Earthquake early warning simulation result generation system and method

Abstract

The invention discloses an earthquake early warning simulation result generation system. The system comprises a data acquisition module, a data processing module, a seismic condition prediction module, an information processing module and a three-dimensional visualization module. The data acquisition module is used for acquiring and sending real-time seismic data; the data processing module is used for carrying out data cleaning on the real-time seismic data based on a preset data cleaning rule to obtain target seismic data, and the seismic condition prediction module is used for processing the target seismic data to obtain a seismic condition prediction result; the information processing module is used for processing the received earthquake condition prediction result to obtain earthquake evaluation information matched with the earthquake condition prediction result; and the three-dimensional visualization module is used for generating a three-dimensional earthquake early warning model based on the target earthquake data and the earthquake evaluation information. Through the technical scheme of the invention, the generation of the earthquake early warning simulation result can be realized, and the intuition, accuracy and timeliness of the earthquake early warning simulation result are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of earthquake early warning, and in particular to a system and method for generating earthquake early warning simulation results. BACKGROUND

[0002] Earthquakes are a natural disaster with great destructive power, causing huge loss of life and property to human society. In order to reduce the harm caused by earthquakes, timely and accurate earthquake early warning is crucial.

[0003] With the continuous progress of science and technology, especially the rapid development of sensor technology, data processing technology and computer simulation technology, new possibilities have been provided for earthquake early warning. Modern earthquake monitoring systems can obtain more abundant and accurate real-time earthquake data, including the propagation speed, amplitude, frequency and other multi-dimensional information of seismic waves. However, how to effectively integrate and analyze these massive data to generate accurate, intuitive and practical earthquake early warning simulation results remains a technical problem to be solved. Currently, some existing earthquake early warning systems have deficiencies in data processing and result display: the data cleaning of some systems is not perfect, resulting in noise and outliers interfering with subsequent analysis and prediction; some earthquake condition prediction models are not accurate enough to accurately reflect the propagation range and intensity changes of seismic waves; some systems lack the ability to comprehensively consider multiple factors in the information processing and evaluation process, making the earthquake evaluation information not comprehensive and accurate enough. In addition, in terms of result display, most systems use two-dimensional graphics or simple charts, which are difficult to intuitively present the complex characteristics and potential impact of earthquakes, and are not conducive to relevant departments and the public to quickly understand and respond.

[0004] In summary, the existing earthquake early warning simulation result generation system has the problem of poor intuitiveness, accuracy and timeliness of earthquake early warning simulation results. SUMMARY

[0005] The present application provides an earthquake early warning simulation result generation system, which can solve the problem of poor intuitiveness, accuracy and timeliness of existing earthquake early warning simulation results.

[0006] According to an aspect of the present application, an earthquake early warning simulation result generation system is provided, which comprises a data acquisition module, a data processing module, an earthquake condition prediction module, an information processing module and a three-dimensional visualization module connected in sequence.

[0007] The data acquisition module is configured to obtain at least one real-time earthquake data from a seismic station and send it to the data processing module.

[0008] The data processing module is configured to perform data cleaning on each real-time seismic data based on a preset data cleaning rule after receiving the real-time seismic data, to obtain each target seismic data and send the target seismic data to the seismic condition prediction module and the three-dimensional visualization module.

[0009] The seismic condition prediction module is configured to process each target seismic data after receiving the target seismic data, to obtain a seismic condition prediction result and send the seismic condition prediction result to the information processing module, wherein the seismic condition prediction result comprises a seismic wave propagation range and intensity variation information of a target earthquake.

[0010] The information processing module is configured to process the received seismic condition prediction result, to obtain seismic evaluation information matched with the seismic condition prediction result and send the seismic evaluation information to the three-dimensional visualization module.

[0011] The three-dimensional visualization module is configured to generate a three-dimensional earthquake early warning model based on each target seismic data and the seismic evaluation information received, and display the three-dimensional earthquake early warning model.

[0012] Optionally, the seismic condition prediction module specifically comprises: a feature extraction unit configured to perform feature extraction processing on each target seismic data based on a preset formula to obtain a data feature set, wherein F(ω) is a data feature, ω is an earthquake angular frequency in the target seismic data, S(t) is a seismic condition-time sequence in the target seismic data, which represents a change of the seismic data with time, and t is a time parameter in the seismic condition-time sequence; a normalization unit configured to perform normalization processing on each data feature contained in the data feature set to obtain a normalized data set; and a deep learning unit configured to perform processing on the normalized data set based on a pre-trained earthquake prediction model to obtain a seismic condition prediction result matched with the target seismic data.

[0013] Optionally, the information processing module specifically comprises: a seismic wave propagation simulation unit configured to perform calculation based on a preset physical model to obtain a seismic wave propagation path, a propagation speed and an attenuation condition matched with the seismic condition prediction result after receiving the seismic condition prediction result, and send each calculation result to a geographic information system combination unit; and the geographic information system combination unit is configured to obtain satellite remote sensing data, and obtain seismic evaluation information matched with the seismic condition prediction result based on the satellite remote sensing data and each calculation result, wherein the seismic evaluation information is used to evaluate a hazard degree of an earthquake.

[0014] Further, the preset physical model comprises at least one of an elastic wave equation model, an anisotropic medium model, a viscoelastic medium model and a layered medium model.

[0015] Optionally, the geographic information system combination unit further comprises a satellite connection unit configured to connect with a satellite data platform and acquire satellite remote sensing data of a specific region; and a data combination unit configured to receive the satellite remote sensing data and the calculation results, and process the satellite remote sensing data and the calculation results based on a pre-set mountain landslide susceptibility model to obtain the earthquake assessment information.

[0016] Optionally, the three-dimensional visualization module is further configured to receive an operation instruction of a user to perform a perspective switching or size adjustment operation on the three-dimensional earthquake early warning model.

[0017] Optionally, the seismic condition prediction module further comprises a historical data acquisition unit configured to acquire historical sample data from a seismic station, wherein the historical sample data comprises historical earthquake data and historical earthquake results matched with the historical earthquake data, and the historical earthquake results are used as labeled data; and a model training unit configured to train a convolutional neural network using the historical sample data to obtain an earthquake prediction model.

[0018] Optionally, the earthquake early warning simulation system further comprises a real-time updating module configured to continuously monitor the latest data of the seismic station according to a monitoring instruction of a user, and periodically send the updated data to the data acquisition module according to a pre-set data sending frequency.

[0019] Optionally, the earthquake early warning simulation system further comprises a risk early warning publishing module configured to generate corresponding risk early warning information according to the earthquake assessment information, and publish the risk early warning information to relevant departments through a pre-set channel.

[0020] According to another aspect of the present application, an earthquake early warning simulation result generation method applied to an earthquake early warning simulation result generation system is provided, and the method comprises:

[0021] acquiring at least one real-time earthquake data from a seismic station through a data acquisition module and sending the real-time earthquake data to a data processing module;

[0022] performing data cleaning on the real-time earthquake data based on a pre-set data cleaning rule through the data processing module after receiving the real-time earthquake data, obtaining target earthquake data, and sending the target earthquake data to a seismic condition prediction module and a three-dimensional visualization module;

[0023] processing the target earthquake data through the seismic condition prediction module after receiving the target earthquake data, obtaining a seismic condition prediction result, and sending the seismic condition prediction result to an information processing module, wherein the seismic condition prediction result comprises a seismic wave propagation range and intensity change information of a target earthquake;

[0024] The information processing module processes the received seismic situation prediction result, obtains seismic evaluation information matched with the seismic situation prediction result, and sends the seismic evaluation information to the three-dimensional visualization module;

[0025] The three-dimensional visualization module generates a three-dimensional earthquake early warning model based on the received target seismic data and seismic evaluation information, and displays the three-dimensional earthquake early warning model through a WEBGL network graphics library.

[0026] The technical scheme of the embodiment of the present application first acquires at least one real-time seismic data from a seismic station through the data acquisition module and sends the real-time seismic data to the data processing module. After receiving each real-time seismic data, the data processing module performs data cleaning on each real-time seismic data based on a preset data cleaning rule, obtains each target seismic data, and sends the target seismic data to the seismic situation prediction module and the three-dimensional visualization module. Then, the seismic situation prediction module processes each target seismic data after receiving the target seismic data, obtains a seismic situation prediction result, and sends the seismic situation prediction result to the information processing module. Then, the information processing module processes the received seismic situation prediction result, obtains seismic evaluation information matched with the seismic situation prediction result, and sends the seismic evaluation information to the three-dimensional visualization module. Finally, the three-dimensional visualization module generates a three-dimensional earthquake early warning model based on the received target seismic data and seismic evaluation information, and displays the three-dimensional earthquake early warning model through a WEBGL network graphics library. The problem of poor intuitiveness, accuracy and timeliness of the existing earthquake early warning simulation result is solved, the generation of the earthquake early warning simulation result is realized, and the intuitiveness, accuracy and timeliness of the earthquake early warning simulation result are improved.

[0027] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0029] Figure 1 is a structural schematic diagram of an earthquake early warning simulation result generation system provided by the first embodiment of the present application;

[0030] Figure 2 is a structural schematic diagram of an earthquake early warning simulation result generation system provided by the second embodiment of the present application;

[0031] Figure 3This is a flowchart of a method for generating earthquake early warning simulation results provided according to embodiment three of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0033] 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 the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Example 1

[0035] Figure 1 This is a structural diagram of an earthquake early warning simulation result generation system provided in Example 1 of the present invention. This embodiment is applicable to the situation where earthquake early warning simulation results are generated based on a seismic station and a WEBGL network graphics library.

[0036] like Figure 1 As shown, the system includes a data acquisition module 110, a data processing module 120, an earthquake prediction module 130, an information processing module 140 and a three-dimensional visualization module 150 which are connected in sequence.

[0037] The data acquisition module 110 is used to acquire at least one real-time seismic data from a seismic station and send the data to the data processing module 120 .

[0038] The term "seismic station" refers to a facility or institution dedicated to monitoring and recording seismic activity. It is typically equipped with a series of high-precision seismic monitoring instruments, such as seismographs and accelerometers, that can sense seismic waves generated by crustal movement and convert these physical signals into analyzable digital data, i.e., the real-time seismic data in this embodiment.

[0039] In particular, in the present embodiment, the real-time seismic data includes at least one of the magnitude of the earthquake, the focal depth, the epicenter location, the seismic wave propagation speed, the duration of the earthquake, the ground acceleration, the ground displacement, the seismic angular frequency, and the earthquake-time sequence.

[0040] The data processing module 120 is configured to perform data cleaning on the real-time seismic data based on preset data cleaning rules after receiving the real-time seismic data, to obtain target seismic data and send the target seismic data to the seismic condition prediction module 130 and the three-dimensional visualization module 150.

[0041] In particular, the data processing module 120 in the present embodiment can be composed of a high-performance computer system and specific data processing software. After receiving the real-time seismic data from the data acquisition module 110, the data processing module 120 performs a series of processing operations on the data based on preset data cleaning rules. The data cleaning rules can include operations such as removing noise data, correcting erroneous data, and filling missing values. Through these operations, the original real-time seismic data is converted into accurate and effective target seismic data. Subsequently, the target seismic data that has been cleaned and processed is sent to the seismic condition prediction module 130 and the three-dimensional visualization module 150, respectively, for subsequent analysis and display.

[0042] The seismic condition prediction module 130 is configured to process the target seismic data after receiving the target seismic data, to obtain a seismic condition prediction result and send the seismic condition prediction result to the information processing module 140. The seismic condition prediction result includes the seismic wave propagation range and the intensity variation information of the target earthquake.

[0043] The seismic wave propagation range refers to the range of areas where the seismic waves generated by an earthquake can propagate on the surface or underground. It is usually described in the form of spreading around the epicenter. For example, the seismic wave propagation range of an earthquake can be an area 100 kilometers around the epicenter. Further, the intensity variation information is a description of the variation of the intensity of the earthquake at different locations and different times. In the present embodiment, the intensity of the earthquake can be measured by various indicators, such as the Richter magnitude, ground acceleration, etc. The intensity variation information can include the intensity at different distances from the epicenter, or the increase or decrease of the intensity in different time periods after the earthquake. For example, for a certain earthquake, the intensity near the epicenter can reach 8, and as the distance from the epicenter increases, the intensity gradually decreases, such as to 6 at a distance of 50 kilometers; or the intensity is strong in the first few minutes after the earthquake, and then gradually weakens.

[0044] The seismic condition prediction module 130 specifically includes a feature extraction unit configured to extract features of the target seismic data based on a preset formula The received target seismic data is subjected to feature extraction processing to obtain a data feature set, wherein F(ω) is a data feature, ω is an earthquake angular frequency in the target seismic data, S(t) is a seism- time sequence used to represent the change of the seismic data with time in the target seismic data, and t is a time parameter in the seism- time sequence; a normalization unit is configured to normalize each data feature in the data feature set to obtain a normalized data set; and a deep learning unit is configured to process the normalized data set based on a pre-trained seismic prediction model to obtain a seism prediction result matched with the target seismic data.

[0045] The normalization processing is a data processing method that maps data to a specific range, usually converting data to values between 0 and 1, or -1 and 1. The main purpose of normalization is to eliminate the differences in magnitude, dimension and numerical range of different features, so that different features have the same weight and influence in subsequent data analysis and model training, thereby improving the performance and accuracy of the algorithm. For example, if there are two features, one feature value is between 0 and 100, and the other feature value is between 0 and 1, without normalization processing, the former may have too much influence on the analysis result. By normalization, the value of the first feature can also be mapped to 0 to 1, so that the two features can participate in calculation and analysis equally. Optionally, in the embodiment, the normalization method configured by the normalization unit can be min-max normalization or Z-score standardization, etc.

[0046] In the embodiment, the seismic prediction model can be used to obtain a seism prediction result matched with the current earthquake by analyzing the target seismic data of the received current earthquake; wherein the seism prediction result includes at least one of the following: the occurrence time of aftershocks of the current earthquake, the epicenter location prediction, the magnitude prediction, the potential disaster area, the estimated results of personnel casualties and property losses, the possibility of secondary disasters, the estimated results of earthquake duration, and the assessment of the damage degree of buildings and infrastructure. It should be noted that the specific data types included in the seism prediction result can be configured by the developer when training the seismic prediction model according to the needs of the actual implementation scenario, and the embodiment does not limit this.

[0047] Further, the seism prediction module 130 further includes a historical data acquisition unit configured to acquire historical sample data from a seismic station, wherein the historical sample data includes historical seismic data and historical seismic results matched with the historical seismic data, and the historical seismic results are used as labeled data; and a model training unit configured to train a convolutional neural network using the historical sample data to obtain a seismic prediction model.

[0048] Specifically, in actual application scenarios, seismic data usually has complex spatio-temporal characteristics and patterns, and convolutional neural networks can automatically learn these hidden features and patterns from a large amount of seismic-related data. For example, by inputting historical seismic data, including the magnitude, focal depth, epicenter location, seismic wave propagation characteristics, and other multi-dimensional information of earthquakes, the convolutional layers in the convolutional neural network can capture local features in these data, such as the seismic activity patterns in a specific region or the correlation features between earthquakes of different magnitudes. The pooling layer can filter and compress these features, extracting the most critical information and reducing the dimensionality and computational complexity of the data. The fully connected layer integrates these filtered and extracted features, and finally outputs the prediction results of the likelihood of future earthquakes, the magnitude, the possible epicenter location, and other aspects. By training the convolutional neural network using a large amount of historical seismic data, it can continuously optimize its parameters, thereby improving the accuracy and reliability of earthquake prediction. For example, a trained convolutional neural network, i.e., an earthquake prediction model, can discover the pattern that after a period of frequent small earthquakes in a certain region, the likelihood of a larger magnitude earthquake increases, and make corresponding predictions based on this pattern.

[0049] The information processing module 140 is configured to process the received seismic condition prediction result, obtain seismic evaluation information matched with the seismic condition prediction result, and send the seismic evaluation information to the three-dimensional visualization module 150.

[0050] The three-dimensional visualization module 150 is configured to generate and display a three-dimensional earthquake early warning model based on the received target seismic data and seismic evaluation information through a WEBGL network graphics library.

[0051] The WEBGL (Web Graphics Library) is a web-based graphics library for implementing interactive 3D graphics and 2D graphics in web browsers. WEBGL is based on the OpenGL ES specification, allowing web developers to use scripting languages such as JavaScript to create and display complex graphics on web pages, including 3D scenes, models, animations, etc., without the need to install additional plug-ins. It utilizes the computer's graphics processing unit to accelerate graphics rendering, enabling smooth real-time graphics effects. This makes it possible to create games, scientific visualizations, architectural design displays, geographic information systems, etc. with realistic visual effects on web pages. Accordingly, in this embodiment, WEBGL can convert the complex data into intuitive three-dimensional earthquake warning models based on the obtained target earthquake data and earthquake assessment information, such as displaying the depth and specific coordinates of the earthquake epicenter inside the Earth in three-dimensional form, simulating the dynamic process of earthquake wave propagation from the epicenter to the surrounding area, and using different colors and brightness to represent the intensity range and possible disaster areas affected by the earthquake. Such a three-dimensional earthquake warning model can provide more clear and intuitive earthquake situation awareness for relevant departments and the public, helping to make more effective response decisions.

[0052] Further, the three-dimensional visualization module 150 is further configured to receive user operation instructions to perform perspective switching or size adjustment operations on the three-dimensional earthquake warning model.

[0053] Specifically, the user's operation instructions are specific instruction information conveyed by the user to the system through input devices such as mouse, keyboard, touch screen, etc. For example, the user issues instructions by clicking specific buttons, sliding the scroll wheel with the mouse, or performing specific gesture operations on the touch screen, etc.; the perspective switching operation allows the user to observe the three-dimensional earthquake warning model from different angles. For example, from directly above to view the earthquake-affected area, or from the side to view the propagation direction and range of the earthquake wave; further, the size adjustment operation allows the user to change the size of the three-dimensional earthquake warning model according to their own needs. For example, enlarge the model to view local details more clearly, or reduce the model to grasp the overall impact of the earthquake. By receiving these operation instructions from the user, the system can provide a more personalized and flexible interactive experience, allowing the user to gain a deeper and more comprehensive understanding of earthquake warning information.

[0054] Optionally, on the basis of the above-mentioned modules, the earthquake early warning simulation system further comprises: a real-time updating module, configured to continuously monitor the latest data of the seismic station according to a monitoring instruction of a user, and periodically send the updated data to the data acquisition module 110 according to a preset data sending frequency; and a risk early warning publishing module, configured to generate corresponding risk early warning information according to the earthquake assessment information, and publish the risk early warning information to relevant departments through a preset channel.

[0055] In one specific embodiment of the present embodiment, the real-time updating module can be a functional module for continuously tracking and acquiring the latest data of the seismic station. For example, it can be a software program running on a high-performance server. The module will continuously monitor the latest dynamics of the seismic station according to the monitoring instruction of the user; wherein the monitoring instruction of the user is a specific requirement and condition set by the user according to the user's demand for earthquake monitoring in a specific area, and the preset data sending frequency is a time interval for transmitting updated data to the data acquisition module 110. For example, it can be set to send updated data every 5 minutes.

[0056] Further, the risk early warning publishing module is used to generate targeted risk early warning information according to the earthquake assessment information; wherein the preset channel can be a dedicated earthquake early warning publishing platform, a communication network of the government emergency management department, a mobile phone short message push service, etc. For example, through the dedicated network connection with the government emergency command center, the risk early warning information can be timely transmitted to the relevant departments, so that they can quickly take corresponding emergency measures and rescue actions.

[0057] The technical scheme of the present embodiment first acquires at least one real-time earthquake data from the seismic station through the data acquisition module and sends it to the data processing module, and then, after receiving each real-time earthquake data, the data processing module performs data cleaning on each real-time earthquake data based on a preset data cleaning rule, obtains each target earthquake data and sends it to the seismic condition prediction module and the three-dimensional visualization module. Then, after receiving each target earthquake data, the seismic condition prediction module processes each target earthquake data, obtains a seismic condition prediction result and sends it to the information processing module. Then, the information processing module processes the received seismic condition prediction result, obtains earthquake assessment information matching the seismic condition prediction result and sends it to the three-dimensional visualization module. Finally, the three-dimensional visualization module generates a three-dimensional earthquake early warning model based on the received each target earthquake data and earthquake assessment information through the WEBGL network graphics library and displays it. The problem of poor intuitiveness, accuracy and timeliness of the existing earthquake early warning simulation result is solved, the generation of the earthquake early warning simulation result is realized, and the intuitiveness, accuracy and timeliness of the earthquake early warning simulation result are improved.

[0058] Embodiment two

[0059] Figure 2 A structural schematic diagram of a seismic early warning simulation result generation system provided for embodiment two of the present application, and the embodiment can be applicable to the case of generating a seismic early warning simulation result based on a seismic station and a WEBGL network graphics library.

[0060] As shown in Figure 2 The seismic early warning simulation result generation system comprises, in sequence, a data acquisition module, a data processing module, a seismic condition prediction module, an information processing module, and a three-dimensional visualization module.

[0061] The information processing module specifically comprises a seismic wave propagation simulation unit 210 and a geographic information system combination unit 220.

[0062] The seismic wave propagation simulation unit 210 is configured to, after receiving a seismic condition prediction result, calculate a seismic wave propagation path, a propagation speed, and an attenuation condition that match the seismic condition prediction result based on a preset physical model, and send each calculation result to the geographic information system combination unit 220.

[0063] Specifically, the preset physical model comprises at least one of an elastic wave equation model, an anisotropic medium model, a viscoelastic medium model, and a layered medium model.

[0064] In one specific implementation of the present embodiment, the elastic wave equation is a mathematical expression describing the propagation of seismic waves within the Earth. It is based on the elastic properties of the medium and is used to calculate the propagation characteristics of seismic waves, such as propagation speed, path, etc. By solving the elastic wave equation, the behavior of seismic waves in different geological structures can be predicted; further, the anisotropic medium refers to a medium whose physical properties differ in different directions. Due to the inhomogeneity of the structure and composition of rocks in different directions within the Earth, the medium exhibits anisotropy. For example, the propagation speed of seismic waves in certain rock layers may be different in the horizontal and vertical directions, which can be used in the present embodiment to calculate the propagation speed of seismic waves in different media, and thus the propagation range; further, the viscoelastic medium is a medium that combines viscous and elastic characteristics. During the propagation of seismic waves, the viscoelastic medium not only stores and releases energy like an elastic medium, but also dissipates energy due to internal viscosity, thereby affecting the propagation speed and attenuation of seismic waves. The viscoelastic medium model can be used to calculate the propagation of seismic waves in a viscoelastic medium; further, the layered medium is a stratified structure of the Earth's interior, which is often characterized by stratification. This stratified geological structure is referred to as a layered medium. Different rock layers have different physical properties, and the propagation path, speed, and attenuation of seismic waves will change accordingly when they pass through these layered media. The layered medium model can be used to calculate the propagation of seismic waves in a layered medium.

[0065] Further, the geographic information system combination unit 220 is configured to obtain satellite remote sensing data, and obtain earthquake assessment information matching the seismic condition prediction result based on the satellite remote sensing data and the calculation results. The earthquake assessment information is used to assess the damage degree of the earthquake.

[0066] The satellite remote sensing data includes, but is not limited to, topographic relief, landform features, land use types, vegetation distribution, and the like related to the current earthquake. The specific data types included in the satellite remote sensing data can be set by relevant personnel based on actual application conditions, and the present embodiment does not limit this.

[0067] In the present embodiment, the geographic information system combination unit 220 further includes a satellite connection unit configured to establish a connection with a satellite data platform and obtain satellite remote sensing data of a specific region; and a data combination unit configured to receive the satellite remote sensing data and the calculation results, and process the satellite remote sensing data and the calculation results based on a pre-set landslide susceptibility model to obtain the earthquake assessment information.

[0068] Specifically, the satellite data platform is used to collect, store, manage and distribute various data acquired by satellites, which are from a wide range of sources and can include different types of satellites such as meteorological satellites, earth observation satellites, communication satellites, etc. The types of data are also diverse, covering images of the earth's surface, geographic information, meteorological parameters, ocean conditions, environmental monitoring data, etc. Further, in this embodiment, the landslide susceptibility model is used to evaluate and predict the possibility of landslides in a specific area under the influence of an earthquake based on input satellite remote sensing data and the calculation results of the earthquake wave propagation path, propagation speed, attenuation, etc. to obtain earthquake assessment information.

[0069] The technical scheme of the embodiment of the present application first acquires at least one real-time earthquake data from a seismic station through the data acquisition module and sends it to the data processing module. After receiving each real-time earthquake data, the data processing module performs data cleaning on each real-time earthquake data based on a preset data cleaning rule, obtains each target earthquake data and sends it to the earthquake condition prediction module and the three-dimensional visualization module. Then, the earthquake condition prediction module processes each target earthquake data after receiving it, obtains an earthquake condition prediction result and sends it to the information processing module. Then, the earthquake wave propagation simulation unit calculates the earthquake wave propagation path, propagation speed and attenuation matching the earthquake condition prediction result based on a preset physical model, and sends each calculation result to the geographic information system combination unit. The geographic information system combination unit acquires satellite remote sensing data based on the satellite remote sensing data and each calculation result to obtain earthquake assessment information matching the earthquake condition prediction result. Finally, the three-dimensional visualization module generates a three-dimensional earthquake warning model based on each target earthquake data and earthquake assessment information through the WEBGL network graphics library and displays it. The present application solves the problems of poor intuitiveness, accuracy and timeliness of existing earthquake warning simulation results, realizes the generation of earthquake warning simulation results, and improves the intuitiveness, accuracy and timeliness of earthquake warning simulation results.

[0070] Embodiment Three

[0071] Figure 3 A flowchart of a method for generating an earthquake warning simulation result provided by the third embodiment of the present application. The present embodiment can be applied to the generation of an earthquake warning simulation result based on a seismic station and a WEBGL network graphics library. The method can be performed by an earthquake warning simulation result generation system, which can be realized in the form of hardware and / or software. The earthquake warning simulation result generation system can be configured in a terminal or a server with earthquake warning simulation result generation function.

[0072] As shown in Figure 3 , the method comprises:

[0073] S310, acquiring at least one real-time seismic data from a seismic station through a data acquisition module and sending to a data processing module.

[0074] S320, after receiving each real-time seismic data, performing data cleaning on each real-time seismic data based on a preset data cleaning rule through the data processing module, obtaining each target seismic data and sending to a seismic condition prediction module and a three-dimensional visualization module.

[0075] S330, after receiving each target seismic data, processing each target seismic data through the seismic condition prediction module, obtaining a seismic condition prediction result and sending to an information processing module.

[0076] The seismic condition prediction result includes the seismic wave propagation range and intensity change information of the target earthquake.

[0077] S340, processing the received seismic condition prediction result through the information processing module, obtaining seismic evaluation information matched with the seismic condition prediction result and sending to the three-dimensional visualization module.

[0078] S350, based on the received each target seismic data and seismic evaluation information, generating a three-dimensional earthquake warning model through a WEBGL network graphics library and displaying through the three-dimensional visualization module.

[0079] The technical scheme of the embodiment of the application first acquires at least one real-time seismic data from a seismic station through a data acquisition module and sends to a data processing module, and after receiving each real-time seismic data, performs data cleaning on each real-time seismic data based on a preset data cleaning rule through the data processing module, obtains each target seismic data and sends to a seismic condition prediction module and a three-dimensional visualization module, then after receiving each target seismic data, processes each target seismic data through the seismic condition prediction module, obtains a seismic condition prediction result and sends to an information processing module, then processes the received seismic condition prediction result through the information processing module, obtains seismic evaluation information matched with the seismic condition prediction result and sends to the three-dimensional visualization module, finally based on the received each target seismic data and seismic evaluation information, generates a three-dimensional earthquake warning model through a WEBGL network graphics library and displays through the three-dimensional visualization module. The problem of poor intuitiveness, accuracy and timeliness of the existing earthquake warning simulation result is solved, the generation of the earthquake warning simulation result is realized, and the intuitiveness, accuracy and timeliness of the earthquake warning simulation result are improved.

[0080] Specific implementation scenario

[0081] In order to more clearly describe the technical scheme provided by the embodiment of the application, the embodiment will briefly introduce a specific implementation scenario obtained according to the embodiment.

[0082] Suppose in a certain earthquake-prone area, the earthquake early warning simulation result generation system provided by the embodiment starts to operate.

[0083] Step 1: The data acquisition module quickly obtains a plurality of real-time earthquake data from the local seismic station, which contains the key information of the preliminary vibration intensity of the earthquake, the source location, etc., and sends it to the data processing module.

[0084] Step 2: After receiving the real-time earthquake data, the data processing module removes the noise and error data according to the preset data cleaning rules, arranges and optimizes the effective data, and obtains the accurate target earthquake data. Then, the target earthquake data is sent to the seismic condition prediction module and the three-dimensional visualization module at the same time.

[0085] Step 3: After receiving the target earthquake data, the seismic condition prediction module analyzes and processes it in depth. Through a series of operations such as feature extraction, normalization, and the use of pre-trained earthquake prediction model, the seismic condition prediction result is obtained, which includes the target earthquake's seismic wave propagation range and intensity change information. The result is sent to the information processing module.

[0086] Step 4: The information processing module further processes the received seismic condition prediction result, comprehensively considers factors such as geographical environment and population distribution, generates detailed earthquake assessment information matching the seismic condition prediction result, such as possible disaster areas, estimated losses, and landslide possibility assessment results, and sends it to the three-dimensional visualization module.

[0087] Step 5: After receiving the target earthquake data and earthquake assessment information, the three-dimensional visualization module first analyzes and classifies these data, extracts the epicenter location, focal depth, magnitude and other key information from the target earthquake data, and organizes the detailed content of the earthquake assessment information about the possible disaster area range, building damage prediction, and personnel evacuation suggestions. Then, using the powerful functions of WEBGL network graphics library, it starts to build a three-dimensional scene. Based on the three-dimensional model of the Earth, the focal point is accurately marked on the corresponding geographical coordinates according to the epicenter location, and the propagation range and intensity change of the seismic wave are represented by light or lines of different colors and brightness. For the possible disaster area, specific colors or textures are used for identification, such as red for high-risk areas and yellow for moderate-risk areas. In the three-dimensional scene, according to the building damage prediction, the buildings are displayed in different shapes or colors, such as severely damaged buildings showing collapse or tilt, and slightly damaged buildings showing cracks. At the same time, dynamic arrow indicators for personnel evacuation, as well as related warning signs and text explanations are added to the scene. In this way, relevant departments and personnel can intuitively understand the situation of the earthquake, so as to make timely and effective response decisions.

[0088] Note that the above merely describes preferred embodiments of the application and the principles of the technology applied. Those skilled in the art will understand that the application is not limited to the specific embodiments described herein, and that various obvious changes, modifications and substitutions can be made to the application without departing from the scope of the application. Therefore, although the application has been described in detail by the above embodiments, the application is not limited to the above embodiments, and can include more other equivalent embodiments without departing from the concept of the application, and the scope of the application is determined by the appended claims.

Claims

1. A system for generating earthquake early warning simulation results, characterized in that: include: A data acquisition module, a data processing module, an earthquake prediction module, an information processing module and a three-dimensional visualization module connected in sequence; The data acquisition module is used to obtain at least one real-time seismic data from the seismic station and send it to the data processing module; The data processing module is used to perform data cleaning on each real-time seismic data based on preset data cleaning rules after receiving the real-time seismic data, obtain each target seismic data and send it to the earthquake prediction module and the three-dimensional visualization module; The earthquake prediction module is used to process each target earthquake data after receiving it, obtain earthquake prediction results, and send them to the information processing module. The earthquake prediction results include the propagation range and intensity change information of the seismic wave of the target earthquake; The information processing module is used to process the received earthquake prediction results, obtain earthquake assessment information matching the earthquake prediction results, and send the information to the three-dimensional visualization module; The three-dimensional visualization module is used to generate and display a three-dimensional earthquake early warning model through a WEBGL network graphics library based on the received target earthquake data and earthquake assessment information.

2. The system according to claim 1, wherein: The earthquake prediction module specifically includes: Feature extraction unit, used to extract features based on preset formulas Perform feature extraction on each received target seismic data to obtain a data feature set, where F(ω) is the data feature, ω is the seismic angular frequency in the target seismic data, S(t) is the seismic condition-time series used to represent the change of seismic data over time in the target seismic data, and t is the time parameter in the seismic condition-time series; A normalization unit, used to normalize each data feature contained in the data feature set to obtain a normalized data set; A deep learning unit is used to process the normalized data set based on a pre-trained earthquake prediction model to obtain an earthquake prediction result that matches the target earthquake data.

3. The system according to claim 1, wherein: The information processing module specifically includes: The seismic wave propagation simulation unit is used to calculate the seismic wave propagation path, propagation speed and attenuation matching the seismic prediction result based on a preset physical model after receiving the seismic prediction result, and send the calculation results to the geographic information system integration unit; The geographic information system combining unit is used to obtain satellite remote sensing data, and obtain earthquake assessment information matching the earthquake prediction result based on the satellite remote sensing data and various calculation results, and the earthquake assessment information is used to assess the degree of earthquake damage.

4. The system according to claim 3, characterized in that The preset physical model includes at least one of an elastic wave equation model, an anisotropic medium model, a viscoelastic medium model, and a layered medium model.

5. The system according to claim 3, wherein: The geographic information system combining unit further comprises: Satellite connection unit, used to establish a connection with the satellite data platform and obtain satellite remote sensing data of a specific area; The data combining unit is used to receive satellite remote sensing data and various calculation results, and process the satellite remote sensing data and various calculation results based on a preset landslide susceptibility model to obtain earthquake assessment information.

6. The system according to claim 1, wherein: The three-dimensional visualization module is further used to: Receive a user's operation instruction to switch the perspective or adjust the size of the three-dimensional earthquake early warning model.

7. The system according to claim 1, wherein: The earthquake prediction module further includes: A historical data acquisition unit is used to acquire historical sample data from a seismic station, wherein the historical sample data includes historical earthquake data and historical earthquake results matching the historical earthquake data, and the historical earthquake results serve as labeled data; The model training unit is used to train the convolutional neural network using historical sample data to obtain an earthquake prediction model.

8. The system according to claim 1, wherein: The earthquake early warning simulation system further includes: The real-time update module is used to continuously monitor the latest data of the seismic station according to the user's monitoring instructions, and periodically send the updated data to the data acquisition module according to the preset data sending frequency.

9. The system according to claim 1, wherein: The earthquake early warning simulation system further includes: The risk warning release module is used to generate corresponding risk warning information based on earthquake assessment information and release it to relevant departments through preset channels.

10. An earthquake early warning simulation method, characterized in that: An earthquake early warning simulation system according to any one of claims 1 to 8, comprising: Acquire at least one real-time seismic data from the seismic station through the data acquisition module and send it to the data processing module; After receiving each real-time earthquake data, the data processing module cleans the real-time earthquake data based on the preset data cleaning rules, obtains each target earthquake data and sends it to the earthquake prediction module and the three-dimensional visualization module; After receiving the target earthquake data, the earthquake prediction module processes the target earthquake data to obtain the earthquake prediction results and sends them to the information processing module. The earthquake prediction results include the propagation range and intensity change information of the target earthquake's seismic wave; The received earthquake prediction results are processed by the information processing module to obtain earthquake assessment information matching the earthquake prediction results and send the information to the three-dimensional visualization module; Based on the received target earthquake data and earthquake assessment information, a three-dimensional earthquake early warning model is generated and displayed through the WEBGL network graphics library through the three-dimensional visualization module.

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