Low-voltage electric appliance line emergency protection method and device based on multi-sensor monitoring

Abstract

The invention relates to the technical field of electrical safety, and discloses a low-voltage electric appliance line emergency protection method and device based on multi-sensor monitoring, and the device comprises a multi-sensor array, a main control board, an Internet of Things communication module, a power supply control module, and a protection housing. According to the method, environmental parameters such as rainfall, wind power and water level are collected in real time through multiple sensors, preprocessed by a main control board and then transmitted to an emergency management platform through an Internet of Things communication module, an AI algorithm is used for analyzing and evaluating the environmental risk level, and when the risk exceeds a safety threshold value, early warning information is automatically sent and a main power supply is cut off. And power supply is automatically recovered after the environment recovers to be normal. The problems that an existing protection device is slow in response and lacks preventive protection and remote monitoring capacity are solved, intelligent and precise protection of low-voltage electric appliance lines in atrocious weather is achieved, and safety and reliability of equipment operation are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of electrical safety technology, and in particular to an emergency protection method and device for low-voltage electrical circuits based on multi-sensor monitoring. It is mainly applied to outdoor power equipment such as electric bicycle charging piles and new energy vehicle charging piles, and is used to cope with the safety risks brought by disasters such as typhoons, thunderstorms, and flooding, so as to realize emergency protection of low-voltage electrical circuits. Background Technology

[0002] With the widespread adoption of new energy vehicles and electric bicycles, charging stations, as core supporting facilities, are deployed extensively in outdoor environments, inevitably facing threats from severe weather conditions such as typhoons, thunderstorms, strong winds, and flooding. Existing charging station protection devices primarily address electrical faults such as overcurrent and leakage, lacking specific protection measures against severe weather conditions, thus exhibiting significant technical deficiencies. Firstly, the response is passive. Existing protection devices mostly activate protection only after equipment experiences faults such as short circuits or leakage, which is a "post-event remedy" and cannot provide preventative protection before severe weather causes faults, which can easily lead to equipment damage and safety accidents. Secondly, there is insufficient monitoring capability. There is a lack of real-time monitoring methods for environmental parameters such as rainfall, wind force, and water level. Administrators cannot keep abreast of changes in the on-site environment and find it difficult to deploy protective measures in advance. Third, communication reliability is low. Existing devices mostly use a single communication method, which makes it impossible to transmit data and remotely control the device when network interruptions are caused by severe weather. Fourth, the protection strategy is too simplistic and cannot be adjusted according to the severity of the weather. This results in either over-protection that affects normal use or insufficient protection that cannot withstand risks. Fifth, existing protection devices have performance defects. For example, the overvoltage protector disclosed in Chinese patent application CN85200061U has a response time of several seconds, making it difficult to effectively deal with lightning overvoltage and switching surges. The structure of the varistor and the current-blocking inductor connected in series between the electrode leads in Chinese patent application CN87203635U may cause short circuits and fires in actual use.

[0003] Therefore, developing an emergency protection solution for low-voltage electrical circuits that can monitor environmental climate in real time, intelligently determine risk levels, and has reliable communication capabilities and differentiated protection strategies has become an urgent need to address safety hazards of outdoor charging piles and other equipment. Summary of the Invention

[0004] The main objective of this invention is to provide an emergency protection method and device for low-voltage electrical circuits based on multi-sensor monitoring, which can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The low-voltage electrical circuit emergency protection device based on multi-sensor monitoring consists of five parts: a sensor module, a main control board, a communication module, a power control module, and a housing structure. The functions of each part are as follows: The sensor module consists of an optical rain gauge, an ultrasonic anemometer and wind direction sensor, and an electrode-type water immersion sensor. The optical rain gauge, based on the principle of optical scattering, monitors rainfall intensity (accuracy 0.1 mm / h) and cumulative rainfall in real time. The ultrasonic anemometer and wind direction sensor calculates wind speed (range 0-60 m / s, accuracy 0.1 m / s) and wind direction (range 0-360°, accuracy 1°) by measuring the time difference of ultrasonic waves in the air. The electrode-type water immersion sensor monitors water level (accuracy 0.1 cm) and the rate of water level rise by detecting the conductivity between electrodes. The sensors are distributed and installed at the top, bottom, and surrounding key locations of the charging pile to ensure comprehensive data collection coverage.

[0006] Main control board: Utilizing a domestically produced 32-bit ARM architecture microprocessor and running the FreeRTOS real-time operating system, it boasts efficient data processing capabilities. The main control board is equipped with 256KB SRAM for temporary data storage and 1MB Flash memory for storing historical monitoring data, AI algorithm models, and system programs. An integrated real-time clock module supports adjusting protection thresholds based on different time periods (such as peak charging times during the day and off-peak times at night) to achieve differentiated protection. The main control board incorporates data preprocessing algorithms to filter, amplify, denoise, and extract features from the raw data collected by the sensors, providing high-quality data input for AI risk assessment.

[0007] IoT Communication Module: Utilizing the Quectel EC200S series IoT module, the core supports 4G Cat4 cellular network communication, offering high data transmission rates and wide coverage. It also integrates an NB-IoT low-power wide-area network module as a backup communication channel, automatically switching to the NB-IoT network when the 4G / 5G network is interrupted, ensuring normal transmission of monitoring data and control commands. The module has built-in GNSS positioning functionality, enabling real-time reporting of device location information, facilitating asset management and fault location for outdoor charging stations. The communication protocol adopts the Ito IoT protocol to ensure the security and compatibility of data transmission. To address potential reliability issues with the IoT communication module, a dual redundancy mechanism of "local independent protection + platform collaborative protection" is constructed. This mechanism includes the following: First, enhanced communication module reliability: Based on 4G / 5G+NB-IoT dual-mode communication, LoRa wireless communication is added as a backup channel, achieving "cellular network + LPWAN + LoRa" triple-mode redundancy. The module has a built-in signal strength monitoring and automatic switching algorithm. When the main network signal strength is ≤-100dBm or the data transmission timeout is ≥3 seconds, it automatically switches to the backup network with a switching time ≤1 second. A data caching mechanism is adopted, storing monitoring data locally (maximum storage of 100,000 records) during network interruption and automatically retransmitting it after network recovery to ensure no data loss. Second, local independent protection logic: The main control board has a built-in offline AI simplified model and a preset security threshold library. When the communication interruption exceeds 5 seconds, it automatically switches to local independent operation mode. The local model quickly determines risks based on core characteristic parameters (such as instantaneous wind speed ≥25m / s, water level ≥5cm, and current exceeding the rated value by 1.5 times) and directly triggers protection actions; preset thresholds support local storage and remote updates, and administrators can adjust offline protection parameters according to regional characteristics through the emergency management platform; thirdly, platform collaborative backup: the emergency management platform synchronizes the operating status and protection parameters of all devices in real time. When a device's communication is interrupted, the platform makes auxiliary judgments based on monitoring data of surrounding devices in the same area and regional meteorological early warning information, and issues emergency control commands (such as remote power cut-off) to the target device through LoRa or SMS modules, forming a dual guarantee of "local rapid response + platform remote backup".

[0008] Power control module: Utilizing a dual-relay redundancy design, two 40A silver alloy contact relays are connected in parallel. The silver alloy material possesses high arc resistance and low contact resistance, ensuring reliable power disconnection under high current conditions. The module incorporates overcurrent protection devices, rapidly triggering protection when the line current exceeds the rated value. An integrated arc-extinguishing circuit absorbs arc energy, preventing short circuits or equipment damage caused by arcing during power outages. The module supports two control modes: local automatic control (automatically triggered by the main control board based on risk level) and remote manual control (administrators send control commands via the emergency management platform), with seamless switching between the two modes. The power control module parameters are optimized to match line protection requirements, clearly defining core technical parameters and performance indicators: Firstly, precise matching of breaking capacity: The power control module adopts a dual-relay + high-speed circuit breaker combination design. The relays use silver-tin oxide alloy contacts (with arc resistance twice that of silver alloy). A single relay has a rated current of 60A, and with the dual parallel redundancy design, the rated breaking current reaches 120A, with a short-circuit breaking capacity ≥10kA (400V). The module features several improvements: 1) AC coverage, meeting the extreme fault current requirements of outdoor charging piles (maximum load current 100A); 2) Optimized action time: relay action time ≤10ms, circuit breaker short-circuit tripping time ≤2ms, and total response time of both ≤15ms, meeting the rapid disconnection requirements of short-circuit faults; 3) Enhanced protection functions: the module incorporates five-fold protection including overcurrent, overvoltage, undervoltage, leakage current, and overtemperature. Overcurrent protection thresholds can be set according to the rated current of the line (32A, 63A, 100A), and overvoltage thresholds are 420V and 420A respectively. The voltage threshold is 200V, the leakage protection threshold is 30mA (adjustable), and the over-temperature protection threshold is 90℃. The arc extinguishing circuit design is optimized, using a combination of metal grid and RC absorption circuit to extinguish the arc, increasing the arc extinguishing capacity to 20kA and completely eliminating the risk of short circuits caused by arcing during power outages. Fourth, the control mode is upgraded: local automatic control and remote manual control support priority settings. By default, local automatic control has higher priority than remote control (for rapid response to emergency faults). Administrators can temporarily adjust the priority through the platform (such as setting remote control to take priority during maintenance). The module has a built-in status feedback sensor to monitor the status of relay contacts and circuit continuity in real time. The feedback data error is ≤1%, ensuring that the protection action is executed properly.

[0009] Housing Structure: The main housing is made of glass fiber reinforced epoxy resin composite material, which improves weather resistance and impact resistance by 30% compared to polypropylene, and upgrades the protection level to IP67. The sensor housing is designed separately, with optical rain sensors and ultrasonic wind speed and direction sensors achieving an IP68 protection level, and electrode-type water immersion sensors achieving an IP69K protection level, capable of withstanding high-pressure spray cleaning and long-term immersion. The housing surface is coated with a fluorocarbon coating, with an anti-UV aging life of ≥10 years and salt spray corrosion resistance of ≥5000 hours. The connection cable between the sensor and the main control board uses a double-shielded cable (inner aluminum foil + outer braided mesh), improving the electromagnetic interference resistance to 100V / m. The cable interface is protected against... The water-resistant aviation connector (IP68 protection) features a built-in sealing ring and anti-pull clips; outdoor cabling is protected by conduit (PVC pipe + anti-corrosion coating), with a bending radius ≥10 times the cable diameter to prevent rainwater infiltration and mechanical damage; a new anti-electromagnetic interference design is added, with built-in metal shielding covers on the main control board and communication module, providing shielding effectiveness ≥80dB (10kHz-1GHz); for high-temperature environments, the heat dissipation channel of the outer shell adopts a "fin + guide groove" structure, increasing the heat dissipation area by 50%, and with the selection of low-power components, it ensures stable operation of the device under extreme conditions such as a wide temperature range of -40℃ to 85℃, relative humidity of 0-100% (including condensation), and wind speed ≤60m / s.

[0010] The emergency protection method for low-voltage electrical circuits based on multi-sensor monitoring is implemented using the aforementioned device, and the specific process is as follows: S1. System Initialization: After the device is powered on, it automatically performs a self-test, including sensor calibration, main control board program self-test, communication module network registration, and power control module status detection. After passing the self-test, it enters standby mode, waiting for data acquisition commands. S2. Data Acquisition: The main control board sends acquisition commands to the sensor modules according to a preset cycle (default 1 second / time, which can be adjusted through the management platform). Multiple sensors simultaneously collect environmental parameters such as rainfall, wind force, and water level, and transmit the raw data to the main control board. S3. Local data processing: The main control board filters (removes random interference signals), amplifies (enhances weak signals), and extracts features (extracts key features such as the rate of change of rainfall intensity, peak wind speed, and rate of water level rise) from the raw data to generate standardized data. S4. Risk Level Assessment: The AI ​​algorithm model built into the main control board receives standardized data and time series information, combines it with pre-stored historical meteorological data (such as local typhoon and rainstorm history records) and equipment failure data, performs feature fusion and pattern recognition, and outputs a risk level score of 0-10 (0-3 is low risk, 4-7 is medium risk, and 8-10 is high risk). At the same time, the main control board uploads the standardized data and risk level score to the emergency management platform through the communication module, and the platform can perform secondary verification in conjunction with regional meteorological early warning information. S5. Protect the implementation of decisions: S5.1 Low Risk (Level 0-3): Maintain normal power supply, continuously monitor environmental parameters, and report status data to the management platform every 5 minutes; S5.2, Medium Risk (Levels 4-7): Immediately send an early warning notification to the administrator (via SMS, APP push, etc.) to inform them of the on-site environmental risk situation. The administrator can remotely view real-time data and decide whether to take further measures. The device continuously monitors environmental changes at high frequency (0.5 seconds / time). S5.3, High Risk (Level 8-10): The system automatically sends an emergency warning to the administrator and triggers the power control module to cut off the main power supply to prevent accidents such as short circuits and leakage caused by rain, water accumulation, and strong winds. After the power is cut off, the system records the operation log (including power outage time, risk level, triggering conditions, etc.) and uploads it to the management platform. S6. Power restoration and status maintenance: After a power outage, the device continuously monitors environmental parameters, collects data every second and performs risk assessment. When the risk level drops to the low-risk range (level 0-3) for 3 consecutive minutes and the emergency management platform confirms that the regional weather warning has been lifted, the power supply to the protected equipment is automatically restored. After the power supply is restored, the device continues to monitor for 30 minutes. After confirming that the equipment is operating normally, the regular monitoring frequency is restored.

[0011] Compared with the prior art, the present invention has the following beneficial effects: Achieving preventative protection: By monitoring environmental parameters in real time through multiple sensors and combining AI algorithms to predict disaster and climate risks in advance, the power supply is proactively cut off before equipment failure occurs, shifting from "post-event remediation" to "pre-event prevention," effectively avoiding equipment damage and safety accidents; Accurate Risk Assessment: Employing AI algorithms based on convolutional neural networks, historical and real-time data are integrated to conduct risk assessments, generate quantitative risk levels, reduce protection errors caused by misjudgments of single parameters, and improve the accuracy of protection strategies. Reliable communication and control: It adopts a 4G / 5G+LPWAN dual-mode communication design to ensure that communication can still be maintained when the main network is interrupted due to severe weather; the power control module adopts a dual-relay redundant design, which supports local automatic control and remote manual control, improving the reliability of power failure operation; Convenient remote management: The emergency management platform enables real-time viewing of environmental data, receiving of risk warnings, remote control, and query of operation logs. Administrators can monitor the status of all outdoor equipment without on-site supervision, reducing management costs. Strong environmental adaptability: The outer shell of the device is made of corrosion-resistant composite material with IP65 protection level, and the internal structure is waterproof and heat dissipation structure. The core components adopt industrial-grade standards and can operate stably for a long time in harsh environments such as -40℃~85℃, high humidity, rainstorm, and sandstorm. Flexible differentiated protection: It supports adjusting protection thresholds according to time strategies, and can formulate personalized protection schemes based on the equipment usage and environmental characteristics at different times, so as to avoid over-protection affecting normal use. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating the workflow of the emergency protection method for low-voltage electrical circuits based on multi-sensor monitoring according to the present invention. Detailed Implementation

[0013] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0014] The emergency protection method and device for low-voltage electrical circuits based on multi-sensor monitoring first involves hardware installation and debugging: Sensor Installation: The optical rain sensor is installed on the top of the charging pile in an unobstructed location to ensure uninterrupted rainfall monitoring; the ultrasonic anemometer is installed on the top of the charging pile above surrounding obstacles to avoid obstruction of wind speed measurement; the electrode-type water immersion sensor is installed at the bottom of the charging pile near the ground and in areas prone to water accumulation around the charging pile to ensure timely monitoring of water accumulation; the sensors are connected to the main control board via shielded cables, and the cable interfaces are sealed with waterproof connectors; to address the insufficient correlation between sensor parameters and low-voltage line protection, a new core electrical parameter monitoring module is added to the existing environmental parameter monitoring, achieving comprehensive coverage of external environmental risks and internal line faults. A new current sensor, voltage sensor, leakage current sensor, and temperature sensor are added to monitor the real-time line current (range 0-120A, accuracy 0.1A), three-phase voltage (range 0-450V, accuracy 0.1V), leakage current (range 0-500mA, accuracy 1mA), and line connector temperature (range -40℃~150℃, accuracy 0.5℃), respectively. The sensors are installed using a combination of series and adhesive mounting. Current and voltage sensors are connected in series in the main circuit, leakage current sensors are mounted on the outside of the neutral and live wires, and temperature sensors are attached to key heat-generating components such as line joints and circuit breakers. Data from multiple sensors is fused according to a dual-dimensional approach of "environmental risk + electrical status" to construct a complete protection logic: when environmental parameters trigger a medium-risk scenario, the focus is on monitoring the trend of electrical parameter changes; when environmental parameters reach a high-risk level or electrical parameters exceed safety thresholds (such as current exceeding the rated value by 1.2 times, leakage current exceeding 30mA, or temperature exceeding 85℃), protection actions are triggered. Through this dual-dimensional monitoring, the system not only resists external threats such as natural disasters but also prevents internal faults such as line aging, short circuits, and leakage, filling the protection blind spots of pure environmental monitoring and achieving full-scenario safety coverage.

[0015] Main control board and communication module installation: The main control board and communication module are fixed in a dry area inside the charging pile. The antenna of the communication module is installed in an unobstructed position outside the charging pile to ensure good signal reception. The main control board and power control module are connected through industrial-grade terminals, and the wiring is wrapped with insulating tape to prevent short circuits. The main control board preprocessing technology details and data fusion strategy include the following steps: First, data preprocessing optimization: A combination algorithm of "Kalman filter + moving average" is adopted. Kalman filter quickly removes random noise from the sensor, and moving average (window size of 5 sampling points) smooths data fluctuations, with processing delay controlled within 10ms. For weak signals (such as leakage current, low wind speed), an instrumentation amplifier INA128 is used for amplification (amplification factor is adjustable, up to 10). First, the signal identification accuracy is ensured by 10 times. Second, the multi-sensor data fusion strategy is implemented: a spatiotemporal synchronization mechanism is established, with all sensors using the main control board's real-time clock as the reference, and the data acquisition time deviation is controlled within 1ms. A credibility weight allocation algorithm is adopted to dynamically adjust the weights according to the sensor's accuracy, installation location, and working status (e.g., increasing the weight of the rain sensor during heavy rain and increasing the weight of the current sensor during high load). Abnormal sensor data is automatically downweighted (weight ≤ 0.2) to avoid misjudgment caused by a single sensor failure. Third, the response speed is guaranteed: the main control board uses a dual-core ARM Cortex-M7 processor with a main frequency increased to 480MHz and a data processing throughput of 2MB / s. Preprocessing and AI risk assessment are executed in parallel, and sensor data acquisition and preprocessing are performed synchronously. The total response time from data acquisition to protection decision output is ≤ 50ms, which is more than 10 times faster than the existing technology, meeting the rapid protection requirements of sudden faults such as short circuits and lightning strikes.

[0016] Power control module installation: The power control module is connected in series in the main power line of the charging pile. The input end is connected to the power supply, and the output end is connected to the internal circuit of the charging pile. During installation, ensure that the relay contacts are in good contact and the arc extinguishing circuit is wired correctly. Debugging: After installation, power on the device. The system will automatically perform a self-test, checking the data from each sensor, communication stability, and the power control module's ability to respond to control commands via the emergency management platform. Simulate different environmental conditions (e.g., artificial spraying to simulate rainfall, fan blowing to simulate strong winds, and water injection to simulate water accumulation) to test the accuracy of the AI ​​algorithm's risk assessment and the execution of protection strategies. Once debugging is complete, it can be put into use. To address the issue of limited input data for the AI ​​algorithm, the input system of the risk assessment model can be reconstructed, integrating four core data categories: real-time monitoring data from multiple sensors (including environmental and electrical parameters); historical data (regional meteorological data for the past 5 years, equipment operation records, and fault maintenance files, updated quarterly); line topology information (static parameters such as line length, conductor cross-section, load type, and number of connectors); and dynamic load data (real-time charging power and load change rate). Optimize the AI ​​algorithm architecture by adopting a hybrid model of "convolutional neural network + gradient boosting tree": the convolutional neural network extracts temporal features (e.g., rainfall intensity change rate, current fluctuation period), the gradient boosting tree processes static features (e.g., line cross-section, load type), and the output layer combines the results of both to generate a risk score of 0-10 levels. A new load adaptation mechanism has been added, setting differentiated weight coefficients for different charging powers (e.g., 3.5kW for electric bicycles, 60kW for new energy vehicles) and different line conditions (no-load, full-load, overload). For example, temperature and current parameters are given higher weights under high load conditions, while the influence of environmental parameters is emphasized under low load conditions. The model training dataset is categorized and labeled according to three dimensions: "environmental scenario + load level + line status". The accuracy of the test set has been improved to over 98%, ensuring differentiated risk identification for different line states under the same environmental conditions and avoiding over-protection or delayed protection.

[0017] Next, the software algorithm is trained and updated: Algorithm Training: Collect meteorological data (including rainfall, wind speed, wind direction, water accumulation, etc.) and charging pile fault records for the target area over the past 5 years. Clean and label the data (labeling the environmental conditions corresponding to the faults) to construct a training dataset. Divide the dataset into training and testing sets in a 7:3 ratio for training and validation of the convolutional neural network model. Adjust parameters such as the number of network layers and neurons during training to ensure that the model's risk assessment accuracy is not less than 95%. Algorithm Updates: After model deployment, the latest meteorological data and equipment operation records are collected quarterly to fine-tune and update the model online, adapting to climate change and site environment changes (such as the impact of new buildings around charging stations on wind speed measurement), ensuring the continuous accuracy of risk assessment.

[0018] As the core of remote management, the emergency management platform has the following functions: Data monitoring: Real-time display of environmental data (rainfall, wind speed, water level, etc.), risk level, equipment operating status (power supply / power outage), geographical location, etc. for all connected devices; In order to solve the safety hazards of automatic power restoration, the principle of "detect before power supply" is strictly followed, and the power restoration logic is reconstructed: First, the environmental restoration judgment standard is quantified: the environmental parameters must meet the low risk threshold for 3 consecutive minutes (rainfall intensity ≤2mm / h, wind speed ≤6m / s, water level ≤1cm and no upward trend), and the emergency management platform will simultaneously lift the regional meteorological warning; Second, a secondary detection of line status is added: after the environment meets the standards, the device automatically starts the line insulation detection and continuity test process. The system employs a weak detection voltage (50V) output from the power control module to measure the line insulation resistance (safety threshold ≥1MΩ) and circuit continuity, confirming the absence of short circuits, insulation damage, or other issues. Third, a tiered power restoration strategy is implemented: after passing the test, the power control module provides pre-power (50% of the rated voltage for 5 seconds) to monitor line current and voltage stability. Once no abnormalities are detected, the system switches to full power supply, while continuous high-frequency monitoring continues for 30 minutes. If any parameter abnormalities occur during this period, the power is immediately cut off and reported to the platform. Fourth, a manual confirmation fallback mechanism is in place: for high-risk power outages, administrators must conduct secondary confirmation through the platform before automatic power restoration. Remote viewing of line detection data is supported, with options for "agree to restore" or "restore after on-site investigation," preventing secondary accidents caused by hidden line damage.

[0019] Early warning management: Receives early warning information sent by the receiving device, supports multiple notification methods such as SMS, APP push, and email, and can filter early warning information by device type, region, and risk level; Remote control: Administrators can send manual power-off and power-restoring commands to the device through the platform, and the execution results are fed back in real time after the commands are issued; Log query: Stores operation logs (power outage, power restoration, parameter adjustment, etc.), warning logs, and fault logs for all devices, and supports querying and exporting by time, device number, operation type, and other conditions; Data analysis: Statistical analysis of historical environmental data and risk level data is performed to generate trend charts (such as monthly rainfall intensity distribution, quarterly high-risk period statistics, etc.) to provide data support for equipment layout optimization and protection strategy adjustment.

[0020] 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 this invention is defined by the appended claims and their equivalents.

Claims

1. An emergency protection method for low-voltage electrical circuits based on multi-sensor monitoring, characterized in that, Includes the following steps: S1. Real-time collection of environmental data through a multi-sensor array, including rainfall intensity, cumulative rainfall, wind speed, wind direction, water level and water level rise rate; S2. The main control board receives data collected by the sensor array and performs preprocessing operations such as filtering, amplification, and feature extraction. S3. The main control board transmits the pre-processed monitoring data to the emergency management platform in real time through the Internet of Things communication module; S4, the emergency management platform and the AI ​​algorithm built into the main control board work together to conduct joint analysis by combining historical meteorological data and real-time monitoring data to assess the current environmental risk level; S5. When the risk level reaches the preset medium risk threshold, send an early warning notification to the administrator; when the risk level reaches the preset high risk threshold, immediately send an emergency early warning message to the administrator and automatically cut off the main power supply of the protected equipment. S6. Continuously monitor environmental parameters in real time. When the AI ​​algorithm identifies that the environmental conditions have returned to a safe range, it automatically restores the power supply to the protected equipment and reports the recovery status to the emergency management platform.

2. The emergency protection method for low-voltage electrical circuits based on multi-sensor monitoring according to claim 1, characterized in that: The multi-sensor array includes an optical rain sensor, an ultrasonic wind speed and direction sensor, and an electrode-type water immersion sensor. The sensors are installed in a distributed layout at key locations on the charging pile to ensure the comprehensiveness and accuracy of data collection.

3. The emergency protection method for low-voltage electrical circuits based on multi-sensor monitoring according to claim 1, characterized in that: The IoT communication uses Ito IoT technology, which supports dual-mode communication of 4G / 5G cellular networks and LPWAN low-power wide area networks. The main network and backup network automatically switch to ensure the continuity and reliability of data transmission.

4. The emergency protection method for low-voltage electrical circuits based on multi-sensor monitoring according to claim 1, characterized in that: The AI ​​algorithm adopts an abnormal weather recognition model based on convolutional neural networks. The input layer receives real-time data and time series information from sensors, the hidden layer performs feature extraction and pattern recognition, and the output layer generates a risk level score of 0-10. The model is updated online once a quarter based on the latest meteorological data and equipment operation records.

5. The emergency protection method for low-voltage electrical circuits based on multi-sensor monitoring according to claim 1, characterized in that: The power control module adopts a dual-relay redundant design, supports both remote manual control and local automatic control modes, and can reliably cut off a maximum current of 100A. It has built-in overcurrent protection and arc extinguishing circuit to avoid the risk of arcing during power outages.

6. An emergency protection device for low-voltage electrical circuits based on multi-sensor monitoring, characterized in that, Includes sensor module, main control board, communication module, power control module and housing structure; The sensor module further includes a rainfall monitoring unit, a wind monitoring unit, and a water immersion monitoring unit, which respectively employ an optical rainfall sensor, an ultrasonic wind speed and direction sensor, and an electrode-type water immersion sensor to achieve high-precision acquisition of environmental parameters. The main control board adopts a 32-bit ARM architecture microprocessor, runs the FreeRTOS real-time operating system, is equipped with 256KBSRAM and 1MB Flash memory, integrates a real-time clock module, supports adjusting the protection threshold according to time strategy, and realizes differentiated protection for different time periods. The communication module adopts Quectel EC200S series IoT module, which supports 4G Cat4 communication, GNSS positioning function and NB-IoT backup communication channel to realize two-way communication and location tracking with the emergency management platform; The power control module includes two 40A silver alloy contact relays (parallel redundant design), overcurrent protection devices, and arc extinguishing circuits to ensure safe power disconnection in emergency situations. The outer shell structure is made of corrosion-resistant composite material, with an internal waterproof sealing structure and heat dissipation channels, achieving an IP65 protection level, and is suitable for harsh outdoor environments such as high temperature, high humidity, heavy rain, and sandstorms.

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