High earth and rockfill dam earthquake damage rapid assessment system

Abstract

The invention relates to the technical field of high earth and rockfill dam earthquake damage assessment, in particular to a high earth and rockfill dam earthquake damage rapid assessment system, which comprises a cloud computing module used for acquiring and transmitting data of other servers and preprocessing the acquired data; the fog computing module is connected with the cloud computing module and is used for analyzing and computing the data preprocessed by the cloud computing module and writing a computing result into a database; and the twinborn body synchronous rendering module is connected with the fog computing module and is used for performing mapping processing on the computing result in the database by utilizing a digital twinborn technology to obtain a virtual detection model. Dam earthquake damage assessment is carried out through an advanced distributed architecture by utilizing edge, fog and cloud computing, autonomous and online digital twinning of an extra-high earth and rockfill dam can be realized, a physical object, a virtual model and a mapping network are combined by utilizing a digital twinning technology, the digital twinning body of each dam can provide comprehensive and real representation, and the digital twinning of each dam can be realized. Therefore, effective monitoring, analysis and decision-making processes are realized.

Description

Technical Field

[0001] The present application relates to the technical field of earthquake damage assessment for high earth-rock dams, and in particular to a rapid earthquake damage assessment system for high earth-rock dams. Background Art

[0002] Southwest my country is rich in hydropower resources, and many high-dam hydropower station projects are planned, constructed and operated, including Xiaowan, Nuozadu and other high-dam projects over 200m. At the same time, the southwest region is also a high-incidence earthquake area, and the seismic safety of high dams faces severe challenges. Due to the huge reservoir capacity, once the high dam collapses, the reservoir water will flow out of control, which will cause huge casualties and property losses downstream, and the consequences will be disastrous. According to the requirements of the specifications, medium and large-scale water conservancy projects are generally equipped with relatively complete instrument observation projects to monitor the changes in physical quantities such as dam deformation, seepage, and stress, and at the same time combine daily inspections, annual detailed inspections, and special inspections. Due to the discrete arrangement of dam safety monitoring instruments, and the inspections are mainly visual, they can often only see the surface situation. Therefore, instrument observation and inspection alone are not enough to fully perceive and grasp the safety of the dam. And the existing technology cannot make a quick and accurate assessment of the safety status of the dam after a strong earthquake.

[0003] In summary, how to design an intelligent and rapid seismic damage assessment system for high earth-rock dams is an urgent problem to be solved. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first purpose of this application is to propose a rapid earthquake damage assessment system for high earth-rock dams to solve the problem that existing technical means are unable to make a rapid and accurate assessment of the safety status of dams after a strong earthquake.

[0006] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a high earth-rock dam earthquake damage rapid assessment system, comprising:

[0007] The cloud computing module is used to obtain and transmit data from other servers and pre-process the obtained data;

[0008] A fog computing module is connected to the cloud computing module, performs analysis and calculation on the data preprocessed by the cloud computing module, and writes the calculation results into a database;

[0009] The twin synchronous rendering module is connected to the fog computing module, and maps the calculation results in the database using the digital twin technology to obtain a virtual detection model.

[0010] Preferably, the cloud computing module uses a parallel multi-threaded MQTT protocol to transmit data.

[0011] Preferably, the parallel multithreading includes:

[0012] The MQTT proxy instructs the MQTT client in the cloud server database to subscribe to the sensor data;

[0013] The MQTT proxy accepts subscription requests for loading data from the MQTT client in the fog server and commands the MQTT client in the cloud server database to publish the loaded data for FEA;

[0014] The MQTT proxy instructs the MQTT client in the cloud server database to subscribe to the FEA response data from the MQTT client in the fog server and store it;

[0015] The MQTT proxy commands the MQTT client in the cloud server visualization module to subscribe to the sensor data and finite element response data from the MQTT client in the cloud server database.

[0016] Preferably, analyzing and calculating the data preprocessed by the cloud computing module and writing the calculation results into a database comprises:

[0017] For a seismic wave calculation request, the fog computing module downloads and processes the seismic wave data into a preset format. After the calculation is completed, the displacement time history curve corresponding to the specified node ID and the results of the dam seismic analysis are converted into JSON data format and stored in the database.

[0018] Preferably, the fog computing module further includes: an intermediate module, connected to the cloud computing module, and configured to communicate in the form of publishing and subscribing via a message queue telemetry protocol.

[0019] Preferably, the intermediate module loads multiple seismic waves at one time and processes them in sequence until all seismic waves are evaluated.

[0020] Preferably, the cloud computing module adopts a slice-oriented layered architecture to separate each API processing flow and dynamically cut the code into a specified position of the class.

[0021] Preferably, the layered architecture comprises:

[0022] Router layer, used to handle API routing;

[0023] The Validation layer is connected to the Router layer and is used to verify the data carried by each request;

[0024] The Service business logic layer is connected to the Validation layer and is used to process the overall data flow;

[0025] The Model layer is connected to the Service business logic layer and is used to interact with the database model;

[0026] The Exception layer is connected to the Model layer and is used to process abnormal data.

[0027] Preferably, the twin synchronous rendering module includes: node rendering and grid rendering.

[0028] Preferably, the grid rendering comprises:

[0029] Get all element types and node composition order;

[0030] defining a triangle decomposition criterion for each type of element and decomposing all elements into triangles according to the element type;

[0031] The triangles are rendered and the triangle mesh is deformed by updating the node coordinates calculated from the original coordinates and the FEA residual displacement, and the deformed triangle mesh is colored.

[0032] The present application provides a high earth-rock dam seismic damage rapid assessment system, which uses edge, fog and cloud computing to perform dam seismic damage assessment through an advanced distributed architecture, full-process unmanned intelligent analysis, an intelligent seismic load identification algorithm triggered by an automatic system, a three-dimensional real-time virtual model visualization algorithm, and various connection APIs with the independently developed programmable FEM software Geodyna. It can realize autonomous and online digital twins of ultra-high earth-rock dams, and use digital twin technology to combine physical objects, virtual models and mapping networks. The digital twin of each dam can provide a comprehensive and realistic representation, thereby realizing an effective monitoring, analysis and decision-making process.

[0033] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0035] Figure 1 This is a schematic diagram of the structure of a high earth-rock dam earthquake damage rapid assessment system provided by the present invention;

[0036] Figure 2 This is a schematic diagram of the backend slicing-oriented design pattern;

[0037] Figure 3 This is a schematic diagram of a WEB-based finite element real-time rendering engine;

[0038] Figure 4 This is a schematic diagram of the overall system architecture;

[0039] Figure 5 Schematic diagram of the finite element rendering method based on WebGL. DETAILED DESCRIPTION

[0040] The core of the present invention is to provide a high earth-rock dam earthquake damage rapid assessment system, which uses edge, fog and cloud computing to conduct dam earthquake damage assessment through an advanced distributed architecture, thereby achieving effective monitoring and analysis of the dam's safety status after a strong earthquake.

[0041] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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 work are within the scope of protection of the present invention.

[0042] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of a high earth-rock dam earthquake damage rapid assessment system provided by the present invention; the details are as follows:

[0043] A high earth-rock dam seismic damage rapid assessment system, comprising:

[0044] The cloud computing module is used to obtain and transmit data from other servers and pre-process the obtained data;

[0045] The cloud computing module uses the parallel multi-threaded MQTT protocol to transmit data, and the parallel multi-threaded protocol includes:

[0046] The MQTT proxy instructs the MQTT client in the cloud server database to subscribe to the sensor data;

[0047] The MQTT proxy accepts subscription requests for loading data from the MQTT client in the fog server and commands the MQTT client in the cloud server database to publish the loaded data for FEA;

[0048] The MQTT proxy instructs the MQTT client in the cloud server database to subscribe to the FEA response data from the MQTT client in the fog server and store it;

[0049] The MQTT proxy commands the MQTT client in the cloud server visualization module to subscribe to the sensor data and finite element response data from the MQTT client in the cloud server database.

[0050] The cloud computing module adopts a slice-oriented layered architecture to separate each API processing flow and dynamically cut the code into the specified position of the class. The layered architecture includes:

[0051] Router layer, used to handle API routing;

[0052] The Validation layer is connected to the Router layer and is used to verify the data carried by each request;

[0053] The Service business logic layer is connected to the Validation layer and is used to process the overall data flow;

[0054] The Model layer is connected to the Service business logic layer and is used to interact with the database model;

[0055] The Exception layer is connected to the Model layer and is used to process abnormal data.

[0056] A fog computing module is connected to the cloud computing module, performs analysis and calculation on the data preprocessed by the cloud computing module, and writes the calculation results into a database;

[0057] For a seismic wave calculation request, the fog computing module downloads and processes the seismic wave data into a preset format. After the calculation is completed, the displacement time history curve corresponding to the specified node ID and the results of the dam seismic analysis are converted into JSON data format and stored in the database.

[0058] The fog computing module also includes: an intermediate module, which is connected to the cloud computing module and is used to communicate in the form of publishing and subscribing through a message queue telemetry protocol. The intermediate module loads multiple seismic waves at one time and processes them in sequence until all seismic waves are evaluated.

[0059] The twin synchronous rendering module is connected to the fog computing module, and maps the calculation results in the database using the digital twin technology to obtain a virtual detection model, including: node rendering and grid rendering.

[0060] The grid rendering includes:

[0061] Get all element types and node composition order;

[0062] defining a triangle decomposition criterion for each type of element and decomposing all elements into triangles according to the element type;

[0063] The triangles are rendered and the triangle mesh is deformed by updating the node coordinates calculated from the original coordinates and the FEA residual displacement, and the deformed triangle mesh is colored.

[0064] This embodiment provides a high earth-rock dam seismic damage rapid assessment system, which builds a dam digital twin network based on digital twin technology, spatially reconstructs the dam structure, and ensures seamless integration of real-time data acquisition, data processing and visualization. By combining physical objects, virtual models and mapping networks, the digital twin of each dam can provide a comprehensive and realistic representation. Through an advanced distributed architecture, edge, fog and cloud computing are used to conduct dam seismic damage assessment, unmanned intelligent analysis of the entire process, intelligent seismic load identification algorithms triggered by automatic systems, three-dimensional real-time virtual model visualization algorithms, and various connection APIs with the independently developed programmable FEM software Geodyna, efficient and accurate monitoring, analysis and decision-making of the safety status of the dam after the earthquake are achieved.

[0065] Based on the above embodiment, this embodiment describes the high earth-rock dam earthquake damage rapid assessment system, as follows:

[0066] This system mainly adopts "fog-cloud" collaborative computing, in which the cloud computing module uses the Linux operating system, and the fog computing module uses the Windows system to host the large-scale geotechnical engineering three-dimensional nonlinear high-performance effective static and dynamic analysis software GEODYNA for calculation, and uses the MQTT protocol for data synchronization between the fog computing module and the cloud computing module. The advantage of such an architectural design is that it can not only maximize the computing power of the powerful configuration of the fog computing module, but also realize the triggering of calculations in the form of a network, the post-processing of the calculation structure, and the presentation of the cloud platform through the data storage and distribution capabilities of the cloud computing module, so that users can access the earthquake rapid assessment platform at any location and at any time through the browser, respond to earthquake events in a timely and rapid manner, and obtain the dam earthquake damage assessment results as soon as possible.

[0067] The cloud server needs to collect and transmit data from all other servers. In addition, in the case of large data volume and large network latency, it is necessary to minimize blocking. Therefore, the parallel multi-threaded MQTT is used to realize the automatic data transmission of the entire digital twin network. MQTT is an OASIS standard messaging protocol widely used in the Internet of Things (IoT). Unlike the HTTP protocol that uses a request / response mechanism, MQTT uses a publish / subscribe mechanism to establish a Transmission Control Protocol (TCP) connection with the server. MQTT consists of an MQTT broker in the Eclipse mosquito software and an MQTT client in the MQTTx software. The MQTT broker is the hub of MQTT information transmission and is responsible for publishing data to the MQTT client based on demand. It is also responsible for managing the MQTT client to ensure smooth and accurate communication between clients. In the proposed digital twin network, the MQTT broker is deployed in the cloud server, making it the central brain and having the highest command for data transmission between all servers. The MQTT client is installed in all edge servers, fog servers, and cloud servers.

[0068] There are four main parallel threads in the digital twin process: (1) The MQTT proxy commands the MQTT client in the cloud server database to subscribe to sensor data for storage from the MQTT client in the edge server. (2) The MQTT proxy accepts the subscription demand for loading data from the MQTT client in the fog server, and then commands the MQTT client in the cloud server database to publish the loaded data to it for FEA. (3) The MQTT proxy commands the MQTT client in the cloud server database to subscribe to FEA response data from the MQTT client in the fog server for storage. (4) The MQTT proxy commands the MQTT client in the cloud server visualization module to subscribe to sensor data and finite element response data from the MQTT client in the cloud server database for 2D and 3D visualization. In addition, due to its lightweight and small network bandwidth characteristics, MQTT can achieve stable data transmission of remote devices when the hardware is severely limited and the network bandwidth is low.

[0069] The cloud computing module is mainly responsible for hosting the front and back ends of the cloud platform. The front end of the cloud computing module is mainly written in vue.js, using the Ant Design design language for UI design, and using vue-router to implement single-page applications. The back-end platform is mainly developed using the high-performance back-end FastAPI framework, which has performance comparable to NodeJS and Go, and has the ability to automatically generate standard API documents, making later maintenance more efficient. In this project, a layered architecture based on the slice-oriented design pattern was developed based on the back-end development framework, which separates the processing flow of each API. During the operation of the back-end service, the code is dynamically cut into the specified method and specified position of the class, which greatly reduces the complexity of the code and greatly improves the reusability of the class, solves the problem of the inability to connect between classes in object-oriented design, and improves the robustness of the code.

[0070] like Figure 2 As shown, in the design of the embodiment, the backend architecture is divided into 5 layers, namely the Router layer for processing API routing, the dependency injection Validation layer is used in the Router layer, and the data carried by each request is verified before entering the Service business logic layer. The business logic layer mainly handles the overall flow of data, such as earthquake report generation and other functions, and communicates with the Model layer. The Model layer mainly interacts with the database model, and defines a dedicated method for each method of the database, so that the logic layer can call the Model layer, thereby realizing the isolation of logic and data and improving the robustness of the system.

[0071] The fog computing module is the core of the entire system computing. Since GEODYNA is a software system developed based on the Windows system, it requires high computing power for computing. Therefore, the fog computing module is designed to adapt the operating system and computing power. In order to enable the GEODYNA computing software to communicate with the cloud computing module in a two-way manner, a middleware running on the fog computing module was developed in the platform of this project. The middleware communicates with the cloud computing module in a publish and subscribe mode through the message queue telemetry protocol (MQTT). For a seismic wave calculation request, a cloud service API is requested from the browser to upload the seismic wave data to the cloud server. After the cloud server verifies the seismic wave data, it will send an MQTT data to the MQTT agent, so that the computing node of the fog computing module can subscribe and receive the incoming instructions. The instructions carry the download address of the verified seismic wave. The fog computing module will download the seismic wave data and pre-process it into the file format required for GEODYNA calculation. After the calculation is completed, the displacement time history curve corresponding to the ID of the specified node and the results of the dam seismic analysis will be post-processed into the JSON data format for easy network transmission and reading. The data will be uploaded and written to the database with the specified API, so that the cloud computing module can access the analysis results. Due to the long calculation time of seismic waves, the middleware of the fog computing module is also designed with a task queue function, which can load multiple seismic waves at one time. Even if the cloud platform system is turned off, it will continue to calculate in sequence in the fog computing module until all seismic waves are evaluated.

[0072] It has always been a difficult problem to render finite element data on the browser client side. On the one hand, the amount of finite element analysis results is very large and not suitable for network transmission. On the other hand, the browser's client rendering performance is limited and complex data cannot be rendered. Therefore, a finite element heterogeneous data method based on JSON format is designed in this system for data transmission and analysis, and a real-time rendering engine based on finite element solid mesh is developed. Figure 3 As shown in the figure, the selection of finite element node data is realized through the computer graphics ray detection algorithm. In order to facilitate the selection of node data and realize the rendering of only nodes, a point material is defined, and the color format defined by Jetmap is used to render the data in different formats from small to large. Since the rendering is asynchronous, the asynchronous callback method is used to obtain the maximum and minimum displacement values ​​after rendering, and the acquisition of the medium displacement range of the finite element is realized. The finite element web real-time rendering engine function is realized.

[0073] like Figure 4As shown, the key components of the digital twin network proposed in this embodiment include the edge server, which is responsible for acquiring data from various types of sensors and effectively and intelligently identifying seismic loads for system triggering; the fog server is responsible for the hydrostatic pressure, dynamic seismic finite element analysis and safety evaluation of each dam; the cloud server, as the central hub of the network, is responsible for data storage, data storage and real-time virtual model visualization driven by finite element responses.

[0074] It is worth noting that all programs in the digital twin network run in the Linux system. Linux provides stability, reliability and compatibility for the deployed applications. In addition, the local area network (LAN) connected by wires with a total length of more than 1,000 kilometers facilitates communication between the super dam cluster and the city safety command center. The LAN establishes a strong and reliable means of data transmission, enabling seamless connection between the dam cluster and the command center. In addition, the digital twin system is designed to be accessible online for easy use. However, as before, in order to prioritize data security, access is strictly limited to the LAN. By limiting access to the LAN, data protection is enhanced and potential risks posed by external network connections are reduced. As a result, the digital twin system can maintain high standards of high performance, data security and accessibility.

[0075] A large amount of data is generated in the seismic monitoring of the dam, including sensor data and finite element responses. The data is stored and managed using a MySQL database based on a relational database management system (RDBMS). The characteristics of the MySQL database are as follows: (1) Data is stored in the form of tables, similar to the Excel spreadsheet format. (2) The first row contains column headers. (3) Each column stores data corresponding to a specific record. (4) Each column is associated with a specific data field. (5) Multiple tables form a database structure, which can achieve sequential recording and facilitate fast and efficient retrieval of data through search queries. By using the MySQL database, sequential recording and efficient data query and retrieval are achieved.

[0076] Unlike the static visualization and lack of interactivity of previous finite element software or some digital twin systems, the visualization of the virtual model in this digital twin network is dynamically presented in real time based on the constantly updated finite element response. To achieve this, the network uses the Web Graphics Library (WebGL) framework, a framework that utilizes the JavaScript API of OpenGL ES. It enables high-performance interactive 3D and 2D graphics rendering in a variety of compatible web browsers. As a result, the digital twin system is able to provide an immersive and dynamic visualization experience, allowing engineers to examine and analyze the real-time behavior of the virtual model driven by the FEA response.

[0077] There are two optional 3D rendering modes: clickable node rendering and mesh rendering. Node rendering means only the nodes of the FEM are rendered, omitting the elements. This rendering mode utilizes the GL_Points component of WebGL along with the FEM / FEA information associated with each node. This information includes the node coordinates (x,y,z) to determine its position in virtual space, the real-time FEM analysis residual displacement to drive its deformation, and the real-time FEM analysis residual displacement or stress to calculate its color representation, ranging from blue (minimum) to red (maximum). The nodes are designed to be clickable, allowing interaction. The LineSegmentIntersector component of WebGL is used to pick up events, perform Raycaster collision detection on nodes in the scene, and enable clickable functionality. Upon clicking a node, the associated FEA response is displayed along with its ID and location. The node rendering mode is lightweight, so rendering is fast, especially for computers with lower performance. This mode provides efficient digital twin capabilities for large structures such as dams.

[0078] Mesh rendering mode refers to the rendering of finite element entities. The rendering algorithm diagram is as follows Figure 4 As shown. It is constructed by combining the GL_Triangles component of WebGL with the CG triangle composition criteria of the unit and the finite element / finite element information of each unit (including the unit type, the composition order and coordinates of the nodes that make up the unit, and the real-time finite element residual displacement or stress of the node). Mesh rendering is divided into 5 steps.

[0079] Step 1: Get the type and node composition order of all elements.

[0080] Step 2: Define the triangle decomposition criteria for each type of element, and decompose all elements into triangles according to the element type. For example, a common 6-face 8-node hexahedral element can be decomposed into 12 triangles, 36 vertex coordinates, and the 36 vertex coordinates share 8 node coordinates through indices.

[0081] Step 3: Use the GL_Triangles component to render the triangle. Enter the vertex coordinates of the triangle in a counterclockwise order.

[0082] Step 4: Deform the triangle (mesh) by updating the node coordinates calculated from the original coordinates and their FEA residual displacements.

[0083] Step 5: Use the GL_Shader component to add color to the mesh. The RGB value of each node is determined by the ratio of the finite element residual displacement or stress of its node to the maximum value of all nodes.

[0084] Finally, a comprehensive system visualization integrating the three dams was developed using a Web architecture based on a B / S (Browser / Server) framework and HTML (Hypertext Markup Language). Figure 5 In addition, it has been deployed on a robust cloud server to facilitate the overall management of the DAM cluster.

[0085] A high earth-rock dam earthquake damage rapid assessment system provided by an embodiment of the present invention uses an advanced distributed architecture to realize unmanned intelligent analysis of the entire process of dam earthquake damage assessment by using edge, fog and cloud computing. The architecture allocates complex dam cluster monitoring computing tasks in a hierarchical manner to specific departments responsible for management, so that each department can conveniently and professionally maintain and modify the system. In addition, evenly distributing workloads on multiple machines enhances the performance, robustness, fault tolerance and overall reliability of the digital twin system. An intelligent seismic load identification algorithm triggered by an automatic system, a three-dimensional real-time virtual model visualization algorithm, and various connection APIs with the independently developed programmable FEM software Geodyna have been developed to realize autonomous and online digital twins of ultra-high earth-rock dams. Finally, a comprehensive, autonomous and distributed earth-rock dam earthquake disaster prevention and mitigation emergency software platform was established.

[0086] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0087] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0088] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0089] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0090] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0091] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0092] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0093] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0094] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0095] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0096] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0097] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0098] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0099] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A high earth-rock dam earthquake damage rapid assessment system, characterized in that: include: The cloud computing module is used to obtain and transmit data from other servers and pre-process the obtained data; A fog computing module is connected to the cloud computing module, performs analysis and calculation on the data preprocessed by the cloud computing module, and writes the calculation results into a database; The twin synchronous rendering module is connected to the fog computing module, and maps the calculation results in the database using the digital twin technology to obtain a virtual detection model.

2. The high earth-rock dam earthquake damage rapid assessment system according to claim 1 is characterized in that: The cloud computing module uses the parallel multi-threaded MQTT protocol to transmit data.

3. The high earth-rock dam earthquake damage rapid assessment system according to claim 2 is characterized in that: The parallel multithreading includes: The MQTT proxy instructs the MQTT client in the cloud server database to subscribe to the sensor data; The MQTT proxy accepts subscription requests for loading data from the MQTT client in the fog server and commands the MQTT client in the cloud server database to publish the loaded data for FEA; The MQTT proxy instructs the MQTT client in the cloud server database to subscribe to the FEA response data from the MQTT client in the fog server and store it; The MQTT proxy commands the MQTT client in the cloud server visualization module to subscribe to the sensor data and finite element response data from the MQTT client in the cloud server database.

4. The high earth-rock dam earthquake damage rapid assessment system according to claim 1 is characterized in that: The step of analyzing and calculating the data preprocessed by the cloud computing module and writing the calculation results into a database includes: For a seismic wave calculation request, the fog computing module downloads and processes the seismic wave data into a preset format. After the calculation is completed, the displacement time history curve corresponding to the specified node ID and the results of the dam seismic analysis are converted into JSON data format and stored in the database.

5. The high earth-rock dam earthquake damage rapid assessment system according to claim 1 is characterized in that: The fog computing module also includes: an intermediate module, connected to the cloud computing module, and used for communicating in the form of publishing and subscribing through a message queue telemetry protocol.

6. The high earth-rock dam earthquake damage rapid assessment system according to claim 1 is characterized in that: The intermediate module loads multiple seismic waves at one time and processes them in sequence until all seismic waves are evaluated.

7. The high earth-rock dam earthquake damage rapid assessment system according to claim 1 is characterized in that: The cloud computing module adopts a slice-oriented layered architecture to separate each API processing flow and dynamically cut the code into the specified position of the class.

8. The high earth-rock dam earthquake damage rapid assessment system according to claim 7 is characterized in that: The layered architecture includes: Router layer, used to handle API routing; The Validation layer is connected to the Router layer and is used to verify the data carried by each request; The Service business logic layer is connected to the Validation layer and is used to process the overall data flow; The Model layer is connected to the Service business logic layer and is used to interact with the database model; The Exception layer is connected to the Model layer and is used to process abnormal data.

9. The high earth-rock dam earthquake damage rapid assessment system according to claim 1 is characterized in that: The twin synchronous rendering module includes: node rendering and grid rendering.

10. The high earth-rock dam earthquake damage rapid assessment system according to claim 9 is characterized in that: The grid rendering includes: Get all element types and node composition order; defining a triangle decomposition criterion for each type of element and decomposing all elements into triangles according to the element type; The triangles are rendered and the triangle mesh is deformed by updating the node coordinates calculated from the original coordinates and the FEA residual displacement, and the deformed triangle mesh is colored.

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