a node consensus verification process based on blockchain technology. Multiple verification nodes distributed across the cloud, regional master stations, and on-site mobile terminals will verify the legality and rationality of the command. This consensus mechanism is designed to tolerate no more than 1 / 3 of the nodes failing or malicious attacks, preventing accidental activation by a single maliciously attacked node. Once a command is verified, it is stamped with an immutable timestamp and recorded on the command timestamp chain, achieving full lifecycle traceability of the operation. During command execution, the system will also dynamically adjust control parameters based on the actual state of the grounding ring (such as whether the temperature begins to drop and stabilizes below 65℃ within 30 minutes after current limiting). After execution, the self-calibration mechanism will collect feedback on the effect of strategy execution, compare it with the control expectation, and use this to optimize the generation of the next strategy.

[0081] In summary, this application's embodiments, through real-time perception and intelligent analysis, achieve precise insight and early warning of potential risks, transforming the traditional passive response mode into proactive and reliable preventative protection, fundamentally curbing the occurrence of accidents and greatly improving the inherent safety level of power grid operation. Regarding operation and maintenance efficiency, its user-friendly structural design and intelligent strategy push significantly simplify operating procedures, reduce the labor intensity of personnel, and make maintenance work more convenient and efficient. In terms of economy and long-term reliability, the application of high-performance materials extends equipment lifespan, while advanced verification and self-learning capabilities ensure the accuracy of system decisions and continuous evolution capabilities, forming a constantly improving intelligent operation and maintenance ecosystem.

[0082] This application proposes a method for an integrated micro-sensor and cloud-based operation and maintenance system for voltage detection and grounding rings. For example... Figure 10 As shown, the method for a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance includes the following steps:

[0083] In step S101, the first state data of the grounding ring collected by the micro sensor is obtained.

[0084] Understandably, this application implements real-time, multi-dimensional acquisition of key operating parameters of the grounding ring, such as temperature, current, vibration, and partial discharge, using miniature sensors. This not only comprehensively senses the real-time status of the equipment, providing raw data support for subsequent compressed transmission and cloud-based in-depth analysis based on low-power wide-area IoT, but also improves the power industry's exclusive rule base and historical training model through continuously accumulated dynamic data. This enables accurate capture and type identification of abnormal features, providing an objective basis for generating graded response safety control strategies and iteratively optimizing control logic. This ensures early warning and efficient handling of potential faults, significantly improving the intelligence level and operational reliability of grounding ring maintenance.

[0085] Specifically, S101 collects status data based on a miniature electrical parameter acquisition unit and an environmental status sensing unit. The miniature electrical parameter acquisition unit integrates a miniature voltage sensor and a current sensor to collect electrical operation data during the operation of the grounding ring in real time. The electrical operation data includes voltage values, current values, and leakage parameters. The environmental status sensing unit integrates a temperature and humidity sensor and a gas sensor to collect environmental impact data of the environment in which the grounding ring is located in real time. The environmental impact data includes temperature, humidity, and the concentration of corrosive gases in the surrounding environment. The status data includes electrical operation data and environmental impact data.

[0086] In step S102, based on the operating data collected by the micro-sensors, the first state data is compressed using an adaptive frequency band data compression algorithm based on low-power wide-area IoT and transmitted to the cloud server. Combined with the power industry's exclusive dynamic rule base and historical data, a historical data training model is constructed, and the abnormal level of the grounding loop is determined based on the historical data training model.

[0087] Historical data training model refers to the process of iteratively analyzing historical data through algorithms, adjusting model parameters to learn patterns and rules in the data, and thus making predictions or providing decision support for unknown data.

[0088] Step S102 includes:

[0089] S102-1: The multi-dimensional feature data extraction unit accurately extracts key operational feature parameters of different types, such as current, voltage, temperature, and grounding resistance, from the raw operational data obtained from the data acquisition module using a sliding window statistical algorithm. This yields the third operational data, providing a standardized data foundation for subsequent analysis. The specific process is shown in Formula Group 1.

[0090] Sliding window statistical algorithm formula (formula group 1):

[0091]

[0092]

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] in, for The arithmetic mean of the data within the time window; The length of the window; The current moment; For summation index variables; For the time series The specific values ​​of each sampling point (i.e., the first state data, including current, voltage, temperature, humidity, etc.); for The root mean square value of the data within the time window; For the first The square of the data from each sampling point; The variance of the data within the window; Standard deviation; The maximum value of the data within the window; The sampled value at the current moment; The minimum value of the data within the window; The slope of the data change within the window; This represents the time interval between two adjacent sampling points.

[0099] Specifically, all data combinations in Formula Group 1 are matrixed and defined as the third running data. The key feature vectors (including mean, root mean square, variance, standard deviation, maximum and minimum values, and slope) of the grounding loop running data extracted by Formula Group 1 through the sliding window statistical algorithm are used as the replacement input of the original sensor signal x[n] in the adaptive frequency band data compression algorithm of Formula Group 2, thereby realizing standardized feature compression processing and frequency division processing.

[0100] S102-2: The adaptive frequency band data compression unit uses an adaptive frequency band data compression algorithm (i.e., formula group 2) to compress and divide data according to the frequency band characteristics of different types of characteristic data (i.e., key operating characteristic parameters and third operating data) to obtain second state data (including two components: high frequency and low frequency). This maximizes the reduction of data volume while ensuring data accuracy, and adapts to low power transmission requirements.

[0101] Adaptive frequency band data compression algorithm formula (Formula group 2):

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] in, This refers to the low-frequency component of the signal; The degree of coarsness in wavelet decomposition; This indicates the specific position of the coefficient on the time axis; The original sensor signal; This refers to the time sampling point number; It is a wavelet function; For time-frequency analysis coordinate system;

[0108] These are the high-frequency components of the signal; It is a scaling function; For the first after threshold processing The detail factor of the layer; To preserve the sign of the coefficients; For the first Layer detail factor; For use in the first Adaptive threshold for layer detail coefficients; For the first Adaptive weighting factor for the layer; For conditional judgment; This is a location index that controls the time position. Weights based on the importance of information; For the first The energy of the band coefficient within the current time window; The total energy across all frequency bands; Bandwidth-limited weights; For transmission of the first Historical average bitrate of frequency band data; The maximum bit rate allowed by the network link; These are the top-level approximation coefficients after quantization; It is a rounding function; These are the top-level approximation coefficients; The basic unit used in numerical quantization.

[0109] S102-3: Low-power wide-area IoT transmission unit transmits second-state data stably to the cloud server through low-power wide-area IoT, while controlling transmission energy consumption to adapt to long-term outdoor operation scenarios of grounding loop.

[0110] S102-4: The cloud-based intelligent analysis and anomaly determination unit uses a dynamic rule base and historical data specific to the power industry to build a historical data training model. Then, it uses the historical data training model to accurately determine the anomaly level of the grounding loop.

[0111] S102-4-1: Constructing historical data to train the model:

[0112] Formula for training a model using historical data (Formula group 3):

[0113]

[0114]

[0115]

[0116]

[0117] Specifically, the historical data training model directly extracts 16-dimensional composite features from the original data, such as "contact resistance-leakage current dynamic coupling coefficient", "temperature and humidity-partial discharge time-series correlation", and "conductor tension weekly variation standard deviation". The training adopts the LightGBM algorithm and embeds a 10kV distribution network-specific dynamic rule base, such as the annual oxidation resistance increment threshold of copper-aluminum crimp terminals in rural areas with frequent tree obstruction (annual average +8mΩ) and the monthly average growth curve of leakage current of insulators in heavily polluted areas in urban-rural fringe areas (monthly average +0.15mA). The model classifies the three-level state of "initial anomaly-mid-term deterioration-near-term fault" and five sub-categories of anomalies.

[0118] Specifically, the model is built using raw or standardized data that has been decompressed or reconstructed from compressed data. The main purpose of compressing the data is to improve low-power transmission efficiency. On the cloud server side, the data is processed into a format suitable for model training.

[0119] S102-4-2: Decompress and standardize the second state data, input it into the constructed historical data training model, and output the grounding loop anomaly level (formula group 4).

[0120]

[0121]

[0122] in, The data points are standardized (i.e., the second-state data after decompression and standardization). These are the original data points (i.e., the first state data). This is the mean of the feature; The standard deviation of this feature; The value of the loss function; This represents the total number of samples; This represents the total number of exception types. For sample index; For the sample The true label; Predict samples for the model Category The probability of; It is the natural logarithm function; for The hidden state vector at time step; It is the hyperbolic tangent activation function; This is the weight matrix from hidden state to hidden state; for The hidden state vector at time step; The weight matrix from the input features to the hidden state; for The input feature vector at time step; This represents the reconstruction error; The length of the time series; For time step index; Reconstructed for LSTM decoder Time-time feature vector; To determine the category that maximizes the expression within the parentheses ; These are the weighting coefficients predicted by the model; Predicting the category to which a sample belongs for the model The probability of; For expert rules to categories Trigger confidence level; These are model parameters; The learning rate; To reflect the impact of parameter changes on loss; This describes the adjustment range of the parameter.

[0123] It should be noted that the power industry-specific dynamic rule library refers to a set of rules that focuses on core scenarios such as the safe operation of the power system, market transactions, dispatch and maintenance, metering and settlement, integrates industry policies, regulations and technical standards, and is updated and optimized in real time with policy adjustments, technological innovations and market supply and demand dynamics, providing dynamically adapted institutional and technical support for the standardized operation of the industry.

[0124] It is understood that, by learning from the power industry's proprietary dynamic rule base and long-term accumulated historical operating data, the embodiments of this application can accurately capture the abnormal pattern characteristics of grounding loops under different operating conditions, effectively improving the accuracy and efficiency of abnormality type identification. At the same time, the model can achieve self-iterative optimization based on the continuous input of historical data, adapting to diverse scenarios such as equipment aging and environmental changes, and making up for the limitations of a single rule base in covering complex anomalies. This model provides a reliable decision-making basis for the subsequent generation of multi-dimensional anomaly level responses and safety control strategies, helping the system shift from post-processing to pre-judgment, and significantly enhancing the intelligence and operational reliability of grounding loop status monitoring.

[0125] For example, in the training model of historical data of 189 sets of grounding rings in the 10kV distribution network of a county-level power supply company, the model is directly built based on the full amount of operation data from 2020 to 2025. The input data includes original time-series data such as contact resistance (±0.5mΩ accuracy), conductor crimping terminal temperature (-30℃~100℃), ambient temperature and humidity (-20℃~60℃, 10%-95%RH), 10kHz-50kHz partial discharge quantity (0.5pC resolution) and leakage current (0-10mA) collected every 10 minutes. It is also linked to 453 historical abnormal samples marked by manual inspection, such as "oxidation and ablation of crimping terminals", "precursor of surface pollution discharge of insulators", and "rust and broken strands of grounding down conductors". The historical data training model directly extracts 16-dimensional composite features from the raw data, including "contact resistance-leakage current dynamic coupling coefficient," "temperature and humidity-partial discharge time-series correlation," and "conductor tension weekly variation standard deviation." The training employs the LightGBM algorithm, embedding a dynamic rule base specific to 10kV distribution networks—such as the annual oxidation resistance increment threshold for copper-aluminum crimp terminals in rural areas with frequent tree obstructions (annual average +8mΩ) and the monthly average leakage current growth curve for insulators in heavily polluted urban-rural fringe areas (monthly average +0.15mA). This classifies the model into three levels of states: "initial anomaly - mid-term degradation - imminent failure," and five subcategories of anomalies. After three years of operational data validation, the model achieves a 91.8% accuracy rate in identifying "crimp terminal oxidation" anomalies in grounding rings that have been in operation for more than five years, and provides a 52-hour advance warning for "insulator pollution flashover." Furthermore, through iterative iteration of newly added rural power grid renovation grounding ring data each year, the feature weights are dynamically optimized (e.g., the weight of the "contact resistance-leakage current coupling coefficient" increases from 0.19 to 0.34), accurately adapting to the anomaly evolution patterns in different areas of the 10kV distribution network.

[0126] In step S103, a multi-dimensional anomaly level response with semantic tags is generated based on the anomaly level of the grounding ring, a target safety control strategy is generated, and a target safety control instruction is generated based on the dynamically updated risk assessment matrix and real-time data, and an iterative control strategy library is constructed.

[0127] The control strategy library is a structured collection that stores various control strategies used for system adjustment, optimization, or management, and supports on-demand invocation to guide specific control processes.

[0128] Step S103 includes:

[0129] S103-1: The abnormal semantic tag unit matches a unique semantic tag for each abnormal level based on the determined abnormal level of the grounding ring. The tags include: scope of impact, degree of harm, and frequency of occurrence quantification level.

[0130] S103-2: The risk assessment matrix unit dynamically updates the weight coefficients of the risk assessment matrix based on power industry safety standards, anomaly level labels, and real-time collected status data of the connection loop, and generates target safety control instructions to provide risk priority basis for subsequent safety strategy generation.

[0131] S103-3: The safety control strategy generation unit retrieves the basic control scheme from the power operation and maintenance specification library based on the anomaly level and risk priority (i.e., the weight coefficient of the risk assessment matrix) and the target safety control instructions, and adapts and adjusts it in combination with real-time operating conditions (i.e., the status data of the connection loop collected in real time) to generate the target safety control strategy.

[0132] S103-4: The strategy library self-iteration unit stores all generated target safety control strategies and records the grounding loop status feedback data (i.e., the electrical operation data of the grounding loop collected in real time) after the target safety control strategy is executed. By comparing the expected effect of the strategy with the actual effect, the strategy parameters are automatically optimized and the control strategy library is continuously upgraded through self-iteration.

[0133] It should be noted that the multi-dimensional anomaly levels are divided into three levels: mild, moderate, and severe. Mild anomalies refer to operating parameters deviating from the rated threshold by ≤10%, lasting for <5 minutes, and affecting only a single grounding ring. The semantic tag is "slight parameter deviation - short time - single ring independent impact". The response is mainly cloud-based early warning and high-frequency monitoring. Moderate anomalies refer to parameters deviating by 10%-30%, lasting for 5-30 minutes, or causing signal fluctuations in 1-2 surrounding related grounding rings. The semantic tag is "resistance exceeding standard - moderate - lasting for 15 minutes - local related impact". The response includes generating inspection work orders and fine-tuning local parameters. Severe anomalies refer to parameters deviating by >30%, lasting for more than 30 minutes, or affecting the stability of the main line. The semantic tag is "sudden current increase - severe - lasting for 40 minutes - main line impact". The response is that the linkage control strategy library executes emergency measures.

[0134] It is understood that the embodiments of this application, through centralized storage and dynamic updating of security control strategies, enable the system to quickly generate accurate control commands based on real-time data and risk assessment matrices, and adaptively adjust parameters according to the grounding loop status, thereby improving response efficiency and accuracy. At the same time, its self-iterative capability continuously learns and improves strategies through a feedback optimization mechanism, enhancing the system's ability to handle abnormal situations. Combined with blockchain verification and operation traceability, it ensures the security and traceability of commands, ultimately improving the overall intelligence and reliability of operation and maintenance, and reducing the risk of human intervention.

[0135] For example, after the grounding ring of a 10kV distribution line is put into operation, a miniature sensor continuously collects temperature, contact resistance, and vibration data. One summer afternoon, the cloud server receives the grounding ring's temperature data via a low-power wide area network—a real-time temperature of 92℃ (historical baseline for the same period is 65-75℃), and the contact resistance rises to 120μΩ (normal threshold <100μΩ). The system calls the corresponding strategy for "high temperature + high resistance composite anomaly" in the control strategy library: first, it triggers a level 3 warning; simultaneously, based on a dynamic risk assessment matrix (combined with historical fault records of this link, current load rate of 85%, and ambient humidity of 70%), it calculates the probability of fault occurrence as 78%, and generates a "step-by-step load reduction + on-site verification" control command: it sends a command to the distribution network automation master station to reduce the load of the feeder where the grounding ring is located to 60% (to prevent further overheating); and pushes the task to the nearest maintenance personnel (2km from the site) via a mobile terminal APP, requiring them to arrive on-site within 30 minutes with an infrared thermometer and torque wrench for verification. After the operation was executed, the system continuously collected temperature data (which dropped to 78°C after 1 hour). Combined with the on-site inspection results of the maintenance personnel (slight oxidation of the cable clamp), the system fed back the data and effects of the entire process of "high temperature-high resistance-load reduction-manual intervention" to the strategy library, automatically optimizing the load reduction threshold for similar anomalies (adjusted from 70% to 65%) and the maintenance response time (shortened to 25 minutes).

[0136] In step S104, based on the control strategy library, the parameters are dynamically adjusted according to the grounding ring status. The target security control command is verified through blockchain node consensus. After verification, the operation is traced by combining the timestamp chain of the target security control command. The grounding ring is operated according to the target security control strategy, and the target security control strategy is optimized through feedback through a self-calibration mechanism.

[0137] The instruction timestamp chain is a chain sequence that links and records the occurrence time of each instruction in chronological order, used to ensure the timing and verifiability of the execution of target security control instructions.

[0138] Step S104 includes:

[0139] S104-1: The dynamic parameter adjustment unit dynamically fine-tunes the operating parameters based on the real-time status (i.e., status data) of the grounding ring to ensure that the target safety control command is accurately matched with the current equipment operating condition (i.e., status data).

[0140] S104-2: The interactive verification and traceability unit uses the blockchain node consensus mechanism to perform multiple interactive verifications on the target security control instructions to prevent misoperation or malicious instructions, and records the operation sequence completely through the timestamp chain to achieve full-process tamper-proof traceability.

[0141] S104-3: After the target safety control command is verified, the safety execution unit matches the corresponding target safety control strategy and drives the physical execution mechanism to complete the actual operation of the grounding ring according to the predetermined target safety control strategy;

[0142] S104-4: The feedback optimization unit compares the electrical operation data of the grounding ring after the operation with the expected effect to form feedback, driving the self-calibration and continuous optimization of the control strategy library.

[0143] It is understood that the embodiments of this application use blockchain technology to bind each security control instruction with a precise timestamp to form a continuous chain, which not only ensures the verifiability and integrity of the operation instructions—the issuance time, execution order and modification record of any instruction are permanently stored and need to be verified by consensus nodes; but also enables full-link trusted traceability from instruction generation to execution, which can quickly locate the responsible link when an anomaly occurs, and at the same time provide a precise data time sequence analysis basis for subsequent strategy optimization, thereby significantly improving the transparency of operation and maintenance audit and the system's anti-tampering capability.

[0144] For example, in a magnetic grounding ring system of a 10kV distribution network, the grounding ring micro-sensor collected data in real time showing that the grounding circulation current jumped from the normal 0.2A to 9.8A (far exceeding the first grounding circulation current measurement threshold of 5A). At the same time, the cable surface temperature reached 42℃ (above the temperature alarm threshold of 40℃). After these data were transmitted to the cloud via low-power wide-area IoT, and combined with the standard in the power industry dynamic rule base of "circulation current exceeding the threshold and abnormal temperature are judged as insulation defects", the model was trained with historical data to confirm that the anomaly matching degree reached 89%, and finally it was judged as "insulation aging caused by defects in the semi-conductive layer of the cable intermediate joint". The system then generates a control command to "trigger the surge arrester of this section of the line and push a maintenance warning." After the command is verified by consensus among three blockchain nodes—the substation terminal, the regional operation and maintenance center, and the provincial safety master station (100% pass rate)—a chain record is automatically generated, containing "command generation timestamp (associated with initial data: circulating current 9.8A, temperature 42℃), node verification pass timestamp (associated with the digital signatures of each node), surge arrester terminal reception timestamp, and action feedback timestamp (associated with action current 22A)." During subsequent verification by maintenance personnel, the complete timestamp chain can be retrieved by entering the command ID. Leveraging the immutability of blockchain, the entire process can be traced, and the data within the chain can be used to accurately locate defects, providing a reliable basis for fault handling.

[0145] According to the embodiments of this application, a method for a grounding ring detection system integrating micro-sensing and cloud-based operation and maintenance is proposed. The data acquisition module provides the system with accurate and real-time grounding ring operation data, forming the foundation for all subsequent intelligent analysis and control. The data transmission and intelligent analysis module, leveraging low-power wide-area IoT and adaptive frequency-segmented data compression algorithms, ensures both high-efficiency and low-energy data transmission. It also combines a dynamic rule base specific to the power industry and historical data training models to quickly and accurately determine the anomaly level of the grounding ring. The anomaly response and strategy generation module performs multi-dimensional anomaly level responses with semantic tags based on the anomaly judgment results. It generates scientific target safety control strategies based on a dynamically updated risk assessment matrix and constructs an iterative control strategy library to continuously improve the system's ability to cope with different anomaly scenarios. The command verification and control module ensures the security and credibility of control commands through blockchain node consensus verification. Combined with the command timestamp chain, it achieves full traceability of the operation and can dynamically adjust parameters according to the grounding ring status and provide feedback optimization strategies through a self-calibration mechanism, enabling intelligent operation and maintenance of the entire grounding ring process. This significantly improves the operational safety, maintenance efficiency, and management reliability of the power system's grounding links. This solves the problems of insufficient data acquisition capabilities, rigid anomaly response mechanisms, and significantly low maintenance efficiency in existing technologies.

[0146] The following will illustrate a method for an integrated micro-sensor and cloud-based operation and maintenance system for voltage detection and grounding rings through a specific embodiment. Figure 11 As shown, it includes:

[0147] A key milestone has been reached in the intelligent transformation of a 10kV community distribution substation in the old city of a certain city – a voltage detection and grounding ring system integrating micro-sensors and cloud-based operation and maintenance has been officially put into operation, injecting "digital security genes" into this substation, which has been built for more than 15 years and is responsible for the electricity supply of 320 households, 5 community shops and 1 primary school. This distribution area is connected to a 500kVA distribution transformer via a 10kV overhead line. The four grounding rings in the ring main unit were once the core components to ensure maintenance safety. However, due to its location next to a river (the groundwater level is only 0.8 meters from the foundation of the ring main unit) and long-term exposure to damp salt spray corrosion, there have been two personal safety accidents involving maintenance personnel accidentally touching live busbars in the past three years. On average, there are as many as 12 complaints about power outages caused by poor grounding to low-voltage users per year. The drawbacks of the traditional "regular manual inspection + emergency repair after failure" model have been exposed: the monthly inspection of the four grounding rings of a single ring main unit takes 2 hours, with labor costs exceeding 2,000 yuan. Moreover, it is impossible to detect hidden faults such as oxidation and corrosion of contact terminals in real time. The operation and maintenance efficiency and safety are difficult to meet the requirements of modern power distribution networks. Faced with the four major pain points of 10kV grounding rings—"compact space makes it difficult to deploy sensors, harsh environment makes them susceptible to corrosion, heavy safety responsibilities, and high operation and maintenance costs"—the power supply company has launched a digital transformation from "passive emergency repair" to "proactive defense" with the goals of "miniaturized sensing, low-power transmission, intelligent diagnosis, and traceable management and control".

[0148] The system first embeds customized miniature composite sensors into each grounding ring body to specifically address the special needs of 10kV equipment: a temperature and humidity sensor (accuracy ±0.5℃ / ±2%RH) is attached to the inner wall of the shielding cover to monitor the microenvironment of the cabin in real time, triggering a corrosion warning when the humidity exceeds 70%RH; a contact resistance sensor (measurement range 0.1μΩ-5mΩ, accuracy ±0.8%) is connected in series at the connection point between the copper-aluminum transition terminal and the busbar to directly collect the contact resistance value, marking an anomaly when it exceeds 5μΩ (the maintenance threshold for 10kV grounding rings); a partial discharge sensor (frequency band 100kHz-5MHz, sensitivity -95dBm) is attached to the surface of the insulating cover to capture discharge pulse signals and identify micro-damage to the insulation layer; and a corrosion rate sensor (based on electrochemical impedance spectroscopy) is embedded under the zinc coating to calculate the corrosion rate by measuring impedance changes and provide early warning of the risk of zinc layer peeling. These sensors are all IP68 waterproof certified, suitable for the high humidity and dusty environment of ring main units, and operate in a low-power "sleep-wake" mode: normally collecting basic data (temperature, humidity, contact resistance) every 15 minutes; when the local discharge exceeds -90dBm or there is a sudden change in temperature and humidity (ΔT>5℃ / ΔRH>10%), they automatically switch to high-frequency sampling (contact resistance once per second, partial discharge once every 0.5 seconds). At 10:30 on August 15, the G2 grounding ring sensor was the first to trigger an anomaly: the contact resistance suddenly increased from 0.9μΩ to 8.2μΩ (exceeding the threshold by 64%), and the temperature and humidity simultaneously rose to 32℃ / 85%RH (an increase of 10%RH compared to the previous day). The partial discharge sensor captured a continuous pulse of -88dBm in the 250kHz frequency band. After the data was filtered by the front-end wavelet to remove glitches other than ±3σ, it was transmitted efficiently using an adaptive frequency band compression algorithm—temperature and humidity were extracted for daily cycle trends through wavelet transform (preserving 24-hour fluctuation characteristics and compressing) With a compression rate of 88%, contact resistance only compresses redundant timestamps (retaining key "time-value" abrupt changes, with a compression rate of 65%), and partial discharge uses differential coding to record pulse amplitude / frequency (e.g., "250kHz / -88dBm pulse lasting 5 seconds", with a compression rate of 70%). Ultimately, the average daily communication traffic of a single grounding ring is reduced from 18MB to 5.2MB, and the total traffic of 128 grounding rings in the community is only about 666MB / day. Communication costs are reduced by 60%, and the lifespan of the sensor lithium battery is extended from 2.5 years to 4 years, laying the foundation for long-term stable monitoring.

[0149] After the flood of data entered the power industry's dedicated cloud platform, the system initiated a three-level diagnostic process to accurately pinpoint the root cause of the fault: The first level invoked the 10kV power industry dynamic rule library, finding that the G2 grounding ring contact resistance of 8.2μΩ exceeded the standard (64% over the standard), and the partial discharge of -88dBm was close to the "attention value" (-85dBm), immediately triggering a level-two warning; the second level correlated with historical data, retrieving G2's operating records for the past 5 years, revealing that during the peak summer season of 2019, humidity caused the contact resistance to briefly rise to 7.5μΩ (which was later restored after cleaning), and after the zinc coating was replaced in 2021, the corrosion rate increased from 0.03mm / year to 0.04mm / year (approaching the design threshold of 0.05mm / year), combined with small... Environmental data showing high groundwater levels and a measured soil resistivity of 120 Ω·m (exceeding the safe value of 80 Ω·m) in the district during summer pinpointed a combined cause of "high humidity corrosion + insulation micro-damage". The third level of inference, based on an improved random forest + attention mechanism model (which has learned from 5000+ 10kV grounding ring fault samples), indicates that the zinc layer of the copper-aluminum transition terminal is rapidly peeling off due to the high humidity soil (the current corrosion rate has reached 0.05 mm / year, exceeding the design value). The surge in contact resistance has caused micropore discharge in the insulation layer (corresponding to a 250kHz pulse characteristic). If not addressed within 48 hours, this could develop into a grounding failure, posing a risk of backfeeding from the 380V generators of three shops on the low-voltage user side, threatening the safety of maintenance personnel and equipment.

[0150] The diagnostic conclusions drive the generation of semantic multi-dimensional strategies. The system responds collaboratively from three dimensions: safety, operation and maintenance, and users. The safety dimension is marked as "medium-high risk (level 2)," clearly indicating that the affected area is 120 households and 3 shops downstream of the G2 grounding ring, and the urgency level is set as "the fault needs to be isolated within 4 hours." The operation and maintenance dimension is linked to historical maintenance records (G2 has been in operation for 7 years, and the last maintenance showed no abnormalities), spare parts inventory (the warehouse has spare parts of the same model of stainless steel terminals, 15 kilometers away from the site), and the nearest operation and maintenance team (the third shift of power distribution operation and maintenance, currently working in the community 3 kilometers away). The user dimension assesses the impact of the power outage (replacing the terminals requires a short power outage of 2 hours, which is expected to affect 120 households, and the complaint risk level is "yellow"), automatically triggering the property notification interface to push the power outage plan (15:00-17:00) and the contact information of the emergency generator vehicle through the owner group (to prevent backfeeding from the shops' own power supply). Meanwhile, the dynamic risk assessment matrix (inputting real-time soil moisture of 28%, probability of rainfall in the next 24 hours of 60%, and historical fault recurrence rate of 25%) further optimized the strategy: additional drones were dispatched to retest the resistivity of the soil around the G2 grounding ring (confirming 120Ω·m), and it was recommended that the grounding ring in this area be replaced with stainless steel in the future; the operation and maintenance shift 3 was coordinated to adjust the work plan, prioritizing the G2 grounding ring (originally scheduled to arrive at 16:00, now moved forward to 14:30), and requiring them to carry 10kV special tools such as insulating gloves and voltage detectors to ensure efficiency and safety in handling the situation.

[0151] After the maintenance instruction is generated, the system packages it into a blockchain transaction and submits it to a consortium blockchain node consisting of the power supply station (regulator), the third maintenance team (executor), the equipment manufacturer (a power fittings factory, spare parts provider), and the community property management (user representative). Each node rigorously verifies the instruction using the PBFT (Practical Byzantine Fault Tolerance) consensus algorithm: permission verification confirms that the maintenance personnel hold a 10kV grounding ring maintenance special operation certificate and are currently located within 300 meters of the ring main unit; instruction integrity verification includes the equipment ID, policy hash value, and timestamp; risk assessment calculates a risk value of 8.2 (below the threshold of 10) using an identifier confirmation matrix, confirming the necessity of the action. After successful verification, the instruction is written to the blockchain at 14:25, generating an immutable log: "14:15 Instruction generated - 14:20 Power supply station node verification - 14:22 Maintenance team node verification - 14:25 On-chain". Upon arrival at the site, maintenance personnel scanned the QR code on the grounding ring using a handheld terminal, retrieved the blockchain record to confirm the instructions were correct, and then performed the following operations: First, they remotely locked the outgoing switch of the ring main unit containing the G2 grounding ring to prevent accidental closing; then, they disassembled the old copper-aluminum terminals and found that a large area of ​​the zinc layer had peeled off, exposing the oxidized and blackened copper substrate with a corrosion depth of 1.2mm; next, they installed new stainless steel terminals and applied conductive paste (increasing the contact area by 30%); finally, they tested the contact resistance (0.6μΩ, meeting the standard), partial discharge (-96dBm, no abnormality), and temperature and humidity (26℃ / 78%RH, returned to normal). The entire 12-minute operation was recorded on the blockchain, with the timestamp accurate to the second, ensuring "traceable operation and definable responsibility."

[0152] After the anomaly was eliminated, the system entered a continuous optimization phase: G2 grounding ring data was monitored continuously for 72 hours, with contact resistance stabilizing at 0.5-0.7μΩ, partial discharge <-97dBm, and temperature and humidity fluctuating synchronously with the environment. The model determined that the anomaly had been completely eliminated. This case (fault cause, handling time, sensor data comparison) was entered into the 10kV dedicated control strategy library, and new rules were added such as "quarantine layer thickness of grounding rings in high humidity areas" and "shops must be notified 4 hours in advance of the risk of reverse power supply to the user side." Analysis of the entire process revealed that the original model's prediction error for "zinc layer corrosion rate in areas with high groundwater levels" reached 15% (original prediction 0.03mm / year, actual 0.05mm / year). Therefore, 50 sets of grounding ring corrosion data from the past 3 years in this area were used for retraining, and the corrosion coefficient was corrected (from 0.002mm / month to 0.003mm / month). The effectiveness of "drone resistivity detection" was summarized and incorporated into the standard process for grounding ring inspection in high humidity areas. This step will be triggered by default in subsequent similar scenarios.

[0153] In this incident, the system completed the handling of the data anomaly in just 3.5 hours (compared to 8-12 hours for traditional manual inspections), preventing power outages for 120 households and damage to shop equipment. User complaints decreased by 60% that month. Over the next six months, the 10kV grounding ring fault rate in the community reached zero, and maintenance costs decreased by 55% (manual inspection frequency dropped from twice a month to once a quarter). More importantly, the system, through 10kV scenario adaptation, verified the feasibility of the "micro-sensor + LPWAN + AI diagnostics + blockchain traceability" model in low-voltage distribution networks—its self-iterative strategy library can predict poor contact faults in similar humid environments up to 96 hours in advance, truly achieving a leap from "firefighting-style maintenance" to "predictive protection."

[0154] In summary, this application's embodiment utilizes a full-chain digital design—including precise perception through micro-sensors, cost reduction and efficiency improvement through low-power transmission, intelligent diagnosis through AI models, and enhanced control through blockchain traceability—to move the safety defense line forward, shifting from traditional "post-fault repair" to "early warning of potential hazards." Rapid response within 3.5 hours avoids power outages and backfeed risks for 120 households, significantly improving personal and equipment safety. Operation and maintenance efficiency is dramatically improved, with annual maintenance costs per ring main unit reduced by 55%, manual inspection frequency reduced from twice a month to once a quarter, and the failure rate reduced to zero. This addresses the maintenance challenges of "numerous points, wide distribution, and harsh environment" in old urban distribution networks. The model is replicable and scalable, verifying the adaptability of "micro-sensors + LPWAN + AI + blockchain" technology on the 10kV distribution side. It provides a "low-cost, high-return" model for the intelligent transformation of similar old distribution areas nationwide, promoting the transformation of distribution networks from "passive maintenance" to "predictive protection," and providing a grassroots practice model for the construction of a new power system safety foundation.

[0155] Figure 12 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0156] The memory 1201, the processor 1202, and the computer program stored on the memory 1201 and executable on the processor 1202.

[0157] When the processor 1202 executes the program, it implements a method for a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance provided in the above embodiments.

[0158] Furthermore, electronic devices also include:

[0159] Communication interface 1203 is used for communication between memory 1201 and processor 1202.

[0160] The memory 1201 is used to store computer programs that can run on the processor 1202.

[0161] The memory 1201 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0162] If the memory 1201, processor 1202, and communication interface 1203 are implemented independently, then the communication interface 1203, memory 1201, and processor 1202 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 12 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0163] Optionally, in a specific implementation, if the memory 1201, processor 1202, and communication interface 1203 are integrated on a single chip, then the memory 1201, processor 1202, and communication interface 1203 can communicate with each other through an internal interface.

[0164] The processor 1202 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0165] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for an integrated micro-sensor and cloud-based operation and maintenance grounding ring system.

[0166] Furthermore, embodiments of this application also provide a computer program product, including a computer program or instructions, which, when executed, implement the above-described method for an integrated micro-sensor and cloud-based operation and maintenance grounding ring system.

[0167] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0168] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0169] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0170] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0171] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0172] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance, characterized in that, include: The module comprises a data acquisition module, a data transmission and intelligent analysis module, an anomaly response and strategy generation module, and an instruction verification and control module; among which, The data acquisition module is used to acquire the first state data of the grounding ring based on the micro sensor; The data transmission and intelligent analysis module is used to compress and divide the first state data based on low-power wide-area Internet of Things using an adaptive frequency band data compression algorithm to obtain the second state data, and then transmit it to the cloud server. The data transmission and intelligent analysis module includes: a cloud-based intelligent analysis and anomaly determination unit, which is used to combine the power industry's exclusive dynamic rule base and historical data to build a historical data training model, which is used to determine the anomaly level of the grounding loop; The data transmission and intelligent analysis module is also used to train a model based on the second state data and historical data to determine the current grounding loop anomaly level; The anomaly response and strategy generation module is used to semantically label the current grounding ring anomaly level and generate target security control strategies and target security control instructions. The target security control strategies and target security control instructions are also used to build an iterative control strategy library. The instruction verification and control module is used to verify the target security control instruction through consensus among multiple blockchain nodes based on the control strategy library. After successful verification, the module performs operation tracing by combining the timestamp chain of the target security control instruction to obtain the corresponding target security control strategy. The module then performs maintenance operations on the grounding ring according to the target security control strategy and optimizes the control strategy library through a self-calibration mechanism.

2. The voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to claim 1, characterized in that, The data acquisition module includes: a miniature electrical parameter acquisition unit and an environmental status sensing unit. The miniature electrical parameter acquisition unit integrates a miniature voltage sensor and a current sensor to collect electrical operation data in real time during the operation of the grounding ring. The electrical operation data includes voltage values, current values, and leakage parameters. The environmental state sensing unit is used to integrate temperature and humidity sensors and gas sensors to collect environmental impact data of the environment where the grounding ring is located in real time. The environmental impact data includes temperature, humidity and the concentration of corrosive gases in the surrounding environment. The status data includes electrical operation data and environmental impact data.

3. The voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to claim 2, characterized in that, The data transmission and intelligent analysis module includes: a multi-dimensional feature data extraction unit, an adaptive frequency band data compression unit, a low-power wide-area IoT transmission unit, and a cloud-based intelligent analysis and anomaly detection unit. The multi-dimensional feature data extraction unit is used to extract key operating feature parameters of the grounding ring, such as current, voltage, temperature, and grounding resistance, through a sliding window statistical algorithm. The adaptive frequency band data compression unit uses an adaptive frequency band data compression algorithm to compress and divide the data according to the frequency band characteristics of different types of first state data to obtain second state data. The low-power wide-area IoT transmission unit is used to stably transmit the second state data to the cloud server via low-power wide-area IoT. The cloud-based intelligent analysis and anomaly determination unit constructs a historical data training model using a dynamic rule base specific to the power industry and historical data, and determines the anomaly level of the grounding loop using the historical data training model.

4. The voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to claim 3, characterized in that, The adaptive frequency band data compression unit is also used to compress and divide the first state data of the grounding ring according to a preset scaling function, bandwidth limit weight, information importance weight, historical average code rate and maximum code rate allowed by the network link, to obtain the second state data.

5. The voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to claim 4, characterized in that, The anomaly response and policy generation module includes: an anomaly semantic tagging unit, a risk assessment matrix unit, a security control policy generation unit, and a policy library self-iteration unit. The abnormal semantic tagging unit is based on the abnormality level of the grounding ring and matches a unique semantic tag for each abnormality level. The tags include: scope of impact, degree of harm, and frequency of occurrence quantification level. The risk assessment matrix unit dynamically updates the weight coefficients of the risk assessment matrix based on power industry safety standards, anomaly level labels, and real-time collected status data of the connection loop, and generates target safety control instructions. The security control strategy generation unit generates a target security control strategy based on the anomaly level, the weight coefficients of the risk assessment matrix, and the target security control instructions. The strategy library self-iteration unit is used to store all generated target safety control strategies, and at the same time record the electrical operation data of the grounding ring after the target safety control strategy is executed. By comparing the expected effect and the actual effect of the target safety control strategy, the strategy parameters are optimized and the control strategy library is continuously upgraded through self-iteration.

6. The voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance according to claim 5, characterized in that, The instruction verification and control module includes: a dynamic parameter adjustment unit, an interactive verification and tracing unit, a secure execution unit, and a feedback optimization unit. The dynamic parameter adjustment unit dynamically fine-tunes the target safety control command based on the real-time status data of the grounding ring to ensure that the target safety control command matches the current real-time status data of the grounding ring. The interactive verification and traceability unit performs multiple interactive verifications on the target security control instructions through the blockchain node consensus mechanism to prevent misoperation or malicious instructions, and records the operation sequence completely through the timestamp chain to achieve full-process tamper-proof traceability. After the target security control command is verified, the security execution unit matches the corresponding target security control strategy and drives the physical execution mechanism to complete the actual operation of the grounding ring according to the predetermined target security control strategy. The feedback optimization unit compares the electrical operation data of the grounding ring after the operation with the expected effect to form feedback, driving the self-calibration and continuous optimization of the control strategy library.

7. A method for applying an integrated micro-sensing and cloud-based operation and maintenance grounding ring system according to any one of claims 1-6, characterized in that, include: The first state data of the grounding ring is collected based on a miniature sensor; Based on low-power wide-area IoT, the first state data is compressed and divided using an adaptive frequency band data compression algorithm to obtain the second state data, which is then transmitted to the cloud server. By combining a dynamic rule base specific to the power industry and historical data, a historical data training model is constructed. This historical data training model is used to determine the anomaly level of grounding loops. The model is trained based on the second state data and historical data to determine the current grounding loop anomaly level; The current grounding loop's anomaly level is semantically tagged to generate target security control strategies and target security control instructions. These target security control strategies and instructions are also used to construct an iterative control strategy library. Based on the control strategy library, the target security control command is verified through consensus among multiple blockchain nodes. After verification, the operation is traced back to obtain the corresponding target security control strategy by combining the timestamp chain of the target security control command. The grounding ring is maintained according to the target security control strategy, and the control strategy library is optimized through feedback through a self-calibration mechanism.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for a voltage detection grounding ring system integrating micro-sensing and cloud-based operation and maintenance as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the method of the voltage detection grounding ring system integrating micro-sensing and cloud operation and maintenance as described in any one of claims 1-7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the method of the voltage detection grounding ring system integrating micro-sensing and cloud operation and maintenance as described in any one of claims 1-7.

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