Intelligent pre-repositioning method for stacker crane based on digital twinning technology

Abstract

The application discloses a kind of based on digital twinborn technology's intelligent pre-compensation of stack way crane method.The application carries out image recording and automatic detection to the hoisting target object feature in the construction site environment of stack way crane, obtains the weight of hoisting target object by the target object of existing feature in database;According to hoisting weight, crane lifting arm length calculates the compensation weight parameter of compensation weight block, including compensation weight and compensation weight block barycenter position, the center of gravity of crane system when preset hoisting target object is located at balance point, while through compensation mechanism automatically moves compensation weight block to the preset position.When actual crane hoisting target object, load sensor on lifting hook verifies hoisting weight, if there is greater error with image recognition result, then fine-tune is carried out to balance point preset position, and is displayed in real time on virtual environment.The application digital twinborn technology automatically identifies hoisting target object weight, automatically pre-compensation, so that stack way crane is automatically in safe balanced state, and improve construction efficiency.

Description

Technical Field

[0001] This invention belongs to the field of mechanical control methods, specifically relating to an intelligent pre-balanced method for a trestle crane based on digital twin technology. Background Technology

[0002] In the engineering field, cranes are indispensable equipment, playing a crucial role, especially in the construction, maintenance, and hoisting of trestle and bridge structures operating in the air. However, existing cranes have certain shortcomings in pre-balancing, often relying on manual judgment of lifting weight and lacking intelligent and automated balancing solutions. This not only affects construction efficiency but may also lead to safety hazards due to human error. Therefore, there is an urgent need to develop an intelligent pre-balancing method for trestle cranes based on digital twin technology, which can help solve the problems in existing technologies. Summary of the Invention

[0003] The purpose of this invention is to solve the problems existing in the prior art and to provide an intelligent pre-balanced method for trestle cranes based on digital twin technology, which aims to realize an automated and intelligent pre-balanced process, reduce safety risks, and improve construction efficiency.

[0004] The specific technical solution adopted in this invention is as follows:

[0005] A method for intelligent pre-counterweighting of a trestle crane based on digital twin technology, comprising:

[0006] S1. Before the trestle crane lifts the target object at the construction site, it first collects the features of the target object through a video monitoring system, and then matches the collected features in a pre-built database to determine the estimated weight of the target object that matches the features.

[0007] S2. In the digital twin model pre-built for the trestle crane, based on the estimated weight of the determined lifting target and the preset optimal counterweight principle, the optimal pre-counterweight scheme for the lifting process is generated through counterweight simulation.

[0008] S3. The optimal pre-counterweight scheme is fed back to the counterweight control system of the trestle crane, and the counterweight control system pre-adjusts the counterweight blocks to ensure that the trestle crane is in a state of maximum balance during the lifting operation of the target object.

[0009] Preferably, the database stores different data information for two different types of lifting objects: commonly used lifting objects and non-standard components. The commonly used lifting objects are uniquely identified by a code set at the factory, and the database stores the mapping relationship between the code and weight of each commonly used lifting object. The non-standard components require pre-training of a recognition model based on video recognition and machine learning. This recognition model can identify the type and shape / size parameters of the lifting target based on video images of the lifting target obtained from the video monitoring system at the construction site. The database stores the mapping relationship between the shape / size parameters and weight of different types of components.

[0010] Preferably, the identification code is a QR code.

[0011] Preferably, the digital twin model of the trestle crane needs to be established in a virtual scene using digital twin technology, based on the actual trestle crane equipment parameters used in the construction and the actual construction site environment.

[0012] Preferably, the optimal pre-weighting scheme should include the weight of the pre-adjusted counterweight and its location information.

[0013] Preferably, the optimal counterweight principle is that the pre-adjusted weight and position of the counterweight block should maintain the overall center of gravity of the trestle crane system at the equilibrium origin during the lifting operation of the target object.

[0014] Preferably, the trestle crane uses a monitor to display the digital twin model in the virtual environment, thereby enabling visualization of the lifting target, the crane boom, and the counterweight features.

[0015] Preferably, the hook of the trestle crane is equipped with a load sensor to obtain the actual weight of the object to be lifted. During the lifting process, the actual weight of the object to be lifted needs to be compared with the estimated weight in real time. If the error between the two exceeds the allowable range, the counterweight simulation needs to be performed again in the digital twin model based on the actual weight of the object to be lifted and the preset optimal counterweight principle. After updating the optimal pre-counterweight scheme, the counterweight blocks are immediately readjusted.

[0016] Preferably, during the actual lifting of the target object by the trestle crane, the digital twin model adjusts the counterweight scheme in real time based on the current lifting status information and feeds it back to the actual crane for execution.

[0017] Preferably, the digital twin model predicts the future state and behavior of the crane in real time based on the current lifting status information, and issues an early warning according to preset warning rules when a potential danger or malfunction is predicted.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] The present invention provides an intelligent pre-balanced method for trestle cranes based on digital twin technology. This method automatically identifies the weight of the target object to be lifted using video recognition and machine learning technologies, and automatically pre-balances the load using a digital twin model. This ensures that the crane system is in a state of maximum balance when lifting the target object, achieving both safety and rapid construction. The present invention enables an automated and intelligent pre-balanced process during trestle laying, reducing safety risks and improving construction efficiency. Attached Figure Description

[0020] Figure 1 A schematic diagram illustrating the steps of an intelligent pre-balanced method for a trestle crane based on digital twin technology;

[0021] Figure 2 A schematic diagram of a digital twin model of a trestle crane;

[0022] Figure 3 A schematic diagram of the equilibrium origin and equilibrium safety boundary;

[0023] Figure 4 This is a flowchart of an intelligent pre-balanced method for a trestle crane based on digital twin technology. Detailed Implementation

[0024] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below. Technical features in the various embodiments of the present invention can be combined accordingly without mutual conflict.

[0025] In the description of this invention, it should be understood that when an element is considered to be "connected" to another element, it can be a direct connection to the other element or an indirect connection, i.e., there is an intermediate element. Conversely, when an element is said to be "directly" connected to another element, there is no intermediate element.

[0026] In the description of this invention, it should be understood that the terms "first" and "second" are used only for descriptive purposes and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature.

[0027] like Figure 1As shown, in a preferred embodiment of the present invention, a smart pre-balanced method for a trestle crane based on digital twin technology is provided, comprising:

[0028] S1. Before the trestle crane lifts the target object at the construction site, it first collects the features of the target object through a video monitoring system, and then matches the collected features with a pre-built database to determine the estimated weight of the target object that matches the features.

[0029] It should be noted that the video monitoring system in this invention can be implemented using any monitoring equipment installed at the construction site, as long as it can accurately capture video images of the lifting target, and the image quality should meet the requirements for subsequent image processing. For example, if the lifting target has an identification code, the identification code recognition requirements must be met; if the lifting target does not have an identification code, the input requirements for the subsequent recognition model must be met.

[0030] It should be noted that the identification code in this invention can take the form of a QR code, barcode, etc. In the embodiments of this invention, the identification code is preferably a QR code.

[0031] It should be noted that the lifting target in this invention is not limited and can be any component or equipment that may be involved in the construction of the trestle.

[0032] Furthermore, in embodiments of the present invention, the features of the lifting target collected by the video monitoring system can be divided into two categories: the first category is an identification code used to uniquely identify the lifting object, and the second category is the external shape features of the lifting target. The first category of identification codes is suitable for commonly used lifting objects on construction sites. These commonly used lifting objects are generally standard components, and their dimensions and mass are basically fixed. Therefore, their weight can be measured in advance, and then mapped using the identification code. Subsequently, only the identification code needs to be identified to find the corresponding weight. The second category is suitable for some less commonly used non-standard components. The shape and dimensions of these non-standard components often cannot be determined in advance, so they need to be determined on-site. In this invention, a recognition model based on video recognition and machine learning can be used to determine the component type and shape and size parameters, thereby estimating its corresponding weight.

[0033] Therefore, in order to adapt to the method of determining the weight of components based on the above two types of characteristics, the present invention needs to pre-build a database before actual operation to match it with the characteristics of the lifting target object identified on site, and finally determine the weight of the lifting target object. In the embodiments of the present invention, since the lifting target object has two different forms, namely commonly used lifting objects and non-standard components, the database also needs to store different data information for the two different forms of commonly used lifting objects and non-standard components.

[0034] For commonly used lifting objects, each type corresponds to a standard component, and each is uniquely identified at the factory by a unique identification code. The database stores the mapping relationship between the identification codes and weights of each commonly used lifting object. Therefore, by identifying the identification code, the unique number or name of the current lifting target can be determined, and the corresponding weight can then be retrieved from the database.

[0035] For non-standard components, a recognition model based on video recognition and machine learning needs to be pre-trained. This model can identify the type and shape / size parameters of the lifting target object based on video images of the construction site acquired by the aforementioned video monitoring system. The database stores the mapping relationship between the shape / size parameters and weight of different types of components. It should be noted that the specific form of this recognition model is not limited and can be constructed using video recognition and machine learning techniques. For example, a target detection network using video images as input can be used to locate the non-standard component target object in the video image. Then, the shape and size of the non-standard component target object in the video image can be determined based on the mapping relationship between pixel coordinates and real-world coordinates. Furthermore, a classification network using video images as input can be used to identify the type of the non-standard component target object. Different types of non-standard components have different densities. For example, common steel and wooden components can have corresponding density coefficients set, and the component mass can be calculated based on the shape, size, and density coefficient of the non-standard component target. Alternatively, for each type of non-standard component target, the weight of the component corresponding to different shapes and sizes can be determined directly using empirical methods or measured data. After determining the shape, size, and type of the non-standard component target in the video image, the corresponding component weight can be determined directly using a lookup table method. Of course, the above recognition model needs to be trained in advance using labeled data, which is existing technology and will not be elaborated further.

[0036] As an embodiment of the present invention, each construction unit has its own commonly used lifting objects according to the actual process adopted. Therefore, each construction unit can build its own aforementioned database to form an enterprise-level database.

[0037] S2. In the digital twin model pre-built for the trestle crane, based on the estimated weight of the determined lifting target and the preset optimal counterweight principle, the optimal pre-counterweight scheme for the lifting process is generated through counterweight simulation.

[0038] It should be noted that the aforementioned digital twin model of the trestle crane can be constructed using digital twin technology, a relatively mature technology, and the specific implementation will not be elaborated further. In this invention, the counterweighting process cannot only consider the trestle crane itself but also the current construction environment, as various terrain factors at the construction site will affect the counterweighting. Therefore, in the embodiments of this invention, the digital twin model of the trestle crane needs to be established in a virtual scene using digital twin technology based on the actual trestle crane equipment parameters used in construction (including the weight, position, moving speed, various geometric parameters, sensor arrangement, etc.) and the actual construction site environment. For example... Figure 2 The diagram shown is a schematic representation of a trestle crane in an embodiment of the present invention.

[0039] Furthermore, the "optimal pre-counterweight scheme" mentioned in this invention refers to the optimal counterweight scheme that ensures the safe and stable operation of the crane, determined based on the estimated weight of the current lifting target object under a preset "optimal counterweight principle" benchmark. This optimal pre-counterweight scheme should include the pre-adjusted counterweight weight and its position information, as both of these will change the torque balance of the trestle crane.

[0040] The aforementioned optimal counterweight principle can be set according to actual project needs. Theoretically, the optimal counterweight principle is that the pre-adjusted weight and position of the counterweight blocks should maintain the overall center of gravity of the trestle crane system at its equilibrium point during lifting operations. The equilibrium point of the trestle crane is its own center of gravity when it is not lifting any heavy objects. At this equilibrium point, the trestle crane can guarantee absolute self-balance and will not have any risk of overturning. Of course, the actual optimal counterweight principle can also be flexibly adjusted according to the actual project and site conditions, based on the requirements of the construction site and relevant specifications.

[0041] Furthermore, the optimal pre-counterweight scheme for the lifting process in this invention is generated through counterweight simulation in a digital twin model. The digital twin model can reflect the actual working environment and equipment parameters of the trestle crane in real time. Therefore, different counterweight schemes can be simulated and parameters optimized within the model to determine the optimal pre-counterweight scheme that satisfies the principle of optimal counterweight, i.e., determining the optimal counterweight weight and its location, thereby ensuring optimal stability of the crane system.

[0042] S3. The determined optimal pre-counterweight scheme is fed back to the counterweight control system of the trestle crane, which then pre-adjusts the counterweight blocks to ensure maximum balance during the lifting operation of the trestle crane on the target object.

[0043] Furthermore, since a digital twin model is established in a virtual environment in this invention, in a preferred embodiment, a monitor can be installed on the trestle crane to display the digital twin model in the virtual environment, thereby realizing the visualization of the lifting target, the crane boom, and the counterweight features. The specific visualization parameters can be adjusted according to the actual engineering situation. For example, the lifting target can display parameters such as weight and position, the crane boom can display posture and parameters, and the counterweight features can display weight and position.

[0044] Additionally, it should be noted that the weight of the target object determined in step S1 above is only an estimated weight, and it may differ from the actual weight. Therefore, after the simulated pre-balance is completed, it is best to perform corresponding verification and correction during the actual lifting operation. The verification and correction methods will be explained later through examples.

[0045] In a preferred embodiment, a load sensor is installed on the hook of the trestle crane to obtain the actual weight of the object to be lifted. During the lifting process, the actual weight of the object to be lifted needs to be compared with the estimated weight in real time. If the error between the two exceeds the allowable range, the counterweight simulation needs to be performed again in the digital twin model based on the actual weight of the object to be lifted and the preset optimal counterweight principle. After updating the optimal pre-counterweight scheme, the counterweight blocks are immediately readjusted. Thus, the present invention can flexibly adapt to changes in the lifting environment. Even if the weight estimation is inaccurate, the digital twin model can automatically adjust the counterweight scheme for verification and correction to adapt to different environments and working conditions. The adjustment of the counterweight blocks can be achieved by increasing or decreasing the mass of the counterweight blocks within the existing counterweight arm range, or by adjusting the position of the counterweight blocks.

[0046] Under this verification and correction method, such as Figure 3 As shown, the intelligent pre-counterweight process of the present invention can be described as follows: First, the characteristics of the lifting target object in the construction site environment of the trestle crane are recorded and automatically detected by image. The weight of the lifting target object is obtained through existing target objects that meet the characteristics in the database. The counterweight parameters of the counterweight block are calculated based on the lifting weight and the crane boom length, including the counterweight weight and the center of gravity position of the counterweight block. The center of gravity of the crane system is preset to be located at the center of the balance point when lifting the target object. At the same time, the counterweight block is automatically moved to the preset position by the counterweight mechanism. When the crane actually lifts the target object, the load sensor on the hook verifies the lifting weight. If there is a large error with the image recognition result, the preset position of the balance point is finely adjusted and displayed in real time on the virtual environment.

[0047] Similarly, besides the issue of inaccurate weight estimation of the target object, there are also situations requiring adjustment of the counterweight due to environmental changes, changes in the optimal counterweight principle, or malfunctions of the trestle crane. Therefore, in another preferred embodiment, during the actual lifting of the target object by the trestle crane, the digital twin model can adjust the counterweight scheme in real time based on the current lifting status information and feed it back to the actual crane for execution.

[0048] In another embodiment, based on a digital twin model, intelligent early warning functionality can also be provided for the trestle crane. Specifically, the digital twin model predicts the crane's future state and behavior in real time based on the current lifting status information, and issues a warning according to preset rules when a potential danger or malfunction is predicted. In practical applications, because the entire lifting process is dynamic, the overall center of gravity of the trestle crane, counterweight, and the lifted object is constantly changing. Therefore, as... Figure 4 As shown, a certain range of balance safety boundary can be set based on the balance origin of the trestle crane. This balance safety boundary should be located within the support range of the chassis. The overall center of gravity can still be considered safe within the balance safety boundary, but it is considered dangerous if it exceeds the balance safety boundary, and corresponding warnings are required.

[0049] In summary, the intelligent pre-balanced method for trestle cranes based on digital twin technology provided by this invention automatically identifies the weight of the target object to be lifted through technologies such as video recognition and machine learning, and automatically pre-balances the weight through a digital twin model. This allows the crane system to be in a state of maximum balance when lifting the target object, ensuring safety while also promoting rapid construction and improving work efficiency.

[0050] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all technical solutions obtained through equivalent substitution or transformation fall within the protection scope of the present invention.

Claims

1. A method for intelligent pre-counterweighting of a trestle crane based on digital twin technology, characterized in that, include: S1. Before the trestle crane lifts the target object at the construction site, it first collects the features of the target object through a video monitoring system, and then matches the collected features in a pre-built database to determine the estimated weight of the target object that matches the features. S2. In the digital twin model pre-built for the trestle crane, based on the estimated weight of the determined lifting target and the preset optimal counterweight principle, the optimal pre-counterweight scheme for the lifting process is generated through counterweight simulation. S3. Feedback the optimal pre-counterweight scheme to the counterweight control system of the trestle crane, and the counterweight control system pre-adjusts the counterweight blocks to ensure that the trestle crane is in a state of maximum balance during the lifting operation of the target object. The database stores different data information for two different types of lifting objects: commonly used lifting objects and non-standard components. The commonly used lifting objects are uniquely identified by a code set at the factory, and the database stores the mapping relationship between the identification codes and weights of each commonly used lifting object. The non-standard components require pre-training of a recognition model based on video recognition and machine learning. This recognition model can identify the type and shape / size parameters of the lifting target based on video images of the lifting target obtained from the video monitoring system at the construction site. The database stores the mapping relationship between the shape / size parameters and weights of different types of components. The hook of the trestle crane is equipped with a load sensor to obtain the actual weight of the object to be lifted. During the lifting process, the actual weight of the object to be lifted must be compared with the estimated weight in real time. If the error between the two exceeds the allowable range, the counterweight simulation must be carried out again in the digital twin model based on the actual weight of the object to be lifted and the preset optimal counterweight principle. After updating the optimal pre-counterweight scheme, the counterweight blocks must be readjusted immediately.

2. The intelligent pre-balanced method for a trestle crane based on digital twin technology as described in claim 1, characterized in that, The identification code is a QR code.

3. The intelligent pre-balanced method for a trestle crane based on digital twin technology as described in claim 1, characterized in that, The digital twin model of the trestle crane needs to be established in a virtual scene using digital twin technology, based on the actual trestle crane equipment parameters used in the construction and the actual construction site environment.

4. The intelligent pre-balanced method for a trestle crane based on digital twin technology as described in claim 1, characterized in that, The optimal pre-weighting scheme should include the weight of the pre-adjusted counterweight and its location information.

5. The intelligent pre-balanced method for a trestle crane based on digital twin technology as described in claim 1, characterized in that, The principle of optimal counterweight is that the pre-adjusted weight and position of the counterweight should maintain the overall center of gravity of the trestle crane system at the equilibrium origin during the lifting operation of the target object.

6. The intelligent pre-balanced method for a trestle crane based on digital twin technology as described in claim 1, characterized in that, The trestle crane uses monitors to display a digital twin model in a virtual environment, thereby enabling visualization of the lifting target, crane boom, and counterweight features.

7. The intelligent pre-balanced method for a trestle crane based on digital twin technology as described in claim 1, characterized in that, During the actual lifting of the target object by the trestle crane, the digital twin model adjusts the counterweight scheme in real time based on the current lifting status information and feeds it back to the actual crane for execution.

8. The intelligent pre-balanced method for a trestle crane based on digital twin technology as described in claim 1, characterized in that, The digital twin model predicts the future state and behavior of the crane in real time based on the current lifting status information, and issues an early warning according to preset warning rules when a potential danger or malfunction is predicted.

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