Earthquake judgment method and device based on network traffic, equipment and storage medium

Abstract

The invention provides an earthquake judgment method and device based on network flow, equipment and a storage medium, and relates to the technical field of electrical digital data processing. The method comprises the following steps: acquiring flow data corresponding to historical station waveform data of an earthquake monitoring station and historical flow data among nodes on a data transmission link; establishing a traffic data vector set based on the traffic data corresponding to the historical station waveform data and historical traffic data between nodes on the data transmission link; wherein vectors in the traffic data vector set represent vectors composed of the traffic data corresponding to the historical station waveform data and the historical traffic data between the nodes on the data transmission link at the same moment; and inputting the flow data vector set into a preset earthquake judgment model, and judging whether an earthquake occurs or not. According to the invention, whether an earthquake event occurs can be determined timely and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to an earthquake determination method, device, equipment and storage medium based on network flow. Background Art

[0002] The diverse businesses and services of the existing earthquake monitoring station network are deployed in a multi-cloud network environment, including heterogeneous architectures such as private cloud, public cloud, and hybrid cloud. In-cloud network traffic analysis is mainly used to monitor the health status of network, security, computing storage and other equipment, such as detecting server load, network congestion, or identifying security threats such as DDoS attacks and malware. These applications focus on the performance and security of the network itself. Traditional earthquake discrimination carries out phase identification based on waveform data from multiple stations from a seismological perspective. The current earthquake event judgment method does not involve the judgment of earthquake events from other perspectives. Although the traditional method of judging earthquake events through phase identification is accurate enough, there is still a gap in how to judge earthquake events from other perspectives. It is impossible to judge earthquake events from multiple perspectives to make the identification of earthquake events more accurate. Summary of the invention

[0003] The embodiments of the present invention provide an earthquake judgment method, device, equipment and storage medium based on network traffic to supplement the existing earthquake event judgment method, realize the judgment of earthquake events in a timely and accurate manner from another perspective, and make the judgment of earthquake events more accurate.

[0004] In a first aspect, an embodiment of the present invention provides an earthquake determination method based on network traffic, comprising: Obtain the flow data corresponding to the historical station waveform data of the seismic monitoring station and the historical flow data between each node on the data transmission link; wherein each node on the data transmission link includes: seismic monitoring station, data processing node, information release node and user terminal.

[0005] A flow data vector set is established based on the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link; wherein the vector in the flow data vector set represents the vector composed of the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link at the same moment.

[0006] The flow data vector set is input into the preset earthquake judgment model to determine whether an earthquake has occurred.

[0007] In a possible implementation, the preset earthquake judgment model is a preset long short-term memory neural network model; the flow data vector set is input into the preset earthquake judgment model to judge whether an earthquake occurs, including: The flow data vector set is input into the preset long short-term memory neural network model to obtain the prediction vector.

[0008] The real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the real-time value of the flow data between each node on the data transmission link are obtained, which are recorded as the first real-time value and the second real-time value respectively; and a real-time vector is established based on the first real-time value and the second real-time value.

[0009] If the Euclidean distance between the predicted vector and the real-time vector is less than or equal to a first preset value, it is determined that the earthquake has not occurred.

[0010] If the Euclidean distance between the predicted vector and the real-time vector is greater than a first preset value, it is determined that an earthquake has occurred.

[0011] In a possible implementation, the calculation formula of the Euclidean distance between the prediction vector and the real-time vector is:

[0012] in, represents the Euclidean distance between the prediction vector and the real-time vector, represents the prediction vector, represents the real-time vector, It represents the real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the total number of real-time values ​​of the flow data between each node on the data transmission link.

[0013] In a possible implementation, the training process of the preset earthquake judgment model is as follows: The flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link are divided into a training set and a test set.

[0014] The training set is input into the preset earthquake judgment model to calculate the training prediction value.

[0015] With the goal of minimizing the difference between the test set and the training prediction value, iterations are performed to optimize the hyperparameters of the preset earthquake judgment model until the difference between the test set and the training prediction value no longer decreases or the upper limit of the number of iterations is reached.

[0016] In a possible implementation, optimizing the hyperparameters of the preset earthquake judgment model includes: The hyperparameters of the preset earthquake judgment model are optimized through particle swarm algorithm or annealing algorithm.

[0017] In one possible implementation, the historical traffic data between each node on the data transmission link includes: historical traffic data between the seismic monitoring station and the data processing node, historical traffic data between the data processing node and the information publishing node, and historical traffic data between the information publishing node and the user terminal.

[0018] In a possible implementation, the flow data corresponding to the historical station waveform data of the seismic monitoring station and the historical flow data between the nodes on the data transmission link are obtained, including: The flow data corresponding to the historical station waveform data of the seismic monitoring station and the initial data of the historical flow data between each node on the data transmission link are obtained.

[0019] The initial data is de-noised, outliers are removed, and missing values ​​are filled to obtain the flow data corresponding to the historical station waveform data of the seismic monitoring station and the historical flow data between each node on the data transmission link.

[0020] In a second aspect, an embodiment of the present invention provides an earthquake determination device based on network traffic, comprising: The data acquisition module is used to obtain the flow data corresponding to the historical station waveform data of the seismic monitoring station, as well as the historical flow data between each node on the data transmission link; wherein each node on the data transmission link includes: seismic monitoring station, data processing node, information release node and user terminal.

[0021] A data processing module is used to establish a flow data vector set based on the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link; wherein the vector in the flow data vector set represents a vector composed of the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link at the same time.

[0022] The result output module is used to input the flow data vector set into a preset earthquake judgment model to judge whether an earthquake occurs.

[0023] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method in the first aspect or any possible implementation of the first aspect is implemented.

[0024] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method in the first aspect or any possible implementation of the first aspect.

[0025] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the method in the first aspect or any possible implementation manner of the first aspect.

[0026] In an embodiment of the present invention, the flow data corresponding to the historical station waveform data of the earthquake monitoring station and the historical flow data between each node on the data transmission link are input into the earthquake judgment model to obtain a predicted value, and the predicted value is compared with the real-time collected value to determine whether an earthquake has occurred. Whether an earthquake event has occurred is judged from the network flow level. The machine learning method is used to greatly improve the accuracy of the judgment result, and then the method of conducting phase identification and judging earthquake events based on waveform data of multiple stations can be combined to judge earthquake events from multiple angles, avoid false alarms, and make the judgment of earthquake events more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of an implementation of an earthquake determination method based on network traffic provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of an earthquake determination device based on network traffic provided by an embodiment of the present invention; Figure 3 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0029] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0030] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0032] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0033] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0034] The following will be combined with the attached Figure 1 To Attachment Figure 3 , the embodiments of the present invention are described in detail.

[0035] See also Figure 1 , which shows a flowchart of an implementation of an earthquake determination method based on network traffic provided by an embodiment of the present invention, and is described in detail as follows: Step 101, obtain the flow data corresponding to the historical station waveform data of the earthquake monitoring station, and the historical flow data between each node on the data transmission link; wherein each node on the data transmission link includes: an earthquake monitoring station, a data processing node, an information release node and a user terminal.

[0036] Exemplarily, the historical traffic data between each node on the data transmission link includes: historical traffic data between the earthquake monitoring station and the data processing node, historical traffic data between the data processing node and the information publishing node, and historical traffic data between the information publishing node and the user terminal.

[0037] Exemplarily, step 101 may include: The flow data corresponding to the historical station waveform data of the seismic monitoring station and the initial data of the historical flow data between each node on the data transmission link are obtained.

[0038] The initial data is de-noised, outliers are removed, and missing values ​​are filled to obtain the flow data corresponding to the historical station waveform data of the seismic monitoring station and the historical flow data between each node on the data transmission link.

[0039] For example, by denoising the initial data, removing outliers and filling in missing values, the flow data corresponding to the historical station waveform data of the earthquake monitoring station and the historical flow data between each node on the data transmission link can be improved, thereby ensuring the accuracy of subsequent earthquake judgments.

[0040] Step 102, based on the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link, establish a flow data vector set; wherein the vector in the flow data vector set represents a vector composed of the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link at the same time.

[0041] For example, in order to facilitate the understanding of the flow data vector set, it is assumed that the flow data corresponding to the historical station waveform data is A, and the historical flow data between each node on the data transmission link is B, C and D, where A, B, C and D are the flow data at the same time, then the flow data vector is (A, B, C, D). It is not difficult to understand that the flow data vectors at multiple times constitute the flow data vector set.

[0042] Step 103, inputting the flow data vector set into a preset earthquake judgment model to judge whether an earthquake occurs.

[0043] Exemplarily, the preset earthquake judgment model is a preset long short-term memory neural network model.

[0044] Exemplarily, step 103 may include: The flow data vector set is input into the preset long short-term memory neural network model to obtain the prediction vector.

[0045] The real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the real-time value of the flow data between each node on the data transmission link are obtained, which are recorded as the first real-time value and the second real-time value respectively; and a real-time vector is established based on the first real-time value and the second real-time value.

[0046] If the Euclidean distance between the predicted vector and the real-time vector is less than or equal to a first preset value, it is determined that the earthquake has not occurred.

[0047] If the Euclidean distance between the predicted vector and the real-time vector is greater than a first preset value, it is determined that an earthquake has occurred.

[0048] For example, the prediction vector is calculated using historical data, so that the final result can be obtained by simple Euclidean distance judgment as soon as the real-time vector is obtained, avoiding the operation of a large amount of data and improving the real-time nature of earthquake judgment. In addition, it can also ensure the timeliness of judging earthquake events in combination with traditional methods (methods based on multi-station waveform data to identify seismic phases and judge earthquake events), and realize multi-angle and rapid judgment of earthquake events.

[0049] Exemplarily, the calculation formula of the Euclidean distance between the prediction vector and the real-time vector may be:

[0050] in, represents the Euclidean distance between the prediction vector and the real-time vector, represents the prediction vector, represents the real-time vector, It represents the real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the total number of real-time values ​​of the flow data between each node on the data transmission link.

[0051] Exemplarily, the training process of the preset earthquake judgment model may be: The flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link are divided into a training set and a test set.

[0052] The training set is input into the preset earthquake judgment model to calculate the training prediction value.

[0053] With the goal of minimizing the difference between the test set and the training prediction value, iterations are performed to optimize the hyperparameters of the preset earthquake judgment model until the difference between the test set and the training prediction value no longer decreases or the upper limit of the number of iterations is reached.

[0054] Exemplarily, optimizing the hyperparameters of the preset earthquake judgment model may include: The hyperparameters of the preset earthquake judgment model are optimized through particle swarm algorithm or annealing algorithm.

[0055] For example, by using a particle swarm algorithm or an annealing algorithm, the iteration speed of the earthquake judgment model can be accelerated, the convergence speed of the earthquake judgment model can be accelerated, and the processing efficiency can be improved.

[0056] Specifically, the method can also combine the results of the method with the seismic phase results obtained from real-time data to comprehensively judge the earthquake event, identify the same event from multiple angles, and further improve the accuracy of the event identification results.

[0057] The above-mentioned earthquake judgment method based on network traffic inputs the traffic data corresponding to the historical station waveform data of the earthquake monitoring station and the historical traffic data between each node on the data transmission link into the earthquake judgment model to obtain the predicted value, and compares the predicted value with the real-time collected value to determine whether an earthquake has occurred. It judges whether an earthquake event has occurred from the network traffic level and adopts a machine learning method, which can greatly improve the accuracy of the judgment result. It can then be combined with the method of conducting phase identification and earthquake event judgment based on waveform data of multiple stations to judge earthquake events from multiple angles, avoid false alarms, and make the judgment of earthquake events more accurate.

[0058] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0059] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0060] Figure 2 The schematic diagram of the structure of the earthquake judgment device based on network traffic provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows: like Figure 2 As shown, the earthquake judgment device based on network traffic includes: The data acquisition module 201 is used to obtain the flow data corresponding to the historical station waveform data of the earthquake monitoring station, and the historical flow data between each node on the data transmission link; wherein each node on the data transmission link includes: an earthquake monitoring station, a data processing node, an information release node and a user terminal.

[0061] The data processing module 202 is used to establish a flow data vector set based on the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link; wherein the vector in the flow data vector set represents a vector composed of the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link at the same time.

[0062] The result output module 203 is used to input the flow data vector set into a preset earthquake judgment model to judge whether an earthquake occurs.

[0063] In a possible implementation, the preset earthquake judgment model is a preset long short-term memory neural network model.

[0064] In a possible implementation, the result output module 203 may be used to: The flow data vector set is input into the preset long short-term memory neural network model to obtain the prediction vector.

[0065] The real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the real-time value of the flow data between each node on the data transmission link are obtained, which are recorded as the first real-time value and the second real-time value respectively; and a real-time vector is established based on the first real-time value and the second real-time value.

[0066] If the Euclidean distance between the predicted vector and the real-time vector is less than or equal to a first preset value, it is determined that the earthquake has not occurred.

[0067] If the Euclidean distance between the predicted vector and the real-time vector is greater than a first preset value, it is determined that an earthquake has occurred.

[0068] In a possible implementation, the calculation formula of the Euclidean distance between the prediction vector and the real-time vector may be:

[0069] in, represents the Euclidean distance between the prediction vector and the real-time vector, represents the prediction vector, represents the real-time vector, It represents the real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the total number of real-time values ​​of the flow data between each node on the data transmission link.

[0070] In a possible implementation, the training process of the preset earthquake judgment model may be: The flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link are divided into a training set and a test set.

[0071] The training set is input into the preset earthquake judgment model to calculate the training prediction value.

[0072] With the goal of minimizing the difference between the test set and the training prediction value, iterations are performed to optimize the hyperparameters of the preset earthquake judgment model until the difference between the test set and the training prediction value no longer decreases or the upper limit of the number of iterations is reached.

[0073] In a possible implementation, optimizing the hyperparameters of the preset earthquake determination model may include: The hyperparameters of the preset earthquake judgment model are optimized through particle swarm algorithm or annealing algorithm.

[0074] In one possible implementation, the historical traffic data between each node on the data transmission link includes: historical traffic data between the seismic monitoring station and the data processing node, historical traffic data between the data processing node and the information publishing node, and historical traffic data between the information publishing node and the user terminal.

[0075] In a possible implementation, the data acquisition module 201 may also be used to: The flow data corresponding to the historical station waveform data of the seismic monitoring station and the initial data of the historical flow data between each node on the data transmission link are obtained.

[0076] The initial data is de-noised, outliers are removed, and missing values ​​are filled to obtain the flow data corresponding to the historical station waveform data of the seismic monitoring station and the historical flow data between each node on the data transmission link.

[0077] Figure 3 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, the steps in the above-mentioned method embodiments are implemented. Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are implemented.

[0078] Exemplarily, the computer program 32 may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 32 in the electronic device 3.

[0079] The electronic device 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will appreciate that Figure 3 It is only an example of the electronic device 3 and does not constitute a limitation of the electronic device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 3 may also include input and output devices, network access devices, buses, etc.

[0080] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0081] The memory 31 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the electronic device 3. The memory 31 is used to store the computer program 32 and other programs and data required by the electronic device 3. The memory 31 may also be used to temporarily store data that has been output or is to be output.

[0082] For the convenience and simplicity of description, only the division of the above functional modules / units is used as an example for illustration. In actual applications, the above functions can be assigned to different functional modules / units as needed. The above modules / units can be implemented in the form of hardware, software, or a combination of hardware and software.

[0083] The embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the methods in the above method embodiments are implemented.

[0084] The embodiment of the present invention further provides a computer program product, including a computer program. When the computer program is executed by a processor, the methods in the above method embodiments are implemented.

[0085] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. Computer readable media may include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0086] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments. If there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form a new embodiment according to their internal logical relationship.

[0087] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for earthquake determination based on network traffic, characterized in that: include: Obtaining flow data corresponding to historical station waveform data of the seismic monitoring station and historical flow data between various nodes on the data transmission link; wherein the various nodes on the data transmission link include: seismic monitoring station, data processing node, information release node and user terminal; A flow data vector set is established based on the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link; wherein the vector in the flow data vector set represents a vector composed of the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link at the same time; The flow data vector set is input into a preset earthquake judgment model to judge whether an earthquake occurs.

2. The earthquake determination method based on network traffic according to claim 1, characterized in that: The preset earthquake judgment model is a preset long short-term memory neural network model; the flow data vector set is input into the preset earthquake judgment model to judge whether an earthquake occurs, including: Inputting the flow data vector set into a preset long short-term memory neural network model to obtain a prediction vector; Acquire the real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the real-time value of the flow data between each node on the data transmission link, and record them as the first real-time value and the second real-time value respectively; and establish a real-time vector based on the first real-time value and the second real-time value; If the Euclidean distance between the predicted vector and the real-time vector is less than or equal to a first preset value, it is determined that the earthquake has not occurred; If the Euclidean distance between the predicted vector and the real-time vector is greater than the first preset value, it is determined that an earthquake has occurred.

3. The earthquake determination method based on network traffic according to claim 2, characterized in that: The calculation formula of the Euclidean distance between the predicted vector and the real-time vector is: in, represents the Euclidean distance between the predicted vector and the real-time vector, represents the prediction vector, represents the real-time vector, It represents the real-time value of the flow data corresponding to the station waveform data of the seismic monitoring station and the total number of real-time values ​​of the flow data between each node on the data transmission link.

4. The earthquake determination method based on network traffic according to claim 2, characterized in that: The training process of the preset earthquake judgment model is as follows: Dividing the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link into a training set and a test set; Inputting the training set into the preset earthquake judgment model to calculate the training prediction value; With the goal of minimizing the difference between the test set and the training prediction value, iterations are performed to optimize the hyperparameters of the preset earthquake judgment model until the difference between the test set and the training prediction value no longer decreases or reaches an upper limit on the number of iterations.

5. The earthquake determination method based on network traffic according to claim 4, characterized in that: The optimizing the hyper parameters of the preset earthquake determination model includes: The hyperparameters of the preset earthquake judgment model are optimized by using a particle swarm algorithm or an annealing algorithm.

6. The earthquake determination method based on network traffic according to claim 1, characterized in that: The historical traffic data between the nodes on the data transmission link include: historical traffic data between the earthquake monitoring station and the data processing node, historical traffic data between the data processing node and the information publishing node, and historical traffic data between the information publishing node and the user terminal.

7. The earthquake determination method based on network traffic according to claim 1, characterized in that: The method of obtaining the flow data corresponding to the historical station waveform data of the seismic monitoring station and the historical flow data between each node on the data transmission link includes: Acquire the flow data corresponding to the historical station waveform data of the seismic monitoring station and the initial data of the historical flow data between each node on the data transmission link; The initial data is subjected to denoising, outlier elimination and missing value completion to obtain flow data corresponding to the historical station waveform data of the seismic monitoring station and historical flow data between each node on the data transmission link.

8. An earthquake judgment device based on network traffic, characterized in that: include: A data acquisition module, which acquires the flow data corresponding to the historical station waveform data of the seismic monitoring station, and the historical flow data between each node on the data transmission link; wherein each node on the data transmission link includes: a seismic monitoring station, a data processing node, an information release node and a user terminal; A data processing module, used to establish a flow data vector set based on the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link; wherein the vector in the flow data vector set represents a vector composed of the flow data corresponding to the historical station waveform data and the historical flow data between each node on the data transmission link at the same time; The result output module is used to input the flow data vector set into a preset earthquake judgment model to judge whether an earthquake occurs.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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