Truss construction monitoring method, device, equipment and computer readable storage medium

Abstract

Embodiments of the present application provide a truss construction monitoring method, device, equipment and computer-readable storage medium. The method includes obtaining current truss information and construction process data; constructing a BIM model based on the truss information; constructing a finite element model including truss geometry and material information based on the BIM model; performing hoisting construction simulation based on the finite element model to determine the optimal number and position of hoisting points; importing the optimal number and position of hoisting points into the BIM model to obtain a target BIM model; generating a visualized three-dimensional model based on the target BIM model and the current truss construction process data; and completing the construction monitoring of the truss through the visualized three-dimensional model. In this way, an accurate assessment of the stress condition of the steel truss during the hoisting and sliding construction process is achieved, effectively improving the defects of many unfavorable stress and strain points and large deformation during the construction process.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of truss construction monitoring, and in particular, to a truss construction monitoring method, device, equipment, and computer-readable storage medium. Background Art

[0002] At present, the lifting and sliding construction of steel trusses has difficulties such as heavy weight, many welds, large space projection, large intersection area with the main structure construction, limited operating space, etc., and accidents such as crane overturning and component falling may occur. At present, ordinary lifting and sliding construction is difficult to ensure construction quality.

[0003] During the lifting process, the stress and strain of the steel truss will change as the stress conditions change. When the crane starts to apply tension, the steel truss will be subjected to tensile force. At this time, the strain of the steel truss will increase, but the stress is small. When the crane continues to apply tension and lift heavy objects, the stress and strain of the steel truss will continue to increase. When the stress of the steel truss reaches its maximum value, if tension continues to be applied, plastic deformation or damage will occur. Therefore, during the lifting process, the tension of the crane needs to be controlled according to the load-bearing capacity of the steel truss and the weight of the heavy object to ensure that the steel truss is not damaged.

[0004] However, in the existing lifting system, operators cannot understand the geometric shape and stress-strain state of the steel truss in real time during operation, which is insufficient in ensuring its safety. Summary of the invention

[0005] According to the embodiments of the present application, a truss construction monitoring solution is provided, which realizes accurate assessment of the stress condition of the steel truss during the lifting and sliding construction process, effectively improves defects such as many unfavorable stress and strain points and large deformation during the construction process, ensures the safety and reliability of the structural construction and use status, greatly improves the structural quality, shortens the construction period, and reduces the construction cost.

[0006] In a first aspect of the present application, a truss construction monitoring method is provided. The method comprises:

[0007] Get current truss information and construction process data;

[0008] Based on the truss information, a BIM model is constructed; based on the BIM model, a finite element model including truss geometry and material information is constructed;

[0009] Perform hoisting construction simulation based on the finite element model to determine the optimal number and positions of hoisting points; import the optimal number and positions of hoisting points into the BIM model to obtain a target BIM model;

[0010] Generate a visualized three-dimensional model based on the target BIM model and the construction process data of the current truss;

[0011] The construction monitoring of the truss is completed through the visualized three-dimensional model.

[0012] Furthermore, the current truss information includes the force, material and geometry information of the current truss.

[0013] Furthermore, constructing a BIM model based on the truss information includes:

[0014] Processing the truss information through three-dimensional scanning technology to obtain point cloud data of the truss;

[0015] Thinning the point cloud data by a preset density to obtain target point cloud data;

[0016] Based on a preset modeling area, the target point cloud data is segmented to obtain a plurality of blocks;

[0017] Rasterize each block to form a Recap file;

[0018] Based on the Recap file, parametric modeling is performed to construct a BIM model.

[0019] Furthermore, the rasterization process for each block includes:

[0020] Convert the point cloud data in each block into a feature vector;

[0021] Input the feature vector into the trained random forest model to obtain point cloud data that meets the requirements;

[0022] Mapping the points in the point cloud data that meet the requirements into grid cells to complete the rasterization processing of the current block;

[0023] The random forest model can be constructed in the following way:

[0024] Generate a training sample set, wherein the training sample includes point cloud data with annotation information; the annotation information is a label of a grid where a point in the point cloud data is located;

[0025] The samples in the training sample set are trained by the random forest algorithm, with the sample files as input and the annotation information as output. When the unification rate of the output annotation information and the annotated label meets the preset threshold, the training of the random forest model is completed.

[0026] Furthermore, it also includes:

[0027] The random forest algorithm is optimized in the following way:

[0028] Define a fitness function for evaluating the random forest algorithm;

[0029] Randomly generate a group of particles, each particle represents a set of parameters of the random forest algorithm; the parameters include the number of decision trees, the size of the feature subset, and the maximum depth of the decision tree;

[0030] For each particle, the training data set is classified using the random forest algorithm to calculate its fitness value;

[0031] Based on the fitness value, the current speed and position of the particle, and the global optimum and individual optimum information, update the speed and position of the current particle;

[0032] Based on the updated particle positions, the random forest algorithm is used to classify the training data set, calculate its fitness value, and update the global optimal and individual optimal information at the same time;

[0033] Repeat the above steps until the termination condition is met to obtain the optimal parameters of the random forest algorithm.

[0034] Furthermore, the hoisting construction simulation based on the finite element model to determine the optimal number and positions of hoisting points includes:

[0035] Define the objective function based on the distance between the lifting points, stability, number of lifting points and stress data of the truss during the lifting process;

[0036] Initialize a group of particles, each particle represents a solution for a set of lifting points and lifting quantities;

[0037] Update the current particle's speed and position according to the particle's current speed, position and optimal solution;

[0038] Calculating a fitness value according to the objective function and the current position of each particle;

[0039] According to the fitness value, updating the global optimal solution;

[0040] If the fitness of a particle is better than the current global optimal solution, the position of the particle is updated to the global optimal solution;

[0041] Repeat the above steps until the preset number of iterations is met and the optimal lifting point and lifting quantity are obtained.

[0042] Furthermore, it also includes:

[0043] The BIM model is modified through construction drawings.

[0044] In a second aspect of the present application, a truss construction monitoring device is provided. The device comprises:

[0045] Acquisition module, used to obtain current truss information and construction process data;

[0046] A construction module is used to construct a BIM model based on the truss information; based on the BIM model, a finite element model including truss geometry and material information is constructed;

[0047] A determination module is used to perform a hoisting construction simulation based on the finite element model to determine the optimal number and positions of hoisting points; and import the optimal number and positions of hoisting points into the BIM model to obtain a target BIM model;

[0048] A generation module, used for generating a visual three-dimensional model based on the target BIM model and the construction process data of the current truss;

[0049] The monitoring module is used to complete the construction monitoring of the truss through the visualized three-dimensional model.

[0050] In a third aspect of the present application, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0051] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method according to the first aspect of the present application is implemented.

[0052] The truss construction monitoring method provided in the embodiment of the present application obtains current truss information and construction process data; constructs a BIM model based on the truss information; constructs a finite element model including truss geometry and material information based on the BIM model; performs hoisting construction simulation based on the finite element model to determine the optimal number and positions of hoisting points; imports the optimal number and positions of hoisting points into the BIM model to obtain a target BIM model; generates a visualized three-dimensional model based on the target BIM model and the current truss construction process data; completes the construction monitoring of the truss through the visualized three-dimensional model, realizes accurate assessment of the stress condition of the steel truss during the hoisting and sliding construction process, effectively improves the defects of many unfavorable stress and strain points and large deformation during the construction process, ensures the safety and reliability of the structural construction and use status, greatly improves the structural quality, shortens the construction period, and reduces the construction cost.

[0053] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0055] Figure 1 is a flow chart of a truss construction monitoring method according to an embodiment of the present application;

[0056] Figure 2 is a schematic diagram of a BIM model according to an embodiment of the present application;

[0057] Figure 3 is a schematic diagram of a finite element model according to an embodiment of the present application;

[0058] Figure 4 A schematic diagram of the positions and numbers of hanging points according to an embodiment of the present application;

[0059] Figure 5 is a block diagram of a truss construction monitoring device according to an embodiment of the present application;

[0060] Figure 6 It is a schematic diagram of the structure of a terminal device or a server suitable for implementing the embodiments of the present application. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0062] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0063] Figure 1 A flow chart of a truss construction monitoring method according to an embodiment of the present disclosure is shown. The method comprises:

[0064] S110, obtaining current truss information and construction process data.

[0065] The truss information generally includes the force, material and / or geometric information of the current truss.

[0066] In this embodiment, the execution subject of the truss construction monitoring method can obtain the current truss information and construction process data through a wired or wireless connection.

[0067] Furthermore, the execution subject may obtain the current truss information and construction process data sent by the electronic device to which it is in communication, or the current truss information and construction process data pre-stored locally.

[0068] For example, the force information of the truss can be obtained through sensors pre-installed on the truss; the geometric information of the truss can be obtained through three-dimensional scanning, etc.

[0069] S120: construct a BIM model based on the truss information; and construct a finite element model including truss geometry and material information based on the BIM model.

[0070] In some embodiments, Figure 2 As shown, the truss information can be processed by three-dimensional scanning technology to obtain point cloud data of the truss; the point cloud data can be thinned by a preset density to obtain target point cloud data; based on a preset modeling area, the target point cloud data can be segmented to obtain multiple blocks; each block can be rasterized to form a Recap file; based on the Recap file, parametric modeling can be performed to construct a BIM model.

[0071] Specifically, the truss information can be processed through three-dimensional scanning technology to obtain original point cloud data of the truss. The original point cloud data is thinned using a uniform density to reduce the amount of point cloud data and improve processing efficiency. The thinned point cloud is segmented according to different modeling areas, and each segmented block is processed separately.

[0072] For each block, rasterization is performed separately, and after combining texture information and shape information, the particle swarm optimization algorithm is used to optimize the parameters, realizing the rapid classification of the ground, buildings, and construction components in the survey area. For the classified point cloud data, the ground and other classifications are saved as different Autodesk ReCap data format files. For the ReCap files of each block, splicing and quality inspection are performed to ensure that the quality of the spliced ​​point cloud data meets the requirements. Finally, the point cloud data of all blocks are saved in the ReCap file format, and the ReCap file format is imported into the Revit platform for parametric modeling to form a 3D BIM model.

[0073] Furthermore, the model can be corrected according to the construction drawing to improve the accuracy of the model.

[0074] Among them, for each block, rasterization is performed separately including:

[0075] Convert the point cloud data in each block into a feature vector; the feature vector includes the coordinates, normal vector, color, etc. of the point;

[0076] Input the feature vector into the trained random forest model to obtain point cloud data that meets the requirements;

[0077] Mapping the points in the point cloud data that meet the requirements into grid cells to complete the rasterization processing of the current block;

[0078] The point cloud data that meets the requirements is unlabeled point cloud data, that is, the unlabeled point cloud data is rasterized (points in the point cloud data are mapped to grid cells).

[0079] The random forest model can be constructed in the following way:

[0080] Generate a training sample set, wherein the training sample includes point cloud data with annotation information; the annotation information is a label of a grid where a point in the point cloud data is located;

[0081] The samples in the training sample set are trained by the random forest algorithm, with the sample files as input and the annotation information as output. When the unification rate of the output annotation information and the annotated label meets the preset threshold, the training of the random forest model is completed.

[0082] Furthermore, it also includes:

[0083] The random forest algorithm is optimized in the following way:

[0084] Define a fitness function for evaluating the random forest algorithm;

[0085] Randomly generate a group of particles, each particle represents a set of parameters of the random forest algorithm; the parameters include the number of decision trees, the size of the feature subset, and the maximum depth of the decision tree;

[0086] For each particle, the training data set is classified using the random forest algorithm to calculate its fitness value;

[0087] Based on the fitness value, the current speed and position of the particle, and the global optimum and individual optimum information, update the speed and position of the current particle;

[0088] Based on the updated particle positions, the random forest algorithm is used to classify the training data set, calculate its fitness value, and update the global optimal and individual optimal information at the same time;

[0089] Repeat the above steps until the termination condition is met and the optimal solution is output, and the globally optimal particle position is output, that is, the corresponding parameter of the random forest algorithm. The optimization of the random forest algorithm is completed through the parameter. The termination condition includes the maximum number of iterations or the convergence of the fitness value.

[0090] In some embodiments, the BIM model (3D BIM model) can be imported into the finite element analysis software through a plug-in to form a finite element model with geometric and material information. Figure 3 .

[0091] S130, performing a hoisting construction simulation based on the finite element model to determine the optimal number and positions of hoisting points; importing the optimal number and positions of hoisting points into the BIM model to obtain a target BIM model.

[0092] In some embodiments, Figure 4 As shown, based on the finite element model, the hoisting construction simulation is carried out, the objective function is re-drafted as a comprehensive index of the maximum absolute stress and the maximum deformation, the optimization parameters are determined as the best hanging point position and the number of hanging points, a group of particles are randomly generated, and their initial positions are set to random values ​​within the parameter range. For each particle, the finite element analysis software is used to calculate the fitness value of its current position, that is, the value of the objective function, and the particle with the best fitness is found in the neighborhood of each particle, and its position is used as the local optimal solution to obtain its calculation results. If the structure is overloaded, the hanging points are added or the hoisting points are replaced until the specified stop condition is reached, and the final result is output as the optimal number and position of hanging points.

[0093] Specifically, the objective function is defined based on the distance between the lifting points, the stability, the number of lifting points and the stress data of the truss during the lifting process;

[0094] In the steel truss hoisting problem, the variables include the coordinates of the hoisting points and the hoisting quantity. The coordinates of the hoisting points can be expressed as two-dimensional or three-dimensional coordinates, and the hoisting quantity can be expressed as an integer.

[0095] Initialize a group of particles, each particle represents a solution for a set of lifting points and lifting quantities; the position of the particles can be randomly initialized in the feasible solution space;

[0096] Update the current particle's speed and position according to the particle's current speed, position and optimal solution;

[0097] Calculating a fitness value according to the objective function and the current position of each particle;

[0098] According to the fitness value, updating the global optimal solution;

[0099] If the fitness of a particle is better than the current global optimal solution, the position of the particle is updated to the global optimal solution;

[0100] Repeat the above steps until the preset number of iterations is met (which can be pre-set according to the actual application scenario), and the optimal lifting point and lifting quantity are obtained.

[0101] Furthermore, the optimal number and positions of hanging points are imported into the BIM model to obtain a target BIM model.

[0102] S140: Generate a visualized three-dimensional model based on the target BIM model and the construction process data of the current truss.

[0103] In some embodiments, a visual three-dimensional model is generated based on the target BIM model and the construction process data of the current truss.

[0104] S150, completing the construction monitoring of the truss through the visualized three-dimensional model.

[0105] In some embodiments, the three-dimensional model can change with the steel truss hoisting construction time and display different stress-strain data, that is, the force information and geometric information of the steel truss can be displayed in real time, thereby realizing real-time monitoring of the truss construction.

[0106] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0107] Through the visualized three-dimensional model constructed by the present invention, an accurate assessment of the stress condition of the steel truss during the lifting and sliding construction process is achieved, which effectively improves the defects such as many unfavorable stress and strain points and large deformation during the construction process, ensures the safety and reliability of the structural construction and use status, greatly improves the structural quality, shortens the construction period, and reduces the construction cost.

[0108] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0109] The above is an introduction to the method embodiment. The following is a further explanation of the scheme described in this application through an apparatus embodiment.

[0110] Figure 5 A truss construction monitoring device 500 according to an embodiment of the present application is shown. Figure 5 Shown include:

[0111] An acquisition module 510 is used to acquire current truss information and construction process data;

[0112] A construction module 520 is used to construct a BIM model based on the truss information; based on the BIM model, a finite element model including truss geometry and material information is constructed;

[0113] A determination module 530 is used to perform a hoisting construction simulation based on the finite element model to determine the optimal number and positions of hoisting points; and import the optimal number and positions of hoisting points into the BIM model to obtain a target BIM model;

[0114] A generating module 540 is used to generate a visualized three-dimensional model based on the target BIM model and the construction process data of the current truss;

[0115] The monitoring module 550 is used to complete the construction monitoring of the truss through the visualized three-dimensional model.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0117] Figure 6 A schematic diagram of the structure of a terminal device or server suitable for implementing an embodiment of the present application is shown.

[0118] like Figure 6 As shown, the terminal device or server includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the terminal device or server are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0119] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0120] In particular, according to an embodiment of the present application, the above method flow steps can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a machine-readable medium, and the computer program includes a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the system of the present application are executed.

[0121] It should be noted that the computer-readable medium shown in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0122] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of the code, and the aforementioned module, program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The units or modules involved in the embodiments described in the present application may be implemented by software or hardware. The units or modules described may also be arranged in a processor. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves.

[0124] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are used by one or more processors to execute the method described in the present application.

[0125] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A truss construction monitoring method, characterized in that: include: Get current truss information and construction process data; Based on the truss information, construct a BIM model; Based on the BIM model, a finite element model including truss geometry and material information is constructed; Based on the finite element model, a hoisting construction simulation is performed to determine the optimal number and positions of hoisting points, including: Define the objective function based on the distance between the lifting points, stability, number of lifting points and stress data of the truss during the lifting process; Initialize a group of particles, each particle represents a solution for a set of lifting points and lifting quantities; Update the current particle's speed and position according to the particle's current speed, position and optimal solution; Calculating a fitness value according to the objective function and the current position of each particle; According to the fitness value, updating the global optimal solution; If the fitness of a particle is better than the current global optimal solution, the position of the particle is updated to the global optimal solution; Repeat the above steps until the preset number of iterations is met, and the optimal lifting point and lifting quantity are obtained; Importing the optimal number and positions of hanging points into the BIM model to obtain a target BIM model; Generate a visualized three-dimensional model based on the target BIM model and the construction process data of the current truss; The construction monitoring of the truss is completed through the visualized three-dimensional model.

2. The method according to claim 1, characterized in that The current truss information includes the force, material and geometry information of the current truss.

3. The method according to claim 2, characterized in that The constructing of the BIM model based on the truss information includes: Processing the truss information through three-dimensional scanning technology to obtain point cloud data of the truss; Thinning the point cloud data by a preset density to obtain target point cloud data; Based on a preset modeling area, the target point cloud data is segmented to obtain a plurality of blocks; Rasterize each block to form a Recap file; Based on the Recap file, parametric modeling is performed to construct a BIM model.

4. The method according to claim 3, characterized in that The rasterization process for each block includes: Convert the point cloud data in each block into a feature vector; Input the feature vector into the trained random forest model to obtain point cloud data that meets the requirements; Mapping the points in the point cloud data that meet the requirements into grid cells to complete the rasterization processing of the current block; The random forest model can be constructed in the following way: Generate a training sample set, wherein the training sample includes point cloud data with annotation information; the annotation information is a label of a grid where a point in the point cloud data is located; The samples in the training sample set are trained by the random forest algorithm, with the sample files as input and the annotation information as output. When the unification rate of the output annotation information and the annotated label meets the preset threshold, the training of the random forest model is completed.

5. The method according to claim 4, characterized in that Also includes: The random forest algorithm is optimized in the following way: Define a fitness function for evaluating the random forest algorithm; Randomly generate a group of particles, each particle represents a set of parameters of the random forest algorithm; the parameters include the number of decision trees, the size of the feature subset, and the maximum depth of the decision tree; For each particle, the training data set is classified using the random forest algorithm to calculate its fitness value; Based on the fitness value, the current speed and position of the particle, and the global optimum and individual optimum information, update the speed and position of the current particle; Based on the updated particle positions, the random forest algorithm is used to classify the training data set, calculate its fitness value, and update the global optimal and individual optimal information at the same time; Repeat the above steps until the termination condition is met to obtain the optimal parameters of the random forest algorithm.

6. The method according to claim 5, characterized in that Also includes: The BIM model is modified through construction drawings.

7. A truss construction monitoring device, characterized in that: include: Acquisition module, used to obtain current truss information and construction process data; A construction module, used to construct a BIM model based on the truss information; Based on the BIM model, a finite element model including truss geometry and material information is constructed; A determination module is used to perform a hoisting construction simulation based on the finite element model to determine the optimal number and positions of hoisting points, including: Define the objective function based on the distance between the lifting points, stability, number of lifting points and stress data of the truss during the lifting process; Initialize a group of particles, each particle represents a solution for a set of lifting points and lifting quantities; Update the current particle's speed and position according to the particle's current speed, position and optimal solution; Calculating a fitness value according to the objective function and the current position of each particle; According to the fitness value, updating the global optimal solution; If the fitness of a particle is better than the current global optimal solution, the position of the particle is updated to the global optimal solution; Repeat the above steps until the preset number of iterations is met, and the optimal lifting point and lifting quantity are obtained; Importing the optimal number and positions of hanging points into the BIM model to obtain a target BIM model; A generation module, used for generating a visual three-dimensional model based on the target BIM model and the construction process data of the current truss; The monitoring module is used to complete the construction monitoring of the truss through the visualized three-dimensional model.

8. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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