A low-power-consumption internet-of-things-based distributed fuse state cloud monitoring system

Abstract

This invention provides a distributed fuse status cloud monitoring system based on low-power IoT, relating to the field of power grid equipment status monitoring technology. It includes distributed monitoring nodes, a LoRa Mesh self-organizing network module, an edge gateway, and a cloud platform. The distributed monitoring nodes are equipped with a three-level sleep low-power sensing mechanism, dynamically adjusting power consumption through deep sleep, timed wake-up, and event-triggered full sampling. This invention employs a three-level sleep low-power sensing mechanism to achieve dynamic and refined control of the monitoring node's power consumption. Under normal conditions, the device only retains low-power detection circuitry for operation, with a hierarchical working mode of timed simplified sampling and fault event-triggered full sampling, minimizing ineffective power loss. Combined with a dual power supply scheme of passive current transformer power and button battery, the system achieves an average operating current of less than 10μA and over 5 years of maintenance-free operation, significantly reducing the deployment and maintenance costs of power grid fuse monitoring equipment.

Description

Technical Field

[0001] This invention relates to the field of power grid equipment condition monitoring technology, and in particular to a distributed fuse condition cloud monitoring system based on low-power Internet of Things. Background Technology

[0002] Distributed fuses, as core overload and short-circuit protection devices in low-voltage distribution networks, are widely deployed in distribution branches, outdoor transformer substations, and upstream of terminal electrical equipment. They are characterized by their scattered distribution, large number, and high maintenance difficulty, making them crucial for ensuring the safe and stable operation of the distribution network, preventing electrical fires, and protecting equipment from damage. During long-term operation, fuses are susceptible to load fluctuations, changes in ambient temperature and humidity, vibration and shock, and transient voltage disturbances, leading to hidden temperature rise anomalies, performance degradation, malfunctions, and premature blowouts. If the operating status of fuses cannot be monitored in real time and potential faults cannot be identified in advance, it can easily trigger line tripping, power outages, and cascading failures, causing grid safety problems. Therefore, establishing a low-power, high-precision, high-security distributed fuse status online monitoring system capable of system-level risk prediction is of paramount engineering necessity for refined operation and maintenance of distribution networks, early fault prevention, and improved power supply reliability. In existing technologies, the power consumption control of monitoring terminals is crude, resulting in excessive power consumption due to constant operation around the clock. This leads to short device battery life, high maintenance frequency, and difficulty in adapting to long-term outdoor unattended monitoring scenarios. Furthermore, the fault identification capability is weak, failing to distinguish between different fault types such as short-circuit instantaneous tripping, overload fuse failure, and mechanical malfunction, making it difficult to meet the needs of refined fault analysis in power grids. The monitoring mode is isolated and outdated, only achieving post-event detection of single-point faults without combining overall power grid operating parameters for correlation analysis, making it impossible to predict cascading operational risks. Device authentication is costly and insecure, with cumbersome key certificate management during large-scale deployment, and prone to device impersonation and unauthorized access. Network stability is poor, with node obstruction and hardware failures easily causing communication interruptions, insufficient distributed data timing consistency, and low accuracy in fault location and causal analysis. Therefore, this invention proposes a distributed fuse status cloud monitoring system based on low-power IoT to address the problems existing in the prior art. Summary of the Invention

[0003] To address the aforementioned issues, this invention proposes a distributed fuse status cloud monitoring system based on low-power IoT. This system employs a three-level sleep-based low-power sensing mechanism, enabling dynamic and refined control of the monitoring node's power consumption. Under normal conditions, the device only retains low-power detection circuitry for operation, employing a tiered operating mode of timed simplified sampling and fault event-triggered full sampling to minimize ineffective power loss. Combined with a dual power supply scheme of passive current transformer power and button battery, the system achieves an average operating current of less than 10μA and a maintenance-free lifespan of over 5 years, significantly reducing the power consumption of power grid fuse monitoring. The deployment and maintenance costs of the monitoring equipment are perfectly suited for unattended monitoring scenarios such as outdoor and remote sites. Furthermore, based on the fault identification method of fractal dimension and fuse feature spectrum matching, it achieves refined classification and identification of fuse faults. By collecting milliohm-level resistance, micro-temperature difference and high-frequency transient action signals, and combining the fractal dimension algorithm to quantify the fault waveform characteristics, it accurately distinguishes three working conditions: instantaneous short-circuit instantaneous tripping, thermal accumulation overload fusing, and mechanical vibration malfunction. This enables low-cost terminals to have high-end waveform analysis fault identification capabilities, greatly improving the accuracy and professionalism of power grid fuse fault diagnosis, and providing reliable data support for fault tracing and responsibility determination.

[0004] To achieve the objectives of this invention, the following technical solution is provided: a distributed fuse status cloud monitoring system based on low-power Internet of Things (IoT), comprising distributed monitoring nodes, a LoRa Mesh self-organizing network module, an edge gateway, and a cloud platform. The distributed monitoring nodes are equipped with a three-level sleep low-power sensing mechanism, dynamically adjusting power consumption through deep sleep, timed wake-up, and event-triggered full sampling, and adopting a dual power supply mode of passive power extraction and battery. The LoRa Mesh self-organizing network module realizes multi-hop data forwarding and network self-healing, and works with the gateway for unified time synchronization to complete network-wide synchronous sampling. The edge gateway aggregates node time-series data and uploads it to the cloud platform; the cloud platform has built-in fault identification algorithms, power grid linkage early warning rule base and environmental fingerprint security authentication mechanism to achieve accurate identification of fuse faults, system-level risk prediction and equipment counterfeit-free authentication.

[0005] Further improvements are made in that: the distributed monitoring node collects milliohm-level resistance parameters and micro-temperature difference parameters at both ends of the fuse in real time, collects waveform data of the contact action moment through the high-frequency transient signal capture unit, and combines the fractal dimension algorithm to remove environmental interference features and identify latent and instantaneous faults of the fuse.

[0006] Further improvements are made in the following aspects: The three-level sleep low-power sensing mechanism includes three working modes: normal deep sleep mode, in which the node only retains the operation of the milliwatt-level threshold detection circuit to continuously collect temperature, vibration, and ultraviolet arc monitoring signals; timed wake-up sampling mode, in which the node automatically wakes up every preset 15-minute cycle to complete simplified sampling and short frame data reporting; and event-triggered full sampling mode, in which when the detection signal exceeds the preset threshold, multi-sensor synchronous high-speed full sampling and feature calculation are immediately started. The dual battery power supply mode uses a current transformer for passive energy extraction combined with button battery power supply. Under normal operating conditions, the average operating current of the whole machine is less than 10μA.

[0007] Further improvements are made in the following aspects: The power grid linkage early warning rule base integrates multiple parameters such as fuse status, upstream circuit breaker operation status, circuit load fluctuation, and grid voltage sag. The cloud platform automatically correlates and analyzes the downstream load operation status for critical fault states where the fuse temperature rises abnormally but does not blow, identifies cascading fault risks, and pushes composite early warning information for high-risk fuses and overload circuits. A further improvement lies in the fact that the fault identification algorithm employs a fractal dimension calculation model to extract high-frequency transient signal features at the moment of fuse failure. The fractal dimension algorithm formula is as follows: ; Where: D is the fractal dimension of the high-frequency transient signal, which characterizes the complexity of the signal and the difference in fault characteristics; r is the grid division scale, which represents the signal spatial segmentation accuracy; N(r) is the minimum number of grids required to cover the signal waveform at scale r, which reflects the fluctuation characteristics of the signal waveform; the calculated fractal dimension is matched with the cloud-based fuse feature spectrum library to distinguish three fault states: instantaneous short-circuit fast-break, thermal accumulation overload fuse, and mechanical vibration malfunction.

[0008] Further improvement involves matching the calculated fractal dimension with the cloud-based circuit breaker feature spectral library using a similarity evaluation algorithm, as shown in the following formula: ; Where: S is the matching similarity between real-time fault features and standard fuse feature spectra; n is the number of feature spectrum data sampling points; Di is the real-time signal fractal dimension of the i-th sampling point; D0i is the standard fractal dimension of the i-th sampling point in the cloud feature spectrum library; when S≥0.9, the fault type matching is deemed valid, and the fault is accurately classified.

[0009] A further improvement is made in the cloud platform, which establishes a power grid linkage early warning mechanism based on a power grid linkage early warning rule base and employs a risk correlation assessment algorithm, as shown in the following formula: ; Where: R is the interlocking risk value of the fuse operation; Tr is the real-time temperature rise anomaly coefficient of the fuse; Lf is the continuous overload coefficient of the circuit load; Vd is the voltage sag fluctuation coefficient of the power grid; α, β, and γ are preset weight coefficients, and α+β+γ=1, which correspond to the influence weight of temperature rise, load, and voltage parameters on fault risk, respectively; the cloud platform triggers composite early warning based on the R value threshold to realize the prediction of system-level risks from single-point anomalies.

[0010] A further improvement is made in that the environmental fingerprint security authentication mechanism adopts an environmental fingerprint matching algorithm, the formula of which is as follows: ; Where: M is the matching degree between the real-time environmental fingerprint and the cloud-registered fingerprint; k is the total number of environmental feature dimensions, including the noise spectrum dimension of 100Hz~8kHz and the light intensity dimension; Xi is the i-th cloud-registered environmental feature value; Yi is the i-th terminal real-time sampled environmental feature value; when M≥0.9, the device authentication is deemed successful.

[0011] A further improvement lies in the fact that the environmental fingerprint security authentication mechanism is equipped with a spatiotemporal feature adaptive calibration algorithm, which dynamically corrects the real-time sampled environmental feature values. The formula for the spatiotemporal feature calibration algorithm is as follows: ; Where: Yi' is the calibrated real-time environmental feature value, used for subsequent fingerprint matching and comparison; Yi is the i-th dimension original environmental feature value currently sampled by the terminal in real time; Xi is the device's initial registration standard environmental feature value stored in the cloud; δ is the real-time feature update weight coefficient, with a value range of 0.7~0.9. The larger the weight, the higher the proportion of real-time sampled features, adapting to the characteristics of slow environmental changes; by dynamically optimizing environmental fingerprint features through this algorithm, while preserving the spatiotemporal uniqueness of the device's exclusive environment, it avoids authentication failure caused by environmental micro-disturbances, ensuring the long-term communication authentication stability of the device.

[0012] Further improvements include: the LoRa Mesh self-organizing network module supports multi-hop route self-healing; when a node's direct link is interrupted, adjacent nodes detect the anomaly through a link quality detection mechanism and automatically switch to the optimal multi-hop forwarding path; the entire network adopts a unified gateway time synchronization sampling mechanism, and a distributed time calibration algorithm is configured to achieve accurate alignment of sampling times for all nodes. The time synchronization calibration algorithm formula is as follows: ; Where: Tn is the standard sampling timestamp after calibration of the nth monitoring node; Tg is the globally unified reference timestamp issued by the edge gateway; ΔT is the inherent static deviation of the node's local clock, which is obtained through factory calibration and fixed in the node's local storage; L is the data forwarding hop count, i.e. the number of LoRa Mesh route hops from the current node to the gateway; Td is the average transmission delay of a single-hop link, which is a fixed parameter measured and calibrated across the entire network; the system completes the timestamp correction of all distributed nodes based on this algorithm, keeping the timing synchronization error of the entire network ≤10ms; at the same time, a signal threshold discrimination mechanism is used to detect the network link quality. When the signal-to-noise ratio of node communication is lower than the preset threshold, route reconstruction is immediately triggered to achieve network self-healing.

[0013] The beneficial effects of this invention are as follows: 1. This invention adopts a three-level sleep low-power sensing mechanism to achieve dynamic and fine-grained control of the power consumption of the monitoring node. Under normal conditions, the device only retains the low-power detection circuit for operation. The hierarchical working mode of timed simplified sampling and fault event-triggered full sampling minimizes the loss of ineffective power consumption. Combined with the dual power supply scheme of passive power extraction from current transformer and button battery, the average operating current of the whole machine is less than 10μA and the maintenance-free life of more than 5 years is achieved. This greatly reduces the deployment and maintenance costs of power grid fuse monitoring equipment and is perfectly adapted to unattended monitoring scenarios such as outdoor and remote sites.

[0014] 2. This invention, based on a fault identification method that matches fractal dimension with fuse feature spectrum, achieves refined classification and identification of fuse faults. By collecting milliohm-level resistance, micro-temperature difference, and high-frequency transient action signals, and combining the fractal dimension algorithm to quantify fault waveform characteristics, it accurately distinguishes three operating conditions: instantaneous short-circuit fast trip, thermal accumulation overload fuse, and mechanical vibration malfunction. This enables low-cost terminals to have high-end waveform analysis fault identification capabilities, significantly improving the accuracy and professionalism of power grid fuse fault diagnosis, and providing reliable data support for fault tracing and responsibility determination.

[0015] 3. The two-way linkage early warning mechanism between fuses and power grid operation status constructed in this invention realizes a technological leap from single-point fault detection to system-level risk prediction. By establishing a multi-power grid parameter association rule library through a cloud platform, for critical fault states where fuses fail to blow due to abnormal temperature rise, the mechanism analyzes multi-dimensional data such as circuit load, circuit breaker operation, and voltage fluctuations to identify potential cascading fault risks in advance, push composite early warning information, effectively avoid safety accidents such as power grid overload and cascading tripping, and improve the stability of power grid operation and the ability to prevent and control risks in advance.

[0016] 4. This invention establishes an interactive security authentication mechanism based on environmental perception. It relies on the device's built-in microphone and light intensity sensor to collect environmental noise and illumination characteristics to generate a unique environmental fingerprint. No manual key configuration or additional hardware is required, enabling authentication upon device deployment. It is suitable for large-scale power grid monitoring equipment deployment scenarios. At the same time, the environmental fingerprint has spatiotemporal uniqueness, which can effectively resist device impersonation and unauthorized access. The authentication success rate exceeds 98%, and the authentication time is less than 2 seconds. It takes into account the advantages of convenient deployment, operational security and low cost.

[0017] 5. This invention adopts a LoRa Mesh low-power self-organizing network topology and a unified time synchronization sampling mechanism across the entire network. It features multi-hop forwarding between nodes, automatic optimal routing, and link self-healing functions, ensuring the stability of data transmission in complex power grid environments and avoiding communication interruptions caused by obstructions and hardware failures. The unified timestamp alignment sampling across the entire network ensures the temporal consistency of distributed node monitoring data, providing an accurate time-series data foundation for cross-node fault location and fault causal correlation analysis, and significantly improving the overall collaborative analysis capability of the power grid distributed monitoring system. Attached Figure Description

[0018] Figure 1 This is a diagram illustrating the composition of the present invention. Detailed Implementation

[0019] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0020] Example 1 according to Figure 1 As shown, this embodiment proposes a distributed fuse status cloud monitoring system based on low-power IoT. Addressing the needs for low-power operation and long-term battery life, it implements a three-level dynamic power consumption adjustment mode and a dual power supply scheme. At the hardware level, the monitoring node is equipped with a milliwatt-level threshold detection circuit, a multi-sensor acquisition module, and a LoRa low-power communication module, configured with a miniature current transformer and a CR2450 button battery to form a dual power supply system. At the software level, three levels of operating logic are set: 1) Deep sleep state: Under normal conditions, high-speed sampling, data processing, and high-frequency communication functions are disabled, only the temperature, vibration, and ultraviolet arc threshold detection circuits are maintained to monitor abnormal trigger signals in real time; 2) Timed wake-up state: A fixed 15-minute cycle is set, and the node automatically wakes up to complete one simplified parameter sampling and short frame data upload, with a single working time ≤200ms; 3) Event-triggered state: When a temperature change ≥5℃ / s, vibration amplitude exceeding a preset threshold, or ultraviolet arc signal intensity exceeding the standard is detected, the full sampling mode is immediately triggered, initiating multi-sensor synchronous high-speed acquisition and fault feature calculation.

[0021] This embodiment uses the power consumption calculation formula: Where: Iavg is the average operating current of the whole machine, Is is the sleep state operating current, Ts is the sleep duration, Iw is the timed wake-up operating current, Tw is the timed working duration, If is the fault full sampling operating current, Tf is the full sampling working duration, and Ttotal is the total monitoring duration. Actual measurements show that under normal conditions, Is=2μA, timed wake-up Iw=80μA, fault full sampling If=500μA, and the average daily fault trigger duration is ≤10min. The calculated average operating current of the whole machine is Iavg=8.6μA, which is lower than the 10μA design threshold. Under these conditions, the battery capacity can achieve 5.2 years of continuous operation without the need for on-site maintenance or replacement.

[0022] Example 2 according to Figure 1 As shown, this embodiment proposes a distributed fuse status cloud monitoring system based on low-power IoT. Addressing the need for refined fuse fault identification, it implements a fractal dimension and fuse feature spectrum matching algorithm. Nodes collect milliohm-level resistance and micro-temperature difference data across the fuse in real time, continuously capturing high-frequency transient waveform signals at the moment of contact action. After denoising preprocessing of the collected waveform signals, the fractal dimension algorithm is used to... Calculate the feature dimension of the fault signal. In the formula, D represents the complexity of the fault signal, r is set as the signal sampling grid scale of 0.01s, and N(r) is the number of waveform coverage grids at the corresponding scale.

[0023] The calculated real-time fractal dimension D is compared with the three types of standard circuit breaker feature spectrum data pre-stored in the cloud, and a similarity algorithm is used. The matching and determination process is completed. Here, n represents 1000 high-frequency sampling points, Di is the real-time fractal dimension sequence, and D0i is the standard feature spectrum sequence. A fault match is considered valid when S ≥ 0.9: S matching the short-circuit feature spectrum indicates an instantaneous short-circuit fault; matching the overload feature spectrum indicates a thermal accumulation overload fuse fault; and matching the fluctuation interference feature spectrum indicates a mechanical vibration malfunction fault. Under actual operating conditions, the fault classification accuracy of this algorithm reaches 99.2%, and it can completely distinguish three easily confused fault types.

[0024] Example 3 according to Figure 1 As shown, this embodiment proposes a distributed fuse status cloud monitoring system based on low-power IoT. Addressing the need for power grid system-level risk prediction, a cloud platform-based association rule base and risk assessment model are built. The platform collects four core parameters: fuse temperature rise data, upstream circuit breaker action signals, circuit load current, and grid voltage fluctuations, constructing multi-dimensional association rules. For critical fault scenarios where the fuse temperature rises abnormally but fails to blow, a risk association degree assessment algorithm is activated. The weighting coefficients α=0.4, β=0.35, and γ=0.25 are set to conform to the weighting law of the cascading effects of power grid faults.

[0025] Tr is the temperature rise anomaly coefficient, which increases with higher temperature rises and ranges from 0 to 1; Lf is the load overload coefficient, which increases with higher overload ratios and ranges from 0 to 1; Vd is the voltage sag coefficient, which increases with larger voltage fluctuations and ranges from 0 to 1. A preset risk threshold R = 0.7 is used. When the calculated R ≥ 0.7, a cascading fault risk is identified. The system automatically correlates downstream circuit load operation data to generate a composite early warning message of "high-risk fuse + potential overload circuit" and pushes it to the operation and maintenance platform. In actual testing, overload fuse risk can be predicted 3-5 hours in advance, effectively avoiding cascading faults in the power grid.

[0026] Example 4 according to Figure 1 As shown, this embodiment proposes a distributed fuse status cloud monitoring system based on low-power IoT, addressing the security access requirements of large-scale devices by implementing an environmental fingerprint authentication mechanism without additional hardware support. During device deployment, the terminal collects ambient noise spectrum data from 100Hz to 8kHz using its built-in microphone and real-time light intensity data using a light intensity sensor. This multi-dimensional environmental feature integration generates a unique initial environmental fingerprint, which is automatically registered to the cloud database. Before device communication, the terminal resamples environmental features in real time and uses a fingerprint matching algorithm... Calculate the matching degree.

[0027] In the formula, k represents the 128-dimensional environmental feature dimension, Xi represents the cloud-registered feature value, and Yi represents the real-time sampled feature value. Authentication is successful if the matching degree M ≥ 0.9. Simultaneously, a calibration algorithm is used. The authentication accuracy was optimized by setting δ=0.8 to accommodate minor natural fluctuations in daylight and ambient noise levels. Actual testing showed that the device's average authentication time was 1.6 seconds, with a success rate of 98.7%, effectively avoiding the problems of cumbersome manual key configuration and device spoofing.

[0028] Example 5 according to Figure 1 As shown, this embodiment proposes a distributed fuse status cloud monitoring system based on low-power IoT. Addressing the requirements for stable distributed node networking and accurate time-series data, a LoRa Mesh low-power self-organizing network system is built. Multiple nodes within the same monitoring area form a multi-hop self-healing network, supporting bidirectional data forwarding between nodes. By default, the path with the optimal signal strength and minimum transmission latency is selected to upload data to the edge gateway. When a node experiences a direct link interruption due to obstruction or hardware failure, adjacent nodes monitor the link communication success rate in real time, automatically triggering route reconstruction and completing data forwarding through multi-hop backup links, achieving millisecond-level network self-healing.

[0029] The entire network adopts a unified time synchronization mechanism using edge gateways. The gateway sends a global timestamp once per second, and all monitoring nodes synchronously calibrate their sampling clocks after receiving the timestamp, ensuring that the sampling times of all distributed nodes are perfectly aligned, with a timing synchronization error of ≤10ms. A timing calibration algorithm eliminates data timing deviations, enabling cross-node data linkage analysis. This allows for precise location of faulty nodes, fault occurrence timing, and fault propagation paths within the region, providing complete timing data support for power grid fault tracing. The network communication success rate remains consistently above 99.5%.

[0030] Comparative example: Using a traditional fuse monitoring system as a comparison, the traditional system operates in a fixed power consumption mode without three-level sleep power consumption adjustment. The average operating current of the entire unit is ≥80μA, and the equipment's battery life is only 6-8 months, requiring frequent on-site battery replacements. It uses a single resistance continuity detection method without a fractal dimension fault identification algorithm, making it unable to distinguish between overload fuses, short-circuit instantaneous trips, and mechanical malfunctions, with a fault identification accuracy of only about 75%. It lacks a grid parameter linkage early warning mechanism, only triggering an alarm after the fuse has completely blown, and has no ability to predict risks in advance. It uses a manual key configuration authentication method, with a single device configuration time of ≥30s for large-scale deployment, an authentication success rate of 90%, and is prone to key leakage and device counterfeiting. It uses a single-point direct connection communication topology, lacks network self-healing capabilities, and has a communication interruption rate of ≥15% under obstruction conditions. It also lacks a unified timing synchronization mechanism, with data timing deviations of ≥200ms, and cannot support accurate cross-node fault analysis.

[0031] Validation data: Multi-dimensional performance comparison tests were conducted between this invention and traditional monitoring systems. The test environment was a 10kV outdoor power distribution network, with 30 distributed monitoring nodes deployed. The test ran continuously for 90 days, and the core verification data are as follows: 1) Power consumption and battery life: The average operating current of the whole device is 8.6μA, with a theoretical battery life of 5.2 years. Traditional devices have an average operating current of 82.3μA and a battery life of 7.2 months. The power consumption is reduced by 89.5% and the battery life is increased by 722%. 2) Fault identification: The average accuracy rate of the three types of fault identification in this invention is 99.2%, compared with 74.8% for traditional equipment, representing a 24.4% improvement in identification accuracy; 3) Early warning capability: This invention can predict faults in advance, with an average early warning time of 4.2 hours. Traditional equipment only provides alarms after the fact and has no predictive capability. 4) Equipment certification: The certification process of this invention takes 1.6 seconds, has a success rate of 98.7%, and requires zero manual configuration, while traditional equipment certification takes 32 seconds, has a success rate of 90.1%, and has high manual configuration costs; 5) Network performance: The network communication success rate of this invention is 99.6%, the timing synchronization error is 8ms, and it has network self-healing capability. Traditional equipment has a communication success rate of 84.7%, a timing deviation of 212ms, and no self-healing function.

[0032] Test data shows that the present invention has technical advantages in power consumption control, fault identification, risk warning, security authentication and network stability, and can effectively solve various defects of the existing technology.

[0033] This distributed fuse status cloud monitoring system based on low-power IoT adopts a three-level sleep low-power sensing mechanism, which realizes dynamic and fine-grained control of the power consumption of monitoring nodes. Under normal conditions, the device only retains the low-power detection circuit for operation. The hierarchical working mode of timed simplified sampling and fault event-triggered full sampling minimizes the loss of ineffective power consumption. With the dual power supply solution of passive power extraction from current transformer and button battery, the average operating current of the whole machine is less than 10μA and the maintenance-free life of more than 5 years is achieved. This greatly reduces the deployment and operation and maintenance costs of power grid fuse monitoring equipment and is perfectly adapted to unattended monitoring scenarios such as outdoor and remote sites. This invention, based on a fault identification method that matches fractal dimension with fuse feature spectrum, achieves refined classification and identification of fuse faults. By collecting milliohm-level resistance, micro-temperature difference, and high-frequency transient action signals, and combining the fractal dimension algorithm to quantify fault waveform characteristics, it accurately distinguishes three operating conditions: instantaneous short-circuit instantaneous tripping, thermal accumulation overload tripping, and mechanical vibration malfunction. This enables low-cost terminals to possess high-end waveform analysis fault identification capabilities, significantly improving the accuracy and professionalism of power grid fuse fault diagnosis, and providing reliable data support for fault tracing and responsibility determination. The invention also constructs a two-way linkage early warning mechanism between fuses and power grid operating status, achieving a technological leap from single-point fault detection to system-level risk prediction. Through a cloud platform, a multi-power grid parameter association rule library is established. For critical fault states where fuses fail to trip due to abnormal temperature rise, multi-dimensional data such as circuit load, circuit breaker operation, and voltage fluctuations are analyzed in conjunction with the mechanism to identify potential cascading fault risks in advance, push composite early warning information, effectively avoid power grid overload, cascading tripping, and other safety accidents, and improve the stability and proactive risk prevention capabilities of power grid operation. This invention establishes a non-interactive security authentication mechanism based on environmental perception. It utilizes the device's built-in microphone and light intensity sensor to collect environmental noise and illumination characteristics, generating a unique environmental fingerprint. No manual key configuration or additional hardware is required, enabling authentication upon device deployment. This is suitable for large-scale power grid monitoring equipment deployment scenarios. Furthermore, the environmental fingerprint possesses spatiotemporal uniqueness, effectively resisting device impersonation and unauthorized access. The authentication success rate exceeds 98%, and the authentication time is less than 2 seconds, balancing deployment convenience, operational security, and low cost. This invention employs a LoRa Mesh low-power self-organizing network topology and a unified time synchronization sampling mechanism across the entire network. It features multi-hop forwarding between nodes, automatic optimal routing, and link self-healing capabilities, ensuring the stability of data transmission in complex power grid environments and avoiding communication interruptions caused by obstructions and hardware failures. Unified timestamp alignment sampling across the entire network ensures the temporal consistency of distributed node monitoring data, providing a precise temporal data foundation for cross-node fault location and fault causal correlation analysis, significantly improving the overall collaborative analysis capabilities of the power grid distributed monitoring system.

[0034] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distributed fuse status cloud monitoring system based on low-power Internet of Things, comprising distributed monitoring nodes, LoRa Mesh self-organizing network modules, edge gateways, and a cloud platform, characterized in that: The distributed monitoring node is equipped with a three-level sleep low-power sensing mechanism, which dynamically adjusts power consumption through deep sleep, timed wake-up, and event-triggered full sampling, and adopts a dual power supply mode of passive power extraction and battery; the LoRa Mesh self-organizing network module realizes multi-hop data forwarding and network self-healing, and completes full network synchronous sampling in conjunction with the gateway for unified time synchronization. The edge gateway aggregates node time-series data and uploads it to the cloud platform; the cloud platform has built-in fault identification algorithms, power grid linkage early warning rule base and environmental fingerprint security authentication mechanism to achieve accurate identification of fuse faults, system-level risk prediction and equipment counterfeit-free authentication.

2. The distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 1, characterized in that: The distributed monitoring nodes collect milliohm-level resistance parameters and micro-temperature difference parameters at both ends of the fuse in real time. They also collect waveform data of the contact action moment through a high-frequency transient signal capture unit and combine the fractal dimension algorithm to remove environmental interference features and identify latent and instantaneous faults in the fuse.

3. The distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 2, characterized in that: The three-level sleep low-power sensing mechanism includes three working modes: normal deep sleep mode, in which the node only retains the milliwatt-level threshold detection circuit to work and continuously collect temperature, vibration and ultraviolet arc monitoring signals; timed wake-up sampling mode, in which the node automatically wakes up every 15 minutes to complete simplified sampling and short frame data reporting. Event-triggered full sampling mode: when the detected signal exceeds the preset threshold, multi-sensor synchronous high-speed full sampling and feature calculation are immediately initiated. The dual battery power supply mode uses a current transformer for passive energy extraction combined with button battery power supply. Under normal operating conditions, the average operating current of the whole machine is less than 10μA.

4. The distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 1, characterized in that: The power grid linkage early warning rule base integrates multiple parameters such as fuse status, upstream circuit breaker operation status, circuit load fluctuation, and grid voltage sag. The cloud platform automatically correlates and analyzes the downstream load operation status for critical fault states where the fuse temperature rises abnormally but does not blow, identifies cascading fault risks, and pushes composite early warning information for high-risk fuses and overload circuits.

5. A distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 4, characterized in that: The fault identification algorithm employs a fractal dimension calculation model to extract high-frequency transient signal features at the moment of fuse failure. The fractal dimension algorithm formula is as follows: ； Where: D is the fractal dimension of the high-frequency transient signal, which characterizes the complexity of the signal and the difference in fault characteristics; r is the grid division scale, which represents the signal spatial segmentation accuracy; N(r) is the minimum number of grids required to cover the signal waveform at scale r, which reflects the fluctuation characteristics of the signal waveform; the calculated fractal dimension is matched with the cloud-based fuse feature spectrum library to distinguish three fault states: instantaneous short-circuit fast-break, thermal accumulation overload fuse, and mechanical vibration malfunction.

6. A distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 5, characterized in that: The calculated fractal dimension is matched with the cloud-based circuit breaker feature spectrum library using a similarity evaluation algorithm, as shown in the following formula: ； Where: S is the matching similarity between real-time fault features and standard fuse feature spectra; n is the number of feature spectrum data sampling points; Di is the real-time signal fractal dimension of the i-th sampling point; D0i is the standard fractal dimension of the i-th sampling point in the cloud feature spectrum library; when S≥0.9, the fault type matching is deemed valid, and the fault is accurately classified.

7. A distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 6, characterized in that: In the cloud platform, a power grid linkage early warning mechanism is established based on a power grid linkage early warning rule base, and a risk correlation assessment algorithm is adopted, as shown in the following formula: ； Where: R is the interlocking risk value of the fuse operation; Tr is the real-time temperature rise anomaly coefficient of the fuse; Lf is the continuous overload coefficient of the circuit load; Vd is the voltage sag fluctuation coefficient of the power grid; α, β, and γ are preset weight coefficients, and α+β+γ=1, which correspond to the influence weight of temperature rise, load, and voltage parameters on fault risk, respectively; the cloud platform triggers composite early warning based on the R value threshold to realize the prediction of system-level risks from single-point anomalies.

8. A distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 1, characterized in that: The environmental fingerprint security authentication mechanism uses an environmental fingerprint matching algorithm, as shown in the following formula: ； Where: M is the matching degree between the real-time environmental fingerprint and the cloud-registered fingerprint; k is the total number of environmental feature dimensions, including the noise spectrum dimension of 100Hz~8kHz and the light intensity dimension; Xi is the i-th cloud-registered environmental feature value; Yi is the i-th terminal real-time sampled environmental feature value; when M≥0.9, the device authentication is deemed successful.

9. A distributed fuse status cloud monitoring system based on low-power Internet of Things as described in claim 8, characterized in that: The environmental fingerprint security authentication mechanism is equipped with a spatiotemporal feature adaptive calibration algorithm to dynamically correct the real-time sampled environmental feature values. The formula for the spatiotemporal feature calibration algorithm is as follows: ； Where: Yi' is the calibrated real-time environmental feature value, used for subsequent fingerprint matching and comparison; Yi is the i-th dimension original environmental feature value currently sampled by the terminal in real time; Xi is the device's initial registration standard environmental feature value stored in the cloud; δ is the real-time feature update weight coefficient, with a value range of 0.7~0.

9. The larger the weight, the higher the proportion of real-time sampled features, adapting to the characteristics of slow environmental changes; by dynamically optimizing environmental fingerprint features through this algorithm, while preserving the spatiotemporal uniqueness of the device's exclusive environment, it avoids authentication failure caused by environmental micro-disturbances, ensuring the long-term communication authentication stability of the device.

10. A distributed fuse status cloud monitoring system based on low-power Internet of Things according to claim 1, characterized in that: The LoRa Mesh self-organizing network module supports multi-hop route self-healing. When a node's direct link is interrupted, adjacent nodes detect the anomaly through a link quality detection mechanism and automatically switch to the optimal multi-hop forwarding path. The entire network adopts a unified gateway time synchronization sampling mechanism and is configured with a distributed time calibration algorithm to ensure accurate alignment of sampling times for all nodes. The time synchronization calibration algorithm formula is as follows: ； Where: Tn is the standard sampling timestamp after calibration of the nth monitoring node; Tg is the globally unified reference timestamp issued by the edge gateway; ΔT is the inherent static deviation of the node's local clock, which is obtained through factory calibration and fixed in the node's local storage; L is the data forwarding hop count, i.e. the number of LoRa Mesh route hops from the current node to the gateway; Td is the average transmission delay of a single-hop link, which is a fixed parameter measured and calibrated across the entire network; the system completes the timestamp correction of all distributed nodes based on this algorithm, keeping the timing synchronization error of the entire network ≤10ms; at the same time, a signal threshold discrimination mechanism is used to detect the network link quality. When the signal-to-noise ratio of node communication is lower than the preset threshold, route reconstruction is immediately triggered to achieve network self-healing.

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