A high earth-rock dam earthquake damage rapid assessment system

Abstract

The application relates to the technical field of high earth-rock dam earthquake damage assessment, in particular to a high earth-rock dam earthquake damage rapid assessment system, which comprises a cloud computing module, a fog computing module and a twin body synchronous rendering module; the cloud computing module is used for acquiring and transmitting data of other servers and pre-processing the acquired data; the fog computing module is connected with the cloud computing module, analyzes and calculates the pre-processed data of the cloud computing module, and writes the calculation results into a database; the twin body synchronous rendering module is connected with the fog computing module, maps and processes the calculation results in the database by using a digital twin technology, and obtains a virtual detection model. Through an advanced distributed architecture, dam earthquake damage assessment is carried out by using edge, fog and cloud computing, the self-determination and online digital twin of an extra-high earth-rock dam can be realized, the digital twin of each dam can provide comprehensive and real representation by combining a physical object, a virtual model and a mapping network through the digital twin technology, and thus an effective monitoring, analyzing and decision-making process can be realized.

Description

Technical Field

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

[0002] my country's southwest region boasts abundant hydropower resources, and numerous high-dam hydropower projects are being planned, constructed, and operated, including several dams over 200 meters tall, such as Xiaowan and Nuozhadu. Furthermore, the southwest region is also prone to earthquakes, posing significant challenges to the seismic safety of high dams. Due to their enormous reservoir capacity, a dam failure and uncontrolled water release would cause massive casualties and property losses downstream, with devastating consequences. Regulations require that medium- and large-scale water conservancy projects generally have comprehensive instrumentation to monitor changes in physical quantities such as dam deformation, seepage, and stress. This is combined with routine inspections, annual detailed inspections, and special inspections. However, because dam safety monitoring instruments are discretely deployed and inspections are primarily visual, they often only provide a superficial understanding. Therefore, instrumentation and inspections alone are insufficient to fully understand and assess dam safety. Furthermore, existing technologies are unable to quickly and accurately assess the safety status of dams after a strong earthquake.

[0003] In summary, how to design an intelligent and rapid seismic damage assessment system for high earth-rockfill dams is an urgent problem that needs 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 a certain extent.

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

[0006] To achieve the above objectives, the first embodiment of the present application proposes a high earth-rockfill dam seismic damage rapid assessment system, comprising:

[0007] Cloud computing module, 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 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 broker instructs the MQTT client in the cloud server database to subscribe to sensor data;

[0013] The MQTT proxy accepts subscription requests to load data from the MQTT client in the fog server and instructs 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 includes:

[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 publish and subscribe 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 includes:

[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 includes:

[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-rockfill dam seismic damage rapid assessment system, which uses an advanced distributed architecture to perform dam seismic damage assessment using edge, fog and cloud computing, 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-rockfill 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 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 seismic 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 this invention is to provide a high earth-rock dam earthquake damage rapid assessment system. Through an advanced distributed architecture, it uses edge, fog and cloud computing to perform dam earthquake damage assessment, realizing effective monitoring and analysis of the dam safety status after a strong earthquake.

[0041] In order to enable those skilled in the art to better understand the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without 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-rockfill dam seismic damage rapid assessment system, comprising:

[0044] Cloud computing module, 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-threading includes:

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

[0047] The MQTT proxy accepts subscription requests to load data from the MQTT client in the fog server and instructs 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, separates each API processing flow, and dynamically cuts the code into the specified location 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 publish and subscribe 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 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 rapid assessment system for earthquake damage to high earth-rockfill dams. Based on digital twin technology, a digital twin network of dams is constructed, and the dam structure is spatially reconstructed to ensure 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 perform dam earthquake damage assessment, full-process unmanned intelligent analysis, 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 dams after the earthquake are achieved.

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

[0066] This system primarily utilizes "fog-cloud" collaborative computing, with the cloud computing module utilizing the Linux operating system, and the fog computing module using the Windows system to host GEODYNA, a large-scale, high-performance, and effective three-dimensional nonlinear geotechnical engineering static and dynamic analysis software. The system also utilizes the MQTT protocol for data synchronization between the fog computing and cloud computing modules. This architectural design not only maximizes the computing power of the fog computing module but also leverages the cloud computing module's data storage and distribution capabilities to enable network-based computational triggering, computational structure post-processing, and cloud platform presentation. This allows users to access the earthquake rapid assessment platform from any location and at any time through a browser, enabling them to respond promptly to earthquake events and obtain immediate dam damage assessment results.

[0067] Cloud servers need to collect and transmit data from all other servers. Furthermore, given large data volumes and high network latency, minimal blocking is required. Therefore, MQTT, a parallel multi-threaded messaging protocol, is used to automatically transmit data across the entire digital twin network. MQTT is an OASIS standard messaging protocol widely used in the Internet of Things (IoT). Unlike HTTP, which uses a request / response mechanism, MQTT uses a publish / subscribe mechanism to establish a Transmission Control Protocol (TCP) connection with servers. 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 for MQTT information transmission, responsible for publishing data to MQTT clients upon request. It also manages MQTT clients to ensure smooth and accurate communication between them. In the proposed digital twin network, the MQTT broker is deployed in the cloud server, making it the central brain and the ultimate authority for data transmission between all servers. MQTT clients are installed in all edge servers, fog servers, and cloud servers.

[0068] In the digital twin process, there are four main parallel threads: (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 request to load 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 the 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 in the case of severely limited hardware and low network bandwidth.

[0069] The cloud computing module is primarily responsible for hosting the front-end and back-end of the cloud platform. The front-end of the cloud computing module is primarily written in Vue.js, using the Ant Design design language for UI design, and Vue-router to implement single-page applications. The back-end platform is primarily developed using the high-performance back-end FastAPI framework, which boasts performance comparable to NodeJS and Go, and is capable of automatically generating standard API documentation, making subsequent maintenance more efficient. In this project, a layered architecture based on a slicing-oriented design pattern was developed based on this back-end development framework. This separates the processing flow of each API, and dynamically directs the code to the specified method and location of the class during the operation of the back-end service. This significantly reduces the complexity of the code, greatly improves the reusability of the class, solves the problem of disconnection 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 five layers: the Router layer that handles API routing, the Validation layer that uses dependency injection 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 data flow, such as earthquake report generation and other functions, and communicates with the Model layer. The Model layer mainly interacts with the database model. Each method of the database is defined as a dedicated method for design, allowing the logic layer to call the Model layer, thereby achieving isolation between logic and data and improving the robustness of the system.

[0071] The fog computing module is the computing core of the entire system. Since GEODYNA is a Windows-based software system that requires high computing power, a fog computing module was designed to adapt the operating system and computing power. To enable two-way communication between the GEODYNA computing software and the cloud computing module, a middleware running on the fog computing module was developed within the project's platform. This middleware communicates with the cloud computing module using the Message Queuing Telemetry Protocol (MQTT) in a publish-and-subscribe mode. For a seismic wave calculation request, a browser requests a cloud service API to upload the seismic wave data to the cloud server. After verifying the seismic wave data, the cloud server sends an MQTT message to the MQTT broker, allowing the fog computing module's compute nodes to subscribe and receive the incoming instructions. The instruction carries the download address of the verified seismic wave data. The fog computing module downloads the seismic wave data and preprocesses it into the file format required for GEODYNA calculations. After the calculation is complete, the displacement time history curve corresponding to the specified node ID and the results of the dam seismic analysis are post-processed into a JSON data format for easy network transmission and reading. The data is then uploaded and written to the database using the specified API, allowing the cloud computing module to access the analysis results. Because seismic wave calculations take a long time, the fog computing module's middleware also features a task queue function that can load multiple seismic waves at once. Even if the cloud platform system is shut down, calculations will continue in the fog computing module until all seismic waves are evaluated.

[0072] Rendering finite element data on the browser client side has always been a difficult problem. 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 cannot render complex data. 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, finite element node data selection is achieved through a computer graphics ray detection algorithm. To facilitate node data selection and enable node-only rendering, a point material is defined, using the Jetmap color format. Data is rendered in different formats, from small to large. Because rendering is asynchronous, an asynchronous callback method is used to obtain the maximum and minimum displacement values ​​after rendering, enabling the acquisition of the finite element's mid-range displacement range. This implements the finite element web real-time rendering engine.

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

[0074] Notably, all programs within the digital twin network run on Linux. Linux provides stability, reliability, and compatibility for deployed applications. Furthermore, a local area network (LAN), connected by over 1,000 kilometers of wires, facilitates communication between the super dam cluster and the city's safety command center. The LAN establishes a robust and reliable means of data transmission, enabling seamless connectivity between the dam cluster and the command center. Furthermore, the digital twin system is designed to be accessible online for ease of use. However, as previously mentioned, to prioritize data security, access is strictly restricted 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 performance, data security, and accessibility.

[0075] A large amount of data is generated during 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 convenient, 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 virtual model visualization in this digital twin network is dynamically presented in real time based on the continuously updated finite element responses. To achieve this, the network uses the Web Graphics Library (WebGL) framework, which is a framework that utilizes the JavaScript API of OpenGL ES. It can achieve high-performance interactive 3D and 2D graphics rendering in various compatible web browsers. As a result, the digital twin system is able to provide an immersive 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 FEM nodes are rendered, omitting elements. This rendering mode utilizes WebGL's GL_Points component and the FEM / FEA information associated with each node. This information includes the node coordinates (x, y, z) to determine its position in virtual space, real-time FEM residual displacement to drive its deformation, and real-time FEM residual displacement or stress to calculate its color representation, ranging from blue (minimum) to red (maximum). Nodes are designed to be clickable, allowing for interaction. WebGL's LineSegmentIntersector component is used to pick up events, perform Raycaster collision detection on nodes in the scene, and enable clickability. After clicking a node, the associated FEA response is displayed, along with its ID and location. Node rendering mode is lightweight, making rendering fast, especially on lower-performance computers. This mode provides efficient digital twinning 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 in the figure, it is constructed by combining the WebGL GL_Triangles component with the element's CG triangle composition criteria and each element's finite element / finite element information (including element type, the order and coordinates of the nodes that make up the element, and the real-time finite element residual displacement or stress of the nodes). Mesh rendering is divided into five steps.

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

[0080] Step 2: Define the triangle decomposition criteria for each element type and decompose all elements into triangles based on the element type. For example, a common 6-faced 8-node hexahedron 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 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-rockfill 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 utilizing edge, fog and cloud computing. This architecture distributes complex dam cluster monitoring computing tasks in a hierarchical manner to specific departments responsible for management, enabling each department to conveniently and professionally maintain and modify the system. In addition, evenly distributing workloads across multiple machines enhances the performance, robustness, fault tolerance and overall reliability of the digital twin system. An intelligent earthquake 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-rockfill dams. Ultimately, a comprehensive, autonomous and distributed earth-rockfill 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 is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0091] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0092] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean 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 expressions 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, unless they are mutually inconsistent.

[0093] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0094] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment 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 be performed out of 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 those skilled in the art to which the embodiments of the present application belong.

[0095] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, 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 it in another suitable manner if necessary, and then storing it in a computer memory.

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

[0097] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0098] In addition, the functional units in the various embodiments 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 a 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 is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A high earth-rock dam earthquake damage rapid assessment system, characterized by: include: The cloud computing module is used to acquire and transmit data from other servers and pre-process the acquired data. The edge server 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 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 transmission, data storage and real-time virtual model visualization driven by finite element responses. The cloud computing module uses the parallel multi-threaded MQTT protocol to transmit data, and the parallel multi-threading includes: The MQTT broker instructs the MQTT client in the cloud server database to subscribe to sensor data; The MQTT proxy accepts subscription requests to load data from the MQTT client in the fog server and instructs 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; The fog computing module is connected to the cloud computing module, analyzes and calculates the data preprocessed by the cloud computing module, and writes the calculation results into the database, including: 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. The twin synchronous rendering module is connected to the fog computing module, and maps the calculation results in the database using 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 fog computing module further includes: an intermediate module connected to the cloud computing module and configured to communicate in a publish and subscribe manner via a message queue telemetry protocol.

3. The high earth-rock dam earthquake damage rapid assessment system according to claim 2 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.

4. 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.

5. The high earth-rock dam earthquake damage rapid assessment system according to claim 4 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.

6. 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.

7. The high earth-rockfill dam earthquake damage rapid assessment system according to claim 6 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.

Images