Internet of Things Multi-Channel Earthquake Early Warning Emergency Linkage System

Abstract

The Internet of Things multi-channel earthquake early warning emergency linkage system belongs to the field of earthquake early warning technology. To address the deficiencies of the existing technology in terms of real-time performance, reliability, scenario coverage, and security, the present invention constructs a hierarchical architecture with linkage among the Internet of Things perception layer, edge computing layer, and cloud platform analysis module. The perception layer collaboratively collects data through multiple sensors to eliminate environmental interference and ensure high-precision seismic wave detection. The edge computing layer uses advanced algorithms to filter noise in real time and confirm seismic events through multi-dimensional comparison, significantly improving local processing efficiency and greatly reducing the cloud load. The cloud platform integrates multi-source data and combines machine learning models to dynamically optimize the early warning threshold, enhancing the accuracy of earthquake type classification and intensity prediction, achieving full-link optimization from data collection to intelligent decision-making. A multi-channel distribution module is designed to transmit instructions hierarchically according to priorities, ensuring full-scenario coverage in urban and rural areas through multiple communication technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake early warning, and particularly to an Internet of Things multi-channel earthquake early warning emergency linkage system. Background Art

[0002] Earthquake early warning is a disaster reduction technology that issues an alarm to potential disaster-affected areas after an earthquake occurs and before the arrival of destructive seismic waves, using the difference in the propagation speed of seismic waves and the fast transmission characteristics of electromagnetic waves. Through the strategy of "exchanging space for time", earthquake early warning cannot completely eliminate the earthquake risk, but significantly reduces the disaster losses. Its effectiveness depends on technical optimization, infrastructure improvement, and the enhancement of public emergency awareness.

[0003] Published patent: An earthquake early warning information broadcasting system (Publication No.: CN119049228A), belonging to the field of earthquake early warning, solves the problems of how to achieve early detection of seismic waves during an earthquake to extend the warning response time, and at the same time make a safer and more effective strategy for the emergency stop of elevators when only the arrival of P waves is identified; the system includes: an earthquake monitoring and analysis module analyzes and judges whether the operation of the elevators in the target building complex will be threatened by the earthquake according to the seismic parameter time series data detected by several earthquake monitoring devices arranged around the target building complex, and generates an early warning signal of the corresponding seismic wave type; the elevator earthquake early warning analysis module obtains the early warning signal, analyzes and obtains the operation control instructions of the corresponding elevators in the target building complex, and sends the obtained operation control instructions of the corresponding elevators to the elevator controllers of the corresponding elevators, and at the same time generates an earthquake early warning prompt signal and sends it to the voice broadcast device of the corresponding elevator.

[0004] It only focuses on the emergency control of elevators, does not involve the linkage response of urban lifeline systems such as subways and power, has a single scene coverage, cannot achieve multi-facility collaborative disaster prevention, lacks multi-sensor collaborative calibration, is vulnerable to environmental interference, the collected data has great limitations, needs to directly upload the original data to the cloud for processing, has a high delay, is difficult to meet the second-level early warning requirements, only relies on a single network channel to distribute early warnings, cannot cover areas without network, does not pre-store a local emergency instruction library, and cannot trigger key operations such as elevator stops when the network is disconnected, and the system reliability is insufficient. Summary of the Invention

[0005] The purpose of the present invention is to provide an Internet of Things multi-channel earthquake early warning emergency linkage system, which solves the problems of the deficiencies in real-time performance, reliability, scene coverage, and security of the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions: An Internet of Things multi-channel earthquake early warning emergency linkage system, including an Internet of Things perception layer, an edge computing layer, a cloud platform analysis module, a multi-channel distribution module, a dynamic fault tolerance module, an emergency linkage module, a self-checking and fault recovery module, and a user interaction interface. The Internet of Things perception layer is responsible for the acquisition and preliminary processing of seismic wave data. The edge computing layer performs noise filtering, waveform comparison, and strategy pre-computation locally to reduce the load on the cloud. The cloud platform analysis module realizes global optimization through machine learning and dynamic threshold adjustment and generates encryption parameters. The multi-channel distribution module ensures that early warning information reaches different scenarios through multi-path communication. The dynamic fault tolerance module monitors the network status and performs redundancy checks to ensure the reliability of the system under abnormal conditions. The emergency linkage module controls elevators, subways, and power equipment according to early warning instructions to achieve a fast and safe response. The self-checking and fault recovery module includes a health monitoring unit, a fault switching and recovery unit, and a log and feedback unit. The self-checking and fault recovery module automatically detects the health status of sensors every 24 hours and switches to a standby sensor in case of a fault. The user interaction interface provides a visual early warning map and emergency guidance, supporting functions such as voice broadcast, vibration reminder, and multi-language switching.

[0007] Further, the Internet of Things perception layer includes low-power seismic monitoring sensors and a data acquisition unit. The low-power seismic monitoring sensors include a three-axis accelerometer, a geomagnetic sensor, and a temperature compensation unit. The data acquisition unit includes a Zigbee module and an analog-to-digital converter. The three-axis accelerometer, the geomagnetic sensor, and the temperature compensation unit are all deployed on building foundations, bridge nodes, and subway tunnels. The three-axis accelerometer and the geomagnetic sensor are used to detect ground vibration and geomagnetic field changes in real time. The three-axis accelerometer captures three-dimensional vibration signals based on MEMS technology. The geomagnetic sensor monitors geomagnetic anomalies. The temperature compensation unit automatically calibrates the influence of ambient temperature on sensor data to ensure measurement accuracy. The data acquisition unit converts the analog signal output by the sensor into a digital signal through the analog-to-digital converter. The Zigbee module transmits the data to the edge computing layer through a low-power wireless protocol and supports self-organizing networks to expand the coverage range.

[0008] Furthermore, the edge computing layer includes a noise filtering module, a waveform comparison module, and a local cache unit. The noise filtering module includes an input layer, a convolutional neural network, and an output layer. The waveform comparison module includes an amplitude comparison unit, a frequency comparison unit, and a period similarity unit. The local cache unit includes an FPGA chip and a pre-computation strategy library. The noise filtering module uses a convolutional neural network to remove noise from the original seismic waveform. The input layer receives data, the convolutional layer extracts features, and the output layer marks valid signals. The waveform comparison module confirms the type of seismic wave through three comparisons. The amplitude comparison unit calculates the amplitude difference between adjacent sensors, the frequency comparison unit analyzes the main frequency difference, and the period similarity unit calculates the waveform cosine similarity. The FPGA chip stores the pre-computed elevator docking strategy and the prediction of the arrival time of seismic waves. The pre-computation strategy library caches the elevator docking plan and directly calls the local strategy to perform emergency operations when the network is disconnected.

[0009] Furthermore, the cloud platform analysis module includes a machine learning model, a dynamic threshold adjustment unit, and an encryption parameter generation unit. The machine learning model includes a support vector machine and a random forest model. The dynamic threshold adjustment unit includes a geological data interface, an amplitude threshold generator, and a frequency threshold generator. The encryption parameter generation unit includes an SM3 module and an SM4 key generator. The support vector machine and the random forest model fuse multi-source data, classify seismic types, and predict the affected range. The support vector machine classifies through a hyperplane, and the random forest votes through multiple decision trees. The warning thresholds of amplitude and frequency are dynamically adjusted according to geological data to adapt to different regional geological conditions. The national cryptographic SM3 hash algorithm is used to compress data, and the SM4 dynamically generates a 256-bit symmetric encryption key. The key synchronization is achieved through a blockchain smart contract to prevent man-in-the-middle attacks.

[0010] Furthermore, the multi-channel distribution module includes a communication unit and an offline device wake-up unit. The communication unit includes a Beidou transceiver, a 5G interface, and a digital broadcast transmitter. The offline device wake-up unit includes a Beidou instruction encoder and a rural emergency broadcast terminal. The communication unit covers network-free areas through Beidou satellite short messages. The 5G interface gives priority to pushing warnings to critical facilities. The digital broadcast transmitter issues evacuation guidelines to the public. The offline device wake-up unit wakes up the rural emergency broadcast terminal by sending specific frequency band instructions through Beidou satellites, and the terminal broadcasts pre-stored voice warnings in the offline state.

[0011] Furthermore, the dynamic fault tolerance module includes a network status monitoring unit, a redundancy check unit, and a local emergency instruction library. The network status monitoring unit includes a packet loss rate detector and a delay detector. The redundancy check unit includes a CRC32 checker and a data retransmission controller. The local emergency instruction library includes an elevator docking plan library and a subway braking instruction library. The network status monitoring unit real-time detects the packet loss rate and delay of the 4G / 5G network. After the packet loss rate and delay reach the set values, it triggers the communication channel switching. The redundancy check unit verifies the data integrity through the CRC32 check algorithm and automatically retransmits when the check fails to ensure the transmission reliability. The local emergency instruction library stores pre-calculated elevator docking plans and subway braking instructions and directly calls and executes them when the network is disconnected to avoid relying on the real-time network.

[0012] Furthermore, the emergency linkage module includes an elevator intelligent docking unit, a subway braking unit, and a power cut-off unit. The elevator intelligent docking unit includes a laser rangefinder and an elevator controller. The subway braking unit includes a braking signal generator and a hydraulic braking actuator. The power cut-off unit includes an intelligent circuit breaker and a power status feedback module. The elevator intelligent docking unit real-time locates the elevator position through the laser rangefinder, and the elevator controller moves the elevator to the nearest safe floor within 2 seconds. The subway braking unit triggers the hydraulic braking system through the braking signal generator and completes the emergency braking within 3 seconds. After receiving the instruction through the intelligent circuit breaker, the power cut-off unit cuts off the power supply in the high-risk area within 1 second, and the power status feedback module real-time transmits back the power grid status.

[0013] Furthermore, the system operation process is as follows:

[0014] S1. Data acquisition and preliminary processing

[0015] S1.1 Sensor network startup: The low-power seismic monitoring sensors deployed on the building foundation, bridge, and subway tunnel real-time collect ground vibration, geomagnetic field change, and environmental temperature data;

[0016] The sensor eliminates environmental interference through the temperature compensation unit to ensure data accuracy;

[0017] S1.2 Signal conversion and transmission: The data acquisition unit converts the analog signal output by the sensor into a digital signal through an analog-to-digital converter, and the digital signal is transmitted to the edge computing layer through the Zigbee module using a low-power wireless protocol, supporting self-organizing network to expand the coverage range;

[0018] S2. Edge computing layer real-time processing

[0019] S2.1 Noise filtering: The noise filtering module of the edge computing layer uses a convolutional neural network to eliminate noises such as traffic vibration and mechanical interference and retains the effective seismic signals;

[0020] S2.2 Waveform Comparison and Confirmation: The waveform comparison module performs three comparisons:

[0021] Amplitude comparison: Calculate the amplitude difference between adjacent sensors to exclude local interference;

[0022] Frequency comparison: Extract the main frequency through fast Fourier transform to determine whether it is a seismic event;

[0023] Period similarity comparison: Calculate the cosine similarity of waveforms to confirm the consistency of multi-sensor data;

[0024] If all three comparisons pass, it is determined to be a valid seismic wave;

[0025] S2.3 Local Policy Pre-storage: The local cache unit stores pre-computed emergency policies through the FPGA chip and directly calls them when the network is disconnected;

[0026] S2.4 Data Upload: The processed key data is uploaded to the cloud platform analysis module;

[0027] S3. Cloud Platform Global Optimization and Instruction Generation

[0028] S3.1 Multi-source Data Fusion: The machine learning model fuses sensor data, geological database, and historical seismic data to classify earthquake types and predict intensity and impact range;

[0029] S3.2 Dynamic Threshold Adjustment: Dynamically optimize the amplitude and frequency warning thresholds according to geological conditions to reduce the false alarm rate;

[0030] S3.3 Encryption and Instruction Generation: The encryption parameter generation unit encrypts data using the national cryptographic SM3 / SM4 algorithm and synchronizes the encryption keys to the edge nodes in combination with blockchain technology;

[0031] Generate global warning instructions;

[0032] S4. Multi-channel Redundant Distribution

[0033] S4.1 Priority Distribution Strategy: The communication unit pushes instructions according to priority:

[0034] First-level priority: Real-time transmission through the 5G private network;

[0035] Second-level priority: Release evacuation guidelines through digital broadcasting;

[0036] Third-level priority: Send encrypted short messages through Beidou satellites to wake up the emergency broadcast terminal;

[0037] S4.2 Wake-up of Offline Devices: The Beidou instruction encoder sends specific encoding instructions to wake up the rural emergency broadcast terminal and broadcast pre-stored voice warnings;

[0038] S5. Dynamic Fault Tolerance and Emergency Execution

[0039] S5.1 Network exception handling: The dynamic fault tolerance module monitors the network packet loss rate and latency in real time:

[0040] When the main network is abnormal, it automatically switches to the Beidou satellite or the local broadcast link;

[0041] The redundancy check unit ensures the integrity of the instruction through CRC32 check and automatic retransmission;

[0042] S5.2 Offline emergency response: The local emergency instruction library calls the pre-stored strategy to directly trigger the operation of elevators, subways, and power equipment;

[0043] S5.3 Terminal device linkage: The emergency linkage module performs operations after receiving instructions:

[0044] Intelligent elevator docking: The laser rangefinder locates the elevator position and docks it to a safe floor;

[0045] Subway hierarchical braking: Generates hierarchical braking signals to complete emergency braking;

[0046] Power cut-off: Cut off the power supply in high-risk areas and reserve the emergency power supply for hospitals and subways;

[0047] S6. User interaction and feedback closed-loop

[0048] S6.1 Multi-modal warning reach: The user interaction interface publishes warning information to the public through voice announcements, vibration reminders, and graphic push, and supports one-key navigation to the shelter;

[0049] S6.2 Visual map display: Dynamically renders the epicenter position, evacuation routes, and safe areas, and adapts to large-screen command centers and mobile terminals;

[0050] S6.3 User feedback collection: Users report their status through interface buttons or voice commands, and the data is encrypted and transmitted back to the cloud platform to optimize the strategy;

[0051] S7. System health management and maintenance

[0052] S7.1 Periodic self-check: The self-check and fault recovery module detects the status of sensors, networks, and hardware, and warns of low battery or overload;

[0053] S7.2 Fault automatic switching: When the sensor fails, it switches to the standby node, and when the process crashes, it automatically restarts the service;

[0054] S7.3 Log and optimization: Encrypt and store the fault logs, and push maintenance suggestions in combination with the machine learning model.

[0055] Compared with the prior art, the beneficial effects of the present invention are:

[0056] 1. The present invention constructs a hierarchical architecture with linkage among the Internet of Things perception layer, edge computing layer, and cloud platform analysis module. The perception layer collaboratively collects data through multiple sensors to eliminate environmental interference and ensure high-precision seismic wave detection. The edge computing layer uses advanced algorithms to filter noise in real time and confirm seismic events through multi-dimensional comparison, significantly improving local processing efficiency and greatly reducing the cloud load. The cloud platform integrates multi-source data and combines machine learning models to dynamically optimize the warning threshold, enhancing the accuracy of earthquake type classification and intensity prediction, and achieving full-link optimization from data collection to intelligent decision-making.

[0057] 2. The system of the present invention designs a multi-channel distribution module to transmit instructions hierarchically according to priorities, ensuring full-scenario coverage in urban and rural areas through multiple communication technologies. The dynamic fault tolerance module monitors the network status in real time, automatically switches to the backup link when the main network is abnormal, and ensures the integrity of instructions through the verification and retransmission mechanism. The local emergency instruction library pre-stores key strategies and directly calls and executes operations when the network is disconnected. Combining encryption technology to synchronize keys, seamless emergency response under network interruption is achieved.

[0058] 3. The emergency linkage module of the present invention achieves rapid response through precise terminal control, ensuring the safe docking of elevators, hierarchical braking of subways, and power cut-off in high-risk areas. The user interaction interface integrates dynamic map rendering, supports multi-modal warnings, and collects user feedback to optimize strategies. The self-check and fault recovery module periodically detects the system status, automatically switches to backup nodes or restarts services, forming a full-closed-loop earthquake-resistant system, significantly enhancing the real-time performance, reliability of the urban lifeline system, and the public's emergency response ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is the system architecture diagram of the present invention;

[0060] Figure 2 is the system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] To solve the problems of deficiencies in real-time performance, reliability, scenario coverage, and security, as Figure 1-2 shown, the following preferred technical solutions are provided:

[0063] The Internet of Things multi-channel earthquake early warning emergency linkage system includes an Internet of Things perception layer, an edge computing layer, a cloud platform analysis module, a multi-channel distribution module, a dynamic fault tolerance module, an emergency linkage module, a self-check and fault recovery module, and a user interaction interface. The Internet of Things perception layer is responsible for the acquisition and preliminary processing of seismic wave data. The edge computing layer performs noise filtering, waveform comparison, and policy pre-computation locally to reduce the cloud load. The cloud platform analysis module achieves global optimization through machine learning and dynamic threshold adjustment and generates encryption parameters. The multi-channel distribution module ensures that early warning information reaches different scenarios through multi-path communication. The dynamic fault tolerance module monitors the network status and performs redundancy checks to ensure the reliability of the system under abnormal conditions. The emergency linkage module controls elevators, subways, and power equipment according to early warning instructions to achieve a fast and safe response. The self-check and fault recovery module includes a health monitoring unit, a fault switching and recovery unit, and a log and feedback unit. The self-check and fault recovery module automatically detects the health status of sensors every 24 hours and switches to standby sensors in case of a fault. The user interaction interface provides a visual early warning map and emergency guidance, supporting functions such as voice broadcast, vibration reminder, and multi-language switching.

[0064] The Internet of Things perception layer includes low-power seismic monitoring sensors and a data acquisition unit. The low-power seismic monitoring sensors include a three-axis accelerometer, a geomagnetic sensor, and a temperature compensation unit. The data acquisition unit includes a Zigbee module and an analog-to-digital converter. As the data acquisition front-end of the earthquake early warning system, the Internet of Things perception layer shoulders the important task of real-time collecting seismic wave data and completing preliminary signal processing and transmission. By deploying a three-axis accelerometer, a geomagnetic sensor, and a temperature compensation unit, it realizes the rapid perception of earthquake events, achieves multi-sensor collaborative acquisition, temperature compensation calibration, efficient signal conversion and transmission. With high-precision acquisition and low-latency transmission of seismic wave data, it builds a data foundation for the real-time performance and reliability of the earthquake early warning system, and also provides high-quality raw data for subsequent edge computing and cloud analysis. The Internet of Things perception layer is composed of low-power seismic monitoring sensors and a data acquisition unit. The low-power seismic monitoring sensors are deployed at building foundations, bridge nodes, and subway tunnels. The three-axis accelerometer, based on MEMS technology, captures ground three-dimensional vibration signals in real time and outputs digital signals. The geomagnetic sensor uses the magnetoresistance effect to monitor the change in geomagnetic field strength and propagation direction, and assists in identifying geomagnetic anomalies. The temperature compensation unit monitors the ambient temperature in real time through a thermistor, corrects the sensor drift error according to the pre-stored calibration curve, and calibrates the interference of the ambient temperature on the sensor data to ensure the measurement accuracy. These components work together to detect ground vibration and geomagnetic field changes in real time, collect three-dimensional vibration signals and geomagnetic field data for identifying earthquake precursors. The analog-to-digital converter uses a successive approximation ADC, which can increase the sampling frequency and reduce the quantization error, and converts the analog signal output by the low-power seismic monitoring sensor into a digital signal. Subsequently, the digital signal is transmitted to the edge computing layer through the Zigbee module using a low-power wireless protocol, and the Zigbee module supports self-organizing networking, which can expand the coverage range and achieve efficient data transmission.

[0065] The edge computing layer includes a noise filtering module, a waveform comparison module, and a local cache unit. The noise filtering module includes an input layer, a convolutional neural network, and an output layer. The waveform comparison module includes an amplitude comparison unit, a frequency comparison unit, and a period similarity unit. The local cache unit includes an FPGA chip and a pre-computation strategy library. As the local data processing core of the earthquake early warning system, the edge computing layer performs key tasks such as real-time noise filtering, waveform comparison, and strategy pre-computation at the edge nodes close to the data source. Its core objective is to reduce the cloud load and improve the system response speed through local processing, ensuring that the earthquake early warning can achieve second-level real-time performance. The edge computing layer receives the original seismic wave data transmitted from the Internet of Things sensing layer, processes it through a series of operations, and then uploads the key data to the cloud platform to serve the subsequent global optimization analysis, realizing efficient, reliable, and low-latency earthquake early warning data processing, laying a solid foundation for cloud global optimization and emergency response. The noise filtering module uses a convolutional neural network model to remove noise from the original seismic waveform to improve the signal effectiveness. The input layer of this module receives the standardized waveform data. The convolutional neural network extracts the local features of the waveform through 3 convolutional kernels and uses the ReLU activation function to enhance the non-linear expression ability. The formula is: , is the original seismic wave waveform data transmitted from the Internet of Things sensing layer, is the 3 convolutional kernels, is the rectified linear activation function, is the classifier, is the filtered effective seismic signal. The output layer uses the Softmax classifier to label the effective signal, reducing the mis-deletion rate, and outputs the filtered effective seismic signal for use by the waveform comparison module. The waveform comparison module confirms the type and consistency of the seismic event through multi-dimensional comparison. Among them, the amplitude comparison unit calculates the amplitude difference between adjacent sensors to exclude local interference. The frequency comparison unit uses the fast Fourier transform to extract the main frequency. When the difference is less than 3Hz, it is determined as a seismic event. The formula is: , is the frequency eigenvalue obtained after the FFT calculation of the seismic wave signal, is the original seismic wave time-domain data collected by the Internet of Things sensing layer, is the number of data points for a single sampling, determined by the sensor sampling frequency, and is the signal length for frequency-domain analysis, is the frequency index, corresponding to different frequency components, , is the imaginary unit, used for complex number operations to realize the conversion from time domain to frequency domain. The period similarity unit calculates the waveform cosine similarity through a sliding window to confirm the consistency of multi-sensor data. The formula is: , is the seismic waveform vector collected by adjacent sensors in the same sliding window, is the waveform vector at The amplitude / frequency value at each time point corresponds to the discrete sampling point of the seismic wave signal. is the data length of the sliding window, used for local waveform consistency analysis, It is the waveform similarity value, ranging from [-1,1]. The closer the absolute value is to 1, the more similar the waveform is. If all three comparisons are passed, it is determined to be a valid seismic wave and the early warning process is triggered. The local cache unit is responsible for storing the pre-calculated emergency strategy to ensure that the system can still respond quickly in the case of network disconnection. The FPGA chip relies on the hardware parallel computing capability to pre-store the elevator parking strategy and the seismic wave arrival time prediction model to optimize the local decision-making speed. The pre-calculated strategy library caches the emergency instructions in extreme scenarios. When the network is disconnected, the local strategy can be directly called to ensure that elevators, subways and other equipment complete the corresponding operations before the S wave arrives.

[0066] The cloud platform analysis module includes a machine learning model, a dynamic threshold adjustment unit and an encryption parameter generation unit. The machine learning model includes a support vector machine and a random forest model. The dynamic threshold adjustment unit includes a geological data interface, an amplitude threshold generator and a frequency threshold generator. The encryption parameter generation unit includes an SM3 module and an SM4 key generator. As the intelligent decision-making center of the earthquake early warning system, the cloud platform analysis module undertakes the important responsibilities of global data integration, dynamic threshold optimization and secure encryption. It uses a machine learning model to integrate and analyze local sensor data, earthquake monitoring center signals and geological database data, dynamically adjusts early warning parameters and generates encryption instructions to ensure that early warning information can be accurately and securely distributed to terminals. It receives pre-processed data uploaded by the edge computing layer, and simultaneously accesses the authoritative data of the earthquake monitoring center. After a series of processing, it pushes the global warning instructions to key facilities such as subways, elevators, and electricity, as well as public terminals through a multi-channel distribution module, realizing the integration of intelligent decision-making, dynamic optimization, and security encryption, providing core computing power and security for the earthquake early warning system. The machine learning model uses support vector machines and random forest models to classify earthquake types and predict earthquake intensity and impact range to improve the accuracy of early warning. The support vector machine constructs a classification hyperplane based on the radial basis kernel function, which can distinguish earthquake wave types with high accuracy. By learning and training historical earthquake data, it can identify abnormal waveforms. The formula is: ,in, is the seismic wave eigenvector, are seismic wave samples to be classified, each of which contains multiple multidimensional features. is the feature vector of the sample, is the amplitude, is the main frequency, is the waveform period, is the kernel function parameter that controls the "tightness" of the feature space mapping. It is determined by training with historical earthquake data and is the square of the Euclidean distance between two-sample feature vectors, used to measure feature differences. The random forest model integrates 100 decision trees and judges earthquake intensity and propagation range by voting. It integrates multi-source data, including sensor frequency, waveform period, fault distribution, soil stiffness coefficient, regional geological classification, epicenter location, magnitude, focal depth, historical earthquake waveforms, earthquake influence range, and a frequency threshold adjusted in real time. The formula is: , is the input sample, including sensor data and geological parameters. is the th decision tree's prediction result for the sample . is the predefined earthquake intensity level or influence range category. is the indicator function. is the earthquake intensity / influence range finally predicted by the random forest. The dynamic threshold adjustment unit dynamically optimizes the warning threshold according to geological conditions to reduce the misjudgment rate. The geological data interface accesses the GIS geographic information system to obtain fault distribution and soil stiffness coefficient data, and maps these geological data to the warning area after parsing. The amplitude threshold generator sets a dynamic amplitude range according to different geological conditions, and the frequency threshold generator filters out non-seismic frequency band interference for different geological conditions, making the warning threshold adapt to different geological conditions. The encryption parameter generation unit is committed to ensuring the security of data transmission, preventing information from being tampered with and stolen. The SM3 module compresses the data using the hash algorithm to generate a 256-bit digest to ensure data integrity and can adapt to high-concurrency scenarios. The SM4 key generator dynamically generates a 256-bit symmetric encryption key, combines with the blockchain smart contract deployed based on the Ethereum private chain, and synchronizes the key to the edge nodes in the light node mode to form a blockchain network, greatly improving the key synchronization success rate. In the case of network disconnection, the preset key library is enabled to store the keys in the recent 24 hours to ensure the continuity of encryption.

[0067] The multi-channel distribution module includes a communication unit and an off-line device wake-up unit. The communication unit includes a Beidou transceiver, a 5G interface, and a digital broadcast transmitter. The off-line device wake-up unit includes a Beidou instruction encoder and rural emergency broadcast terminals. As the information transmission center of the earthquake early warning system, the multi-channel distribution module ensures that early warning information can reach different scenarios quickly and reliably by building redundant communication paths. It reasonably allocates communication resources according to the priority of the recipients and has the ability to wake up emergency broadcast terminals in areas without network through Beidou satellite instructions. After receiving the early warning instructions issued by the cloud platform, the module distributes them to different terminals according to the priority, realizing high-reliability, full-scenario, and low-latency early warning information transmission, and building a communication guarantee for earthquake emergency response. The communication unit transmits early warning information through multiple channels, aiming to achieve full coverage of urban and remote areas. The Beidou transceiver sends short messages containing encrypted instructions based on the RDSS protocol to cover areas without network and effectively prevent information from being tampered with. The 5G interface adopts the NSA / SA dual-mode networking mode to allocate exclusive bandwidth for key facilities with the first-level priority such as subways and hospitals, which can not only quickly push early warning instructions but also support real-time video linkage. The digital broadcast transmitter sends evacuation guides through FM radio, and can synchronously push voice broadcasts and text information to transmit early warning content to areas with the second-level priority such as schools and shopping malls. The off-line device wake-up unit is mainly responsible for waking up rural emergency broadcast terminals without network connection. After encoding the wake-up instructions into BCD format by the Beidou instruction encoder, they are sent to rural emergency broadcast terminals through Beidou satellites. These terminals support the off-line working mode and are equipped with built-in lithium batteries, widely covering rural administrative villages and natural villages. Once receiving the Beidou satellite instructions, the terminals will automatically broadcast the pre-stored voice early warnings to ensure that the rural areas, that is, the areas with the third-level priority, can also receive early warning information in a timely manner. When the main network fails, the multi-channel distribution module can automatically switch to the standby channel and trigger the off-line device wake-up function to ensure the stability of early warning information transmission.

[0068] The dynamic fault tolerance module includes a network status monitoring unit, a redundancy check unit, and a local emergency instruction library. The network status monitoring unit includes a packet loss rate detector and a latency detector. The redundancy check unit includes a CRC32 checker and a data retransmission controller. The local emergency instruction library includes an elevator docking plan library and a subway braking instruction library. As the core for ensuring the reliability of the earthquake early warning system, the dynamic fault tolerance module maintains the continuity of system functions in case of network anomalies or hardware failures. It ensures the accurate distribution and execution of early warning signals under extreme conditions by monitoring the network status in real time, using redundancy checks to ensure data integrity, and invoking locally stored instructions, achieving a three-in-one fault tolerance ability of network self-healing, reliable data, and offline response, providing all-weather and full-scenario stability guarantee for the earthquake early warning system. The network status monitoring unit uses distributed probe technology to collect network quality indicators in real time and continuously monitor the network communication status to determine whether to trigger the fault tolerance mechanism. This unit counts the packet loss ratio through the packet loss rate detector and issues an alarm once the trigger threshold is reached to identify network congestion or link interruption. At the same time, the latency detector measures the round-trip time of packets to determine the network response ability. When a high-latency scenario is detected, it promptly triggers the switching of communication channels. By continuously counting the packet loss in data transmission and combining with a dynamic threshold model to judge the network health, measuring the round-trip time of data transmission to identify network congestion or interruption, and timely triggering the switching between the primary network and the standby communication channel according to the detection results. The redundancy check unit adopts a hierarchical check mechanism to ensure the integrity and reliability of data transmission. The CRC32 checker uses cyclic redundancy check codes to verify the received data, filtering out transmitted errors or damaged data to ensure the integrity of data packets. Once the check fails, the data retransmission controller automatically retransmits the data packet until it is successfully received or the maximum retransmission times are reached, improving the delivery reliability of critical instructions, excluding errors during transmission, and ensuring that the instruction transmission is tamper-free and lossless. The local emergency instruction library stores pre-computed emergency instructions to ensure that the system can still perform critical operations when the network is completely interrupted. The elevator docking plan library stores the corresponding docking logic and priorities for different floor heights and can be directly invoked during network outages to drive the elevator to dock safely. The subway braking instruction library caches the train braking parameters and triggers the emergency braking of the hydraulic system during network interruption to prevent the train from derailing. Through the version synchronization mechanism, the local emergency instruction library pre-stores the cloud optimization strategy locally to ensure the continuity of emergency operations in offline scenarios.

[0069] The emergency linkage module includes an elevator intelligent docking unit, a subway braking unit, and a power cut-off unit. The elevator intelligent docking unit includes a laser rangefinder and an elevator controller. The subway braking unit includes a braking signal generator and a hydraulic braking actuator. The power cut-off unit includes an intelligent circuit breaker and a power status feedback module. As the terminal execution center of the earthquake early warning system, the emergency linkage module shoulders the important task of accurately converting early warning instructions into equipment control actions. By linking key facilities such as elevators, subways, and power, it aims to minimize the risk of earthquake disasters. It receives early warning instructions issued by the multi-channel distribution module, assigns execution priorities according to preset rules, sends control signals to terminal devices through standardized protocols, and monitors the execution status in real time. Once a device response times out or the execution fails, it automatically triggers an alternative plan. With standardized protocol integration and intelligent priority scheduling, it ensures that the earthquake early warning forms a seamless closed-loop from "information perception" to "terminal execution", significantly enhancing the seismic resilience of the urban lifeline system. The elevator intelligent docking unit is dedicated to ensuring that the elevator safely docks to the designated floor before the arrival of seismic waves to prevent passengers from being trapped. It monitors the elevator position and running status in real time, generates a docking strategy based on the predicted arrival time of seismic waves, and then drives the elevator control system to execute the operation. The laser rangefinder uses time-of-flight technology to accurately measure the distance between the elevator car and the floor to determine whether the elevator is between floors. If it is between floors, it triggers the emergency docking logic. After receiving the docking instruction, if the elevator is between floors, the elevator controller will control the motor to drive the car to move smoothly and quickly to the nearest safe floor, then lock the access control and cut off the operating power to avoid the risk of secondary shaking;If the elevator has stopped, the elevator door will be locked directly and the power supply will be cut off. At the same time, the stopping result will be fed back to the cloud platform to update the floor evacuation map. The subway braking unit is responsible for triggering the emergency braking of the subway train to prevent derailment or collision accidents. The unit predicts the arrival time of the seismic wave based on the propagation speed of the seismic wave and the current position of the train, and then calculates the braking distance and pressure parameters, generates graded braking instructions, performs deceleration or emergency braking operations in stages, drives the hydraulic system to apply braking force, and simultaneously monitors the brake disc temperature and pressure status, and links the platform broadcasting system to release evacuation information. The brake signal generator generates a PWM signal to accurately control the start-up timing and strength of the brake system to avoid passenger injuries or equipment damage due to emergency braking. The hydraulic brake actuator applies braking force through the hydraulic cylinder and monitors the brake disc temperature and pressure in real time. The power cut-off unit can cut off the power supply in high-risk areas and prevent electrical fires or equipment short circuits. Based on the GIS map, it can locate high-risk facilities near the epicenter, generate a list of power-off instructions, and remotely control the circuit breaker to open the gate, give priority to cutting off non-essential loads, while retaining the emergency power supply, and feed back the grid breakpoint information to the command center in real time to provide support for rapid power supply restoration after the disaster. After receiving the opening command, the electromagnetic release of the intelligent circuit breaker triggers the power-off operation. It supports remote reset and status feedback functions, can accurately cut off the target circuit, and retain the emergency power supply line. The power status feedback module monitors the voltage and current parameters of the grid through sensors, and encrypts these data and sends them back to the command center, providing real-time data basis for power supply restoration after the disaster. ;

[0070] The self-check and fault recovery module includes a health monitoring unit, a fault switching and recovery unit, and a logging and feedback unit. As the health management center of the earthquake early warning system, the self-check and fault recovery module achieves real-time fault perception, automatic switching, and recovery by periodically detecting the operating status of the system's hardware and software, ensuring the continuous and reliable operation of the system under abnormal conditions such as equipment failures and network interruptions. Through periodic self-checks, intelligent switching, and closed-loop feedback, it builds a full-link resilience capability from "fault perception" to "rapid recovery" for the earthquake early warning system, significantly enhancing the system's robustness in complex environments. The health monitoring unit detects the operating status of system components in real time through heartbeat signals and data consistency verification, identifying potential faults. It determines whether the sensors are malfunctioning, and simultaneously detects the battery power and temperature drift, issuing warnings for low battery or environmental interference, counting the network packet loss rate and latency, identifying communication link congestion or interruption, and monitoring the CPU and memory usage to prevent data processing delays caused by hardware overload, comprehensively ensuring the stable operating status monitoring of the system's core components. When a fault occurs, the fault switching and recovery unit automatically switches to standby resources to restore system functions. When a sensor fails, it switches to a standby sensor in the same area to maintain the continuity of data collection. When the main communication module fails, the Beidou satellite or local broadcast link is enabled to ensure uninterrupted communication. When a process crash is detected, the service is automatically restarted and the most recent cached state is loaded. When a memory leak occurs, resources are released and an alarm is triggered to notify the operation and maintenance personnel. Through these measures, various types of faults are quickly responded to, and the system is restored to normal operation. The logging and feedback unit is responsible for recording fault information and feeding it back to the cloud to assist in system optimization. It encrypts and stores data such as fault time, type, and recovery measures, facilitating root cause analysis afterwards. Based on historical fault frequencies and recovery efficiencies, it dynamically evaluates the health levels of devices and links, and combines with the machine learning model on the cloud platform to push maintenance suggestions, providing strong data support and decision-making basis for the continuous optimization of the system.

[0071] As the core of information display and user access in the earthquake early warning system, the user interface undertakes the important task of intuitively and efficiently transmitting early warning information, evacuation guidance, and system status to end users. At the same time, it supports user feedback and interactive operations. By receiving real-time early warning data sent from the cloud platform, it generates visual early warning information in combination with the GIS map, and dynamically updates evacuation routes, safe areas, and equipment status. It adapts to different user scenarios through various methods such as voice broadcast, vibration reminder, and graphic and text push, collects feedback from users on confirming receipt of instructions and transmits it back to the cloud platform for optimizing emergency strategies. With the design of the trinity of dynamic visualization, multi-channel alarms, and intelligent feedback, it constructs a complete link from early warning perception to user actions, significantly enhancing the public's emergency response ability and system transparency. It integrates GIS geographic information and real-time earthquake data to generate a visual early warning map, intuitively showing the earthquake impact range, evacuation routes, and the status of key facilities, being able to render the epicenter location, intensity distribution, and safe areas, dynamically mark evacuation routes, shelters, and risk areas, and support various interactive operations. It can adapt to the display of large-screen command centers and mobile terminals, facilitating users to obtain accurate information from different devices. By means of multiple channels, it ensures that users receive early warning information in a timely manner. In public places, it automatically switches the multilingual evacuation guidance broadcast by the public address system according to the regional population distribution. Personal devices receive voice prompts and support adaptive volume adjustment. Mobile terminals prompt emergency alarms through the vibration mode, and the vibration intensity is dynamically adjusted according to the earthquake intensity. At the same time, it pushes graphic and text cards containing key information, and the cards support one-click navigation to the nearest shelter, comprehensively ensuring that users in different scenarios can effectively receive early warnings. It collects user behavior data to optimize the system interaction experience. Users can report their status through the interface buttons, and the data is encrypted and transmitted back to the command center. It supports voice command input, generates emergency requests through NLP parsing. Users can also customize their alarm preferences, and the configured information can be synchronized in the cloud. In addition, it provides an accessible mode to adapt to the needs of elderly and visually impaired users, fully considering the usage experience of different user groups. It displays the system operation status and equipment health for the decision-making reference of operation and maintenance personnel. It integrates an equipment status dashboard, uses red, yellow, and green colors to mark the health levels, shows the execution progress of emergency operations, and supports manual intervention, facilitating operation and maintenance personnel to timely understand the system situation and take corresponding measures.

[0072] After receiving the original seismic wave data transmitted by the Internet of Things perception layer, the edge computing layer first preprocesses the data through a noise filtering module, uses a convolutional neural network model to identify and eliminate environmental noise, and retains the effective seismic signals. Subsequently, the waveform comparison module performs multi-dimensional analysis on the filtered data, including amplitude difference, frequency difference, and waveform period similarity comparison, to confirm the consistency of the seismic wave type and the event. After local processing, the edge computing layer uploads the verification results to the cloud platform analysis module to provide data support for global early warning decision-making. After receiving the comparison results uploaded by the edge computing layer, the cloud platform analysis module fuses multi-source data through a machine learning model, dynamically adjusts the early warning threshold, and generates global early warning instructions. After being encrypted by the encryption parameter generation unit, the instructions are sent to the multi-channel distribution module. According to the preset priority strategy, the multi-channel distribution module pushes the early warning instructions to different terminals: the first-priority instructions are transmitted in real time through the 5G private network; the second-priority instructions are released through digital broadcasting; the third-priority instructions reach offline devices through Beidou satellites to ensure full-scenario coverage. The dynamic fault tolerance module monitors the network communication status in real time. When the detected main network packet loss rate or delay exceeds the limit, it automatically switches to Beidou satellites or the local emergency instruction library. The redundancy check unit performs integrity verification on the transmitted data. If the verification fails, it triggers an automatic retransmission mechanism. When the network is completely interrupted, the local emergency instruction library directly calls the pre-stored strategy to drive the emergency linkage module to execute operations. After receiving the instructions, the emergency linkage module triggers the intelligent docking of elevators, the hierarchical braking of subways, and the power cut-off in high-risk areas, and encrypts and feeds back the execution status to the cloud platform to form a closed-loop disaster tolerance system.

[0073] The system operation process is as follows:

[0074] S1. Data acquisition and preliminary processing

[0075] S1.1 Sensor network startup: Low-power seismic monitoring sensors deployed on building foundations, bridges, and subway tunnels collect ground vibration, geomagnetic field changes, and environmental temperature data in real time;

[0076] The sensor eliminates environmental interference through the temperature compensation unit to ensure data accuracy;

[0077] S1.2 Signal conversion and transmission: The data acquisition unit converts the analog signal output by the sensor into a digital signal through an analog-to-digital converter, and the digital signal is transmitted to the edge computing layer through the Zigbee module using a low-power wireless protocol, supporting self-organizing network to expand the coverage range;

[0078] S2. Edge computing layer real-time processing

[0079] S2.1 Noise filtering: The noise filtering module of the edge computing layer uses a convolutional neural network to eliminate noises such as traffic vibration and mechanical interference, and retains the effective seismic signals;

[0080] S2.2 Waveform Comparison and Confirmation: The waveform comparison module performs three comparisons:

[0081] Amplitude comparison: Calculate the amplitude differences between adjacent sensors to exclude local interference;

[0082] Frequency comparison: Extract the main frequency through fast Fourier transform to determine whether it is a seismic event;

[0083] Period similarity comparison: Calculate the waveform cosine similarity to confirm the consistency of multi-sensor data;

[0084] If all three comparisons pass, it is determined as a valid seismic wave;

[0085] S2.3 Local Policy Pre-storage: The local cache unit stores pre-computed emergency policies through the FPGA chip and directly calls them when the network is disconnected;

[0086] S2.4 Data Upload: The processed key data is uploaded to the cloud platform analysis module;

[0087] S3. Cloud Platform Global Optimization and Instruction Generation

[0088] S3.1 Multi-source Data Fusion: The machine learning model fuses sensor data, geological database, and historical seismic data to classify seismic types and predict intensity and impact range;

[0089] S3.2 Dynamic Threshold Adjustment: Dynamically optimize the amplitude and frequency warning thresholds according to geological conditions to reduce the false alarm rate;

[0090] S3.3 Encryption and Instruction Generation: The encryption parameter generation unit encrypts data using the national secret SM3 / SM4 algorithm and synchronizes the keys to the edge nodes in combination with blockchain technology;

[0091] Generate global warning instructions;

[0092] S4. Multi-channel Redundant Distribution

[0093] S4.1 Priority Distribution Strategy: The communication unit pushes instructions according to priorities:

[0094] First-level priority: Real-time transmission through the 5G private network;

[0095] Second-level priority: Release evacuation guidelines through digital broadcasting;

[0096] Third-level priority: Send encrypted short messages through Beidou satellites to wake up the emergency broadcast terminal;

[0097] S4.2 Wake-up of Offline Devices: The Beidou instruction encoder sends specific encoded instructions to wake up the rural emergency broadcast terminal and broadcast pre-stored voice warnings;

[0098] S5. Dynamic Fault Tolerance and Emergency Execution

[0099] S5.1 Network anomaly handling: The dynamic fault tolerance module monitors the network packet loss rate and latency in real time:

[0100] When the main network is abnormal, automatically switch to the Beidou satellite or local broadcast link;

[0101] The redundancy check unit ensures the integrity of the instruction through CRC32 check and automatic retransmission;

[0102] S5.2 Offline emergency response: The local emergency instruction library calls the pre-stored policy to directly trigger the operation of elevators, subways, and power equipment;

[0103] S5.3 Terminal device linkage: The emergency linkage module executes operations after receiving instructions:

[0104] Intelligent elevator docking: The laser rangefinder locates the elevator position and docks it to a safe floor;

[0105] Subway hierarchical braking: Generate hierarchical braking signals to complete emergency braking;

[0106] Power cut: Cut off the power supply in high-risk areas and reserve the emergency power supply for hospitals and subways;

[0107] S6. User interaction and feedback closed-loop

[0108] S6.1 Multi-modal warning reach: The user interaction interface publishes warning information to the public through voice broadcast, vibration reminder, and graphic push, and supports one-key navigation to the shelter;

[0109] S6.2 Visual map display: Dynamically render the epicenter position, evacuation route, and safe area, and adapt to the large-screen command center and mobile terminals;

[0110] S6.3 User feedback collection: Users report their status through interface buttons or voice commands, and the data is encrypted and transmitted back to the cloud platform to optimize the strategy;

[0111] S7. System health management and maintenance

[0112] S7.1 Periodic self-check: The self-check and fault recovery module detects the status of sensors, networks, and hardware, and warns of low battery or overload;

[0113] S7.2 Fault automatic switching: Switch to the standby node when the sensor fails, and automatically restart the service when the process crashes;

[0114] S7.3 Log and optimization: Encrypt and store the fault logs, and push maintenance suggestions in combination with the machine learning model.

[0115] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0116] As described above, the above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. Internet of Things multi-channel earthquake early warning emergency linkage system, characterized in that, It includes an Internet of Things (IoT) perception layer, an edge computing layer, a cloud platform analysis module, a multi-channel distribution module, a dynamic fault tolerance module, an emergency linkage module, a self-check and fault recovery module, and a user interaction interface. The IoT perception layer is responsible for the acquisition and preliminary processing of seismic wave data. The edge computing layer performs noise filtering, waveform comparison, and strategy pre-computation locally to reduce the load on the cloud. The cloud platform analysis module achieves global optimization through machine learning and dynamic threshold adjustment and generates encryption parameters. The multi-channel distribution module ensures that early warning information reaches different scenarios through multi-path communication. The dynamic fault tolerance module monitors the network status and performs redundancy checks to ensure the reliability of the system under abnormal conditions. The emergency linkage module controls elevators, subways, and power equipment according to early warning instructions to achieve rapid and safe response. The self-check and fault recovery module includes a health monitoring unit, a fault switching and recovery unit, and a log and feedback unit. The self-check and fault recovery module automatically detects the health status of sensors every 24 hours and switches to standby sensors in case of faults. The user interaction interface provides a visual early warning map and emergency guidance, supporting functions such as voice broadcast, vibration reminder, and multi-language switching; The system operation process is as follows: S1. Data acquisition and preliminary processing S1.1 Sensor network startup: Low-power seismic monitoring sensors deployed on building foundations, bridges, and subway tunnels collect ground vibration, geomagnetic field change, and environmental temperature data in real time; The sensor eliminates environmental interference through a temperature compensation unit to ensure data accuracy; S1.2 Signal conversion and transmission: The data acquisition unit converts the analog signal output by the sensor into a digital signal through an analog-to-digital converter. The digital signal is transmitted to the edge computing layer through a Zigbee module using a low-power wireless protocol, supporting self-organizing network to expand the coverage; S2. Real-time processing by the edge computing layer S2.1 Noise filtering: The noise filtering module in the edge computing layer uses a convolutional neural network to eliminate noise from traffic vibration and mechanical interference and retain effective seismic signals; S2.2 Waveform comparison and confirmation: The waveform comparison module performs three comparisons: Amplitude comparison: Calculate the amplitude difference between adjacent sensors to exclude local interference; Frequency comparison: Extract the main frequency through fast Fourier transform to determine whether it is a seismic event; Period similarity comparison: Calculate the waveform cosine similarity to confirm the consistency of multi-sensor data; If all three comparisons pass, it is determined as an effective seismic wave; S2.3 Local strategy pre-storage: The local cache unit stores pre-computed emergency strategies through an FPGA chip and directly calls them in case of network disconnection; S2.4 Data upload: The processed key data is uploaded to the cloud platform analysis module; S3. Global optimization and instruction generation by the cloud platform S3.1 Multi-source data fusion: The machine learning model fuses sensor data, geological database, and historical earthquake data to classify earthquake types and predict intensity and impact range; S3.2 Dynamic threshold adjustment: Dynamically optimize the amplitude and frequency early warning thresholds according to geological conditions to reduce the false alarm rate; S3.3 Encryption and instruction generation: The encryption parameter generation unit encrypts data using the national cryptographic SM3 / SM4 algorithm and synchronizes the key to the edge node in combination with blockchain technology; Generate a global warning instruction; S4. Multichannel redundant distribution S4.1 Priority distribution strategy: The communication unit pushes instructions according to priority: First-level priority: Real-time transmission through the 5G private network; Second-level priority: Release evacuation guidelines through digital broadcasting; Third-level priority: Send encrypted short messages via Beidou satellite to wake up the emergency broadcast terminal; S4.2 Wake-up of offline devices: The Beidou instruction encoder sends encoded instructions to wake up rural emergency broadcast terminals and broadcast pre-stored voice warnings; S5. Dynamic fault tolerance and emergency execution S5.1 Network anomaly handling: The dynamic fault tolerance module monitors the network packet loss rate and latency in real time: When the main network is abnormal, automatically switch to the Beidou satellite or local broadcast link; The redundancy check unit ensures the integrity of the instruction through CRC32 check and automatic retransmission; S5.2 Emergency response to network disconnection: The local emergency instruction library calls the pre-stored strategy to directly trigger operations on elevators, subways, and power equipment; S5.3 Linkage of terminal devices: The emergency linkage module performs operations after receiving instructions: Intelligent elevator docking: The laser rangefinder locates the elevator position and docks it to a safe floor; Subway hierarchical braking: Generate hierarchical braking signals to complete emergency braking; Power cut-off: Cut off the power supply in high-risk areas and retain the emergency power supply for hospitals and subways; S6. User interaction and feedback closed-loop S6.1 Multi-modal warning reach: The user interaction interface publishes warning information to the public through voice announcements, vibration reminders, and graphic and text push, and supports one-key navigation to shelters; S6.2 Visual map display: Dynamically render the epicenter position, evacuation routes, and safe areas, adapting to large-screen command centers and mobile terminals; S6.3 Collection of user feedback: Users report their status through interface buttons or voice commands, and the data is encrypted and transmitted back to the cloud platform to optimize the strategy; S7. System health management and maintenance S7.1 Periodic self-check: The self-check and fault recovery module detects the status of sensors, networks, and hardware, and warns of low battery or overload; S7.2 Automatic fault switching: Switch to a standby node when the sensor fails, and automatically restart the service when the process crashes; S7.3 Logging and optimization: Encrypt and store fault logs, and push maintenance suggestions in combination with machine learning models.

2. The multi-channel earthquake early warning and emergency linkage system for the Internet of Things according to claim 1, characterized in that: The Internet of Things perception layer includes low-power seismic monitoring sensors and data acquisition units. The low-power seismic monitoring sensors include triaxial accelerometers, geomagnetic sensors, and temperature compensation units. The data acquisition units include Zigbee modules and analog-to-digital converters.

3. The multi-channel earthquake early warning emergency linkage system for the Internet of Things according to claim 1, characterized in that: The edge computing layer includes a noise filtering module, a waveform comparison module, and a local cache unit. The noise filtering module includes an input layer, a convolutional neural network, and an output layer. The waveform comparison module includes an amplitude comparison unit, a frequency comparison unit, and a period similarity unit. The local cache unit includes an FPGA chip and a pre-computation strategy library.

4. The multi-channel earthquake early warning emergency linkage system for the Internet of Things according to claim 1, characterized in that: The cloud platform analysis module includes a machine learning model, a dynamic threshold adjustment unit, and an encryption parameter generation unit. The machine learning model includes a support vector machine and a random forest model. The dynamic threshold adjustment unit includes a geological data interface, an amplitude threshold generator, and a frequency threshold generator. The encryption parameter generation unit includes an SM3 module and an SM4 key generator.

5. The multi-channel earthquake early warning and emergency linkage system for the Internet of Things according to claim 4, wherein: The multi-channel distribution module includes a communication unit and an offline device wake-up unit. The communication unit includes a Beidou transceiver, a 5G interface, and a digital broadcast transmitter. The offline device wake-up unit includes a Beidou instruction encoder and a rural emergency broadcast terminal.

6. The multi-channel earthquake early warning and emergency linkage system for the Internet of Things according to claim 1, characterized in that: The dynamic fault tolerance module includes a network status monitoring unit, a redundancy check unit, and a local emergency instruction library. The network status monitoring unit includes a packet loss rate detector and a delay detector. The redundancy check unit includes a CRC32 checker and a data retransmission controller. The local emergency instruction library includes an elevator docking scheme library and a subway braking instruction library.

7. The multi-channel earthquake early warning emergency linkage system for the Internet of Things according to claim 1, characterized in that: The emergency linkage module includes an elevator intelligent docking unit, a subway braking unit, and a power cut-off unit. The elevator intelligent docking unit includes a laser rangefinder and an elevator controller. The subway braking unit includes a braking signal generator and a hydraulic braking actuator. The power cut-off unit includes an intelligent circuit breaker and a power status feedback module.

Images