Earthquake early warning method based on superconducting gravimeter

Abstract

The invention discloses an earthquake early warning method based on a superconducting gravimeter, and the method comprises the following steps: firstly obtaining the original data of the superconducting gravimeter through an edge data collection server, and then analyzing a data transmission protocol of the superconducting gravimeter through the edge data collection server, converting the original data into recognizable data according to the analyzed data transmission protocol; the edge data acquisition server firstly pushes identifiable data to a storage layer of the Internet of Things platform through an MQTT protocol of a transmission layer EMQX, and then the storage layer transfers the identifiable data to a time sequence database OpenTSDB; packaging an OpenAPI service according to an HTTP-API (Hyper Text Transfer Protocol-Application Program Interface) in the time sequence database OpenTSDB to construct a service layer; and the service layer forwards the data to a third-party Kafka through a Kafka protocol. According to the invention, the data transmission and processing efficiency is high.

Description

Technical Field

[0001] The present invention relates to an improvement in seismic monitoring network technology, belonging to the field of Internet of Things, and particularly relates to a seismic early warning method based on a superconducting gravimeter. Background Art

[0002] A seismic monitoring network is a seismic monitoring network / system composed of several seismic monitoring stations. In the seismic monitoring network of the seismic monitoring network, it is necessary to transmit the data collected by the superconducting gravimeter to a relational database for analysis and processing, so as to monitor and give early warnings to relevant regions. Since the data format collected by the superconducting gravimeter is different from the data format of the relational database, it is necessary to manually convert the superconducting gravimeter data and then transmit it to the relational database; this method is not only time-consuming, but also has a negative impact on the timeliness of data; at the same time, when business personnel want to use these data, they usually need to wait for the data to be transmitted back the next day, and also go through a complex data parsing process before they can conduct further analysis and processing. In addition, the query efficiency is very low, and data sharing also requires manual operation, resulting in poor data processing efficiency and inability to give seismic early warnings in a timely and accurate manner.

[0003] The Chinese patent application with the application number CN202111457516.5 and the application date of December 2, 2021 discloses a method for extracting co-seismic gravity signals based on superconducting gravity data. Observation data is collected by a superconducting gravimeter; after preprocessing the observation data, a gravity change value is obtained; the best display frequency bands of co-seismic gravity signals and high-frequency seismic waves in the gravity change value are found; the high-frequency seismic waves are filtered to extract co-seismic gravity signals. For the problem that the superconducting gravity observation data contains various different signals, the power spectral density analysis method combined with low-pass filtering technology is used to effectively suppress signals such as high-frequency seismic waves, pre-seismic gravity anomaly perturbations, and typhoons, and the co-seismic gravity signals are optimally extracted. In addition, the data before the earthquake is subjected to least squares fitting to obtain the first-order change value, and the data after the earthquake is also subjected to first-order least squares fitting, and the difference is compared with the theoretical value of co-seismic gravity change to verify the effectiveness of the extracted co-seismic gravity signals. However, the above method only processes the data of various different signals in the observation data to verify the effectiveness of the signals, and does not solve the problem of giving seismic early warnings in a timely and accurate manner.

[0004] Disclosing the information of this background art section is only intended to increase the understanding of the overall background of this patent application, and should not be regarded as an admission or any form of implication that this information constitutes the prior art already known to those of ordinary skill in the art. Summary of the Invention

[0005] The object of the present invention is to overcome the problem in the prior art that earthquake early warning cannot be carried out in a timely and accurate manner, and a superconducting gravimeter-based earthquake early warning method capable of carrying out earthquake early warning in a timely and accurate manner is provided.

[0006] To achieve the above object, the technical solution of the present invention is: a superconducting gravimeter-based earthquake early warning method, and the superconducting gravimeter-based earthquake early warning method includes the following steps:

[0007] First step, the edge data acquisition server first obtains the original data of the superconducting gravimeter, then the edge data acquisition server analyzes the data transmission protocol of the superconducting gravimeter, and then converts the original data into recognizable data according to the analyzed data transmission protocol;

[0008] Second step, the edge data acquisition server first pushes the recognizable data to the storage layer of the Internet of Things platform through the MQTT protocol of the transport layer EMQX, and then the storage layer transfers and stores the recognizable data into the time series database OpenTSDB;

[0009] Third step, encapsulate the OpenAPI service according to the HTTP—API in the time series database OpenTSDB to construct the service layer;

[0010] Fourth step, the service layer forwards the data to the third-party Kafka through the Kafka protocol for early warning.

[0011] In the first step, the edge data acquisition server first obtains the original data of the superconducting gravimeter, then the edge data acquisition server analyzes the data transmission protocol of the superconducting gravimeter, and then converts the original data into recognizable data according to the analyzed data transmission protocol, specifically: the edge data acquisition server is connected to the superconducting gravimeter through the RS232 protocol, and then receives the ASCII data stream sent by the superconducting gravimeter through the DataReceived receiving event of the serial port control, and then decodes the received ASCII data stream into a character data format.

[0012] Preprocessing the converted data includes filtering and denoising, and saving the preprocessed data to a real-time cache file and a log file.

[0013] In the second step, the storage layer transfers and stores the acquired data into the time series database OpenTSDB, specifically: after the EMQX cluster receives the data, the data will first be stored in the MQTT queue, and then the data is read from the MQTT through the Internet of Things platform and then stored in the OpenTSDB time series database.

[0014] The certificate generated by the Internet of Things platform and sent to the edge data acquisition server is stored by the edge device's edge data acquisition server. Then, TLS is configured on the EMQX server, and encryption and identity authentication are performed on TLS.

[0015] The edge data acquisition server uses the configured MQTT client to connect to the EMQX server of the Internet of Things platform through TLS and perform verification.

[0016] The data transfer and storage also include using transaction management to manage data consistency. If an error occurs during data transfer and storage, the transaction management can roll back to the state before the transfer.

[0017] Configuring the MQTT client on the edge data acquisition server includes the server address, port, client ID, username, and password. The identified data is encapsulated into MQTT messages, and through the QoS level, the message publishing logic is implemented to push the messages to the specified MQTT topic.

[0018] In the service layer, first use the Kafka client library to create a Kafka Producer object and configure the key parameters of the Producer. Then, obtain the data to be forwarded from the data source in the service layer.

[0019] Serializing the data into a format supported by Kafka includes JSON, Avro, and Protobuf.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0021] 1. In the earthquake early warning method based on a superconducting gravimeter of the present invention, first, by using the edge data acquisition function in the Internet of Things technology, it can be directly connected to device instruments to achieve real-time monitoring with sampling per second, and the data is transmitted back to the Internet of Things platform in real-time. Then, the data is stored in the time series database OpenTSDB, which is designed specifically for time series data, can efficiently compress and store data, and provides fast query services. Furthermore, through the message queue technology Kafka, the data can be shared in real-time with various application systems, enabling these systems to also enjoy second-level data updates. This not only improves the real-time nature of the data but also greatly enhances the efficiency of data processing and query, bringing significant progress to the earthquake monitoring network industry. Therefore, the present invention can conduct earthquake early warning in a timely and accurate manner.

[0022] 2. In the earthquake early warning method based on a superconducting gravimeter of the present invention, by performing data preprocessing in the edge layer, the load on the central server is reduced, the overall performance is improved, and the cost can be reduced at the same time. Each layer can optimize the allocation of resources according to requirements, improving the utilization efficiency of resources. Therefore, the server of the present invention has a small load and a high resource utilization rate.

[0023] 3. In the earthquake early warning method based on a superconducting gravimeter of the present invention, through the Kafka message queue technology, the real-time sharing of data is realized, the availability of data and the response speed of the system are improved, and the combination of the Internet of Things technology and the time series database provides a stable data transmission and storage mechanism, enhancing the reliability of the system. Therefore, the present invention has a faster response and stronger reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flowchart of the present invention.

[0025] Figure 2 is a schematic diagram of the Internet of Things platform of the present invention.

[0026] Figure 3 is a schematic diagram of the supported device types of the present invention.

[0027] Figure 4 is a diagram of the connected devices of the present invention.

[0028] Figure 5 is a schematic diagram of the device topology monitoring of the present invention.

[0029] Figure 6 is a diagram of the object template of the present invention.

[0030] Figure 7 is a diagram of the object instance created based on the object template of the present invention.

[0031] Figure 8 is a schematic diagram of the real-time data of the present invention.

[0032] Figure 9 is a schematic diagram of the historical data of the present invention.

[0033] Figure 10 is a schematic diagram of the development interface of the present invention.

[0034] Figure 11 is a schematic diagram of the real-time stream computing of the present invention.

[0035] Figure 12 is a schematic diagram of sharing data in the form of a real-time stream of the present invention.

[0036] Figure 13 is a diagram of sharing data in the form of an API of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] See Figures 1 to 13, a seismic early warning method based on a superconducting gravimeter, the seismic early warning method based on a superconducting gravimeter comprising the following steps:

[0039] First step, the edge data acquisition server first obtains the original data of the superconducting gravimeter, then the edge data acquisition server analyzes the data transmission protocol of the superconducting gravimeter, and then converts the original data into recognizable data according to the analyzed data transmission protocol;

[0040] Second step, the edge data acquisition server first pushes the recognizable data to the storage layer of the Internet of Things platform through the MQTT protocol of the transport layer EMQX, and then the storage layer transfers and stores the recognizable data into the time series database OpenTSDB;

[0041] Third step, encapsulate the OpenAPI service according to the HTTP—API in the time series database OpenTSDB to build the service layer;

[0042] Fourth step, the service layer forwards the data to the third-party Kafka through the Kafka protocol for early warning.

[0043] In the first step, the edge data acquisition server first obtains the original data of the superconducting gravimeter, then the edge data acquisition server analyzes the data transmission protocol of the superconducting gravimeter, and then converts the original data into recognizable data according to the analyzed data transmission protocol. Specifically: the edge data acquisition server connects to the superconducting gravimeter through the RS232 protocol, and then receives the ASCII data stream sent by the superconducting gravimeter through the DataReceived receiving event of the serial port control, and then decodes the received ASCII data stream into a character data format.

[0044] Preprocess the converted data, including filtering and denoising, and save the preprocessed data to the real-time cache file and the log file.

[0045] In the second step, the storage layer transfers and stores the obtained data into the time series database OpenTSDB. Specifically: after the EMQX cluster receives the data, the data will first be stored in the MQTT queue, and then the data will be read from the MQTT through the Internet of Things platform and then stored in the OpenTSDB time series database.

[0046] The Internet of Things platform generates and issues a certificate to the edge data acquisition server. The edge device edge data acquisition server stores the corresponding certificate, then configures TLS on the EMQX server, and encrypts and authenticates the TLS.

[0047] The edge data acquisition server uses the configured MQTT client to connect to the EMQX server of the Internet of Things platform through TLS and perform verification.

[0048] The data transfer and storage also includes using transaction management to manage data consistency. If an error occurs during the data transfer and storage process, the transaction management can roll back to the state before the transfer.

[0049] Configuring the MQTT client on the edge data acquisition server, including the server address, port, client ID, username, and password, encapsulating the identified data into MQTT messages, and implementing the message publishing logic through the QoS level to push the messages to the specified MQTT topic.

[0050] In the service layer, first use the Kafka client library to create a Kafka Producer object and configure the key parameters of the Producer, and then obtain the data that needs to be forwarded from the data source in the service layer.

[0051] Serializing the data into formats supported by Kafka, including JSON, Avro, and Protobuf.

[0052] The supplementary description of the present invention is as follows:

[0053] Collect the data of the superconducting gravimeter back to the Internet of Things platform at a sampling frequency of seconds, then use a time series database to store the data, and finally use the message queue technology to forward and share the data to the application system.

[0054] Embodiment 1:

[0055] A seismic early warning method based on a superconducting gravimeter, the seismic early warning method based on a superconducting gravimeter includes the following steps:

[0056] First step, the edge data acquisition server first obtains the original data of the superconducting gravimeter, then the edge data acquisition server parses the data transmission protocol of the superconducting gravimeter, and then converts the original data into identifiable data according to the parsed data transmission protocol;

[0057] Second step, the edge data acquisition server first pushes the identifiable data to the storage layer of the Internet of Things platform through the MQTT protocol of the transport layer EMQX, and then the storage layer transfers and stores the identifiable data into the time series database OpenTSDB;

[0058] Third step, encapsulate the OpenAPI service according to the HTTP—API in the time series database OpenTSDB to build the service layer;

[0059] Fourth step, the service layer forwards the data to the third-party Kafka through the Kafka protocol for early warning.

[0060] Embodiment 2:

[0061] Embodiment 2 is basically the same as Embodiment 1, and the difference is:

[0062] The edge data acquisition server is connected to the superconducting gravimeter through the RS232 protocol. Then, it receives the ASCII data stream sent by the superconducting gravimeter through the DataReceived receiving event of the serial port control. Next, it decodes the received ASCII data stream into a character data format and preprocesses the converted data, including filtering and denoising. The preprocessed data is saved to a real-time cache file and a log file for subsequent analysis and traceability. The edge data acquisition server interfaces with the superconducting gravimeter for protocol parsing, reducing the dependence on the central processing capacity, improving the data processing efficiency, performing preliminary processing near the data source, reducing data transmission latency, and enhancing the response speed.

[0063] Embodiment 3:

[0064] Embodiment 3 is basically the same as Embodiment 1, except that:

[0065] After the EMQX cluster receives the data, the data is first stored in the MQTT queue, and then the data is read from the MQTT through the Internet of Things platform and stored in the OpenTSDB time series database. Using EMQX as the transport layer can handle high-concurrency data transmission, ensuring the real-time and reliability of the data. The MQTT protocol is designed to be lightweight and suitable for Internet of Things scenarios. Especially in an environment with unstable network, it can ensure the reliable transmission of messages. At the same time, the MQTT protocol supports various levels of message encryption and authentication, guaranteeing the security of data transmission. As the unified medium for all data access of the Internet of Things platform, EMQX is a large-scale distributed MQTT message server, efficiently and reliably connecting a large number of Internet of Things devices, and real-time processing and distributing message and event stream data, helping to build Internet of Things applications for key businesses. It provides a highly reliable, low-latency, and complete data stream for data collection in application scenarios such as emergency handling, earthquake prevention and disaster reduction, disciplinary applications, digital twins of stations, monitoring and operation and maintenance, etc. EMQX has the characteristics of ultra-large scale, high performance, low latency, and high availability. An EMQX single cluster can support up to 100 million MQTT concurrent connections; the transmission and processing throughput of a single server can reach millions of MQTT messages per second; the information transfer is almost real-time, ensuring that the latency is in the sub-millisecond level; high availability and horizontal scalability are achieved through a masterless node distributed architecture;

[0066] Provide efficient and reliable information transfer capabilities for applications and devices by opening standard protocols such as MQTT, HTTP, QUIC, and WebSocket; support two-way MQTT connections encrypted by TLS / SSL and authenticate through username / password, JWT, PSK, X509 certificates, etc.; extract, filter, enrich, and transform millions of continuous event streams through a powerful SQL-based rule engine; as the business grows, EMQX provides high flexibility in terms of extended deployment, whether it is horizontal or vertical scaling.

[0067] Example 4:

[0068] Example 4 is basically the same as Example 1, except that:

[0069] Use OpenTSDB as the medium for data storage. OpenTSDB uses HBase to store all time series data to build a distributed and scalable time series database; it supports second-level data collection for all metrics, supports permanent storage, can perform capacity planning, and is easily integrated into the existing alarm system; OpenTSDB can obtain corresponding metrics from a large-scale cluster (including network devices, operating systems, and applications in the cluster), store, index, and serve them, making this data easier to understand, such as web-based and graphical. OpenTSDB can obtain real-time status information of the infrastructure and services, display various software and hardware errors, performance changes, and performance bottlenecks in the cluster; for managers, OpenTSDB can measure the SLA of the system, understand the interactions between complex systems, and display resource consumption; the overall job situation of the cluster can be used to assist in budgeting and cluster resource coordination; for developers, OpenTSDB can display the main performance bottlenecks and frequently occurring errors in the cluster, so that they can focus on solving important problems.

[0070] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. Any equivalent modifications or changes made by those of ordinary skill in the art according to the disclosed content of the present invention shall be included in the protection scope recorded in the claims.

Claims

1. An earthquake early warning method based on a superconducting gravimeter, characterized in that: The earthquake early warning method based on superconducting gravimeter comprises the following steps: In the first step, the edge data acquisition server first obtains the raw data of the superconducting gravimeter, then parses the data transmission protocol of the superconducting gravimeter, and then converts the raw data into recognizable data according to the parsed data transmission protocol; In the second step, the edge data acquisition server first pushes the identifiable data to the storage layer of the IoT platform through the MQTT protocol of the transport layer EMQX, and then the storage layer transfers the identifiable data to the time series database OpenTSDB; Step 3: Encapsulate the OpenAPI service based on the HTTP-API in the time series database OpenTSDB to build the service layer; Step 4: The service layer forwards the data to the third-party Kafka through the Kafka protocol for early warning.

2. The earthquake early warning method based on superconducting gravimeter according to claim 1, characterized in that: In the first step, the edge data acquisition server first obtains the original data of the superconducting gravimeter, and then the edge data acquisition server parses the data transmission protocol of the superconducting gravimeter, and then converts the original data into recognizable data according to the parsed data transmission protocol. Specifically, the edge data acquisition server is connected to the superconducting gravimeter through the RS232 protocol, and then receives the ASCII data stream sent by the superconducting gravimeter through the DataReceived receiving event of the serial port control, and then decodes the received ASCII data stream into a character data format.

3. The earthquake early warning method based on superconducting gravimeter according to claim 2, characterized in that: The converted data is preprocessed, including filtering and denoising, and the preprocessed data is saved in a real-time cache file and a log file.

4. The earthquake early warning method based on superconducting gravimeter according to claim 1, characterized in that: In the second step, the storage layer transfers the acquired data to the time series database OpenTSDB. Specifically, after the EMQX cluster receives the data, the data will be first stored in the MQTT queue, and then the data will be read from MQTT through the IoT platform and stored in the OpenTSDB time series database.

5. The earthquake early warning method based on superconducting gravimeter according to claim 4, characterized in that: The IoT platform generates and issues a certificate to the edge data acquisition server. The edge device edge data acquisition server stores the corresponding certificate, then configures TLS on the EMQX server, and performs encryption and identity authentication on TLS.

6. The earthquake early warning method based on superconducting gravimeter according to claim 5, characterized in that: The edge data acquisition server uses the configured MQTT client to connect to the EMQX server of the IoT platform through TLS and perform verification.

7. The earthquake early warning method based on superconducting gravimeter according to claim 6, characterized in that: The data transfer also includes managing data consistency using transaction management. If an error occurs during the data transfer process, the transaction management can roll back to the state before the transfer.

8. The earthquake early warning method based on superconducting gravimeter according to claim 1, characterized in that: The configuration of the MQTT client on the edge data acquisition server includes the server address, port, client ID, user name and password, encapsulates the identified data into an MQTT message, implements the message publishing logic through the QoS level, and pushes the message to the specified MQTT topic.

9. The earthquake early warning method based on superconducting gravimeter according to claim 8, characterized in that: In the service layer, the Kafka client library is first used to create a Kafka Producer object, and the key parameters of the Producer are configured, and then the data to be forwarded is obtained from the data source of the service layer.

10. The earthquake early warning method based on superconducting gravimeter according to claim 9, characterized in that: The data is serialized into formats supported by Kafka, including JSON, Avro, and Protobuf.

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