Visualization Rendering Method and System for Structural Mechanics Characteristics of Digital Twin Bridge

Abstract

The present disclosure provides a method and system for visual rendering of the structural mechanical properties of a digital twin bridge, relating to the technical field of visualizing the structural mechanical properties of a bridge, including: obtaining the load distribution state of vehicles on the bridge deck; based on the load distribution state, combining with the finite element model of the bridge to determine the deflection-time curve and the cable force-time curve of the deflection change control points of the bridge main girder; converting the deflection-time curve and the cable force-time curve into the HSV color model by a coloring algorithm based on the Sigmoid function, and converting the HSV color model into the RGB color model; using the UE4 engine to perform visual rendering on the digital twin bridge based on the RGB color model. This method improves the accuracy of bridge health monitoring, and through visualization means, the structural state and potential problems of the bridge are more easily identified and understood. The present disclosure also provides a system for visual rendering of the structural mechanical properties of a digital twin bridge, an electronic device, and a storage medium.

Description

Technical Field

[0001] The present disclosure relates to the technical field of visualization of structural mechanical characteristics of bridges, and in particular to a method and system for visualization rendering of structural mechanical characteristics of digital twin bridges. Background Art

[0002] In the field of bridge engineering, accurate monitoring and timely maintenance of the health of bridges are the key to ensuring their long-term stable operation. During the service life of a bridge, affected by various external environmental factors and continuous use, the key components of its structure gradually suffer damage and changes in the internal force state. These changes may cause varying degrees of attenuation in the stiffness and bearing capacity of the bridge. If they are not identified and handled in time, they may evolve into serious safety hazards and even lead to catastrophic accidents. Therefore, it is crucial to implement effective bridge health monitoring.

[0003] Traditional bridge health monitoring systems are mainly based on IoT technology, using a variety of sensors installed on bridges to collect key performance data about the bridge, such as stress, deflection, and vibration. These data are then analyzed and processed to assess the overall health of the bridge. However, this approach has some limitations. First, due to the independence of monitoring systems and the lack of effective data integration and interoperability, information silos have been created, which limits the overall effectiveness of the monitoring system. Second, traditional monitoring systems mostly rely on periodic report analysis, which makes it difficult to provide real-time feedback on the health of the bridge, especially in emergency situations, which may lead to delayed responses.

[0004] In view of the limitations of traditional methods, the application of digital twin technology provides an innovative solution. Digital twin technology refers to the creation of a digital virtual copy of a physical entity, and the use of historical data, real-time data, and algorithm models to simulate, verify, predict, and control the entire life cycle of the physical entity. In bridge health monitoring, the digital twin model combines the operational safety data of the physical bridge, and simulates the key monitoring parts of the physical bridge on the digital model, such as the bridge deck, guardrails, and piers, thereby achieving real-time monitoring and analysis of the accumulated deformation of the bridge.

[0005] For example, CN114757373B discloses a bridge monitoring and analysis method, equipment and computer storage medium based on digital model analysis. This technology collects safety data in real time by deploying operational safety monitoring terminals at key locations of physical bridges, and accumulates and simulates these data to the corresponding monitoring locations of digital model bridges. This method can perform preventive maintenance management before the bridge shows significant external damage, effectively reducing traces of bridge maintenance and extending its operational life. In this way, the operational safety of the bridge is preventively guaranteed, the risk of structural damage is greatly reduced, and the optimization and intelligence of bridge maintenance is achieved.

[0006] However, in the prior art, the optimization and intelligentization of bridge maintenance are usually achieved by means of numerical models, which are not intuitive and real-time enough. For this reason, a visualization rendering method and system for the structural mechanical properties of a digital twin bridge are proposed. Summary of the Invention

[0007] To solve at least one of the problems described above, the present application discloses a visualization rendering method and system for the structural mechanical properties of a digital twin bridge.

[0008] According to one aspect of the present disclosure, there is provided such a visualization rendering method for the structural mechanical properties of a digital twin bridge, including: obtaining the load distribution state of vehicles on the bridge deck; based on the load distribution state, combining with the bridge finite element model to determine the deflection-time curve and the cable force-time curve of the deflection change control points of the bridge main girder; converting the deflection-time curve and the cable force-time curve into the HSV color model by a coloring algorithm based on the Sigmoid function, and converting the HSV color model into the RGB color model; performing visualization rendering on the digital twin bridge using the UE4 engine based on the RGB color model.

[0009] In some embodiments, the obtaining the load distribution state of vehicles on the bridge deck includes: obtaining the position information of vehicles on the bridge by a video image recognition algorithm based on deep learning, and performing type recognition on the vehicles to obtain the vehicle types; using a dynamic weighing system to measure the pressure of the vehicle axles, converting this pressure into a voltage signal, and calculating the weight and speed of the vehicle; determining the bridge load distribution state based on the vehicle types, the position information of the vehicles, the weight of the vehicles, the speed of the vehicles, and the time.

[0010] In some embodiments, the algorithm for obtaining the position information of vehicles on the bridge by a video image recognition algorithm based on deep learning and performing type recognition on the vehicles to obtain the vehicle types is any one of YOLO, SSD, Faster R-CNN, or EfficientDet.

[0011] In some embodiments, the process of converting the deflection-time curve and the cable force-time curve into the HSV color model by a coloring algorithm based on the Sigmoid function includes:

[0012] Perform normalization on the deflection-time curve and cable force-time curve for linear variation, converting the curves into the range of [0, 1]; use the Sigmoid function to correct the y-axis reference indices of the normalized deflection and cable force curves to amplify the color interval distribution at large deflection and large cable force values; linearly amplify the processed deflection and cable force reference indices within the range of [0, 1] to the range of [0, 240], and determine the hue value H in the HSV color model; set the saturation S and brightness V to 1, and construct the HSV color model based on the hue value H, saturation S, and brightness V.

[0013] In some embodiments, the steps of visualizing and rendering the digital twin bridge using the UE4 engine based on the RGB color model include:

[0014] In the UE4 engine, use the Datasmith tool to import the three-dimensional bridge model and the base scene to create the base of the digital twin model; use the material domain of UE4 to perform material preparation for the scene; apply the UltraDynamicSky plugin to render the sky and lighting for the entire three-dimensional scene; use the UE4_VaREST and JsonBlueprint plugins to establish a connection with the mechanical analysis database, and obtain the results of RGB-time parameters from the background through POST requests; according to the specifications of the mechanical model, disassemble and match the digital twin model accordingly to ensure that the mechanical analysis results at each stage have corresponding representation carriers in the twin model; after receiving the mechanical analysis results, match these results with the material coloring of the bridge model, and perform A-B difference matching on the rendering colors of each segment and adjacent segments to obtain the rendering model.

[0015] In some embodiments, the steps of calculating the weight and speed of the vehicle are as follows: Set several groups of pressure sensors on the road surface to detect the pressure applied by the vehicle axle passing by; convert the detected pressure into a voltage signal, where the conversion is achieved by applying the pressure to a piezoelectric material or a similar sensor to generate a voltage signal proportional to the applied pressure; collect and process the generated voltage signal to analyze the amplitude and duration of the signal, and calculate the weight and speed of the vehicle.

[0016] In some embodiments, the vehicle types at least include: large buses, medium buses, small buses, large sedans, medium sedans, small sedans.

[0017] According to another aspect of the present disclosure, there is provided a visualization rendering system for the structural mechanical properties of a digital twin bridge, including: a data acquisition module for acquiring the load distribution state of a vehicle on the bridge deck; a time-varying graph determination module for determining the deflection-time curve and the cable force-time curve of the deflection change control points of the bridge main girder based on the load distribution state in combination with the bridge finite element model; a color model conversion module for converting the deflection-time and cable force-time change conditions into the HSV color model based on the coloring algorithm of the Sigmoid function and converting the HSV color model into the RGB color model; and a rendering module for performing visualization rendering of the digital twin bridge based on the RGB color model using the UE4 engine.

[0018] According to yet another aspect of the present disclosure, there is provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the visualization rendering method for the structural mechanical properties of the digital twin bridge as described in any one of the above embodiments.

[0019] According to still another aspect of the present disclosure, there is provided a readable storage medium storing a computer program, which is suitable for being loaded by a processor to execute the visualization rendering method for the structural mechanical properties of the digital twin bridge as described in any one of the above embodiments.

[0020] The visualization rendering method for the structural mechanical properties of the digital twin bridge provided by the present disclosure realizes an intuitive display of the response results of the bridge under load by acquiring highway bridge data and integrating video image recognition algorithms, dynamic weighing systems, finite element model analysis, and a coloring algorithm based on the Sigmoid function. This integrated method not only improves the accuracy of bridge health monitoring, but also makes the structural state and potential problems of the bridge easier to identify and understand through visualization means.

[0021] Adopt a real-time data processing and feedback mechanism. By combining the Flask framework and the UE4 engine, the collected data can be quickly processed and presented in a visual form, thereby realizing an immediate assessment of the bridge health condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings illustrate exemplary embodiments of the present disclosure and, together with the description thereof, are used to explain the principles of the present disclosure. These drawings are included to provide a further understanding of the present disclosure and are included in this specification and form a part of this specification.

[0023] Figure 1 Schematic flowchart of the visualization rendering method for the structural mechanical properties of the digital twin bridge according to an embodiment of the present disclosure;

[0024] Figure 2 Schematic diagram of the deflection change of the bridge main girder at a certain moment in an embodiment of the present disclosure;

[0025] Figure 3 Schematic diagram of the method for determining the bridge load distribution state in an embodiment of the present disclosure;

[0026] Figure 4 Flowchart of constructing the HSV color model in an embodiment of the present disclosure;

[0027] Figure 5 Flowchart of constructing the rendering model in an embodiment of the present disclosure;

[0028] Figure 6 Flowchart of calculating the vehicle weight and vehicle speed in an embodiment of the present disclosure;

[0029] Figure 7 Schematic diagram of the rendering effect of the bridge deflection in an embodiment of the present disclosure;

[0030] Figure 8 Schematic diagram of the visualization rendering system of the structural mechanical properties of the digital twin bridge in an embodiment of the present disclosure.

[0031] Figure 9 Schematic diagram of an electronic device in an embodiment of the present disclosure.

[0032] Figure 10 Schematic diagram of a storage medium in an embodiment of the present disclosure. Specific embodiments

[0033] The following further elaborates on the present disclosure in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant content and do not limit the present disclosure. Additionally, it should be noted that for the sake of convenience of description, only the parts related to the present disclosure are shown in the drawings.

[0034] It should be noted that, without conflict, the embodiments in the present disclosure and the features in the embodiments can be combined with each other. The following will detail the technical solutions of the present disclosure with reference to the accompanying drawings and embodiments.

[0035] Unless otherwise specified, the exemplary embodiments / examples shown will be understood to provide exemplary features of various details of some ways that can implement the technical concept of the present disclosure in practice. Therefore, unless otherwise specified, without departing from the technical concept of the present disclosure, the features of various embodiments / examples can be additionally combined, separated, interchanged, and / or rearranged.

[0036] The terms used herein are for the purpose of describing particular embodiments and are not limiting. As used herein, unless the context clearly dictates otherwise, the singular forms "a", "an", and "the" are also intended to include the plural forms. Additionally, when the terms "comprising" and / or "including" and their variants are used in this specification, it is stated that there are the stated features, integers, steps, operations, components, assemblies, and / or groups thereof, but it does not exclude the presence or addition of one or more other features, integers, steps, operations, components, assemblies, and / or groups thereof. It should also be noted that, as used herein, the terms "substantially", "about", and other similar terms are used as approximate terms and not as terms of degree, and thus they are used to explain the inherent deviations of measured, calculated, and / or provided values that would be recognized by a person of ordinary skill in the art.

[0037] Figure 1 Flowchart of the method for visual rendering of the structural mechanics characteristics of the digital twin bridge according to an embodiment of the present disclosure.

[0038] S101 Obtain the load distribution state of vehicles on the bridge deck.

[0039] As Figure 3 shown, the obtaining of the load distribution state of vehicles on the bridge deck includes:

[0040] Based on the video image recognition algorithm of deep learning, obtain the position information of vehicles on the bridge and perform type recognition on the vehicles to obtain vehicle types, where the vehicle types at least include: large buses, medium buses, small buses, large sedans, medium sedans, and small sedans;

[0041] Use the dynamic weighing system to measure the pressure of the vehicle's axles, convert this pressure into a voltage signal, and calculate the weight and speed of the vehicle;

[0042] Determine the load distribution state of the bridge based on the vehicle type, the position information of the vehicle, the weight of the vehicle, the speed of the vehicle, and time.

[0043] In a preferred embodiment, the vehicle position information is obtained through the video image recognition algorithm, and the multi-dimensional information of vehicle position, vehicle type, and vehicle weight is fused by combining the vehicle type recognition algorithm and the dynamic weighing system, so as to obtain the load distribution state of vehicles on the bridge deck.

[0044] The vehicle type recognition and tracking positioning algorithm uses a deep learning algorithm based on YOLO, etc. to classify and recognize the vehicle types passing through the bridge. All vehicle types are divided into 6 types, namely large buses, medium buses, small buses, large sedans, medium sedans, and small sedans.

[0045] As Figure 6As shown, the dynamic weighing sensor can convert the axle pressure of the vehicle acting on it into a voltage signal. The axle action time can be calculated by analyzing the voltage signal, and the corresponding axle weight can be calculated by the voltage. The weighing system often arranges two weighing sensors at a certain distance in the same lane to obtain the axle weight and axle speed. The specific working principle is: the two weighing sensors in the same lane are L apart, and the time for the same axle to pass through the two sensors is t1 and t2 respectively. The axle weights measured by the two sensors are w1 and w2 respectively. The axle weight and axle speed can be calculated according to the following formula.

[0046]

[0047]

[0048] The dynamic weighing system obtains the vehicle weight and speed information, and then according to the image frame when the vehicle passes through the dynamic weighing system, the corresponding model of the vehicle can be obtained, and its position can be tracked to obtain the load conditions of the vehicle on the bridge deck at different times. Taking into account the distribution of each vehicle in the selected time interval, the overall load distribution of the vehicle on the bridge deck in this time interval can be obtained. It should be clearly pointed out that converting axle weight and axle speed into vehicle weight and vehicle speed is common knowledge in the field, and no further explanation is given again.

[0049] For further understanding:

[0050] For a single vehicle, since the time it takes for a vehicle to cross a bridge is generally short, it can be assumed that the vehicle is traveling in a straight line at a constant speed. The real-time position of the vehicle can be calculated based on the time and speed of the vehicle passing the dynamic weighing system.

[0051] Based on the real-time position of a single vehicle and the vehicle weight obtained by the dynamic weighing system, the bridge load distribution at different times when the vehicle is driving on the bridge can be obtained.

[0052] For bridges, by comprehensively considering the time, real-time location, vehicle weight and other information of all vehicles crossing the bridge within a certain time interval and superimposing them, the overall load distribution on the bridge deck at a certain moment can be obtained.

[0053] The steps of calculating the weight and speed of the vehicle are as follows:

[0054] Several groups of pressure sensors are arranged on the road surface to detect the pressure applied by the wheel axles of the vehicles;

[0055] Converting the detected pressure into a voltage signal, wherein the conversion is achieved by applying pressure to a piezoelectric material or similar sensor to produce a voltage signal proportional to the applied pressure;

[0056] Collect and process the generated voltage signal to analyze the amplitude and duration of the signal, and calculate the weight and speed of the vehicle

[0057] The algorithm for the deep learning-based video image recognition to obtain the position information of vehicles on the bridge and identify the vehicle types is any one of YOLO, SSD, Faster R-CNN, t, or EfficientDet

[0058] Step S102: Based on the load distribution state, combine with the bridge finite element model to determine the deflection-time curve and cable force-time curve of the deflection change control points of the bridge main girder

[0059] According to the characteristics of the bridge structure, select the main control sections of the structural response of the bridge under vehicle loads. For the control sections of the main girder deflection response change, generally select the locations such as the piers of the bridge main girder, 1 / 4 span, mid-span, 3 / 4 span, etc.; for cable-stayed bridges and suspension bridges, the cable force change response of each stay cable and suspension cable should also be considered. If this bridge is equipped with a health monitoring system, the deflection change control points should include the layout points of the pressure transmitters for measuring the bridge deflection change. According to the load distribution of vehicles on the bridge surface within the time interval and combined with the bridge finite element model, the structural mechanics analysis of the bridge under the load can obtain the deflection-time change and cable force-time change of the deflection change control points of the bridge main girder. As follows Figure 2 , which is the deflection change of the bridge main girder at a certain moment, and the schematic diagram of the corresponding change of the deflection values of each control section with time

[0060] Step 103: The coloring algorithm based on the Sigmoid function converts the deflection-time curve and cable force-time curve into the HSV color model, and then converts the HSV color model into the RGB color model

[0061] As Figure 4 shown, the process of converting the deflection-time curve and cable force-time curve into the HSV color model by the coloring algorithm based on the Sigmoid function includes

[0062] Perform normalization processing of linear transformation on the deflection-time curve and cable force-time curve, and convert the curve to the range of [0,1];

[0063] Use the Sigmoid function to correct the y-axis reference index of the normalized deflection and cable force curves to amplify the color interval distribution at large deflection and large cable force values;

[0064] Linearly amplify the processed deflection and cable force reference indexes in the range of [0,1] to the range of [0,240] to determine the hue value H in the HSV color model;

[0065] Set the saturation S and brightness V to 1, and construct the HSV color model based on the hue value H, saturation S, and brightness V.

[0066] Exemplarily:

[0067] In order to display the mechanical response effect of the bridge structure on the digital twin model of the bridge, it is necessary to convert the deflection-time and cable force-time changes of the deflection control points into color-time changes at the positions of the components. The visual rendering coloring algorithm for structural response converts the deflection-time and cable force-time changes into the HSV color model, where the deflection and cable force response values correspond to the hue (H) value, and the value range is [0, 240], and the saturation (S) and brightness (V) take fixed values of 1.

[0068] According to engineering experience, the probability of the probability distribution of bridge deflection values having maximum and minimum values (negative maximum values) is relatively low. Usually, under normal conditions, the deflection and cable force change little, and the deflection and cable force values in this case appear with a higher frequency; when special situations occur (such as when heavy vehicles pass or a large number of vehicles pass simultaneously in a short period of time), the deflection and cable force of the main girder change greatly, but the occurrence frequency of such large deflection and cable force values is small. Therefore, converting the deflection-time and cable force-time changes obtained by analysis into color-time changes according to a linear distribution shows a poor effect, and it is easy to have the situation that the color changes little most of the time, the colors of large deflections and large cable forces in the limit state appear for a short time, and the change before and after shows a sudden change, and the transition time before and after the color change is short and not obvious.

[0069] When taking the color values of each section, if the value of the hue (H) cannot be linearly taken with the deflection, it is necessary to use the Sigmoid function for correction to amplify the color richness in the case of larger deflection values, and it can better highlight the situation when the bridge structure response is larger. Normalize the deflection-time and cable force-time curves according to linear changes, and use the Sigmoid function to correct the deflection and cable force reference indicators on the y-axis of the function to amplify the color interval distribution when the deflection and cable force are large. Then linearly amplify the deflection and cable force reference indicators in the range of [0, 1] to the hue (H) value in the range of [0, 240]. Then, combined with the saturation (S) and brightness (V) equal to 1, convert the HSV color model into the RGB color model.

[0070] S104. Use the UE4 engine to perform visual rendering on the digital twin bridge based on the RGB color model.

[0071] As Figure 5 shown, in the UE4 engine, use the Datasmith tool to import the three-dimensional bridge model and the basic scene to create the base of the digital twin model;

[0072] Use the material domain of UE4 to adjust the materials of the scene to enhance the authenticity and visual effects of the scene;

[0073] Apply the UltraDynamicSky plugin to render the sky and lighting of the entire 3D scene to improve the naturalness and lighting effects of the scene;

[0074] Use the UE4_VaREST and JsonBlueprint plugins to establish a connection with the mechanical analysis database and obtain the results of RGB-time parameters from the background through POST requests;

[0075] According to the specifications of the mechanical model, disassemble and match the digital twin model accordingly to ensure that the mechanical analysis results at each stage have corresponding representation carriers in the twin model;

[0076] After receiving the mechanical analysis results, match these results with the material coloring of the bridge model, and perform A-B difference matching on the rendering colors of each segment with adjacent segments to achieve a smooth transition of the colors of the entire model;

[0077] Through the above steps, achieve an intuitive and dynamic visual representation of the mechanical characteristics of the bridge structure on the digital twin model.

[0078] More specifically:

[0079] Use Flask for data transmission to complete the communication between bridge mechanical characteristic analysis, RGB color model parameter setting, and visualization rendering of the bridge main girder in the UE4 engine. On the Python side, use Flask to build a mechanical characteristic analysis service. On the UE4 side, the time interval parameters are transmitted to the Python side in json form through a post request. The Python side reads the vehicle information for this time period according to the time interval, then conducts the main control section structural response results of bridge mechanical analysis, and calculates the RGB-time function through the visualization rendering coloring algorithm. The Python side then converts the results into json form and returns them to the UE4 side.

[0080] In the UE4 engine, the three-dimensional model and the basic scene are imported through Datesmith to form a digital base. Use the material domain to adjust the rendering material to make the scene closer to the real environment. Use UltraDynamicSky to render the sky and lights of the entire scene. After building and rendering the three-dimensional scene, use UE4_VaREST and JsonBlueprint plug-ins to connect to the mechanical analysis database, and request the background to return the RGB-time parameter results in Post mode. In order to accurately reflect the results of the mechanical analysis in the twin model, the twin model needs to be disassembled and matched according to the mechanical model, so that the mechanical analysis results of each stage have corresponding twin carriers to receive the data. After receiving the mechanical analysis result data, this result is matched with the bridge deck material coloring, and the rendering color of each segment is matched with the rendering color of the adjacent segment in the driving direction by AB difference, such as Figure 7 As shown, the overall color change transitions smoothly, thereby achieving a visual rendering of the structural mechanical properties of the bridge.

[0081] At least the following technical effects can be obtained through the above technical solution:

[0082] Enhanced bridge health monitoring capabilities: This invention integrates video image recognition algorithms, dynamic weighing systems, finite element model analysis, and sigmoid function-based coloring algorithms to achieve an intuitive display of the response results of bridges under load. This integrated approach not only improves the accuracy of bridge health monitoring, but also makes the structural status and potential problems of bridges easier to identify and understand through visualization.

[0083] Real-time data processing and feedback: Compared with traditional report-based data analysis, the present invention adopts real-time data processing and feedback mechanism. Through the combined use of Flask framework and UE4 engine, the collected data can be quickly processed and presented in a visual form, thereby achieving instant assessment of the health status of the bridge.

[0084] Improve preventive maintenance efficiency: The implementation of the present invention can help timely detect minor damage or stiffness changes in bridges, so that effective preventive maintenance can be carried out before significant appearance damage occurs. This helps reduce the number and cost of bridge repairs, extend the service life of bridges, and also reduce the greater safety hazards that may be caused by delayed repairs.

[0085] Advantages of technology integration: The technical solution of the present invention integrates modern sensor technology, structural mechanics, data analysis, network communication technology and advanced visualization technology. This multidisciplinary comprehensive application has greatly improved the professionalism and practicality of bridge health monitoring. This integration not only improves the accuracy of the data, but also enhances the availability and interpretability of the data.

[0086] Improvements in intelligence and informatization: By combining digital twin technology with Internet of Things data monitoring, this invention has made significant progress in terms of intelligence and informatization. Such advanced monitoring means can not only provide richer and more detailed data, but also conduct in-depth analysis through algorithm models to provide more predictive and guiding information.

[0087] Embodiment 2

[0088] As Figure 8 shown is a visualization rendering system for the structural mechanics characteristics of a digital twin bridge; Based on Embodiment 1, this embodiment discloses a visualization rendering system 400 for the structural mechanics characteristics of a digital twin bridge. As Figure 8 described, this system executes the visualization rendering method for the structural mechanics characteristics of a digital twin bridge, which has the same principle, means, and effects as Embodiment 1 and will not be elaborated here.

[0089] The system includes: a data acquisition module for acquiring the load distribution state of vehicles on the bridge deck;

[0090] a time variation graph determination module for determining the deflection-time curve and cable force-time curve of the deflection control points of the bridge main girder based on the load distribution state in combination with the bridge finite element model;

[0091] a color model conversion module for converting the deflection-time and cable force-time variation conditions into the HSV color model based on the coloring algorithm of the Sigmoid function and converting the HSV color model into the RGB color model;

[0092] a rendering module for visually rendering the digital twin bridge based on the RGB color model using the UE4 engine.

[0093] Embodiment 3

[0094] As Figure 9 shown, according to another aspect of the present application, an electronic device 500 is also provided. The electronic device 500 may include one or more processors and one or more memories. Among them, computer-readable code is stored in the memory, and when the computer-readable code is run by one or more processors, it can execute the visualization rendering method for the structural mechanics characteristics of the digital twin bridge as described above.

[0095] The method or system according to the embodiments of the present application can also be implemented by means of Figure 9 the architecture of the electronic device shown. As Figure 9As shown, the electronic device 500 may include a bus 501, one or more CPUs 502, a read-only memory (ROM) 503, a random access memory (RAM) 504, a communication port 505 connected to a network, an input / output component 506, a hard disk 507, etc. The storage device in the electronic device 500, such as the ROM 503 or the hard disk 507, may store the method for visual rendering of the structural mechanics characteristics of the digital twin bridge provided in this application. The method for visual rendering of the structural mechanics characteristics of the digital twin bridge may, for example, include: obtaining the vehicle load distribution state on the bridge deck; based on the load distribution state, combining with the bridge finite element model to determine the deflection-time curve and the cable force-time curve of the deflection change control points of the bridge main girder; converting the deflection-time curve and the cable force-time curve into the HSV color model based on the coloring algorithm of the Sigmoid function, and converting the HSV color model into the RGB color model; using the UE4 engine to perform visual rendering on the digital twin bridge based on the RGB color model. Further, the electronic device 500 may also include a user interface 508. Of course, Figure 9 The architecture shown is only exemplary. When implementing different devices, one or more components in the electronic device shown may be omitted according to actual needs. Figure 9

[0096] Embodiment 4

[0097] Figure 10 It is a schematic diagram of the structure of a computer-readable storage medium provided by an embodiment of this application. As Figure 10 shown, it is a computer-readable storage medium 600 according to an embodiment of this application. Computer-readable instructions are stored on the computer-readable storage medium 600. When the computer-readable instructions are run by a processor, the method for visual rendering of the structural mechanics characteristics of the digital twin bridge according to the embodiment of this application described with reference to the above drawings can be executed. The storage medium 600 includes but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may, for example, include random access memory (RAM) and cache, etc. Non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0098] ​In addition, according to an embodiment of the present application, the process described above with reference to the flow chart can be implemented as a computer software program. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions that can be run by a processor to execute instructions corresponding to the method steps provided by the present application. For example: obtaining the load distribution state of the vehicle on the bridge deck; based on the load distribution state, determining the deflection-time curve and the cable force-time curve of the deflection change control points of the bridge main girder in combination with the bridge finite element model; converting the deflection-time curve and the cable force-time curve into the HSV color model based on the coloring algorithm of the Sigmoid function, and converting the HSV color model into the RGB color model; using the UE4 engine to perform visual rendering on the digital twin bridge based on the RGB color model. When the computer program is executed by a central processing unit (CPU), the above functions defined in the method of the present application are executed.

[0099] The method, device, and equipment of the present application can be implemented in many ways. For example, the method, device, and equipment of the present application can be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above order of the steps for the method is only for illustration, and the steps of the method of the present application are not limited to the specific order described above, unless otherwise specifically stated. In addition, in some embodiments, the present application can also be implemented as a program recorded in a recording medium, and these programs include machine-readable instructions for implementing the method according to the present application. Therefore, the present application also covers a recording medium storing a program for executing the method according to the present application.

[0100] In addition, parts of the above technical solutions provided in the embodiments of the present application that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive repetition.

[0101] As described above in the specific embodiments, the purpose, technical solutions, and beneficial effects of the present invention have been further described in detail. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A visualization rendering method for the structural mechanics characteristics of a digital twin bridge, characterized in that, Including: Obtain the load distribution state of the vehicle on the bridge deck; Based on the load distribution state, combine with the bridge finite element model to determine the deflection-time curve and cable force-time curve of the deflection change control points of the bridge main girder; Convert the deflection-time curve and cable force-time curve into the HSV color model by the coloring algorithm based on the Sigmoid function, and convert the HSV color model into the RGB color model; Based on the RGB color model, use the UE4 engine to perform visual rendering on the digital twin bridge. The steps of performing visual rendering on the digital twin bridge based on the RGB color model using the UE4 engine include: In the UE engine, use the Datasmith tool to import the three-dimensional bridge model and the basic scene to create the base of the digital twin model; Use the material domain of UE to perform material preparation for the scene; Apply the UltraDynamicSky plug-in to render the sky and lights for the entire three-dimensional scene; Use the UE4_VaREST and JsonBlueprint plug-ins to establish a connection with the mechanical analysis database, and obtain the results of the RGB-time parameters from the background through the POST request method; According to the specifications of the mechanical model, disassemble and match the digital twin model accordingly to ensure that the mechanical analysis results at each stage have corresponding representation carriers in the twin model; After receiving the mechanical analysis results, match these results with the material coloring of the bridge model, and perform A-B difference matching on the rendering colors of each segment and the adjacent segments to obtain the rendering model; Use the Sigmoid function to correct the y-axis reference index of the normalized deflection and cable force curves to amplify the color interval distribution at large deflection and large cable force values.

2. The visualization rendering method for the structural mechanical properties of the digital twin bridge according to claim 1, characterized in that The obtaining of the load distribution state of the vehicle on the bridge deck includes: Obtain the position information of the vehicle on the bridge by the video image recognition algorithm based on deep learning, and perform vehicle type recognition to obtain the vehicle type; Use the dynamic weighing system to measure the pressure of the vehicle axle, convert this pressure into a voltage signal, and calculate the weight and speed of the vehicle; Determine the bridge load distribution state based on the vehicle type, vehicle position information, vehicle weight, vehicle speed, and time.

3. The method for visual rendering of the structural mechanical properties of a digital twin bridge according to claim 2, characterized in that, The algorithm for obtaining the position information of the vehicle on the bridge by the video image recognition algorithm based on deep learning and performing vehicle type recognition to obtain the vehicle type is any one of YOLO, SSD, Faster R-CNN, or EfficientDet.

4. The method for visual rendering of the structural mechanical properties of the digital twin bridge according to claim 1, characterized in that, The process of converting the deflection-time curve and cable force-time curve into the HSV color model by the coloring algorithm based on the Sigmoid function includes: Perform linear transformation normalization processing on the deflection-time curve and cable force-time curve, and convert the curve to the range of [0, 1]; Linearly amplify the processed deflection and cable force reference indexes in the range of [0, 1] to the range of [0, 240], and determine the hue value H in the HSV color model; Set the saturation S and brightness V to 1, and construct the HSV color model based on the hue value H, saturation S, and brightness V.

5. The visualization rendering method for the structural mechanical properties of the digital twin bridge according to claim 2, characterized in that, The steps of calculating the weight and speed of the vehicle are as follows: A number of groups of pressure sensors are set on the road surface to detect the pressure exerted by the axles of passing vehicles; Convert the detected pressure into a voltage signal, where the conversion is achieved by applying the pressure to a piezoelectric material or a similar sensor to generate a voltage signal proportional to the applied pressure; Collect and process the generated voltage signal to analyze the amplitude and duration of the signal and calculate the weight and speed of the vehicle.

6. The visualization rendering method for the structural mechanical properties of the digital twin bridge according to claim 2, characterized in that, The vehicle types at least include: large buses, medium buses, small buses, large sedans, medium sedans, and small sedans.

7. A visualization rendering system for the structural mechanical characteristics of a digital twin bridge, characterized in that it includes: A data acquisition module for acquiring the load distribution state of the vehicle on the bridge deck; A time-varying graph determination module for determining the deflection-time curve and the cable force-time curve of the deflection change control points of the bridge main girder based on the load distribution state and in combination with the bridge finite element model; A color model conversion module for converting the deflection-time and cable force-time change conditions into the HSV color model based on the coloring algorithm of the Sigmoid function and converting the HSV color model into the RGB color model; A rendering module for visually rendering the digital twin bridge based on the RGB color model using the UE4 engine; The step of visually rendering the digital twin bridge based on the RGB color model using the UE4 engine includes: In the UE engine, use the Datasmith tool to import the three-dimensional bridge model and the basic scene to create the base of the digital twin model; Use the material domain of UE to mix the materials of the scene; Apply the UltraDynamicSky plug-in to render the sky and lights of the entire three-dimensional scene; Use the UE4_VaREST and JsonBlueprint plug-ins to establish a connection with the mechanical analysis database and obtain the results of the RGB-time parameters from the background through a POST request; According to the specifications of the mechanical model, disassemble and match the digital twin model accordingly to ensure that the mechanical analysis results at each stage have corresponding representation carriers in the twin model; After receiving the mechanical analysis results, match these results with the material coloring of the bridge model and perform A-B difference matching on the rendering colors of each segment and adjacent segments to obtain the rendering model; Use the Sigmoid function to correct the y-axis reference indicators of the normalized deflection and cable force curves to amplify the color interval distribution at large deflection and large cable force values.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it realizes the visualization rendering method for the structural mechanical characteristics of the digital twin bridge as described in any one of claims 1 to 6.

9. A readable storage medium, characterized in that, The readable storage medium stores a computer program, and the computer program is suitable for being loaded by the processor to execute the visualization rendering method for the structural mechanical characteristics of the digital twin bridge as described in any one of claims 1 to 6.

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