Earthquake early warning and rapid reporting Internet of Things system, method and device and storage medium

Abstract

The invention discloses an earthquake early warning and rapid reporting Internet of Things system, method and device and a storage medium. The system comprises an earthquake information acquisition module, a cloud server and an early warning information issuing module. The earthquake information acquisition module is used for acquiring earthquake monitoring physical quantities, generating earthquake monitoring data according to the earthquake monitoring physical quantities and sending the earthquake monitoring data to the cloud server; the cloud server is used for estimating an earthquake grade and an earthquake source position when an earthquake event is identified according to the earthquake monitoring data; and the early warning information issuing module is used for determining a to-be-early-warned area according to the earthquake grade and the earthquake source position, and sending earthquake early warning information to a broadcasting system, mobile equipment and an outdoor alarm system in the to-be-early-warned area at the same time. By adopting the technical scheme, the earthquake can be quickly and accurately detected, and the earthquake early warning information can be quickly and accurately spread, so that the influence of earthquake disasters is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of the Internet of Things, and particularly to an earthquake early warning and rapid reporting Internet of Things system, method, device and storage medium. Background Art

[0002] Earthquake early warning and rapid reporting refers to a technology that, after an earthquake occurs, utilizes the principle that the propagation speed of seismic waves is less than that of electromagnetic waves, and issues warning information to areas that may be affected before the seismic waves cause serious damage to the target area by quickly monitoring seismic wave signals.

[0003] Existing earthquake early warning systems generally have the following problems: First, the accuracy of existing earthquake sensors is limited, and the ability to detect weak earthquake signals is weak. Moreover, during long-term operation, the sensors are easily affected by environmental factors, resulting in a decrease in their measurement accuracy or malfunctions. Second, existing warning information is often a general alert for a large area, lacking personalized information for different regions and different populations. For example, for areas closer to and farther from the epicenter, the arrival time of seismic waves and the degree of possible harm are different, but the warning information may not be differentiated, resulting in people in some areas being unable to take more effective risk avoidance measures according to the actual situation. Summary of the Invention

[0004] The present invention provides an earthquake early warning and rapid reporting Internet of Things system, method, device and storage medium, which can achieve rapid and accurate detection of earthquakes and rapid and precise dissemination of earthquake early warning information, thereby reducing the impact of earthquake disasters.

[0005] According to one aspect of the present invention, an earthquake early warning and rapid reporting Internet of Things system is provided, including an earthquake information acquisition module, a cloud server, and a warning information publishing module;

[0006] The earthquake information acquisition module is used to acquire earthquake monitoring physical quantities, generate earthquake monitoring data according to the earthquake monitoring physical quantities, and send the earthquake monitoring data to the cloud server; wherein, the earthquake monitoring data includes triaxial acceleration data, triaxial vibration velocity data, and tilt angle data;

[0007] The cloud server is used to estimate the earthquake magnitude and the earthquake epicenter location when an earthquake event is identified according to the earthquake monitoring data;

[0008] The warning information publishing module is used to determine the area to be warned according to the earthquake magnitude and the earthquake epicenter location, and simultaneously send earthquake early warning information to the broadcast system, mobile devices, and outdoor alarm systems within the area to be warned.

[0009] Optionally, the earthquake information acquisition module includes an acceleration sensor, a seismograph, a ground tilt sensor, and a data acquisition and transmission unit;

[0010] Wherein, one earthquake information acquisition module is set at each earthquake information acquisition point within the earthquake monitoring area, and the acceleration sensor, seismograph, and ground tilt sensor in each earthquake information acquisition module are respectively connected to the data acquisition and transmission unit.

[0011] Optionally, the data acquisition and transmission unit is used for:

[0012] Converting the earthquake monitoring physical quantities collected by the acceleration sensor, seismograph, and ground tilt sensor into digital signals, and using the Global Positioning System to realize the clock synchronization of each digital signal to generate earthquake monitoring data;

[0013] Judging whether the currently collected earthquake monitoring data is related to earthquake activities according to the currently collected earthquake monitoring data and the pre-constructed earthquake activity baseline model;

[0014] If so, enter the high-frequency transmission mode and send all the generated earthquake monitoring data to the cloud server;

[0015] If not, maintain the low-frequency transmission mode, and whenever reaching the specified data upload node, send the most recently generated earthquake monitoring data to the cloud server.

[0016] Optionally, the cloud server includes:

[0017] A feature extraction unit, configured to extract time-domain and frequency-domain features from the three-axis vibration velocity data, and send the extracted time-domain features and frequency-domain features to the seismic wave detection unit;

[0018] A seismic wave detection unit, configured to detect P waves and S waves according to the time-domain features and frequency-domain features;

[0019] A magnitude estimation unit, configured to estimate the earthquake magnitude according to the characteristic parameters of the P wave and S wave extracted by the feature extraction unit after the seismic wave detection unit detects the P wave;

[0020] A seismic source location unit, configured to locate the seismic source location according to the earthquake monitoring data provided by multiple earthquake information acquisition points.

[0021] Optionally, the feature extraction unit is specifically configured to:

[0022] Calculate the signal amplitude and signal zero-crossing rate according to the three-axis vibration velocity data;

[0023] Perform Fourier transform on the three-axis vibration velocity data, obtain the spectrum of the vibration velocity, and calculate the main frequency and frequency band width according to the spectrum.

[0024] Optionally, the magnitude estimation unit is specifically used for:

[0025] Calculate the earthquake magnitude according to the formula ;

[0026] where M is the earthquake magnitude, a, b, c, and d are coefficients corresponding to the area where the earthquake information collection module is located respectively, A P is the signal amplitude of the P wave, A S is the signal amplitude of the S wave, T S is the duration of the S wave, f P and f S are the main frequencies of the P wave and the S wave respectively.

[0027] Optionally, the earthquake source location unit is used for:

[0028] Obtain the arrival time of the P wave collected by each earthquake information collection module and the coordinates of each earthquake information collection module, and establish a plurality of equations according to the arrival time of the P wave and the coordinates of each earthquake information collection module ;

[0029] where v is the propagation speed of the P wave, (x, y) is the coordinate of the earthquake source location, (x i , y i ) and (x j , y j ) are the coordinates of the first earthquake information collection module and the coordinates of the second earthquake information collection module respectively, t i and t j are the arrival time of the P wave of the first earthquake information collection module and the second earthquake information collection module respectively;

[0030] Calculate the earthquake source location according to the established equations.

[0031] According to another aspect of the present invention, there is provided an earthquake early warning and rapid reporting method, which is characterized in that it is applied to an earthquake early warning and rapid reporting Internet of Things system as described in any embodiment of the present invention, and includes:

[0032] Collect earthquake monitoring physical quantities through the earthquake information collection module, generate earthquake monitoring data according to the earthquake monitoring physical quantities, and send the earthquake monitoring data to the cloud server; wherein, the earthquake monitoring data includes three-axis acceleration data, three-axis vibration velocity data, and tilt angle data;

[0033] Through a cloud server, when an earthquake event is identified based on the earthquake monitoring data, estimate the earthquake magnitude and the earthquake epicenter location;

[0034] Through an early warning information publishing module, based on the earthquake magnitude and the earthquake epicenter location, determine the area to be warned, and simultaneously send earthquake early warning information to the broadcast system, mobile devices, and outdoor alarm systems within the area to be warned.

[0035] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0036] At least one processor; and

[0037] A memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the earthquake early warning and rapid reporting method according to any embodiment of the present invention.

[0039] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the earthquake early warning and rapid reporting method according to any embodiment of the present invention when executed.

[0040] The technical solution of the embodiments of the present invention, by configuring an earthquake information acquisition module, a cloud server, and an early warning information publishing module in an earthquake early warning and rapid reporting Internet of Things system, the earthquake information acquisition module is used to collect earthquake monitoring physical quantities, generate earthquake monitoring data according to the earthquake monitoring physical quantities, and send the earthquake monitoring data to the cloud server. The cloud server is used to estimate the earthquake magnitude and the earthquake epicenter location when an earthquake event is identified based on the earthquake monitoring data. The early warning information publishing module is used to determine the area to be warned according to the earthquake magnitude and the earthquake epicenter location, and simultaneously send earthquake early warning information to the broadcast system, mobile devices, and outdoor alarm systems within the area to be warned. By reasonably planning the distribution of sensors, the coverage rate of earthquake monitoring is improved, ensuring that the data collected by each sensor has a high degree of consistency in time, reducing the error in earthquake wave analysis caused by time errors, thereby realizing accurate detection of earthquake waves, and being able to use a combination of a broadcast system, a mobile network, and an outdoor alarm system after detecting an earthquake event to achieve wide dissemination of early warning information. The early warning decision-making module can determine the early warning scope and early warning level of different regions, and issue targeted early warning information. The targeted early warning information can help people in different regions take more effective risk avoidance measures according to the actual situation, and improve the ability of the whole society to respond to earthquake disasters.

[0041] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0043] Figure 1 FIG. 1 is a schematic structural diagram of an earthquake early warning and rapid reporting Internet of Things system according to Embodiment 1 of the present invention;

[0044] Figure 2 FIG. 2 is a schematic structural diagram of another earthquake early warning and rapid reporting Internet of Things system according to an embodiment of the present invention;

[0045] Figure 3 FIG. 3 is a flowchart of an earthquake early warning and rapid reporting method according to Embodiment 2 of the present invention;

[0046] Figure 4 FIG. 4 is a schematic structural diagram of an electronic device for implementing the earthquake early warning and rapid reporting method of the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] In order to enable those skilled in the art to better understand the solutions of the present invention, the following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. 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.

[0048] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. 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 does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0049] Example 1

[0050] Figure 1 As shown in the structural schematic diagram of an earthquake early warning and rapid reporting Internet of Things system provided by Example 1 of the present invention, Figure 1 the system includes: an earthquake information collection module 110, a cloud server 120, and a warning information release module 130.

[0051] The earthquake information collection module 110 is used to collect earthquake monitoring physical quantities, generate earthquake monitoring data according to the earthquake monitoring physical quantities, and send the earthquake monitoring data to the cloud server.

[0052] Among them, the earthquake monitoring data includes triaxial acceleration data, triaxial vibration velocity data, and tilt angle data.

[0053] The cloud server 120 is used to estimate the earthquake magnitude and the earthquake epicenter location when an earthquake event is identified according to the earthquake monitoring data.

[0054] The warning information release module 130 is used to determine the area to be warned according to the earthquake magnitude and the earthquake epicenter location, and send earthquake warning information to the broadcast system, mobile devices, and outdoor alarm systems in the area to be warned at the same time.

[0055] Optionally, the earthquake information collection module 110 is the front-end data collection unit in the entire earthquake early warning Internet of Things system. Through the earthquake information collection module 110, various earthquake-related physical quantity information can be obtained at the earthquake monitoring site. By preliminarily processing and integrating these original information, earthquake monitoring data for subsequent analysis and decision-making can be generated. The earthquake information collection module 110 can be widely deployed in earthquake-active areas or geological areas that need to be monitored key.

[0056] Optionally, the cloud server 120 is a server based on cloud computing technology. It provides computing resources, data storage, and various software services for the earthquake early warning Internet of Things system through the network. The cloud server 120 can store a large amount of earthquake monitoring data. The storage system of the cloud server 120 can adopt a combination of a distributed file system and a relational database to store data, dynamically expand the storage capacity to adapt to the growing data demand, effectively manage and store a large amount of earthquake monitoring data, and ensure the persistence and accessibility of the data.

[0057] Optionally, in the warning information release module 130, an empirical model of magnitude and influence radius can be established in advance. When the earthquake magnitude is determined, the corresponding influence radius can be calculated using this model, so as to delimit a preliminary area to be warned in a circular or elliptical shape.

[0058] It is understandable that the location of the epicenter is different, and the surrounding geographical environment and population distribution are different, which will affect the propagation path of the seismic wave and the actual impact on different areas. For example, if the epicenter is located in a mountainous area, the seismic waves may be refracted, reflected, and other phenomena during the propagation process due to the obstruction of the mountains and the complexity of the geological structure, causing the energy distribution and propagation direction of the seismic waves to change. The earthquake impact may be relatively small on one side of the mountain, while in the valley or near the fault zone of the mountain, it may suffer a stronger earthquake shock. For example, if the epicenter is located below or near a city, the degree of earthquake harm will increase significantly due to the dense population, numerous buildings and complex types in the city. When considering the location of the earthquake source, the warning information release module 130 will combine the geographic information system (GIS) data to analyze the topography, population density, building distribution and other information around the earthquake source, use the contour data in the GIS to determine the direction of the mountain range and the undulation of the terrain, and determine the distribution of densely populated areas and important facilities based on the census data and urban planning data. By comprehensively considering the earthquake level and the location of the earthquake source, as well as these geographical and human factors, the warning information release module 130 can more accurately determine the area to be warned based on the preliminary area to be warned. For example, the area to be warned can be accurately identified as urban blocks, towns and villages, traffic arteries, etc. that are on the high-risk propagation path of earthquake waves, and these areas can be included in the final list of areas to be warned so that targeted warning information can be pushed.

[0059] Optionally, the warning information release module 130 can establish a data connection interface with broadcasting organizations such as radio stations. After the area to be warned is determined, the earthquake warning information is encoded and transmitted in a format that can be received and played by the broadcasting system. For example, information such as the earthquake level, source location, expected arrival time of the earthquake wave, possible hazards caused by the earthquake, and risk avoidance measures that the public should take is converted into voice or a specific broadcast data format, and then broadcast through the radio station.

[0060] Optionally, the warning information release module 130 can also send the earthquake warning information to the core machine room of the mobile network, and the mobile operator can use the SMS gateway system to push the earthquake warning information in the form of SMS to mobile device users in the area to be warned.

[0061] Optionally, the outdoor alarm system can be installed in public places in the city, such as squares, schools, communities and other densely populated areas. The early warning information release module 130 can establish a connection with the outdoor alarm system through communication methods such as Ethernet or wireless LAN. When there is earthquake early warning information, a trigger instruction is sent to these outdoor alarm systems. After receiving the instruction, the outdoor alarm system will start a strong sound and light alarm device.

[0062] Figure 2It is a schematic structural diagram of another optional earthquake early warning and rapid reporting Internet of Things system, as Figure 2 shown. The earthquake information acquisition module 110 includes an acceleration sensor 111, a seismograph 112, a ground tilt sensor 113, and a data acquisition and transmission unit 114; the cloud server 120 includes a feature extraction unit 121, a seismic wave detection unit 122, a magnitude estimation unit 123, and a hypocenter location unit 124.

[0063] Among them, an earthquake information acquisition module 110 is set at each earthquake information acquisition point in the earthquake monitoring area, and the acceleration sensor 111, the seismograph 112, and the ground tilt sensor 113 in each earthquake information acquisition module are respectively connected to the data acquisition and transmission unit 114.

[0064] Optionally, the acceleration sensor 111 is a high-sensitivity and low-noise acceleration sensor, which is installed on the ground surface or in shallow underground at each earthquake information acquisition point to ensure that it can accurately capture the ground acceleration fluctuations caused by seismic waves. When the mass block generates an inertial force under the action of acceleration, the acceleration sensor 111 determines the acceleration value by detecting the magnitude of the force.

[0065] Optionally, the acceleration sensor 111 can detect the acceleration changes of the ground in three mutually perpendicular directions (i.e., the X, Y, and Z axes) during an earthquake. For example, during the propagation of seismic waves, the P-wave (longitudinal wave) arrives first, causing compression and stretching of the ground in the propagation direction. When the subsequent S-wave (transverse wave) arrives, it will cause shear motion of the ground perpendicular to the propagation direction. The acceleration sensor can detect rapidly changing acceleration signals in the corresponding axial directions. The acceleration sensor 111 can be a microelectromechanical system (MEMS) acceleration sensor.

[0066] The advantage of such a setting is that: the MEMS acceleration sensor has the characteristics of small volume, low power consumption, and high sensitivity, and can keenly capture the tiny acceleration fluctuations caused by seismic waves, providing important basic data for earthquake monitoring.

[0067] Optionally, the seismograph 112 can be used to detect the ground vibration situation caused by seismic waves. The seismograph 112 can simultaneously measure the vibration velocities of seismic waves in three mutually perpendicular directions. When an earthquake occurs, the seismic waves propagate in the underground medium and cause ground vibration. The seismograph 112 can sense this vibration and convert it into an electrical signal output, thereby accurately measuring the vibration velocity information of seismic waves.

[0068] Optionally, the ground tilt sensor 113 can be used to monitor the change in the tilt angle of the ground relative to the horizontal direction. Before or during an earthquake, the deformation of the earth's crust may cause the ground to tilt, and the ground tilt sensor can monitor this change in real time and record the corresponding tilt angle data. The ground tilt sensor 113 can be an optical fiber ground tilt sensor.

[0069] Optionally, the physical quantity for earthquake monitoring can be the physical parameters used to describe the characteristics related to earthquake activities measured by the acceleration sensor 111, the geophone 112, and the ground tilt sensor 113, and can include the measured acceleration, vibration velocity, and tilt angle.

[0070] Optionally, after receiving the physical quantity for earthquake monitoring, the data acquisition and transmission unit 114 converts the physical quantity for earthquake monitoring from an analog signal to a digital signal and realizes the clock synchronization of the data, and records the three-axis acceleration data, the three-axis vibration velocity data, and the tilt angle data in the form of a time series.

[0071] Among them, the data acquisition and transmission unit 114 can be used for:

[0072] Convert the physical quantity for earthquake monitoring collected by the acceleration sensor, the geophone, and the ground tilt sensor into a digital signal, and use the global positioning system to realize the clock synchronization of each digital signal to generate earthquake monitoring data;

[0073] According to the currently collected earthquake monitoring data and the pre-constructed earthquake activity baseline model, judge whether the currently collected earthquake monitoring data is related to earthquake activities;

[0074] If so, enter the high-frequency transmission mode and send all the generated earthquake monitoring data to the cloud server;

[0075] If not, maintain the low-frequency transmission mode, and whenever reaching the specified data upload node, send the most recently generated earthquake monitoring data to the cloud server.

[0076] Optionally, the data acquisition and transmission unit 114 may include an analog-to-digital converter. The analog-to-digital converter can sample the analog signal at a certain sampling frequency, discretize the continuous analog signal in time, and convert the sampling value into a digital code according to the set quantization accuracy. For example, for a 12-bit analog-to-digital converter, it can divide the amplitude range of the analog signal into 4096 quantization levels, so as to accurately convert the analog signal into a digital signal for subsequent digital signal processing.

[0077] Optionally, the Global Positioning System can provide a high-precision time reference signal. The data acquisition and transmission unit 114 receives the GPS signal and extracts the time information therein, and then uses this time information as a reference to time-stamp the signals collected by the acceleration sensor 111, the seismograph 112, and the ground tilt sensor 113, so that they have a unified time reference. For example, when the acceleration sensor collects a data point, the current GPS timestamp is recorded simultaneously, and the data collected by the seismograph and the ground tilt sensor are also marked with the corresponding accurate time. In this way, in subsequent data processing, the time difference between the data of different sensors can be accurately calculated, providing a reliable time basis for the analysis of seismic waves.

[0078] The advantage of this setting is that: through the clock synchronization mechanism, the generated seismic monitoring data not only contains the physical quantity values measured by the sensors, but also carries accurate time information, forming seismic monitoring data with time series characteristics.

[0079] Optionally, the seismic activity baseline model is a data model constructed based on a large amount of historical seismic data and background data collected during periods of no obvious seismic activity, which reflects the statistical characteristics and variation laws of the physical quantities of seismic monitoring in a specific area under normal geological conditions. For example, in an area with relatively stable seismic activity, the acceleration data, vibration velocity data, and tilt angle data obtained from long-term monitoring will show certain mean values and fluctuation ranges, and these data characteristics are integrated into the seismic activity baseline model. The seismic activity baseline model may include statistical parameters such as the mean value, standard deviation, and frequency distribution of each seismic monitoring data, as well as the results of correlation analysis between them. The characteristics in the normal state form the basis of the seismic activity baseline model and are used as a reference standard for judging whether the subsequently collected data is abnormal.

[0080] Optionally, when the data acquisition and transmission unit 114 receives newly collected seismic monitoring data, it will compare and analyze it with the pre-constructed seismic activity baseline model to determine whether the current data is related to seismic activity. If all the seismic monitoring data shows a significant deviation from the seismic activity baseline model, and this deviation conforms to the characteristic pattern of seismic waves, then it can be preliminarily determined that the currently collected seismic monitoring data is related to seismic activity; on the contrary, if all the seismic monitoring data fluctuates within the normal baseline model range, or only individual isolated data points are abnormal while the overall characteristics still conform to the normal background situation, it is determined that the currently collected seismic monitoring data is not related to seismic activity.

[0081] Among them, the feature extraction unit 121 is used to extract time-domain and frequency-domain features from the three-axis vibration velocity data, and send the extracted time-domain features and frequency-domain features to the seismic wave detection unit;

[0082] The seismic wave detection unit 122 is used to detect P-waves and S-waves according to the time-domain characteristics and frequency-domain characteristics;

[0083] The magnitude estimation unit 123 is used to estimate the earthquake magnitude according to the characteristic parameters of the P-waves and S-waves extracted by the feature extraction unit after the seismic wave detection unit detects the P-waves;

[0084] The earthquake source location unit 124 is used to locate the earthquake source position according to the seismic monitoring data provided by multiple seismic information acquisition points.

[0085] Among them, the feature extraction unit 121 can specifically be used to:

[0086] Calculate the signal amplitude and the signal zero-crossing rate according to the three-axis vibration velocity data;

[0087] Perform a Fourier transform on the three-axis vibration velocity data to obtain the frequency spectrum of the vibration velocity, and calculate the main frequency and the frequency band width according to the frequency spectrum.

[0088] Optionally, the signal amplitude is an important time-domain characteristic describing the intensity of the three-axis vibration velocity data. For the vibration velocity signal caused by seismic waves, its amplitude reflects the severity of ground vibration. When calculating the signal amplitude, it is first necessary to process the three-axis vibration velocity data. Let the three-axis vibration velocities be (where t represents time). For the vibration velocity signal in each axis, the calculation method of its amplitude is: , where, is the maximum value of the absolute value of the axial vibration velocity signal, is the minimum value of its absolute value. For example, when an earthquake occurs, the P-wave arrives first, causing a certain change in the vibration velocity of the ground in the propagation direction. By calculating , , their respective amplitudes, the vibration intensity of the P-wave in different directions can be initially judged. When the subsequent S-wave arrives, it will cause a larger amplitude of vibration, and the corresponding amplitude will also be larger.

[0089] Optionally, the signal zero-crossing rate refers to the number of times the signal value changes from positive to negative or from negative to positive. For the discrete three-axis vibration velocity signals , , (n = 0, 1,..., N - 1, N is the number of signal samples), taking as an example, the calculation formula of its zero-crossing rate is , where is the sign function. When is greater than or equal to 0, is equal to 1. When is less than 0, It is equal to -1, and the calculation methods of the zero-crossing rates on the other two axes are the same as those described above. By calculating the zero-crossing rates of the three-axis vibration velocity signals, it is possible to assist in distinguishing between P-waves and S-waves.

[0090] Optionally, through Fourier transform, the three-axis vibration velocity data can be converted into a frequency-domain signal, so as to clearly see the different frequency components contained in the signal. In seismic waves, the frequency range of P-waves is usually around 1 - 10 Hz, and the frequency of S-waves is generally around 0.5 - 5 Hz. By performing Fourier transform on the three-axis vibration velocity data, P-waves and S-waves can be distinguished.

[0091] Optionally, the main frequency refers to the frequency point with the largest amplitude in the frequency spectrum. By traversing the amplitudes of each frequency point in the frequency spectrum, the point with the largest amplitude can be determined as the main frequency.

[0092] Optionally, the bandwidth can be determined by calculating the frequency range with a certain amplitude ratio. For example, usually calculate the frequency range where the amplitude is greater than of the maximum amplitude as the bandwidth. There are obvious differences in the main frequency and bandwidth between P-waves and S-waves. The main frequency of P-waves is relatively high and the bandwidth is relatively wide, while the main frequency of S-waves is relatively low and the bandwidth is relatively narrow. By calculating the main frequency and bandwidth of the three-axis vibration velocity data, P-waves and S-waves can be more accurately identified.

[0093] Optionally, the seismic wave detection unit 122 will first perform a preliminary detection of P-waves and S-waves using the characteristic parameters such as the signal amplitude, zero-crossing rate, main frequency, and bandwidth calculated by the feature extraction unit 121. The seismic wave detection unit 122 can also use machine learning algorithms for further detection of P-waves and S-waves. After inputting the signal amplitude, zero-crossing rate, main frequency, and bandwidth into a trained machine learning model, the model will calculate a decision function value based on the feature vector of the data. For example, for an SVM model, it will determine whether it is a P-wave, an S-wave, or a non-seismic wave according to the decision function value. By combining the characteristic parameters with the SVM model for detection, it can better adapt to complex seismic wave signals and environmental interferences, and improve the accuracy and reliability of P-wave and S-wave detection.

[0094] Among them, the magnitude estimation unit 123 can be specifically used for:

[0095] According to the formula , calculate the earthquake magnitude;

[0096] Among them, M is the earthquake magnitude, a, b, c, d are coefficients corresponding to the area where the earthquake information acquisition module is located respectively, A P is the signal amplitude of the P-wave, A S is the signal amplitude of the S-wave, T S is the duration of the S-wave, fP and f S are the main frequencies of the P-wave and S-wave respectively.

[0097] Optionally, the coefficients a, b, c, and d can be calibrated according to the local geological conditions and historical earthquake monitoring data in different earthquake monitoring areas.

[0098] Among them, the earthquake source location unit 124 can be specifically used for:

[0099] Obtain the P-wave arrival time collected by each earthquake information collection module and the coordinates of each earthquake information collection module, and establish multiple equations based on the P-wave arrival time and coordinates of each earthquake information collection module ;

[0100] Among them, v is the propagation speed of the P-wave, (x, y) is the coordinate of the earthquake source location, (x i , y i ) and (x j , y j ) are the coordinates of the first earthquake information collection module and the coordinates of the second earthquake information collection module respectively, t i and t j are the P-wave arrival times of the first earthquake information collection module and the second earthquake information collection module respectively;

[0101] Calculate the earthquake source location according to the established equations.

[0102] The technical solution of the embodiment of the present invention configures a seismic information acquisition module, a cloud server, and a warning information publishing module in the earthquake early warning and rapid reporting Internet of Things system. The seismic information acquisition module is used to collect seismic monitoring physical quantities, generate seismic monitoring data according to the seismic monitoring physical quantities, and send the seismic monitoring data to the cloud server. The cloud server is used to estimate the earthquake magnitude and the earthquake epicenter location when an earthquake event is identified according to the seismic monitoring data. The warning information publishing module is used to determine the area to be warned according to the earthquake magnitude and the earthquake epicenter location, and send earthquake warning information to the broadcast system, mobile devices, and outdoor alarm systems in the area to be warned at the same time. By reasonably planning the distribution of sensors, the coverage rate of seismic monitoring is improved, and the data collected by each sensor is highly consistent in time, reducing the error in seismic wave analysis caused by time error, so as to accurately detect seismic waves. After detecting an earthquake event, the warning information can be widely spread by combining the broadcast system, mobile network, and outdoor alarm system. The warning decision-making module can determine the warning scope and warning level of different regions and issue targeted warning information. The targeted warning information can help people in different regions take more effective risk avoidance measures according to the actual situation and improve the ability of the whole society to respond to earthquake disasters.

[0103] Embodiment 2

[0104] Figure 3 The flowchart of an earthquake early warning and rapid reporting method provided by the second embodiment of the present invention is applicable to the scenario of detecting earthquakes and reasonably publishing warning information when an earthquake event is detected. As Figure 3 shown, the method includes:

[0105] S210. Through the seismic information acquisition module, collect seismic monitoring physical quantities, generate seismic monitoring data according to the seismic monitoring physical quantities, and send the seismic monitoring data to the cloud server.

[0106] Among them, the seismic monitoring data includes three-axis acceleration data, three-axis vibration velocity data, and tilt angle data.

[0107] Optionally, the seismic information acquisition module 110 is the front-end data collection unit in the entire earthquake early warning Internet of Things system. Through the seismic information acquisition module 110, various physical quantity information related to earthquakes can be obtained at the earthquake monitoring site. By preliminarily processing and integrating these original information, seismic monitoring data that can be used for subsequent analysis and decision-making is generated. The seismic information acquisition module 110 can be widely deployed in earthquake-active regions or geological regions that need to be monitored key.

[0108] S220. Through the cloud server, when an earthquake event is identified based on the earthquake monitoring data, estimate the earthquake magnitude and the earthquake epicenter location.

[0109] Optionally, the cloud server 120 is a server based on cloud computing technology, which provides computing resources, data storage, and various software services for the earthquake early warning Internet of Things system through the network. The cloud server 120 can store a large amount of earthquake monitoring data. The storage system of the cloud server 120 can adopt a combination of a distributed file system and a relational database to store data, dynamically expand the storage capacity to adapt to the growing data demand, effectively manage and store a large amount of earthquake monitoring data, and ensure the persistence and accessibility of the data.

[0110] S230. Through the warning information release module, determine the area to be warned based on the earthquake magnitude and the earthquake epicenter location, and simultaneously send earthquake warning information to the broadcast system, mobile devices, and outdoor alarm systems within the area to be warned.

[0111] Optionally, in the warning information release module 130, an empirical model of the magnitude and the influence radius can be established in advance. When the earthquake magnitude is determined, use this model to calculate the corresponding influence radius, so as to delimit a preliminary area to be warned in a circular or elliptical shape.

[0112] It can be understood that due to the different epicenter locations, the surrounding geographical environment and population distribution are different, which will affect the propagation path of seismic waves and the actual impact degree on different regions. For example, if the epicenter is located in a mountainous area, seismic waves may be refracted, reflected, etc. during the propagation process due to the blockage of mountains and the complexity of geological structures, resulting in changes in the energy distribution and propagation direction of seismic waves. The seismic impact may be relatively small on one side of the mountain, while more intense seismic shocks may be suffered in valleys or near fault zones of mountains. Another example is that if the epicenter is located below or near a city, due to the dense population, numerous buildings and complex types in the city, the degree of earthquake hazard will increase significantly. When considering the epicenter location, the warning information release module 130 will combine Geographic Information System (GIS) data, analyze information such as the topography, population density, and building distribution around the epicenter, use the contour data in GIS to judge the mountain range trend and terrain undulation, determine the densely populated areas and the distribution of important facilities based on census data and urban planning data. By comprehensively considering the earthquake magnitude and the epicenter location, as well as these geographical and human factors, the warning information release module 130 can more accurately determine the area to be warned on the basis of the preliminary area to be warned. For example, the area to be warned can be accurately determined to urban blocks, rural villages and towns, traffic arteries, etc. that are on the high-risk propagation paths of seismic waves, and these areas are included in the final list of areas to be warned for targeted warning information push.

[0113] Optionally, the early warning information publishing module 130 may establish a data connection interface with a broadcasting organization such as a radio station. After determining the area to be warned, the earthquake early warning information is encoded and transmitted in a format that can be received and played by the broadcasting system. For example, information such as the earthquake magnitude, the earthquake source location, the estimated time of arrival of the seismic wave, the possible hazards caused by the earthquake, and the risk avoidance measures that the public should take is converted into voice or a specific broadcasting data format, and then the program is broadcast through the radio station.

[0114] Optionally, the early warning information publishing module 130 may also send the earthquake early warning information to the mobile network core computer room, and the mobile operator may use the SMS gateway system to push the earthquake early warning information to the mobile device users in the area to be warned in the form of SMS.

[0115] Optionally, the outdoor alarm system may be installed in public places in the city, such as densely populated areas like squares, schools, and communities. The early warning information publishing module 130 may establish a connection with the outdoor alarm system through communication methods such as Ethernet or wireless local area network. When there is earthquake early warning information, a trigger instruction is sent to these outdoor alarm systems. After receiving the instruction, the outdoor alarm system will activate a strong sound and light alarm device.

[0116] The technical solution of the embodiment of the present invention, by using the earthquake information acquisition module to collect earthquake monitoring physical quantities, generating earthquake monitoring data according to the earthquake monitoring physical quantities, sending the earthquake monitoring data to the cloud server, using the cloud server to estimate the earthquake magnitude and the earthquake source location when an earthquake event is identified according to the earthquake monitoring data, using the early warning information publishing module to determine the area to be warned according to the earthquake magnitude and the earthquake source location, and simultaneously sending earthquake early warning information to the broadcasting system, mobile devices, and outdoor alarm systems in the area to be warned. By reasonably planning the distribution of sensors, the coverage rate of earthquake monitoring is improved, ensuring that the data collected by each sensor has a high degree of consistency in time, reducing the error in seismic wave analysis caused by time error, so as to achieve accurate detection of seismic waves, and after detecting an earthquake event, by combining the broadcasting system, mobile network, and outdoor alarm system, the wide dissemination of early warning information is realized. The early warning decision-making module can determine the early warning range and early warning level of different regions and issue targeted early warning information. The targeted early warning information can help people in different regions take more effective risk avoidance measures according to the actual situation, improving the ability of the whole society to respond to earthquake disasters.

[0117] Embodiment III

[0118] Figure 4FIG. 0 shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0119] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0120] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0121] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the earthquake early warning and rapid reporting method as described in the embodiments of the present invention. That is:

[0122] Through the seismic information acquisition module, seismic monitoring physical quantities are acquired, and seismic monitoring data is generated based on the seismic monitoring physical quantities, and the seismic monitoring data is sent to the cloud server; wherein, the seismic monitoring data includes three-axis acceleration data, three-axis vibration velocity data, and tilt angle data.

[0123] Through the cloud server, when a seismic event is identified based on the seismic monitoring data, the seismic magnitude and the seismic source location are estimated.

[0124] Through the early warning information release module, based on the seismic magnitude and the seismic source location, the area to be warned is determined, and seismic early warning information is simultaneously sent to the broadcast system, mobile devices, and outdoor alarm systems within the area to be warned.

[0125] In some embodiments, the seismic early warning and rapid reporting method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the seismic early warning and rapid reporting method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the seismic early warning and rapid reporting method by any other suitable means (e.g., by means of firmware).

[0126] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs, the one or more computer programs can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general-purpose programmable processor, can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0127] A computer program for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine, or entirely on a remote machine or server.

[0128] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0129] In order to provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, speech input, or tactile input).

[0130] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0131] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0132] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and this is not limited herein.

[0133] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An earthquake early warning and rapid reporting Internet of Things system, characterized in that, It includes a seismic information acquisition module, a cloud server, and an early warning information release module; The seismic information acquisition module is used to acquire seismic monitoring physical quantities, generate seismic monitoring data based on the seismic monitoring physical quantities, and send the seismic monitoring data to the cloud server; wherein, the seismic monitoring data includes three-axis acceleration data, three-axis vibration velocity data, and tilt angle data; The cloud server is used to estimate the earthquake magnitude and the earthquake epicenter location when an earthquake event is identified based on the seismic monitoring data; The early warning information release module is used to determine the area to be warned according to the earthquake magnitude and the earthquake epicenter location, and simultaneously send earthquake early warning information to the broadcast system, mobile devices, and outdoor alarm systems within the area to be warned; Among them, when considering the epicenter location, the early warning information release module will combine geographical information system data, analyze the topography, population density, and building distribution around the epicenter, use the contour line data in the geographical information system data to judge the mountain range trend and terrain undulation, determine the densely populated areas and the distribution of important facilities according to the census data and urban planning data, and determine the area to be warned.

2. The system according to claim 1, characterized in that, The seismic information acquisition module includes an acceleration sensor, a seismograph, a ground tilt sensor, and a data acquisition and transmission unit; Among them, a seismic information acquisition module is set at each seismic information acquisition point within the seismic monitoring area, and the acceleration sensor, seismograph, and ground tilt sensor in each seismic information acquisition module are respectively connected to the data acquisition and transmission unit.

3. The system according to claim 2, characterized in that, The data acquisition and transmission unit is used for: Converting the seismic monitoring physical quantities collected by the acceleration sensor, seismograph, and ground tilt sensor into digital signals, and using the global positioning system to realize the clock synchronization of each digital signal to generate seismic monitoring data; Judging whether the currently collected seismic monitoring data is related to seismic activities according to the currently collected seismic monitoring data and the pre-constructed seismic activity baseline model; If so, enter the high-frequency transmission mode and send all the generated seismic monitoring data to the cloud server; If not, maintain the low-frequency transmission mode, and whenever reaching the specified data upload node, send the most recently generated seismic monitoring data to the cloud server.

4. The system according to claim 3, characterized in that, The cloud server includes: A feature extraction unit, which is used to extract time-domain and frequency-domain features from the three-axis vibration velocity data, and send the extracted time-domain features and frequency-domain features to the seismic wave detection unit; A seismic wave detection unit, which is used to detect P waves and S waves according to the time-domain features and frequency-domain features; A magnitude estimation unit, which is used to estimate the earthquake magnitude according to the characteristic parameters of P waves and S waves extracted by the feature extraction unit after the seismic wave detection unit detects P waves; An epicenter location unit, which is used to locate the earthquake epicenter location according to the seismic monitoring data provided by multiple seismic information acquisition points.

5. The system according to claim 4, characterized in that, The feature extraction unit is specifically used for: Calculating the signal amplitude and the signal zero-crossing rate according to the three-axis vibration velocity data; Performing a Fourier transform on the three-axis vibration velocity data to obtain the frequency spectrum of the vibration velocity, and calculating the main frequency and the frequency band width according to the frequency spectrum.

6. The system according to claim 5, characterized in that, The magnitude estimation unit is specifically configured to: According to the formula , calculate the earthquake magnitude; Among them, M is the earthquake magnitude, and a, b, c, and d are coefficients corresponding to the area where the earthquake information acquisition module is located, respectively. A P is the signal amplitude of the P wave, and A S is the signal amplitude of the S wave, T S is the duration of the S wave, f P and f S are the main frequencies of the P wave and the S wave, respectively.

7. The system according to claim 5, characterized in that, The earthquake source location unit is configured to: Obtain the P-wave arrival times collected by each earthquake information acquisition module and the coordinates of each earthquake information acquisition module, and establish multiple equations based on the P-wave arrival times and coordinates of each earthquake information acquisition module ; Among them, v is the propagation speed of the P-wave, (x, y) are the coordinates of the earthquake source location, (x i , y i ) and (x j , y j ) are the coordinates of the first seismic information acquisition module and the coordinates of the second seismic information acquisition module respectively, t i and t j are the arrival times of the P-wave of the first seismic information acquisition module and the second seismic information acquisition module respectively; Calculate the earthquake source location according to the established equations.

8. An earthquake early warning and rapid reporting method, characterized in that, Applied to an earthquake early warning and rapid reporting Internet of Things system as described in any one of claims 1-7, it includes: Through the earthquake information collection module, collect earthquake monitoring physical quantities, generate earthquake monitoring data according to the earthquake monitoring physical quantities, and send the earthquake monitoring data to the cloud server; wherein, the earthquake monitoring data includes triaxial acceleration data, triaxial vibration velocity data, and tilt angle data; Through the cloud server, when an earthquake event is identified according to the earthquake monitoring data, estimate the earthquake magnitude and the earthquake source location; Through the early warning information release module, determine the area to be warned according to the earthquake magnitude and the earthquake source location, and simultaneously send earthquake early warning information to the broadcast system, mobile devices, and outdoor alarm systems within the area to be warned.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the earthquake early warning and rapid reporting method described in claim 8.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions for causing a processor to implement the earthquake early warning and rapid reporting method described in claim 8 when executed.

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