A dam three-dimensional simulation analysis achievement real-time rendering system

Abstract

This invention relates to the field of model simulation calculation, and particularly to a real-time rendering system for 3D simulation analysis results of dams. The system server, through a simulation model management module, transmits a 3D finite element mesh model, obtains calculation conditions from the system client, performs simulation based on these conditions, outputs calculation results, and transmits the calculation result data to the system client after processing it using an 8-bit compression method. A three-level indexing system is constructed. The calculation result management module manages the calculation results. The system client renders a multi-dimensional physical field cloud map. During precise queries, the system server returns lossless calculation results to the system client based on the three-level indexing system. This invention leverages the long-connection characteristics of WebSocket to achieve on-demand incremental transmission of fragmented data, resolving network congestion caused by full transmission. Through the synergy of data compression and progressive rendering, network utilization is optimized while ensuring the real-time performance and stability of the front-end visualization effects.

Description

Technical Field

[0001] This invention belongs to the field of model simulation calculation, and in particular relates to a real-time rendering system for 3D simulation analysis results of dams. Background Technology

[0002] Reservoirs and dams are crucial national infrastructure projects, and their construction and operational safety is a national strategic requirement, receiving high attention from governments at all levels, relevant departments, and operation and management units. Conducting dam safety monitoring and analysis based on multi-source safety monitoring data is an important means to promptly capture abnormal dam operation characteristics and accurately assess the project's operational status, providing reliable support for the safe operation of the project.

[0003] Dam structure simulation analysis, as a key technical method for safety monitoring and analysis, relies on finite element method (FEM) simulation to construct a three-dimensional numerical model, enabling the simulation of the dam's operational state under the coupled effects of multiple fields such as self-weight, water load, and temperature field. This technology has dual core value in engineering safety management: firstly, by synchronously comparing the physical field data such as displacement, stress, and seepage obtained from simulation calculations with actual monitoring data in time and space, a closed-loop analysis system of "numerical simulation-monitoring verification" is formed, effectively assessing the accuracy and reliability of safety monitoring data; secondly, it allows for virtual simulations of conditions that have not yet occurred, such as super-standard floods and extreme temperature changes, quantitatively analyzing structural response characteristics and safety status, providing forward-looking technical support for engineering operation decisions. Currently, dam structure simulation analysis mainly relies on professional commercial software with a client / server (C / S) architecture (such as Ansys and Abaqus), which has inherent limitations such as system closure, difficulty in integration, and inconvenient interaction. Although some implementations based on a browser / server (B / S) architecture have been attempted, improving access convenience and system integration, significant performance bottlenecks still exist in areas such as three-dimensional model loading, result data processing, and multi-condition simulation. Specifically, the following issues are observed: the 3D model relies on global one-time mesh transmission, resulting in excessive network load and significant front-end rendering blockage; the calculation results are transmitted and persistently stored in the original floating-point format, lacking an efficient index structure, making it difficult to meet the requirements for real-time rendering and efficient querying of simulation results; the visualization process lacks standardized color mapping specifications, resulting in inconsistent rendering expressions between different physical quantities, which severely restricts intuitive comparison and collaborative interpretation in multi-field coupling analysis. Summary of the Invention

[0004] The purpose of this invention is to provide a real-time rendering system for 3D simulation analysis results of dams, which aims to solve the problems of high network load and significant front-end rendering blockage caused by the reliance on global one-time mesh transmission of 3D models; and the problem that the calculation results are transmitted and persistently stored in the original floating-point format, lacking an efficient index structure, making it difficult to meet the requirements of real-time rendering and efficient querying of simulation results.

[0005] This invention is implemented as follows: a real-time rendering system for 3D simulation analysis results of a dam. The system includes a system server and a system client. The system server includes a simulation model management module, a real-time simulation calculation module, and a calculation result management module. The system client includes a calculation condition management module. During simulation, the system client submits simulation tasks. The system server transmits the 3D finite element mesh model to the system client in a progressive manner through the simulation model management module. The system client provides calculation conditions to the system server through the calculation condition management module. The real-time simulation calculation module performs simulation based on the calculation conditions, outputs calculation results, constructs a three-level index system based on the calculation results, and transmits compressed data of the calculation results to the system client. The calculation result management module manages the calculation results. The system client renders a multi-dimensional physical field cloud map online based on the compressed data. When initiating a precise query for node physical quantity data, the system server returns lossless calculation results to the system client based on the three-level index system.

[0006] Preferably, the process by which the simulation model management module progressively transmits the three-dimensional finite element mesh model to the system client includes: A grid unitization encapsulation strategy based on engineering construction is adopted to divide the three-dimensional finite element mesh model into multiple independent data units according to the natural structural boundary of the dam body. Each data unit contains the complete vertex and topological data of its corresponding region, and the block granularity is controlled by a preset data capacity threshold. The data to be transmitted is segmented and then transmitted to the system client in a streaming manner. Based on the natural structure of the dam body, the processing flow is as follows: Initial partitioning: Based on the natural structural boundaries inside the dam type identification model, the initial topological segmentation of the grid is performed to form coarse-grained blocks with engineering semantics. Dynamic optimization of data granularity: Calculate the data volume of the initial block and compare it with the preset capacity threshold; further subdivide the over-limit block according to its substructure; merge adjacent blocks that are smaller than the preset value to finally generate independent transmission units with balanced data volume. Lightweight metadata encapsulation: Encapsulates metadata for each transmission unit, including a unique ID, project tag, and adjacency relationship, and generates a global index file.

[0007] Preferably, after receiving the data fragments, the system client parses the vertex coordinates and unit topology information in real time, uses the WebGL graphics library to dynamically construct the mesh, instantiates the corresponding mesh model and adds it to the 3D scene, and completes the real-time rendering update at the fragment level.

[0008] Preferably, in the step of processing the data to be transmitted in segments and transmitting it to the system client in a streaming manner, the system client and the system server establish a bidirectional persistent communication link based on the WebSocket protocol.

[0009] Preferably, the process of constructing a three-level index system based on the calculation results and transmitting the compressed data of the calculation results to the system client includes: classifying the calculation results according to the type of physical quantity, and processing each type of physical quantity separately; for a single physical quantity, traversing the floating-point data of all nodes and extracting the global maximum value of the physical quantity. V max and minimum value V min This serves as the numerical boundary for subsequent color mapping; the HSV color model is used for standardized color mapping of physical quantities, with fixed saturation and brightness parameters, and the numerical gradient of physical quantities is represented only by continuous hue changes.

[0010] Preferably, the step of characterizing the numerical gradient of a physical quantity through continuous hue changes includes: Node numerical normalization: normalizing the physical quantity values ​​of a single node. V i Normalize to the [0,1] interval according to the extreme values: in, V norm The value is the normalized value; 8. Characteristic phase conversion: Converting the normalized phase... V norm Mapped to an 8-bit hue index H8, with the hue range preset to a continuous color spectrum from blue to red, to represent the physical field gradient: The hue index arrays and their value range parameters corresponding to each physical quantity are serialized and encapsulated, and then pushed to the system client in batches.

[0011] Preferably, the system client receives the encapsulated hue index array and its value range parameters, restores the hue index to RGB color in real time according to the preset HSV parameters, and accurately assigns the color to the corresponding node of the mesh based on the principles of vertex shading and fragment shading.

[0012] Preferably, in the step of constructing a three-level index system, the Hash structure of the Redis in-memory database is used to establish a Key-Field-Value three-level index system. The top-level Key consists of the calculation batch number, structural part identifier, and physical quantity type to achieve multi-dimensional data isolation; the Field is a globally unique number for the node; and the Value stores the original float64 precision value corresponding to the node.

[0013] Preferably, the calculation operating condition management module is used for batch configuration, storage and management of multiple sets of operating condition parameters, as well as for adding, modifying, deleting and batch importing / exporting of operating condition parameters.

[0014] Preferably, the calculation result management module is used to manage the calculation result information in a structured manner, including the calculation name, associated working conditions, model used, material parameters, calculation time, and data storage location.

[0015] The real-time rendering system for dam 3D simulation analysis results provided by this invention uses the dam engineering parts as the basis for division, realizes the structured fragmentation of grid data, and ensures that the data volume of a single fragment is adapted to the carrying capacity of the Web client; relying on the long connection characteristics of WebSocket, it realizes the incremental transmission of fragmented data on demand, and solves the network congestion caused by full transmission; through the synergy of streaming transmission and progressive rendering, it optimizes network utilization while ensuring the real-time performance and stability of the front-end visualization effect. By normalizing the raw floating-point physical quantity data and mapping it to an 8-bit hue index, a high-efficiency compression of 87.5% of the data volume is achieved, significantly reducing the bandwidth overhead of front-end data storage and transmission. A Redis in-memory database hash structured storage scheme is adopted, with the calculation number-engineering part-physical quantity as the top-level key and the node number as the field, to build a four-level precise index system. Relying on the memory addressing characteristics of Redis, efficient single-point data query is achieved, ensuring the instant retrieval of raw data with lossless precision. This not only meets the high efficiency requirements of real-time rendering of simulation results, but also achieves precise retrieval of single-node data, fully adapting to the display requirements of dam engineering simulation results. Attached Figure Description

[0016] Figure 1 This is a flowchart of the real-time rendering system for the dam 3D simulation analysis results of the present invention; Figure 2 This is a schematic diagram of the interface of the simulation model management module in this invention; Figure 3 This is a schematic diagram showing the result of the progressive rendering process of the three-dimensional mesh model in this invention; Figure 4 This is a schematic diagram of the interface of the calculation condition management module in this invention; Figure 5 This is a schematic diagram of the simulation calculation working condition design interface in this invention; Figure 6 This is a schematic diagram showing the real-time rendering results of the simulation results in this invention; Figure 7 This is a schematic diagram of the interface of the calculation result management module in this invention; Figure 8 This is a schematic diagram of a high-precision data storage structure based on Redis provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] like Figure 1 The diagram shows a flowchart of the real-time rendering system for dam 3D simulation analysis results of the present invention. The system includes a system server and a system client. The system server includes a simulation model management module, a real-time simulation calculation module, and a calculation result management module. The system client includes a calculation condition management module. During simulation, the system client submits simulation tasks. The system server transmits the 3D finite element mesh model to the system client in a progressive manner through the simulation model management module. The system client provides calculation conditions to the system server through the calculation condition management module. The real-time simulation calculation module performs simulation based on the calculation conditions, outputs calculation results, constructs a three-level index system based on the calculation results, and transmits the compressed data of the calculation results to the system client. The calculation result management module manages the calculation results. The system client renders a multi-dimensional physical field cloud map online based on the compressed data. When initiating a precise query for node physical quantity data, the system server returns lossless calculation results to the system client based on the three-level index system.

[0019] In this system, such as Figure 2 As shown, the simulation model management module is configured to: realize the structured management and piecewise processing of the dam's three-dimensional finite element mesh model; perform standardized piecewise splitting and management of the three-dimensional finite element mesh model according to the structural zoning of the dam project; and provide import, export, modification, and deletion operations for model pieces.

[0020] In this embodiment of the invention, a lightweight loading and rendering system for dam finite element meshes is constructed, which combines engineering part segmentation, real-time WebSocket streaming, and forward progressive rendering. The specific steps are as follows: ① An engineering-based grid-based encapsulation strategy is adopted, which divides the model into multiple independent data units according to the natural structural boundaries of the dam body (such as gravity dams and arch dams divided by dam sections, and earth-rock dams divided by dam body, core wall, filter layer and upstream and downstream slope protection, etc.). Each data unit contains the complete vertex and topology data of its corresponding region, and the block granularity is controlled by a preset data capacity threshold to balance network transmission load and rendering memory usage.

[0021] ②Establish a bidirectional persistent communication link based on the WebSocket protocol to realize on-demand streaming push of fragmented data: The backend (system server) pushes the corresponding data fragments to the browser client in real time and in an orderly manner through the WebSocket connection according to the frontend (system client) request or rendering status.

[0022] ③ After receiving the data fragments, the front end parses the vertex coordinates and unit topology information in real time, and uses the WebGL graphics library to dynamically construct the mesh. Appropriate geometric and material processing strategies are applied to hexahedral and triangular facet units respectively. By introducing a progressive rendering mechanism, each fragment is received and parsed, and its corresponding mesh model is immediately instantiated and added to the 3D scene, achieving real-time rendering updates at the fragment level.

[0023] like Figure 4 As shown, the calculation condition management module is configured to support batch configuration, storage and management of multiple sets of condition parameters (such as different water level loads, ambient temperatures and operating load combinations), and to perform operations such as adding, modifying, deleting and batch importing / exporting of condition parameters.

[0024] like Figure 5 As shown, the real-time simulation calculation module is configured to: extract preset single-condition parameters or custom single-condition parameters from the calculation condition management module and complete parameter validity verification; based on the input condition information, drive the dam finite element simulation calculation model to perform real-time calculation tasks, quickly generate float64 lossless precision raw data of nodal physical quantities under the condition; after the calculation is completed, automatically verify and organize the result data. On the one hand, automatically perform 8-bit hue index compression of the physical quantity data under the condition, generate compressed data adapted to real-time rendering and push it to the front end in real time to render and generate a three-dimensional physical field cloud map; on the other hand, organize the data according to the format "calc_{calculation number}_{engineering part identifier}_{physical quantity type}", and store it through the Redis in-memory database hash structure to support real-time query of nodal lossless precision results.

[0025] like Figure 1 As shown, in this embodiment of the invention, efficient transmission and lightweight processing of calculation results are achieved through pre-calculation of node coloring values, and real-time querying of high-precision data is achieved based on the Redis in-memory database. The specific implementation steps are as follows: ① Based on real-time monitoring data of the dam, the simulation calculation of its operational performance is driven, and the output includes displacement (Ux, Uy, Uz) and stress (normal stress). shear stress Principal stress The results of multi-physics field nodes, such as temperature (T), are stored on the server side, along with the original double-precision (float64) calculation results.

[0026] ② Classify the simulation results according to the type of physical quantity, and process each type of physical quantity separately: For a single physical quantity, traverse the floating-point data of all nodes and extract the global maximum value of the physical quantity. V max and minimum value V min This serves as the numerical boundary for subsequent color mapping, ensuring that the entire range of values ​​for this physical quantity is covered.

[0027] ③ The HSV color model (Hue, Saturation, Value) is used for standardized color mapping of physical quantities. In this scheme, the saturation (S) and value (V) parameters are fixed, and the numerical gradient of the physical quantity is represented only by continuous changes in hue (H), thereby achieving consistent visual encoding across physical fields. Hue values ​​are quantized to 8-bit precision (i.e., an integer range of 0 to 255), which can generate smooth color transitions. Specifically: Node numerical normalization: normalizing the physical quantity values ​​of a single node. V i Normalize to the [0,1] interval according to the extreme values: Among them, V norm This is the normalized value.

[0028] 8. Characteristic phase conversion: Converting the normalized phase... V norm Mapped to an 8-bit (0~255) hue index H8, with the hue range preset as a continuous spectrum from blue to red to clearly represent the physical field gradient: .

[0029] ④ Serialize and encapsulate the hue index array (H_8) and its value range parameters (V_max, V_min) corresponding to each physical quantity, and push them to the front end in batches via the WebSocket channel. After receiving the data, the front end restores the hue index to RGB color in real time according to the preset HSV parameters (fixed S, V), and accurately assigns the color to the corresponding node of the mesh based on the principles of vertex shading and fragment shading.

[0030] For hexahedral isopleths, a barycentric coordinate interpolation algorithm is used to achieve a smooth gradient of surface color, ultimately enabling real-time overlay rendering of multiple fields, including temperature, stress, and displacement. Users can switch physical quantities through an interactive interface to intuitively compare the distribution characteristics of different operational states.

[0031] ⑤ Utilize the hash structure of the Redis in-memory database to establish a three-level index system of "Key-Field-Value". The top-level Key consists of "calculation batch number-structural part identifier-physical quantity type" (e.g., Calc_20251125_DamSection05_Stress_S1) to achieve multi-dimensional data isolation; the Field is a globally unique node number (e.g., N001234); the Value stores the original float64 precision value corresponding to the node, ensuring lossless data storage and millisecond-level addressing.

[0032] The structure of a three-level index system is as follows: Figure 8 The following table illustrates the three-level indexing system: Top-level unique identifier key calc_{calculation number}_{engineering part identifier}_{physical quantity type} Hash table (a collection of field-value key-value pairs) Locating datasets that include "batch calculations + engineering parts + physical quantities" Hash table field Node number Non-destructive precision calculation value Points to a specific node, associated with the calculated value of the corresponding physical quantity. ⑥ Supports users clicking on specific nodes in the 3D scene to perform data drill-down. The system automatically generates a Redis query key based on the current calculation batch, structural part, and physical quantity type, and uses the target node number as the field to directly retrieve the original high-precision calculated value of the node from the in-memory database by executing the HGET command.

[0033] like Figure 7 As shown, the calculation result management module is configured to: realize the structured management of calculation result information, covering key information such as calculation name, associated working conditions, model used, material parameters, calculation time and data storage location; support quick filtering of historical calculation tasks by calculation name, working condition type and other conditions, and realize one-click retrieval and display of results.

[0034] The overall process of this invention can be summarized as follows: like Figure 1 As shown, in the system client, the simulation analysis project partition is first specified and a request to call the three-dimensional finite element mesh model is initiated. The project partition data transmitted by the system server is received via WebSocket. The three-dimensional finite element mesh model is progressively loaded and rendered to obtain the full project model. After the model is loaded, the calculation conditions are set and the simulation calculation task request is submitted.

[0035] On the system server side, after receiving the model request from the system client side, it first retrieves the source data of the 3D finite element mesh model and performs engineering partitioning and sharding, establishes a WebSocket bidirectional data transmission link, and transmits the model. Upon receiving the calculation request, it calls the dam simulation calculation model to generate the calculation results, performs physical quantity dimension classification and 8-bit hue index compression on the results, and simultaneously constructs a Redis in-memory database hash three-level index system and completes data storage. Subsequently, the system client receives the compressed data via WebSocket and renders a multi-dimensional physical field cloud map online. When a precise query for node physical quantity data is initiated (carrying the calculation number, engineering location identifier, physical quantity type, and node number), the system server completes a hierarchical precise query based on the three-level index, retrieves the node lossless precision calculation results, and returns them. Finally, the system client realizes the visualization display of the node lossless precision results.

[0036] Figure 2 As shown, the simulation model management module enables structured block management of the dam's 3D finite element simulation model, covering information such as project name, part division, model file, number of nodes, and storage path. It also supports custom model loading order and provides pre-configuration support for progressive rendering.

[0037] Figure 3 As shown, the system client initiates a request to call a 3D finite element mesh model. The backend (system server) pushes the corresponding data fragments to the system client according to the request from the frontend (system client). After receiving the data fragments, the system client parses the vertex coordinates and unit topology information in real time and uses the WebGL graphics library to dynamically construct the mesh. After each fragment is received and parsed, the corresponding mesh model is immediately instantiated and added to the 3D scene to achieve real-time rendering and updating at the fragment level.

[0038] Figure 4 As shown, the calculation condition management module enables the setting of working conditions. Load information such as temperature, upstream water level, and downstream water level can be preset and configured in advance to provide load input for structural simulation calculation.

[0039] Figure 5 , Figure 6 As shown, the real-time simulation calculation module drives the dam simulation model to perform online calculations based on preset or custom working condition data, generating multi-physics field data such as dam displacement, stress, and temperature, and driving the 3D model to render and display in real time. On this basis, it automatically performs quantitative extraction and display of displacement, stress, and anti-sliding stability for typical parts of the dam. At the same time, the module supports node-level data penetration query. Users can query the non-destructive accuracy information (such as UX node data) of a point in real time by hovering the mouse on the 3D model.

[0040] Figure 7As shown, the calculation result management module enables structured management of calculation result information, covering basic information such as calculation name, calculation time, calculation type, associated working conditions, and calculation time; it also supports quick filtering of historical calculation results by conditions.

[0041] The effectiveness of this invention is illustrated below using a specific simulation scenario as an example and through comparison: A certain engineering finite element simulation model has a total of 200,000 nodes. Taking a single calculation result (containing 10 different physical quantities) as an example, the rendering effects of rendering using traditional double-precision floating-point data direct transmission and 8-bit hue index compression method are compared under the same hardware and network environment as follows: Double-precision floating-point data transmission and loading 16.2M 0.8M 3.5s -- 10s Large fluctuations 8-bit hue index compressed transmission + three-level index lookup 2M 0.8M 0.3s 87.5% 1.3s Stablize Through comparative analysis of the two technical solutions, the 8-bit hue index compression scheme adopted in this invention demonstrates significant advantages: while maintaining rendering stability, this invention compresses the data volume by 87.5%, significantly reduces transmission time from 10 seconds to 1.3 seconds, and substantially reduces front-end parsing time. This invention, through its efficient indexing mechanism, improves loading speed, reduces resource consumption, and ensures rendering stability, making it particularly suitable for web front-end applications with high real-time requirements and large data volumes.

[0042] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A real-time rendering system for 3D simulation analysis results of a dam, characterized in that, The system includes a system server and a system client. The system server includes a simulation model management module, a real-time simulation calculation module, and a calculation result management module. The system client includes a calculation condition management module. During simulation, the system client submits simulation tasks. The system server transmits the three-dimensional finite element mesh model to the system client in a progressive manner through the simulation model management module. The system client provides calculation conditions to the system server through the calculation condition management module. The real-time simulation calculation module performs simulation based on the calculation conditions, outputs calculation results, constructs a three-level index system based on the calculation results, and transmits the compressed data of the calculation results to the system client. The calculation result management module manages the calculation results. The system client renders a multi-dimensional physical field cloud map online based on the compressed data. When initiating a precise query for node physical quantity data, the system server returns lossless calculation results to the system client based on the three-level index system.

2. The real-time rendering system for dam 3D simulation analysis results according to claim 1, characterized in that, The process by which the simulation model management module progressively transmits the 3D finite element mesh model to the system client includes: A grid unitization encapsulation strategy based on engineering construction is adopted to divide the three-dimensional finite element mesh model into multiple independent data units according to the natural structural boundary of the dam body. Each data unit contains the complete vertex and topological data of its corresponding region, and the block granularity is controlled by a preset data capacity threshold. The data to be transmitted is segmented and then transmitted to the system client in a streaming manner. Based on the natural structure of the dam body, the processing flow is as follows: Initial partitioning: Based on the natural structural boundaries inside the dam type identification model, the initial topological segmentation of the grid is performed to form coarse-grained blocks with engineering semantics. Dynamic optimization of data granularity: Calculate the data volume of the initial block and compare it with the preset capacity threshold; further subdivide the over-limit block according to its substructure; merge adjacent blocks that are smaller than the preset value to finally generate independent transmission units with balanced data volume. Lightweight metadata encapsulation: Encapsulates metadata for each transmission unit, including a unique ID, project tag, and adjacency relationship, and generates a global index file.

3. The real-time rendering system for dam 3D simulation analysis results according to claim 2, characterized in that, After receiving the data fragments, the system client parses the vertex coordinates and cell topology information in real time, uses the WebGL graphics library to dynamically construct the mesh, instantiates the corresponding mesh model and adds it to the 3D scene, and completes the real-time rendering update at the fragment level.

4. The real-time rendering system for dam 3D simulation analysis results according to claim 2, characterized in that, In the step of segmenting the data to be transmitted and transmitting it to the system client in a streaming manner, the system client and the system server establish a bidirectional persistent communication link based on the WebSocket protocol.

5. The real-time rendering system for dam 3D simulation analysis results according to claim 1, characterized in that, The process of constructing a three-level index system based on the calculation results and transmitting the compressed data of the calculation results to the system client includes: classifying the calculation results according to the type of physical quantity, and processing each type of physical quantity separately; for a single physical quantity, traversing the floating-point data of all nodes and extracting the global maximum value of the physical quantity. V max and minimum value V min This serves as the numerical boundary for subsequent color mapping; the HSV color model is used for standardized color mapping of physical quantities, with fixed saturation and brightness parameters, and the numerical gradient of physical quantities is represented only by continuous hue changes.

6. The real-time rendering system for dam 3D simulation analysis results according to claim 5, characterized in that, The steps for characterizing the numerical gradient of a physical quantity through continuous hue changes include: Node numerical normalization: normalizing the physical quantity values ​​of a single node. V i Normalize to the [0,1] interval according to the extreme values: in, V norm The value is the normalized value; 8. Characteristic phase conversion: Converting the normalized phase... V norm Mapped to an 8-bit hue index H8, with the hue range preset to a continuous color spectrum from blue to red, to represent the physical field gradient: The hue index arrays and their value range parameters corresponding to each physical quantity are serialized and encapsulated, and then pushed to the system client in batches.

7. The real-time rendering system for dam 3D simulation analysis results according to claim 6, characterized in that, The system client receives the encapsulated hue index array and its value range parameters, restores the hue index to RGB color in real time according to the preset HSV parameters, and accurately assigns the color to the corresponding node of the mesh based on the principles of vertex shading and fragment shading.

8. The real-time rendering system for dam 3D simulation analysis results according to claim 1, characterized in that, In the process of building a three-level index system, the Hash structure of the Redis in-memory database is used to establish a Key-Field-Value three-level index system. The top-level Key consists of the batch number, structural part identifier, and physical quantity type, so as to achieve multi-dimensional data isolation. Field is a globally unique identifier for the node; Value stores the original float64 precision value corresponding to the node.

9. The real-time rendering system for dam 3D simulation analysis results according to claim 1, characterized in that, The calculation operating condition management module is used for batch configuration, storage and management of multiple sets of operating condition parameters, as well as adding, modifying, deleting and batch importing / exporting of operating condition parameters.

10. The real-time rendering system for dam 3D simulation analysis results according to claim 1, characterized in that, The calculation results management module is used to manage the calculation results information in a structured manner, including the calculation name, associated working conditions, model used, material parameters, calculation time, and data storage location.

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