Distributed earthquake early warning cloud monitoring network system and method

Abstract

The invention relates to the technical field of earthquake early warning systems, in particular to a distributed earthquake early warning cloud monitoring network system and method, and the system comprises a plurality of base stations which are used for collecting earthquake wave signals and environment noise information; the collected signals are transmitted to a cloud center node through a wired / wireless communication network; the cloud center node is connected with the plurality of base stations; performing data preprocessing and protocol conversion based on the received data, and transmitting the processed monitoring data to a public cloud node; the public cloud node is connected with the cloud center node; the central node is connected with the public cloud node; calculating an epicenter position and an earthquake magnitude; when the pre-warning condition is met, the distributed earthquake pre-warning system is triggered, and a pre-warning signal is sent to the base station and the pre-warning information issuing module; the early warning information issuing module is connected with the central node; earthquake early warning information is issued to a target user through multiple communication channels, and a distributed acquisition architecture in which a near station base station and a far station base station are combined is adopted, so that the data transmission time delay is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake early warning systems, and in particular to a distributed earthquake early warning cloud monitoring network system and method. Background Art

[0002] In recent years, the disasters caused by frequent earthquakes have increasingly attracted global attention. As an important technical means to reduce the losses caused by earthquake disasters, the earthquake early warning system has gradually evolved from single-node monitoring to distributed architecture. In existing technologies, a variety of earthquake early warning systems have been studied and applied, but there are still many technical limitations, making it difficult to achieve a comprehensive balance between efficiency, real-time performance and reliability.

[0003] The most representative solutions in existing technologies include centralized earthquake early warning systems and distributed monitoring networks based on the Internet of Things (IoT). The centralized earthquake early warning system relies on a central node to uniformly process data collected from multiple seismic stations. Its structure is relatively simple. However, the system is overly dependent on the central node and is prone to processing bottlenecks when the data volume is large. At the same time, its data transmission path is single, resulting in large delays and uncertainties in the system when dealing with complex earthquake scenarios.

[0004] The distributed earthquake monitoring system based on the Internet of Things attempts to conduct distributed monitoring through wireless sensor networks (WSNs), using IoT devices to collect seismic wave signals and transmit warning information through low-power communication modules. However, the transmission range of this method is limited, and there is a lack of a unified central coordination node, resulting in insufficient data integration and analysis capabilities, and prone to false alarms and missed alarms in complex earthquake environments.

[0005] The closest solution in the prior art is the Chinese invention patent with the authorization publication number CN104077890B and the name of the distributed earthquake early warning cloud monitoring network system. It realizes distributed earthquake early warning through the cloud monitoring network. Its core idea is to use multi-point monitoring of seismic wave signals and generate dynamic seismic wave propagation maps in the cloud. However, this technology has the following specific problems: This method uses GIS to dynamically generate seismic wave propagation maps, but lacks the detailed integration and dynamic adjustment strategy of the monitoring data of far and near stations, and cannot meet the monitoring requirements in high real-time and high reliability scenarios. Although a distributed data acquisition mode is adopted, its cloud processing path design is relatively simple, especially for the integration and real-time sorting mechanism of multi-source data is not perfect, resulting in large data transmission and processing delays. This solution does not fully consider the impact of noise on data accuracy in the seismic wave signal acquisition and processing stage, lacks efficient noise filtering and buffering mechanisms, and increases the possibility of false alarms. Due to the complex data transmission path in the system, the time it takes for the warning signal to be transmitted to the target user exceeds 15 seconds, and it is impossible to provide sufficient response time within the critical time window. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention proposes a distributed earthquake early warning cloud monitoring network system and method, which fundamentally solves the technical bottlenecks of the existing technology in terms of real-time, reliability and applicability through the collaborative work of near-station base stations and far-station base stations, combined with a multi-level data processing architecture of public cloud nodes and central nodes.

[0007] The present invention proposes a distributed earthquake early warning cloud monitoring network system, comprising: Multiple base stations for: Collect seismic wave signals and environmental noise information; Transmit the collected signals to the cloud center node via wired / wireless communication network; The cloud central node is connected to the multiple base stations and is used to: Receiving monitoring data sent by the base station; Perform data preprocessing and protocol conversion based on the received data, and transmit the processed monitoring data to the public cloud node; The public cloud node is connected to the cloud center node and is used to: Aggregate monitoring data sent by multiple cloud center nodes; Partition, merge and sort the data according to the monitoring area; Construct a seismic wave reference sequence and determine whether the distributed earthquake early warning conditions are met; The central node is connected to the public cloud node and is used to: receiving a seismic wave reference sequence transmitted by the public cloud node; Analyze earthquake events based on multidimensional feature vectors and calculate epicenter location and magnitude; When the early warning conditions are met, the distributed earthquake early warning system is triggered to send an early warning signal to the base station and the early warning information release module; The early warning information release module is connected to the central node and is used to: Receiving the early warning signal sent by the central node; Earthquake warning information is released to target users through multiple communication channels, including mobile phone text messages, Internet broadcasts, and emergency broadcasts.

[0008] Preferably, the multiple base stations include: A near-station base station, used to directly transmit monitoring data to the public cloud node via a downlink channel; A remote base station, used to forward monitoring data to the public cloud node via the cloud central node; The near-station base station and the far-station base station both include: An acquisition control unit, used for acquiring seismic wave signals; A / D converter for converting analog seismic signals into digital signals; An encryption module, used for encrypting digital signals; The data transmission unit is used to transmit the encrypted monitoring data to the upper-level node via a wireless or wired network.

[0009] Preferably, the cloud central node includes: A data receiving module, used for receiving monitoring data sent by multiple base stations; A protocol conversion module, used to convert the monitoring data into a unified format; A data cache module, used for short-term storage of received monitoring data to smooth the data flow; The data forwarding module is used to transmit the processed monitoring data to the public cloud node.

[0010] Preferably, the public cloud node further comprises: A data buffer management module is used to dynamically adjust the buffer size to adapt to data flow fluctuations; A signal sorting module, used to sort the monitoring data based on timestamps; Seismic wave construction module, used to construct seismic wave reference sequence and determine whether the distributed earthquake early warning conditions are met; The partition merging module is used to integrate data from multiple base stations according to the monitoring area.

[0011] Preferably, the central node further comprises: A feature extraction module is used to extract peak values, main frequencies and time features from a seismic wave reference sequence; A similarity calculation module is used to construct a similarity matrix between base stations based on seismic wave characteristics; The epicenter positioning module is used to determine the epicenter location by combining the base station location information and the similarity matrix; The magnitude calculation module is used to estimate the magnitude of earthquake events.

[0012] Preferably, the warning information release module includes: Multi-channel communication unit, used to release information through SMS, WeChat, and broadcast; The priority scheduling module is used to release information to users in high-risk areas in priority based on their geographic location and warning level.

[0013] Preferably, the system further comprises: Redundant backup module, used to automatically switch to the slave node when the master node fails; The fault detection module is used to monitor the system operation status in real time and trigger the fault recovery mechanism.

[0014] Preferably, an asynchronous communication mode is adopted between the modules; Data transmission uses TCP / IP protocol to ensure transmission reliability.

[0015] Preferably, the system further supports: Cross-regional coordination mechanism for sharing monitoring data among multiple distributed cloud nodes; Real-time update mechanism is used to dynamically adjust the warning threshold to adapt to the earthquake characteristics of different regions.

[0016] The distributed earthquake early warning cloud monitoring network method based on the system comprises the following steps: The base station collects seismic wave signals and environmental noise information and transmits them to the cloud center node; The cloud center node performs protocol conversion on the received data and forwards it to the public cloud node; The public cloud nodes partition, merge, and sort the data and construct a seismic wave reference sequence; The public cloud node transmits the seismic wave reference sequence to the central node; The central node calculates the epicenter location and magnitude based on the seismic wave characteristics; The central node issues a warning signal to the target user when the warning conditions are met.

[0017] The beneficial effects of the present invention are mainly reflected in the following aspects: 1. The present invention adopts a distributed collection architecture combining near-station base stations and far-station base stations to effectively reduce data transmission delay. At the same time, through the dynamic buffering mechanism of the public cloud node, the dynamic balance of data traffic is achieved to avoid the processing bottleneck of the centralized system.

[0018] 2. The modules are connected logically and physically to achieve efficient collaboration. For example, the central node uses multi-dimensional feature vectors to analyze the epicenter location and integrates the partitioned data of the public cloud nodes to achieve real-time and accurate earthquake event analysis.

[0019] 3. The contradiction between real-time performance and accuracy is resolved by introducing a dynamic threshold adjustment mechanism. For example, dynamic adjustment of noise filtering parameters enables the system to respond in a timely manner while avoiding false alarms.

[0020] 4. The present invention introduces data processing optimization strategies at multiple levels, such as noise filtering algorithms, seismic wave sorting mechanisms, and reference sequence construction methods. The superposition of these technologies enables the system to maintain high efficiency and stability in complex earthquake scenarios.

[0021] 5. The early warning information release module of the present invention significantly shortens the early warning reach time through a multi-channel communication mechanism. The entire process from epicenter positioning to early warning information release only takes about 6 seconds, which saves users valuable response time.

[0022] To sum up, the distributed earthquake early warning cloud monitoring network system and method of the present invention have comprehensively improved and optimized the main problems of the prior art, especially in terms of the real-time data processing, the high efficiency of module collaboration, and the accuracy of early warning delivery, reflecting significant technological progress and application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a logic block diagram of the whole system of the present invention.

[0024] Figure 2 It is a logic block diagram of multiple base stations of the present invention.

[0025] Figure 3 It is a logical block diagram of the cloud center node of the present invention.

[0026] Figure 4 This is a logical block diagram of the public cloud node of the present invention.

[0027] Figure 5 It is a logic block diagram of the central node of the present invention.

[0028] Figure 6 It is a logic block diagram of the early warning information release module of the present invention.

[0029] Figure 7 This is a graph showing how the accuracy of the present invention changes over time.

[0030] Figure 8 This is a graph showing the variation of data processing delay over time according to the present invention.

[0031] Fig. 9 This is a graph showing the false alarm rate of the present invention changing over time. DETAILED DESCRIPTION

[0032] See also Figure 1-9 The present invention proposes a distributed earthquake early warning cloud monitoring network system, including multiple base stations 1, a cloud center node 2, a public cloud node 3, a center node 4 and an early warning information release module 5.

[0033] Multiple base stations 1 are used to collect seismic wave signals and environmental noise information. Preferably, the base stations can be set as near-station base stations 11 and far-station base stations 12 according to the geographical distribution density, respectively adapting to monitoring needs of different ranges. The collected signals are transmitted to the cloud center node 2 via a wired or wireless communication network to ensure the stability and real-time performance of signal transmission.

[0034] The cloud center node 2 receives monitoring data sent by multiple base stations 1. Preferably, the monitoring data is received by the data receiving module 21, the data is standardized by the protocol conversion module 22, and the data is temporarily stored by the data cache module 23 to alleviate the impact of data traffic fluctuations. The processed data is transmitted to the public cloud node 3 by the data forwarding module 24.

[0035] The public cloud node 3 receives and aggregates the data sent by the cloud center node 2, and partitions, merges, and sorts the data according to the monitoring area to build a seismic wave reference sequence. At the same time, the public cloud node determines whether the distributed earthquake early warning conditions are met. When the conditions are met, the data is transmitted to the center node 4.

[0036] The central node 4 analyzes the seismic wave reference sequence, calculates the epicenter position and magnitude through the epicenter positioning module 41 in combination with the multi-dimensional feature vector, and triggers the distributed earthquake early warning system.

[0037] The warning information release module 5 receives the warning signal from the central node 4 and quickly transmits the warning information to the target users through various channels such as SMS, Internet broadcast, emergency broadcast, etc. Preferably, the information priority is adjusted based on the user's geographical location and the distance from the epicenter.

[0038] For example, the signal sampling frequency of multiple base stations 1 can be set to 100 Hz, and the signal transmission delay of the remote base station 12 does not exceed 50 ms to meet the real-time requirements. The protocol conversion module 22 of the cloud center node 2 can use the JSON format to standardize the data of multiple signals. The construction of the seismic wave reference sequence in the public cloud node 3 is realized by the following formula: , in, is the reference sequence, is the number of base stations, is the weight coefficient, For base station The acquisition signal.

[0039] In a specific embodiment of the present invention, the multiple base stations 1 of the present invention are further divided into a near-station base station 11 and a far-station base station 12. Among them: The near-station base station 11 directly transmits the monitoring data to the public cloud node 3 through the downlink channel without the need for intermediate node forwarding, which is suitable for areas close to the earthquake source. Preferably, the communication protocol of the near-station base station 11 can use TCP / IP to ensure data integrity.

[0040] The remote base station 12 forwards the monitoring data to the public cloud node 3 through the cloud center node 2, which is suitable for areas far away from the earthquake source. The remote base station 12 includes an acquisition control unit 121 for acquiring seismic wave signals; an A / D converter 122 for converting analog signals into digital signals; an encryption module 123 for encrypting data; and a data transmission unit 124 for transmitting data via wireless communication.

[0041] The acquisition control unit 121 of the remote base station 12 preferably uses a 24-bit high-precision analog-to-digital converter, and the signal acquisition dynamic range can reach 120dB. The encryption module 123 uses the AES-256 algorithm to ensure that the data is protected from tampering and leakage during transmission.

[0042] In a specific embodiment of the present invention, the cloud center node 2 further refines its functional modules, including: The data receiving module 21 is used to receive monitoring data sent by multiple base stations 1. Preferably, multiple protocols (such as HTTP, MQTT) are supported to ensure compatibility.

[0043] The protocol conversion module 22 converts the received data into a unified format, such as JSON or Protobuf.

[0044] The data cache module 23 adopts a ring buffer structure to ensure stability during data processing.

[0045] The data forwarding module 24 transmits the processed monitoring data to the public cloud node 3, using an asynchronous communication mechanism to improve efficiency.

[0046] For example, the buffer size of the data cache module 23 is set to 2MB, which can store at least 30 seconds of continuous monitoring data, and is suitable for earthquake event monitoring scenarios with high data traffic. The data forwarding module 24 supports fast transmission based on UDP, and the delay can be controlled within 10ms.

[0047] The present invention realizes comprehensive coverage and rapid response of distributed earthquake early warning through efficient collaboration of multiple base stations 1, cloud central node 2, public cloud node 3 and central node 4. At the same time, the multi-channel mechanism of the early warning information release module 5 ensures that the early warning information can quickly reach the target users. Through detailed module design, parameter optimization and algorithm support, the present invention significantly improves the real-time, reliability and applicability of the earthquake early warning system.

[0048] In a specific embodiment of the present invention, the public cloud node 3 of the present invention is further optimized, including a data buffer management module 31, a signal sorting module 32, a seismic wave construction module 33 and a partition merging module 34.

[0049] In a preferred embodiment of the present invention, the data buffer management module 31 dynamically adjusts the buffer size to adapt to the fluctuation of the monitoring data flow. For example, when the received monitoring data rate is higher than the preset value, the buffer can be automatically expanded to twice the original size to avoid data loss. The signal sorting module 32 sorts the monitoring data based on the timestamp to ensure the integrity of the data in time sequence processing. Preferably, the sorting algorithm is calculated using the following formula: , in, For base station timestamp, is the data generation time, is the transmission delay correction amount.

[0050] The seismic wave construction module 33 uses the sorted data to generate a seismic wave reference sequence for subsequent analysis of seismic events. The partition merging module 34 merges the monitoring data from different regions and generates regional partitions according to the monitoring range to ensure the spatial integrity of the seismic wave signal.

[0051] In practical applications, when there are multiple base stations in the area, for example, the number of near-station base stations 11 is 20 and the number of far-station base stations 12 is 30, the public cloud node 3 generates monitoring area data through the partition merging module 34. The seismic wave construction module 33 constructs reference seismic waves based on these data, and outputs the sorted data to the central node 4 for further processing.

[0052] In a preferred embodiment of the present invention, the central node 4 of the present invention realizes the analysis and processing of earthquake events through a feature extraction module 41 , a similarity calculation module 42 , an epicenter positioning module 43 and a magnitude calculation module 44 .

[0053] Preferably, the feature extraction module 41 extracts multi-dimensional features such as peak value, main frequency and time based on the received seismic wave reference sequence. The extracted features are used to construct a similarity matrix between base stations through the similarity calculation module 42, which is expressed by the following formula: , in, For base station and The similarity between and Base Station and The characteristic value of and are their respective means.

[0054] The epicenter location module 43 combines the seismic wave propagation time and the base station location to calculate the epicenter location using the least square method. The magnitude calculation module 44 calculates the magnitude of the earthquake event based on the following formula: , in, is the magnitude, is the seismic wave amplitude, is the epicenter distance, is the correction factor.

[0055] In a specific application, the feature extraction module 41 extracts the peak value from the monitoring data of 20 base stations. , main frequency The matrix calculated by the similarity calculation module 42 is This indicates that the number of base station pairs with a similarity higher than 0.9 is 50. The epicenter positioning module 43 calculates the epicenter position as (34.56°N, 118.24°E) based on this information, and the magnitude calculation module 44 obtains a magnitude of 6.8.

[0056] In a preferred embodiment, the warning information release module 5 of the present invention includes a multi-channel communication unit 51 and a priority scheduling module 52 .

[0057] The multi-channel communication unit 51 uses a variety of communication methods, such as SMS, WeChat, emergency broadcast, etc., to quickly transmit the warning information to the target user. Preferably, the unit automatically selects the fastest communication method based on the user's geographical location information and the epicenter location. For example, for users within 10 kilometers around the epicenter, emergency broadcast is used first.

[0058] The priority scheduling module 52 dynamically adjusts the order of sending warning information based on the magnitude and epicenter of the earthquake event. Specifically, the module 52 sets events with a magnitude greater than 6.0 as high priority and preferentially sends warning information to users in high-risk areas.

[0059] Assume that the epicenter of an earthquake is (35.67°N, 139.77°E), the magnitude is 7.2, and the number of high-risk users within a 10 km radius of the epicenter is 100,000. After receiving the warning signal from the central node 4, the priority scheduling module 52 first sends information to these high-risk users, and at the same time issues warnings to medium and low-risk areas within a 50 km radius through Internet broadcasting.

[0060] The system of the present invention further includes a redundant backup module 61 and a fault detection module 62 .

[0061] In the embodiment of the present invention, the redundant backup module 61 ensures that the system can automatically switch to the slave node to continue working when the master node fails through the master-slave node redundancy mechanism. For example, the running status of the master node performs a heartbeat detection every 5 seconds. When the detection fails for 3 consecutive times, the slave node automatically takes over the work.

[0062] The fault detection module 62 is used to monitor the system operation status in real time and detect indicators such as network delay, data packet loss rate, etc. Preferably, when it is detected that the network delay exceeds 100ms or the packet loss rate is higher than 5%, the module 62 automatically issues an alarm and starts the fault tolerance mechanism.

[0063] During an earthquake early warning process, the fault detection module 62 detected that the network delay of the public cloud node 3 reached 150ms, and the redundant backup module 61 immediately started the switching mechanism and took over the data processing work from the node to ensure the continuous operation of the system during the fault.

[0064] The present invention ensures that the distributed earthquake early warning cloud monitoring network system has high efficiency, reliability and rapid response capability by further optimizing the public cloud node 3, the central node 4, the early warning information release module 5 and the system reliability module.

[0065] The modules of the present invention use an asynchronous communication mechanism and realize data transmission through the TCP / IP protocol. In a preferred embodiment of the present invention, the data flows from the base station 1 to the cloud center node 2, further to the public cloud node 3, and finally to the center node 4. The data flow is realized by combining the request-response mechanism and the push subscription mechanism.

[0066] Preferably, the asynchronous communication mechanism allows the separation of data reception and processing, thereby improving the system's processing capabilities in high data traffic scenarios. For example, in an area monitoring high-frequency seismic activity, each base station 1 can collect about 100 data packets per second, and the asynchronous mechanism ensures that all data can be accurately forwarded to the upper node in time sequence without affecting the overall transmission due to the delay of a single node.

[0067] In data transmission, TCP / IP protocol is used to ensure the reliability and integrity of data. For example, base station 1 sends data to cloud center node 2 through TCP / IP protocol. When data packets are lost in the network channel, the protocol can automatically retransmit to ensure the accuracy of the data.

[0068] In actual operation, for example, the data transmission frequency of the near-station base station 11 is set to 10 times per second, and the data transmission frequency of the far-station base station 12 is set to 5 times per second. The asynchronous communication mechanism enables the public cloud node 3 to receive and process the monitoring data from 50 base stations in sequence, and the transmission delay is controlled within 100ms. At the same time, the reliability of the TCP / IP protocol ensures that the data of all base stations can be transmitted to the central node 4 intactly.

[0069] The system of the present invention further supports a cross-regional coordination mechanism and a real-time update mechanism. In a preferred embodiment of the present invention, the cross-regional coordination mechanism is implemented by data sharing between multiple distributed cloud nodes 3. For example, between two monitoring areas A and B, when area A detects an abnormal seismic wave signal, the relevant information can be synchronized to the cloud node of area B in real time, thereby realizing cross-regional coordinated early warning.

[0070] The real-time update mechanism can dynamically adjust the warning threshold to adapt to the characteristics of earthquake activity in different regions. For example, in areas with a high incidence of earthquakes, the early warning system can set the amplitude threshold of the seismic wave to m / , while in areas with less seismic activity, the threshold can be adjusted to m / , to ensure a balance between sensitivity and false alarm rate, to ensure a balance between sensitivity and false alarm rate.

[0071] Assume that the distance between the cloud nodes in region A and region B is 500 kilometers. The cross-regional coordination mechanism implemented through the high-speed optical fiber network enables the public cloud node 3 in region A to detect the peak amplitude of m / After an abnormal signal is detected, the information can be synchronized to the cloud node in area B within 50ms. The real-time update mechanism automatically adjusts the warning threshold to m / , thereby improving the prediction accuracy of the area that may be affected in area B.

[0072] The distributed earthquake early warning cloud monitoring network method of the present invention comprises the following steps: The base station collects seismic wave signals and environmental noise information and transmits them to the cloud center node; in a preferred embodiment of the present invention, the near-station base station 11 and the far-station base station 12 collect seismic wave signals and environmental noise information respectively. The signal collection frequency can be preferably set to 100 Hz. The collected data is encrypted by AES-256 through the encryption module 123 and transmitted to the cloud center node 2 through a wireless or wired network.

[0073] The cloud center node performs protocol conversion on the received data and forwards it to the public cloud node; the cloud center node 2 performs protocol conversion and cache processing on the data sent by the base station 1. Preferably, the protocol format adopts JSON to ensure compatibility.

[0074] The public cloud nodes partition, merge, and sort the data and construct a seismic wave reference sequence; The public cloud node transmits the seismic wave reference sequence to the central node; the public cloud node 3 receives and integrates the data from multiple cloud central nodes 2, partitions and merges them according to the monitoring area, and constructs the seismic wave reference sequence by time sorting. The sorted reference sequence is input to the central node 4 for further processing.

[0075] The central node calculates the epicenter location and magnitude based on the seismic wave characteristics; the central node 4 analyzes the earthquake event through the epicenter positioning module 43 and the magnitude calculation module 44. Preferably, the epicenter location is calculated using the least squares method; When the central node 4 determines that the warning condition is met, the warning information is released to the target user through the warning information release module 5. Preferably, the release method includes multiple channels such as text messages, broadcasts, and the Internet to ensure rapid transmission of information.

[0076] Assume that in an earthquake, the near-station base station 11 detects the amplitude m / , the remote base station 12 detects m / After the cloud center node 2 standardized the signal, the public cloud node 3 integrated and sorted it to build a reference sequence. After analysis, the center node 4 concluded that the epicenter was (36.12°N, 117.54°C) and the magnitude was 6.7, and issued warning information to users in the target area within 30 seconds.

[0077] In specific implementations, the distributed earthquake early warning cloud monitoring network method of the present invention significantly improves the ability to prevent earthquake disasters through efficient signal acquisition, data processing and real-time early warning.

[0078] In order to verify the superiority of the distributed earthquake early warning cloud monitoring network system and method of the present invention, an embodiment is designed for comparison with two comparative examples in combination with specific application scenarios. Through the detection and analysis of key technical indicators, the technical advantages of the system of the present invention in earthquake early warning are fully reflected.

[0079] The test methods and test standards are as follows: 1. Test environment: The test was conducted in a simulated earthquake environment using artificially generated seismic wave signals, including P waves and S waves, simulating different magnitudes, epicenter locations and background noise conditions.

[0080] 2. Detection indicators: Data transmission delay (ms): the time difference from the acquisition of seismic waves to the arrival of data at the public cloud node. Epicenter location accuracy (km): the deviation between the calculated epicenter location and the actual epicenter location.

[0081] False alarm rate (%): The proportion of false alarms triggered by the system due to noise or abnormal signals.

[0082] Warning reach time (s): the time from when the warning is triggered to when the warning information is delivered to the user.

[0083] 3. Testing standards: Data transmission delay: required to be less than 50ms to ensure the real-time performance of the system.

[0084] Epicenter positioning accuracy: the error is less than 1km, which meets the needs of earthquake emergency response.

[0085] False alarm rate: less than 5%, ensuring the reliability of the early warning system.

[0086] Warning response time: less than 10 seconds, meeting rapid emergency needs.

[0087] Embodiment: The distributed earthquake early warning cloud monitoring network system of the present invention is used, including the near-station base station and the far-station base station working together, integrating data through the public cloud node, and the central node completing the analysis and early warning of earthquake events.

[0088] Comparative Example 1: Using a traditional centralized earthquake early warning system, the system directly relies on a central node to process data from all base stations without using a distributed architecture.

[0089] Comparative Example 2: Using an earthquake early warning system based on a single sensor network, data is only uploaded in real time through sensors, without a collaborative processing mechanism.

[0090] The test results are shown in the following table: index Example Comparative Example 1 Comparative Example 2 Data transmission delay (ms) 35 85 60 Epicenter location accuracy (km) 0.8 1.5 2.3 False alarm rate (%) 3.2 7.5 12.4 Warning contact time (s) 6.5 15 12 As can be seen from the above table, the data transmission delay of the embodiment is significantly lower than that of Comparative Example 1 and Comparative Example 2. This is due to the multi-level data transmission design in the present invention, which effectively reduces the transmission delay through the collaborative work of the near-station base station and the far-station base station and the buffering mechanism of the public cloud node, while Comparative Example 1 of the traditional centralized architecture increases the delay due to excessive reliance on the central node to process all data.

[0091] The present invention uses multi-dimensional eigenvector analysis and least squares method to calculate the epicenter position through the central node, and its error is only 0.8km, while the error of comparative examples 1 and 2 is 1.5km and 2.3km respectively. The regional data integration function of the distributed cloud node significantly improves the accuracy of epicenter positioning.

[0092] The false alarm rate of the embodiment is 3.2%, which is significantly lower than that of comparative examples 1 and 2. This is mainly due to the noise filtering algorithm and the dynamic adjustment mechanism of the multi-layer buffer introduced in the public cloud node of the present invention, which effectively reduces the false alarms caused by background noise or abnormal signals.

[0093] The warning reach time of the present invention is only 6.5s, which is much faster than 15s of Comparative Example 1 and 12s of Comparative Example 2. The multi-channel release mechanism of the warning information release module ensures that the warning information can reach the user quickly, while Comparative Examples 1 and 2 both have delayed reach time due to single data processing and transmission paths.

[0094] The distributed earthquake early warning cloud monitoring network system of the present invention significantly improves the real-time, accuracy and reliability of the system in earthquake early warning through a distributed architecture and innovative data processing methods. Through comparative experiments, the embodiments are superior to the comparative examples in all detection indicators, verifying the superiority of the present invention. In particular, the performance of the system of the present invention is particularly outstanding in terms of data transmission delay and early warning reach time, proving its applicability and practicality in emergency early warning scenarios.

[0095] Figure 7 Because the accuracy of earthquake event detection varies over time, the present invention uses near-station base stations and far-station base stations to work together, and realizes the rapid capture and transmission of earthquake wave signals through a distributed architecture. The feature extraction module, similarity calculation module and epicenter positioning module in the system work together to analyze earthquake wave signals from multiple dimensions and quickly identify earthquake event characteristics. In addition, the public cloud node improves the integrity and accuracy of signal integration through time sorting and regional partition merging mechanisms.

[0096] In the initial stage (0-2 seconds), the accuracy is low because the number of base stations collecting signals is small and data processing has not been completed.

[0097] As time goes by, more base station data is integrated, the epicenter positioning and magnitude calculation functions of the central node come into play, the accuracy rate increases rapidly, and eventually stabilizes.

[0098] This improved accuracy over time indicates that the system is able to quickly capture earthquake characteristics and provide high-precision early warnings after data accumulation and processing optimization.

[0099] Figure 8 As data processing delay varies over time, the present invention optimizes the data transmission path through the dynamic buffer management module and the protocol conversion module, effectively reducing the processing delay. The asynchronous communication mechanism in the system allows each module to process data independently, avoiding the bottleneck problem common in the traditional centralized processing architecture. In addition, the public cloud node partitions the monitoring data for parallel processing, greatly improving the overall processing speed.

[0100] In the early stages, processing delays were high because data from some base stations needed to be forwarded through a central node in the cloud, resulting in a longer path.

[0101] As the system enters a stable operating state, the data of each base station is dynamically allocated to the public cloud node for integration, the analysis task of the central node is optimized and completed, and the delay is gradually reduced to 30-50ms.

[0102] The reduction in data processing latency demonstrates the significant advantages of distributed architecture and dynamic optimization in the real-time earthquake early warning.

[0103] Figure 8 In order to prevent the false alarm rate from changing over time, the present invention introduces efficient noise filtering and feature extraction algorithms in the signal processing stage. Through the similarity matrix calculation and the multi-source verification mechanism of the epicenter positioning module, false alarms caused by background noise or abnormal signals are effectively reduced. In addition, the collaborative analysis of the public cloud node and the central node further improves the system's ability to distinguish abnormal signals.

[0104] In the initial stage, due to the limited data collected, the false alarm rate is high, which is related to the large proportion of noise signals.

[0105] Over time, the system improves the model's discriminative ability by accumulating data, especially after performing similarity analysis between multiple base stations, further reducing noise false alarms.

[0106] Between 5 and 10 seconds, the false alarm rate dropped to near 0, indicating that the system can effectively filter noise and accurately identify seismic signals.

[0107] The significant reduction in false alarm rate reflects the advantages of the present invention in noise processing and signal analysis, especially the ability to maintain high reliability in complex environments.

[0108] The collaborative work of multiple base stations (near-site base stations and far-site base stations), combined with the partitioning integration of public cloud nodes and the analysis and processing of central nodes, greatly improves the efficiency of data transmission and processing.

[0109] The combination of functional modules such as feature extraction module, noise filtering algorithm and similarity matrix calculation enables the system to quickly and accurately identify earthquake events, reduce the false alarm rate and improve detection accuracy.

[0110] The modules achieve efficient collaboration through asynchronous communication and dynamic adjustment mechanisms, which not only ensures real-time performance but also maintains system stability in complex earthquake environments.

[0111] The distributed earthquake early warning cloud monitoring network system of the present invention realizes fast and reliable earthquake early warning services through distributed architecture, multi-level data processing and dynamic optimization mechanism. This innovative design not only ensures high detection accuracy and low latency, but also significantly reduces the false alarm rate, providing strong support for earthquake disaster emergency response.

[0112] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Distributed earthquake early warning cloud monitoring network system, characterized in that: include: Multiple base stations for: Collect seismic wave signals and environmental noise information; Transmit the collected signals to the cloud center node via wired / wireless communication network; The cloud central node is connected to the multiple base stations and is used to: Receiving monitoring data sent by the base station; Perform data preprocessing and protocol conversion based on the received data, and transmit the processed monitoring data to the public cloud node; The public cloud node is connected to the cloud center node and is used to: Aggregate monitoring data sent by multiple cloud center nodes; Partition, merge and sort the data according to the monitoring area; Construct a seismic wave reference sequence and determine whether the distributed earthquake early warning conditions are met; The central node is connected to the public cloud node and is used to: receiving a seismic wave reference sequence transmitted by the public cloud node; Analyze earthquake events based on multidimensional feature vectors and calculate epicenter location and magnitude; When the early warning conditions are met, the distributed earthquake early warning system is triggered to send an early warning signal to the base station and the early warning information release module; The early warning information release module is connected to the central node and is used to: Receiving the early warning signal sent by the central node; Earthquake warning information is released to target users through multiple communication channels, including mobile phone text messages, Internet broadcasts, and emergency broadcasts.

2. The distributed earthquake early warning cloud monitoring network system according to claim 1 is characterized in that: The multiple base stations include: A near-station base station, used to directly transmit monitoring data to the public cloud node via a downlink channel; A remote base station, used to forward monitoring data to the public cloud node via the cloud central node; The near-station base station and the far-station base station both include: An acquisition control unit, used for acquiring seismic wave signals; A / D converter for converting analog seismic signals into digital signals; An encryption module, used for encrypting digital signals; The data transmission unit is used to transmit the encrypted monitoring data to the upper-level node via a wireless or wired network.

3. The distributed earthquake early warning cloud monitoring network system according to claim 1 is characterized in that: The cloud center node includes: A data receiving module, used for receiving monitoring data sent by multiple base stations; A protocol conversion module, used to convert the monitoring data into a unified format; A data cache module, used for short-term storage of received monitoring data to smooth the data flow; The data forwarding module is used to transmit the processed monitoring data to the public cloud node.

4. The distributed earthquake early warning cloud monitoring network system according to claim 1, characterized in that: The public cloud node further comprises: A data buffer management module is used to dynamically adjust the buffer size to adapt to data flow fluctuations; A signal sorting module, used to sort the monitoring data based on timestamps; The seismic wave construction module is used to construct a seismic wave reference sequence and determine whether the distributed earthquake early warning conditions are met; The partition merging module is used to integrate data from multiple base stations according to the monitoring area.

5. The distributed earthquake early warning cloud monitoring network system according to claim 1, characterized in that: The central node further comprises: A feature extraction module is used to extract peak values, main frequencies and time features from a seismic wave reference sequence; A similarity calculation module is used to construct a similarity matrix between base stations based on seismic wave characteristics; The epicenter positioning module is used to determine the epicenter location by combining the base station location information and the similarity matrix; Magnitude calculation module, used to estimate the magnitude of earthquake events.

6. The distributed earthquake early warning cloud monitoring network system according to claim 1, characterized in that: The warning information release module includes: Multi-channel communication unit, used to release information through SMS, WeChat, and broadcast; The priority scheduling module is used to release information to users in high-risk areas based on their geographic location and warning level.

7. The distributed earthquake early warning cloud monitoring network system according to claim 1, characterized in that: The system further comprises: Redundant backup module, used to automatically switch to the slave node when the master node fails; The fault detection module is used to monitor the system operation status in real time and trigger the fault recovery mechanism.

8. The distributed earthquake early warning cloud monitoring network system according to claim 1, characterized in that: The modules use an asynchronous communication mode; Data transmission uses TCP / IP protocol to ensure transmission reliability.

9. The distributed earthquake early warning cloud monitoring network system according to claim 1, characterized in that: The system further supports: Cross-regional coordination mechanism for sharing monitoring data among multiple distributed cloud nodes; Real-time update mechanism is used to dynamically adjust the warning threshold to adapt to the earthquake characteristics of different regions.

10. A distributed earthquake early warning cloud monitoring network method based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: The base station collects seismic wave signals and environmental noise information and transmits them to the cloud center node; The cloud center node performs protocol conversion on the received data and forwards it to the public cloud node; The public cloud nodes partition, merge, and sort the data and construct a seismic wave reference sequence; The public cloud node transmits the seismic wave reference sequence to the central node; The central node calculates the epicenter location and magnitude based on the seismic wave characteristics; The central node issues a warning signal to the target user when the warning conditions are met.

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