A virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology

Abstract

The application discloses a virtual pre-assembly method for a steel truss bridge based on a laser point cloud scanning technology, and comprises the following steps: obtaining point cloud data of a prefabricated steel truss bridge component through a laser point cloud scanning module, modeling, obtaining an initial steel truss bridge component model, comparing the steel truss bridge component model data with corresponding standard component model data set in a distributed data node module, obtaining errors of the steel truss bridge component model and the corresponding standard component model, if the errors are within a set error range, the steel truss bridge component model is qualified, the qualified steel truss bridge component model is sent to a corresponding assembly module in a cloud virtual assembly server through the distributed data node, the assembly module assembles according to a component assembly sequence, a steel truss bridge model is obtained, if the error between the obtained steel truss bridge and a standard steel truss bridge model is within a set error range, the assembly is completed.

Description

Technical Field

[0001] This invention relates to the field of bridges, specifically a virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology. Background Technology

[0002] Digital technology is a current trend in industrial production and a key industry for national development. It has enormous application and development potential in all sectors, and the application of digital technology in the steel bridge manufacturing field is no exception.

[0003] Steel truss girders, as one of the main structural forms of steel beams, are widely used in railway and highway bridges. Because steel truss girders use fully bolted connections, they require high manufacturing precision. To ensure the perforation rate during on-site installation, national standards require that parts or the entire bridge be pre-assembled before leaving the factory. Currently, physical pre-assembly is commonly used, but this is costly, time-consuming, and labor-intensive. For a 10,000-ton steel truss girder bridge project, assembly takes 1-2 months. If, due to the special nature of the project construction site, segment transportation is not possible, and the entire bridge needs to be assembled in the processing plant, the pre-assembly cost becomes very high, and the actual assembly time can reach 6 months per 10,000 tons. Therefore, there is an urgent need to develop a virtual assembly technology based on digital technology to replace physical assembly.

[0004] The development of a digital assembly system for steel truss bridges requires addressing three key technologies: precise and rapid acquisition of 3D digital models, digital assembly algorithms, and calculation of fault tolerance during the assembly process and measures for corrective actions to address assembly results. Currently, with the rapid development of digital technology, especially scanning hardware and advancements in 3D processing tools, the technological foundation for developing tools to accurately and rapidly generate 3D models tailored to the characteristics of truss bridge components is in place. Regarding digital assembly algorithms, based on preliminary research and technical verification, a roadmap for algorithm development and initial results have been established. Sufficient data on assembly fault tolerance and error correction schemes has been accumulated through the construction of numerous truss bridges in China over the past few decades. Finally, the establishment of a digital 3D model for each component provides immense convenience for subsequent processes, including virtual assembly, design of transportation and hoisting schemes, on-site control during erection, and digitalization of operation and maintenance. Therefore, digital measurement technology is of great significance and has broad prospects in the bridge manufacturing industry.

[0005] Virtual assembly refers to a technology that uses actual digital models of components to assemble them on a computer using a specific algorithm, replacing traditional physical assembly. As an emerging digital technology, virtual assembly has significant application value in the bridge industry.

[0006] First, due to the complexity and weight of bridge components, traditional physical assembly consumes a huge amount of manpower and financial resources and is inefficient; virtual assembly can greatly reduce assembly costs and construction time.

[0007] Secondly, since the bridge assembly effect involves a variety of factors, traditional assembly methods are not easy to identify and analyze the causes of assembly problems, let alone quickly provide rectification solutions. However, virtual assembly, by adopting effective assembly algorithms and comprehensively considering various assembly adjustment margins, can quickly identify problems and provide assembly reports and rectification solutions. Thirdly, due to the high flexibility of virtual assembly in handling members, it is possible to create a construction model of the entire bridge construction plan based on the actual member dimensions, which is something that traditional physical assembly cannot achieve.

[0008] Finally, traditional physical assembly methods, due to the huge manpower and material resources required, can only assemble partial segments, inevitably resulting in omissions. In contrast, virtual assembly can achieve the assembly of the entire bridge.

[0009] Therefore, how to achieve full bridge assembly through virtual technology is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of the prior art and provide a virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology, comprising the following steps:

[0011] Step 1: Obtain point cloud data of the prefabricated steel truss bridge component through the laser point cloud scanning module, perform modeling, obtain the initial steel truss bridge component model, and upload the obtained steel truss bridge component model data to the distributed data node of the distributed data node module matched with the client module through the client module.

[0012] Step 2: In the distributed data node module, compare the steel truss bridge component model data with the corresponding standard component model data to obtain the error between the steel truss bridge component model and the corresponding standard component model. If the error is within the set error range, it is a qualified steel truss bridge component model, and proceed to Step 4; otherwise, adjust the steel truss bridge component and proceed to Step 3.

[0013] Step 3: By acquiring the error data between the steel truss bridge component model and the corresponding standard component model, the steel truss bridge component is adjusted to obtain the point cloud data of the adjusted steel truss bridge component. This data is then compared and adjusted again with the corresponding standard component model until the error between the truss bridge component model and the corresponding standard component model is within the set error range. The point cloud data of the adjusted steel truss bridge component is then compared with the initial steel truss bridge component model data in the client to obtain point cloud data that is different from the initial steel truss bridge component model data. This point cloud data that is different from the initial steel truss bridge component model data is then uploaded to the matching distributed data node in the distributed data node module through the client to replace the corresponding data in the initial steel truss bridge component model, thus obtaining a qualified steel truss bridge component model.

[0014] Step 4: Send the qualified steel truss bridge component model to the corresponding assembly module of the cloud virtual assembly server through the distributed data nodes. When all the steel truss bridge component models required for assembly are uploaded to the assembly module, the assembly module performs assembly according to the component assembly sequence to obtain a steel truss bridge model. If the error between the obtained steel truss bridge and the standard steel truss bridge model is within the set error range, the assembly is completed.

[0015] Further, the comparison between the steel truss bridge component model data and the corresponding set standard component model data to obtain the error between the steel truss bridge component model and the corresponding standard component model includes:

[0016] Perform coordinate transformation on the steel truss bridge component model to make it coincide with the corresponding standard component model; compare the two coincident models to obtain the non - coincident part between the steel truss bridge component model and the corresponding standard component model, and the non - coincident part is the error.

[0017] Further, the case where if the error is within the set error range, it is a qualified steel truss bridge component model includes:

[0018] If the ratio of the non - coincident part model to the corresponding standard component model is within the set ratio threshold range, it is a qualified steel truss bridge component model.

[0019] Further, the distributed data nodes of the distributed data node module matching the client module include:

[0020] Obtain the weights of the acquisition tasks of each distributed data node in the distributed data node module, sort the distributed data nodes according to the weights of the distributed data node acquisition tasks to obtain an initial distributed data node sequence;

[0021] According to the initial distributed data node sequence, obtain the access latency between the client module and each distributed data node in the initial distributed data node sequence, and sort according to the access latency to obtain the distributed data node sequence corresponding to the client module;

[0022] Calculate respectively the processing rates of each distributed data node in the distributed data node sequence corresponding to the client module for the data uploaded by the client module. According to the processing rate of the distributed data node for the data uploaded by the client module and the weight of the acquisition task of this distributed data node, obtain the weight of the distributed data node acquisition task corresponding to the client module. The distributed data node with the largest weight of the distributed data node acquisition task corresponding to the client module is the distributed data node in the distributed data node module matching the client module.

[0023] A virtual assembly system for steel trusses of a bridge, employing a virtual pre-assembly method based on laser point cloud scanning technology, comprises: a laser point cloud scanning module, a distributed data module, a cloud-based virtual assembly server, and a client module; the laser point cloud scanning module is communicatively connected to the client module, the client module is connected to the distributed data module, and the distributed data module is communicatively connected to the cloud-based virtual assembly server.

[0024] A computer device includes a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the aforementioned virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology.

[0025] A computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the aforementioned virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology.

[0026] The beneficial effects of the present invention are: the technical solution provided by the present invention can achieve the modification of components and complete the virtual pre-assembly of components with less network resources. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating a virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology. Detailed Implementation

[0028] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, and not all of them. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0030] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention. It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0031] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0032] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0033] like Figure 1 As shown, a virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology includes the following steps:

[0034] Step 1: Obtain point cloud data of the prefabricated steel truss bridge component through the laser point cloud scanning module, perform modeling, obtain the initial steel truss bridge component model, and upload the obtained steel truss bridge component model data to the distributed data node of the distributed data node module matched with the client module through the client module.

[0035] Step 2: In the distributed data node module, compare the steel truss bridge component model data with the corresponding standard component model data to obtain the error between the steel truss bridge component model and the corresponding standard component model. If the error is within the set error range, it is a qualified steel truss bridge component model, and proceed to Step 4; otherwise, adjust the steel truss bridge component and proceed to Step 3.

[0036] Step 3: By obtaining the error data between the steel truss bridge component model and the corresponding standard component model, adjust the steel truss bridge components to obtain the point cloud data of the adjusted steel truss bridge components. Then, compare and adjust it with the corresponding standard component model again until the error between the truss bridge component model and the corresponding standard component model is within the set error range. Compare the point cloud data of the adjusted steel truss bridge components with the initial steel truss bridge component model data in the client to obtain the point cloud data different from the initial steel truss bridge component model data. Upload the obtained point cloud data different from the initial steel truss bridge component model data to the matching distributed data node in the distributed data node module through the client, and replace the corresponding data in the initial steel truss bridge component model to obtain a qualified steel truss bridge component model;

[0037] Step 4: Send the qualified steel truss bridge component model to the corresponding assembly module of the cloud virtual assembly server through the distributed data node. When all the steel truss bridge component models required for assembly are uploaded to the assembly module, the assembly module assembles them according to the component assembly sequence to obtain a steel truss bridge model. If the error between the obtained steel truss bridge and the standard steel truss bridge model is within the set error range, the assembly is completed.

[0038] Further, comparing the steel truss bridge component model data with the set corresponding standard component model data to obtain the error between the steel truss bridge component model and the corresponding standard component model includes:

[0039] Perform coordinate transformation on the steel truss bridge component model to make it coincide with the corresponding standard component model; compare the two coincident models to obtain the non - coincident part between the steel truss bridge component model and the corresponding standard component model, and the non - coincident part is the error.

[0040] Further, if the error is within the set error range, it is a qualified steel truss bridge component model, including:

[0041] If the ratio of the non - coincident part model to the corresponding standard component model is within the set ratio threshold range, it is a qualified steel truss bridge component model.

[0042] Further, the distributed data nodes of the distributed data node module matching the client module include:

[0043] Obtain the weights of the acquisition tasks of each distributed data node in the distributed data node module, sort the distributed data nodes according to the weights of the distributed data node acquisition tasks to obtain an initial distributed data node sequence;

[0044] Based on the initial distributed data node sequence, obtain the access latency of each distributed data node in the initial distributed data node sequence for the client module, and sort them according to the access latency to obtain the corresponding distributed data node sequence for the client module.

[0045] Calculate the processing rate of each distributed data node in the distributed data node sequence of the corresponding client module for the data uploaded by the client module. Based on the processing rate of the distributed data node for the data uploaded by the client module and the weight of the task acquired by the distributed data node, obtain the weight of the task acquired by the distributed data node of the corresponding client module. The distributed data node with the largest weight of the task acquired by the distributed data node of the corresponding client module is the distributed data node in the distributed data node module that matches the client module.

[0046] The weight of the acquisition task for each distributed data node in the distributed data node acquisition module includes obtaining the weight of the distributed data node acquisition task based on memory usage, disk usage, and CPU usage, using the following formula:

[0047] p = 1 / a + 1 / b + a / c + d

[0048] Where a represents memory usage, b represents disk usage, c represents CPU usage, and d represents distance weight.

[0049] Based on the initial distributed data node sequence, the access latency of the client module and each distributed data node in the initial distributed data node sequence is obtained. The distributed data node sequence of the corresponding client module is obtained by sorting according to the access latency. This includes: timestamping the client module test data packet and sending it to each distributed data node in the initial distributed data node sequence; obtaining the time when each distributed data node receives the client module test data packet; and obtaining the access latency based on the time when each distributed data node receives the client module test data packet and the timestamp of the client module test data packet.

[0050] The calculation of the processing rate of each distributed data node in the distributed data node sequence of the corresponding client module for the data uploaded by the client module includes: obtaining the time when the distributed data node completes to receive the test data packet of the client module; obtaining the processing time of the test data packet of the client module based on the time when the distributed data node receives the user test data packet and the time when the distributed data node completes to process the client test data; and obtaining the processing rate based on the processing time and the data size of the test data packet of the client module.

[0051] The weight of the task to be acquired by the distributed storage node of the client is obtained by multiplying the obtained processing rate by the weight of the task acquired by the distributed data node.

[0052] A virtual assembly system for steel trusses of a bridge, employing a virtual pre-assembly method based on laser point cloud scanning technology, comprises: a laser point cloud scanning module, a distributed data module, a cloud-based virtual assembly server, and a client module; the laser point cloud scanning module is communicatively connected to the client module, the client module is connected to the distributed data module, and the distributed data module is communicatively connected to the cloud-based virtual assembly server.

[0053] A computer device includes a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the aforementioned virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology.

[0054] A computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the aforementioned virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology.

[0055] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology, characterized in that, Includes the following steps: Step 1: Obtain point cloud data of the prefabricated steel truss bridge component through the laser point cloud scanning module, perform modeling, obtain the initial steel truss bridge component model, and upload the obtained steel truss bridge component model data to the distributed data node of the distributed data node module matched with the client module through the client module. Step 2: In the distributed data node module, compare the steel truss bridge component model data with the corresponding standard component model data to obtain the error between the steel truss bridge component model and the corresponding standard component model. If the error is within the set error range, it is a qualified steel truss bridge component model, and proceed to Step 4; otherwise, adjust the steel truss bridge component and proceed to Step 3. Step 3: By acquiring the error data between the steel truss bridge component model and the corresponding standard component model, the steel truss bridge component is adjusted to obtain the point cloud data of the adjusted steel truss bridge component. This adjusted point cloud data is then compared and adjusted again with the corresponding standard component model until the error between the truss bridge component model and the corresponding standard component model is within the set error range. The point cloud data of the adjusted steel truss bridge component is then compared with the initial steel truss bridge component model data in the client to obtain point cloud data that differs from the initial steel truss bridge component model data. This point cloud data that differs from the initial steel truss bridge component model data is then uploaded to the matching distributed data node in the distributed data node module through the client. The distributed data node in the distributed data node module that matches the client module includes obtaining the weight of the acquisition task of each distributed data node in the distributed data node module. The distributed data nodes are then sorted according to the weight of the acquisition task of the distributed data node to obtain the initial distributed data node sequence. Based on the initial distributed data node sequence, obtain the access latency of each distributed data node in the initial distributed data node sequence for the client module, and sort them according to the access latency to obtain the corresponding distributed data node sequence for the client module. Calculate the processing rate of each distributed data node in the distributed data node sequence of the corresponding client module for the data uploaded by the client module. Based on the processing rate of the distributed data node for the data uploaded by the client module and the weight of the task acquisition of the distributed data node, obtain the weight of the task acquisition of the distributed data node of the corresponding client module. The distributed data node with the largest weight of the task acquisition of the distributed data node of the corresponding client module is the distributed data node in the distributed data node module that matches the client module. Replace the corresponding data in the initial steel truss bridge component model to obtain a qualified steel truss bridge component model. Step four: Send the qualified steel truss bridge component models to the corresponding assembly module of the cloud virtual assembly server through distributed data nodes. When all the steel truss bridge component models required for assembly are uploaded to the assembly module, the assembly module assembles them according to the component assembly order to obtain the steel truss bridge model. If the error between the obtained steel truss bridge and the standard steel truss bridge model is within the set error range, the assembly is completed.

2. The virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology according to claim 1, characterized in that, The step of comparing the steel truss bridge component model data with the corresponding standard component model data to obtain the error between the steel truss bridge component model and the corresponding standard component model includes: performing coordinate transformation on the steel truss bridge component model so that it coincides with the corresponding standard component model; comparing the two coincident models to obtain the non-coincident part between the steel truss bridge component model and the corresponding standard component model, and the non-coincident part is the error.

3. The virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology according to claim 2, characterized in that, The statement that a steel truss bridge component model is considered qualified if the error is within the set error range includes: if the ratio of the non-overlapping part model to the corresponding standard component model is within the set ratio threshold range, then the steel truss bridge component model is considered qualified.

4. A virtual assembly system for a bridge steel truss girder, employing the virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology as described in claim 3, characterized in that... include: The system comprises a laser point cloud scanning module, a distributed data module, a cloud-based virtual assembly server, and a client module. The laser point cloud scanning module is communicatively connected to the client module, the client module is connected to the distributed data module, and the distributed data module is communicatively connected to the cloud-based virtual assembly server.

5. A computer device, characterized in that, include: A memory and a processor, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement a virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions that, when executed by a computer, cause the computer to perform any one of claims 1 to 3, a virtual pre-assembly method for steel truss bridges based on laser point cloud scanning technology.

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