A real-time detector earthquake early warning method based on Internet of Things technology

Abstract

The present invention relates to the field of earthquake early warning technology, and in particular to a real-time earthquake early warning method for geophones based on Internet of Things technology. First, earthquake data for 72 hours of the local surface is collected, and the collected data points are input into this method for superposition operation to generate 24-hour data. Then, it is converted into earthquake amplitudes every 30 seconds as a group. After gain processing of these earthquake amplitudes, they are stored in the early warning file. After officially starting to monitor earthquakes, the current earthquake data is converted every 30 seconds to generate earthquake amplitudes, and compared with the previously stored early warning file to obtain earthquake frequencies and earthquake amplitudes. Finally, earthquake data is sent in real time via 4G. After receiving the data, timely feedback is provided through the earthquake data to warn of the corresponding surface vibration conditions. The present invention can intuitively reflect geological vibrations such as avalanches, cracks, and ice movements in the earthquake detection area, providing timely information for seismic exploration.

Description

Technical Field

[0001] The present invention relates to the technical field of earthquake early warning, and particularly to a real-time earthquake early warning method for geophones based on Internet of Things technology. Background Art

[0002] Earthquake early warning aims to detect seismic waves early and issue alarms quickly, enabling people and institutions in affected areas to take timely shelter measures, thereby reducing the losses caused by earthquake disasters. Traditional earthquake early warning methods mainly rely on seismic waveform analysis of seismic stations. These geophones can monitor ground vibrations in real time and transmit data to the central processing system quickly. However, existing earthquake early warning technologies have certain limitations, especially in terms of real-time performance, data processing efficiency, early warning accuracy, and system adaptability. There are limitations in the following aspects:

[0003] 1) Data processing efficiency and accuracy: When traditional earthquake early warning systems process seismic data collected by geophones, they often adopt fixed data processing procedures and threshold settings, lacking flexibility and self-adaptability. This may lead to false alarms or missed alarms in early warning, especially in the recognition of seismic signals under complex terrain or different geological conditions.

[0004] 2) Real-time performance and response speed: When an earthquake occurs, every second of early warning is crucial. However, existing early warning systems may have delays in data transmission, processing, and decision-making generation, affecting the real-time performance of early warning information.

[0005] 3) Robustness of early warning algorithms: Current early warning algorithms may not be able to accurately distinguish seismic signals from other types of ground vibrations, which will increase the risk of false alarms and reduce the credibility of the system.

[0006] 3) Data fusion and multi-dimensional analysis: Single-type sensor data may not be sufficient to comprehensively evaluate the severity and potential impact of an earthquake. Existing systems often lack effective mechanisms to integrate different types of data, such as GPS location information, temperature information, etc., for multi-dimensional comprehensive analysis. Summary of the Invention

[0007] The present invention provides a real-time earthquake early warning method for geophones based on Internet of Things technology, which can intuitively feedback geological vibrations such as avalanches, cracks, and ice block movements in the earthquake detection area, and provide timely information for seismic exploration.

[0008] In order to achieve the object of the present invention, the technical solution adopted is: A real-time earthquake early warning method for geophones based on Internet of Things technology, including:

[0009] A real-time earthquake early warning method for geophones based on Internet of Things technology, including:

[0010] Step 1: Obtain 72 hours of geophone seismic data. Divide the seismic data into 3 parts at 24-hour intervals, then perform data stacking and multiply by the early warning gain value to obtain a 24-hour data. For the generated 24-hour data, segment it at 30-second intervals, convert the stacked data for each 30-second segment into seismic amplitude, and then store the converted data.

[0011] Step 2: The geophone collects seismic data and converts the data every 30 seconds to generate seismic amplitude and seismic frequency, and sends the seismic data to the seismic platform. The seismic data includes GPS information and seismic information.

[0012] Step 3: After receiving the data, the seismic platform warns about the situation in the seismic detection area based on the received seismic information and GPS information.

[0013] As an optimized solution of the present invention, the seismic platform modifies the early warning gain value in real time according to the environmental noise of the specific area to ensure the quality of the data.

[0014] As an optimized solution of the present invention, for the seismic data stacking process, first let the geophone continuously collect data locally for 72 hours, then split the 72-hour seismic data into 3 time periods, each time period being 24 hours, and then divide each 24-hour time period into 2880 parts, and then perform stacking. The formula is as follows:

[0015]

[0016] t[start,end] = 24h,

[0017] duration = 30s

[0018]

[0019] There are a total of 2880 time periods. x1, y1, z1, xCs, yCs, zCs refer to the energy of one of the 2880 time periods. Convert the stacked data for each 30-second segment into seismic amplitude, and the energy conversion is as follows:

[0020] x1 = Vmax * pow(10, Gain / 6.0)

[0021] Vmax is the maximum value within 30 seconds. Gain is the gain of the alarm threshold for only detecting the ground magnitude. The gain of the seismic alarm threshold is calculated from the minor magnitude. Σpxi represents the longitudinal wave data of the 24 hours generated after stacking the 72-hour seismic data detected.

[0022] Σsxi represents the transverse wave data of the 24 hours generated after stacking the 72-hour seismic data detected.

[0023] Collect seismic data and convert the data every 30 seconds to generate seismic amplitude and seismic frequency;

[0024] At = Vmax,

[0025] X threshold = xt, xi ≥ xt? rt + 1 : rt

[0026] At represents the maximum value within time period t, X threshold represents the judgment threshold within time t, rt represents the seismic frequency exceeding the judgment threshold within time t, and xi represents one of the seismic amplitudes within a 30 - second time period.

[0027] As an optimized solution of the present invention, obtain the seismic amplitude and the number of times. After conversion, the data length is shortened from the original 270,000 - byte data of 30 seconds to 256 bytes, and the data format of the new warning information is JSON.

[0028] As an optimized solution of the present invention, send seismic data using HTTP / MQTT and execute it in a loop.

[0029] As an optimized solution of the present invention, the received seismic information and GPS information warn about the situation in the seismic detection area. Determine whether it is a large surface vibration by the size of the seismic amplitude and the vibration frequency, and judge avalanches and mountain displacements by the GPS position and equipment temperature data.

[0030] As an optimized solution of the present invention, continuously collect data and execute steps 2 and 3 in a loop.

[0031] The present invention has positive effects: 1) The present invention improves the accuracy and real - time performance of earthquake early warning: By continuously collecting data for 72 hours and performing superposition processing, noise can be effectively filtered out and the effective signal can be enhanced, thereby improving the accuracy of earthquake early warning. The 24 - hour data generated after data processing is updated at 30 - second intervals, ensuring the real - time response ability of the earthquake early warning system and helping to timely notify relevant personnel to take measures;

[0032] 2) The present invention reduces the data transmission cost and storage requirements. After the data is processed, the length is reduced from the original 270,000 bytes to 256 bytes, greatly reducing the data transmission cost and the storage space requirements. Using the JSON - formatted data structure facilitates data parsing and processing, further simplifying the system architecture;

[0033] 3) The present invention enhances the adaptability and flexibility of the system: the seismic platform can adjust the warning gain value in real time according to the environmental noise in different regions, ensuring that the system can effectively identify seismic signals in various environments. By repeatedly performing data acquisition and processing, the system can continuously monitor seismic activities, enhancing the system's continuous monitoring ability;

[0034] 4) The present invention improves the comprehensiveness of geological disaster warnings: by combining seismic amplitude and frequency, as well as GPS information, the system can not only warn of earthquakes, but also judge geological disasters such as avalanches, cracks, and ice movements, improving the comprehensiveness and pertinence of warnings. Using equipment temperature data to assist in judging phenomena such as landslide displacement increases the functionality of the warning system.

[0035] In summary, the present invention realizes effective monitoring and warning of earthquakes and related geological disasters through Internet of Things technology, not only improving the accuracy and timeliness of warnings, but also reducing the costs of data processing and transmission, enhancing the adaptability and flexibility of the system, and improving the comprehensiveness of geological disaster warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to the provided drawings.

[0037] Figure 1 is a schematic flowchart of the present invention;

[0038] Figure 2 is a schematic diagram of the data training process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0040] The terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may include steps or units that are not listed.

[0041] As Figure 1 shown, the present invention discloses a real-time earthquake early warning method for geophones based on Internet of Things technology, including:

[0042] Step 1: Obtain 72 hours of geophone earthquake data, divide the earthquake data into 3 portions according to 24 hours per segment, then perform data superposition and multiply by the gain value for early warning to obtain a 24-hour data. For the generated 24-hour data, segment it every 30 seconds, convert the superposed data for each 30-second segment into earthquake amplitude, and then store the converted data;

[0043] Step 2: The geophone collects earthquake data, and converts the data every 30 seconds to generate earthquake amplitude and earthquake frequency, and sends the earthquake data (GPS information and earthquake information) to the earthquake platform;

[0044] Step 3: After the earthquake platform receives the data, it warns about the situation in the earthquake detection area based on the received earthquake information and GPS information.

[0045] Furthermore, the earthquake platform can modify the gain value for early warning in real time according to the environmental noise in a specific area to ensure the quality of the data.

[0046] For earthquake data superposition processing, first let the geophone continuously collect data locally for 72 hours, then split the 72-hour earthquake data into 3 time periods, each time period being 24 hours, and then divide each 24-hour time period into 2880 portions, and then perform superposition. The formula is as follows:

[0047]

[0048] There are a total of 2880 time periods. x1, y1, z1, xCs, yCs, zCs refer to the energy of one of the 2880 time periods. Convert the superposed data for each 30-second segment into earthquake amplitude, and the energy conversion is as follows.

[0049] x1 = Vmax * pow(10, Gain / 6.0)

[0050] Vmax is the maximum value within 30 seconds, and Gain is the gain of the alarm threshold for only detecting the ground earthquake magnitude. The gain of the earthquake alarm threshold is calculated from the minor earthquake magnitude (generally for a magnitude 1 earthquake).

[0051] Σpxi represents the longitudinal wave data of 24 hours generated after superposing 72 hours of earthquake data detected.

[0052] Σsxi represents the transverse wave data of 24 hours generated after superposing 72 hours of earthquake data detected.

[0053] Collect seismic data and convert the data every 30 seconds to generate seismic amplitude and seismic frequency.

[0054] At = Vmax, X threshold = xt, xi ≥ xt? rt + 1: rt

[0055] At represents the maximum value within time period t, X threshold represents the judgment threshold within time t, rt represents the seismic frequency exceeding the judgment threshold within time t, and xi represents one of the seismic amplitudes within a 30 - second time period.

[0056] Obtain the seismic amplitude and the number of times. After conversion, the data length is shortened from the original 270,000 - byte data for 30 seconds to 256 bytes.

[0057] The size of the old transmitted data is as follows: 30s size = (LS + LP + LN) = 30 * 3000 * 3 = 270000, (t[start, end]) is the total sum of the time interval from the start of data collection to the end of data collection, cut time is 30 seconds, and LS, LP, LN are the shear waves and longitudinal waves in three directions.

[0058] The data format of the new warning information is JSON (256 bytes) as follows:

[0059] "deviceId":"11111111","apikey":"11111111","data":{

[0060] "11111111":{

[0061] "LP":{"2024 - 08 - 05T00:49:00.000Z":{"amplitude":0.0

[0062] 42617,"ringing":0}},

[0063] "LS":{"2024 - 08 - 05T00:49:00.000Z":{"amplitude":0.7

[0064] 26171,"ringing":0}},

[0065] "LN":{"2024 - 08 - 05T00:49:00.000Z":{"amplitude":0.2

[0066] 12617,"ringing":0}}}}}

[0067] The data size becomes about 1000 times smaller.

[0068] Send seismic data (GPS information and seismic equipment information) to the seismic platform, using HTTP / MQTT to send to the seismic platform, and execute in a loop.

[0069] The received seismic information and GPS information warn about the situation in the seismic detection area. Determine whether it is a large surface vibration by the magnitude of seismic amplitude and vibration frequency, and judge avalanches and landslides by other seismic equipment information data such as GPS location and equipment temperature.

[0070] Finally, continuously collect data and execute steps 2 and 3 in a loop.

[0071] Embodiment

[0072] Step 102, the geophone collects real-time data;

[0073] Step 103, the data is transformed;

[0074] Step 104, the data is compared, and then seismic amplitude and vibration frequency are generated and sent to the seismic platform;

[0075] Step 105, the seismic platform gives an early warning;

[0076] Step 106, data training, generating a storage file, and jumping to step 104.

[0077] As Figure 1-2 shown, first collect 72 hours of seismic data on the local surface, input the collected data points into this method for superposition operation to generate 24-hour data, then convert it into seismic amplitude every 30 seconds as a group. After gain processing of these seismic amplitudes, store them in the warning file. After officially starting to monitor earthquakes, convert the current seismic data every 30 seconds to generate seismic amplitude, and compare it with the previously stored warning file to obtain seismic frequency and seismic amplitude. Finally, send seismic data in real-time through 4G. After receiving the data, give timely feedback through the seismic data to warn about the corresponding surface vibration situation. The present invention can intuitively feedback geological vibrations such as avalanches, cracks, and ice block movements in the seismic detection area, providing timely information for seismic exploration.

[0078] The specific embodiments described above further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A real-time earthquake warning method based on the Internet of Things technology, characterized in that: include: Step 1, obtain 72 hours of geophone seismic data, divide the seismic data into three parts according to 24 hours, and then superimpose the data and multiply it by the early warning gain value to obtain a 24-hour data. The generated 24-hour data is divided into sections every 30 seconds, and the superimposed data of each section every 30 seconds is converted to obtain the seismic amplitude, and then the converted data is stored; Step 2: The detector collects seismic data and converts the data every 30 seconds to generate seismic amplitude and seismic frequency, and sends the seismic data to the seismic platform. The seismic data includes GPS information and seismic information. Step 3: After receiving the data, the seismic platform warns the situation in the earthquake detection area through the received earthquake information and GPS information; For seismic data stacking, the detector is first allowed to continuously collect data for 72 hours locally, and then the 72 hours of seismic data are divided into three time periods, each of which is 24 hours. Each 24-hour time period is then divided into 2880 parts, and then stacked. The calculation formula is as follows: t[start,end]=24 hours, duration = 30 seconds, There are 2880 time periods in total. The stacked data of each 30-second period is converted to obtain the seismic amplitude. The energy size conversion is as follows: Earthquake amplitude = Vmax*pow(10,Gain / 6.0); Vmax is the maximum detector threshold within 30 seconds, Gain is the gain of the alarm threshold that detects only the magnitude of the earthquake on the ground. The gain of the earthquake alarm threshold is calculated based on the slight magnitude. Σp represents the 24-hour seismic longitudinal wave data generated after the detector superimposes 72 hours of seismic data and splits it; Σs represents the 24-hour seismic shear wave data generated after the detector superimposes 72 hours of seismic data and splits it; Where: counts represents the number of earthquake superpositions within 24 hours, t represents the time period, start represents the beginning of the 24-hour time period, end represents the end of the 24-hour time period, duration represents the time interval, and pow(10,Gain / 6.0) represents 10 raised to the power of Gain / 6.0; x1s represents the maximum seismic shear threshold of the geophone in the first 30-second time period of the first day, y1s represents the maximum seismic shear threshold of the geophone in the first 30-second time period of the second day, and z1s represents the maximum seismic shear threshold of the geophone in the first 30-second time period of the third day; x2s represents the maximum seismic shear threshold of the geophone in the second 30-second time period of the first day, y2s represents the maximum seismic shear threshold of the geophone in the second 30-second time period of the second day, and z2s represents the maximum seismic shear threshold of the geophone in the second 30-second time period of the third day; xCs represents the maximum seismic shear wave threshold of the geophone in the Cth 30-second time period on the first day; yCs represents the maximum seismic shear wave threshold of the geophone in the Cth 30-second time period on the second day; zCs represents the maximum seismic shear wave threshold of the geophone in the Cth 30-second time period on the third day; C represents a natural number; s represents seismic shear wave; x1p represents the maximum seismic P-wave threshold of the geophone in the first 30-second time period of the first day, y1p represents the maximum seismic P-wave threshold of the geophone in the first 30-second time period of the second day, and z1p represents the maximum seismic P-wave threshold of the geophone in the first 30-second time period of the third day; x2p represents the maximum seismic P-wave threshold of the geophone in the second 30-second time period of the first day, y2p represents the maximum seismic P-wave threshold of the geophone in the second 30-second time period of the second day, and z2p represents the maximum seismic P-wave threshold of the geophone in the second 30-second time period of the third day; xCp represents the maximum seismic P-wave threshold of the geophone in the Cth 30-second time period on the first day; yCp represents the maximum seismic P-wave threshold of the geophone in the Cth 30-second time period on the second day; zCp represents the maximum seismic P-wave threshold of the geophone in the Cth 30-second time period on the third day, and p represents seismic P-wave; Collect seismic data and convert the data every 30 seconds to generate seismic amplitude and seismic frequency; At=Vmax,X threshold=xt,xi≥xt? rt+1:rt; At represents the maximum earthquake amplitude value within time period t, X threshold represents the judgment threshold within time period t; xt: represents the threshold of time period t; rt represents the earthquake frequency exceeding the judgment threshold within time period t, and xi represents one of the earthquake amplitudes within a 30-second time period.

2. The real-time early warning method for earthquake using geophones based on Internet of Things technology according to claim 1, characterized in that: The seismic platform modifies the gain value of the early warning in real time according to the environmental noise in the specific area to ensure the quality of the data.

3. The real-time early warning method for earthquake using geophones based on Internet of Things technology according to claim 2 is characterized by: The earthquake amplitude and frequency are obtained. After conversion, the data length is shortened from the original 270,000 bytes of 30 seconds to 256 bytes. The data format of the new warning information is JSON.

4. The real-time early warning method for earthquake using geophones based on Internet of Things technology according to claim 3 is characterized by: Send earthquake data using HTTP / MQTT and execute in a loop.

5. The real-time early warning method for earthquake using geophones based on Internet of Things technology according to claim 4 is characterized by: The received earthquake information and GPS information warn of the situation in the earthquake detection area. The amplitude and frequency of the earthquake are used to determine whether it is a large surface vibration. The GPS position and device temperature data are used to determine avalanches and mountain displacements.

6. The real-time early warning method for earthquake using geophones based on Internet of Things technology according to claim 5, characterized in that: Steps 2 and 3 are performed by continuously collecting data in a loop.

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