Reinforcing cage hoisting control method and system based on digital twinning

Abstract

The application discloses a steel reinforcement cage hoisting control method and system based on digital twinning, the method comprising: selecting hoisting equipment and preliminarily determining hoisting points according to boom parameters and steel reinforcement cage operation information; accurately positioning hoisting operation points by combining real-time hoisting coordinates and stress deformation analysis models; constructing a three-dimensional model of a construction environment through laser point clouds and visual data; extracting spatial, mechanical and process multidimensional hoisting constraint conditions, and generating multiple predicted hoisting trajectories based on the same; calculating trajectory risk indicators by using an evaluation model, and selecting the optimal real-time hoisting trajectory according to the same; and performing hoisting operations according to the trajectory, and finally realizing safe and accurate hoisting through deviation comparison and dynamic control correction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underground engineering construction, and in particular to a steel reinforcement cage hoisting control method and system based on digital twinning. BACKGROUND

[0002] With the acceleration of urbanization, rail transit has gradually become an indispensable means of transportation for modern city development. Underground continuous walls, due to their large stiffness, good integrity, and good displacement control effect, have become an essential and critical task in the construction of urban rail transit underground tunnels. Among them, the hoisting operation of large underground continuous wall steel reinforcement cages is a key link in the construction of underground continuous walls.

[0003] However, due to the poor stiffness, large weight, and high height of the underground continuous wall steel reinforcement cage, the stress state of the steel reinforcement cage during design is different from that during construction hoisting, so the hoisting process of the steel reinforcement cage is extremely demanding. The hoisting operation of the steel reinforcement cage in the hoisting operation of the large underground continuous wall steel reinforcement cage is heavily dependent on the experience of the operator. If the operator's hoisting operation is improper or the coordination is off, it is easy to cause collisions between the large steel reinforcement cage and the hoisting equipment or the surrounding environment, which not only increases the hoisting construction cost of the steel reinforcement cage but also reduces the safety of the hoisting operation. SUMMARY

[0004] To solve the above technical problems, the present application discloses a steel reinforcement cage hoisting control method and system based on digital twinning, which is used to improve the safety of hoisting control.

[0005] To achieve the above purpose, the present application discloses a steel reinforcement cage hoisting control method based on digital twinning, comprising:

[0006] According to the boom information of the hoisting equipment to be selected and the hoisting operation information of the steel reinforcement cage to be hoisted, the hoisting radius is calculated, and collision analysis is performed according to the hoisting radius to select the hoisting equipment from multiple hoisting equipment to be selected;

[0007] According to the preset construction coordinates, the boom information and the hoisting operation information, a plurality of hoisting prediction points of the steel reinforcement cage to be hoisted are determined;

[0008] The hoisting coordinates of the steel reinforcement cage to be hoisted are obtained, and a hoisting operation point is selected from a plurality of hoisting prediction points according to the hoisting coordinates, the hoisting operation information and a preset stress deformation analysis model;

[0009] Laser point cloud data and image data of the construction area are obtained, and an environment model corresponding to the construction area is generated according to the laser point cloud data and the image data;

[0010] extract a hoisting constraint condition corresponding to the to-be-hoisted reinforcement cage from the hoisting operation information, to generate a plurality of predicted hoisting trajectories corresponding to the hoisting equipment according to the hoisting constraint condition, the environment model and the hoisting operation point;

[0011] input the predicted hoisting trajectory and the equipment parameter of the hoisting equipment into a pre-constructed trajectory evaluation model to obtain a trajectory risk of each predicted hoisting trajectory, to select a real-time hoisting trajectory from the plurality of predicted hoisting trajectories according to the trajectory risk;

[0012] control the hoisting equipment to perform the hoisting operation of the to-be-hoisted reinforcement cage according to the real-time hoisting trajectory and collect hoisting data of the to-be-hoisted reinforcement cage in real time, to perform hoisting deviation correction according to the hoisting data and complete the hoisting operation.

[0013] The application discloses a reinforcement cage hoisting control method based on digital twinning, which realizes intelligent control of hoisting operation by constructing virtual-real mapping of construction environment and hoisting process through digital twinning technology. By calculating the hoisting radius and selecting the hoisting equipment in combination with collision analysis, the collision risk that may exist in the traditional manual selection of equipment is avoided; the hoisting prediction point is determined based on the construction coordinates and the hoisting operation information, and the hoisting operation point meeting the mechanical properties is selected in combination with the stress deformation analysis model, so as to ensure the structural stability of the reinforcement cage in the hoisting process; the environment model is generated by using the laser point cloud and image data, so as to provide accurate three-dimensional space constraints for subsequent trajectory planning; a plurality of predicted hoisting trajectories are generated by the hoisting constraint condition and the environment model, and the optimal trajectory is selected based on the trajectory evaluation model, so as to reduce the probability of operation failure; finally, the hoisting process is dynamically adjusted through real-time data collection and deviation correction, so as to improve the hoisting precision and safety.

[0014] As a preferred example, the hoisting radius is calculated according to the hoist arm information of the to-be-selected hoisting equipment and the hoisting operation information of the to-be-hoisted reinforcement cage, and the collision analysis is performed according to the hoisting radius, so as to select the hoisting equipment from a plurality of to-be-selected hoisting equipment, including:

[0015] the hoist arm length, the hoist arm height and the hoist arm hoisting radius of the to-be-selected hoisting equipment are obtained, and the hoisting height and the hoisting weight of the to-be-hoisted reinforcement cage are extracted according to the hoisting operation information;

[0016] the hoisting radius between the to-be-selected hoisting equipment and the to-be-hoisted reinforcement cage is obtained through a preset collision radius calculation function according to the hoist arm height, the hoist arm hoisting radius and the hoisting height;

[0017] According to the hoisting weight, a maximum lifting radius corresponding to the to-be-selected hoisting device is matched, when the lifting radius is less than or equal to the maximum lifting radius, according to a preset construction coordinate, a joint point coordinate of a boom and a device main body in the to-be-selected hoisting device is obtained;

[0018] According to the joint point coordinate, the lifting radius and the lifting height, a collision region corresponding to the to-be-selected hoisting device is determined;

[0019] A planar intersection point of the boom and the to-be-lifted reinforcement cage is obtained, and a hoisting device is selected from the plurality of to-be-selected hoisting devices according to a coincidence detection result of the planar intersection point and the collision region.

[0020] The above scheme realizes accurate selection of a hoisting device through multi-dimensional parameter fusion and dynamic analysis of a collision region. First, by obtaining the boom geometric parameters and the reinforcement cage hoisting parameters, a spatial relationship model between the device and the reinforcement cage is established in combination with a collision radius calculation function, solving the problem that the hoisting radius cannot be quantified in traditional experience judgment. Secondly, based on the selection mechanism of matching the maximum lifting radius with the hoisting weight, devices with insufficient lifting capacity are excluded in the device selection stage, ensuring the basic safety. Then, the three-dimensional collision region is constructed by the boom joint point coordinate, which converts the abstract hoisting radius into a specific space range, providing a visual judgment basis for subsequent collision detection. Finally, through the coincidence detection of the planar intersection point and the collision region, dynamic interference analysis of the device motion trajectory and the spatial position of the reinforcement cage is realized, improving the safety of hoisting.

[0021] As a preferred example, the plurality of hoisting prediction points of the to-be-lifted reinforcement cage are determined according to the preset construction coordinate, the boom information and the hoisting operation information, comprising:

[0022] According to the construction coordinate, a center rotation coordinate of the hoisting device is obtained, so as to determine a plurality of hoisting points corresponding to the to-be-lifted reinforcement cage according to the center rotation coordinate and a boom length of the hoisting device;

[0023] For any one of the hoisting points:

[0024] According to the center rotation coordinate, the boom length, the lifting height and the hoisting point, a first distance is determined;

[0025] According to the hoisting point, three-dimensional size information of the to-be-lifted reinforcement cage, the boom length and the lifting height, a second distance is determined;

[0026] When the second distance is greater than the first distance, the hoisting point is determined as a hoisting prediction point of the to-be-lifted reinforcement cage.

[0027] The above scheme solves the collision risk and structural deformation problem caused by unreasonable lifting point selection by combining construction coordinates and lifting equipment parameters to establish a lifting point screening mechanism. Specifically, first, the initial lifting point is determined based on the center rotation coordinates of the lifting equipment and the length of the lifting arm to ensure that the lifting point is within the operating range of the equipment; then, for each lifting point, the first distance reflecting the reachable range of the lifting equipment and the second distance reflecting the restriction of the size of the reinforcement cage and the lifting height on the space occupation are calculated and compared. When the second distance is greater than the first distance, it indicates that the space occupation of the reinforcement cage under the lifting point exceeds the actual coverage range of the equipment, and the point is marked as a lifting prediction point, thereby excluding the lifting points that may cause collision or exceed the carrying capacity of the equipment, and finally realizing the dual constraints of safety and feasibility of the lifting point and improving the safety of lifting.

[0028] As a preferred example, the lifting coordinates of the reinforcement cage to be lifted are obtained, and a lifting operation point is selected from a plurality of lifting prediction points according to the lifting coordinates, the lifting operation information and a preset stress deformation analysis model, comprising:

[0029] According to the lifting coordinates of the reinforcement cage to be lifted, the prediction point coordinates corresponding to each lifting prediction point are obtained;

[0030] According to the prediction point coordinates, the lifting weight and the pre-marked parameter constant, the lifting stress corresponding to each lifting prediction point is obtained;

[0031] According to the lifting stress and the preset stress deformation analysis model, the horizontal deformation amount of the reinforcement cage corresponding to each lifting prediction point is obtained;

[0032] According to the horizontal deformation amount of the reinforcement cage and the preset deformation amount threshold, a lifting operation point is selected from a plurality of lifting prediction points.

[0033] The above scheme realizes the safety screening of the lifting point by quantitatively analyzing the mechanical response of the lifting point, thereby improving the safety of lifting. First, the spatial position of each prediction point is obtained through the lifting coordinates, providing basic data for subsequent mechanical analysis. Based on the prediction point coordinates, the lifting weight and the pre-calibrated parameter constant, the lifting stress is calculated, combining the actual physical parameters with the theoretical model to ensure the accuracy of the stress calculation. Then, the stress deformation analysis model is used to convert the stress data into the horizontal deformation amount of the reinforcement cage, and the structural deformation of different lifting points is predicted through model simulation. Finally, by comparing the deformation amount with the preset threshold, the lifting operation point whose deformation amount meets the safety requirements is selected, which not only avoids the error of manual experience judgment, but also ensures the structural stability of the reinforcement cage during lifting. The introduction of pre-marked parameter constant can adapt to the material properties of different engineering scenes, and the setting of deformation amount threshold is directly related to the structural safety standard, improving the accuracy of evaluation.

[0034] As a preferred example, the laser point cloud data and the image data of the construction area are acquired to generate an environment model corresponding to the construction area according to the laser point cloud data and the image data, which comprises:

[0035] The laser point cloud data of the construction area is projected onto the image data to obtain first depth environment image data corresponding to the construction area;

[0036] The first depth environment image data and the image data are subjected to bilateral filtering to obtain second depth environment image data;

[0037] The second depth environment image data is converted into point cloud depth image data, and image coordinates of the point cloud depth image data are input into a pre-constructed environment construction model to obtain an environment model corresponding to the construction area.

[0038] In the above scheme, high-precision environment modeling is achieved through multi-source data fusion and optimization processing, and then the safety of hoisting is improved by using high-precision modeling. Among them, by projecting and fusing the laser point cloud data and the image data, the first depth environment image data with depth information is formed, solving the problem of missing data dimension of a single sensor. Then, the fused data is subjected to noise reduction processing by bilateral filtering, which eliminates data noise while retaining edge features, improving the signal-to-noise ratio of environment data. Finally, through point cloud depth image conversion and a pre-constructed environment construction model, two-dimensional image coordinates are converted into a three-dimensional space model, and an environment model containing the spatial distribution characteristics of obstacles is constructed, which not only guarantees the geometric precision of the environment model, but also realizes the digital reconstruction of the construction scene through modeling processing, providing reliable environment constraint conditions for subsequent hoisting trajectory planning and improving the safety of hoisting.

[0039] As a preferred example, the hoisting constraint conditions corresponding to the to-be-hoisted reinforcement cage are extracted from the hoisting operation information to generate a plurality of predicted hoisting trajectories corresponding to the hoisting equipment according to the hoisting constraint conditions, the environment model and the hoisting operation point, which comprises:

[0040] The environment model is processed to obtain a three-dimensional geometric space constraint representing the spatial distribution of static obstacles in the construction area;

[0041] The design parameters and attribute parameters of the to-be-hoisted reinforcement cage are obtained from the hoisting operation information to obtain the mechanical performance constraint of the to-be-hoisted reinforcement cage according to the design parameters and attribute parameters; wherein the mechanical performance constraint includes maximum stress and maximum deflection;

[0042] construct a configuration space containing the geometric space constraint and the mechanical property constraint, and perform parallel solving in the configuration space through a preset path planning algorithm, a predicted point coordinate, and a construction coordinate to obtain a plurality of predicted hoisting trajectories.

[0043] In the above scheme, the intelligent planning of the safe hoisting trajectory is realized by establishing a configuration space with dual constraints of space and mechanics. Firstly, the three-dimensional geometric space constraint is extracted by processing the environmental model to accurately identify the spatial distribution characteristics of static obstacles such as tower cranes and buildings in the construction area, ensuring that the hoisting trajectory avoids all physical obstacles. Secondly, based on the design parameters and material attribute parameters of the reinforcement cage, the mechanical property constraints such as maximum stress and maximum deflection are derived to prevent structural deformation or fracture caused by excessive stress during hoisting. The geometric constraint and the mechanical constraint are fused to construct a multi-dimensional configuration space, breaking through the limitation of traditional path planning which only considers space obstacle avoidance. The path planning algorithm is used to perform parallel solving in the configuration space, which can simultaneously meet the requirements of spatial safety and structural safety. Through multi-objective optimization, a candidate trajectory set that avoids obstacles and meets material strength requirements is generated, improving the safety of hoisting.

[0044] As a preferred example, the predicted hoisting trajectory, the device parameters of the hoisting device are input into a pre-constructed trajectory evaluation model to obtain a trajectory risk of each predicted hoisting trajectory, and a real-time hoisting trajectory is selected from the plurality of predicted hoisting trajectories according to the trajectory risk, comprising:

[0045] For any one of the predicted hoisting trajectories:

[0046] The predicted hoisting trajectory and the device parameters are input into a pre-constructed trajectory evaluation model to extract a time efficiency score, an energy consumption score, and a risk score corresponding to the predicted hoisting trajectory through the trajectory evaluation model;

[0047] The first weight of the time efficiency score, the second weight of the energy consumption score, and the third weight of the risk score are obtained through a preset weight self-attention distribution mechanism in the trajectory evaluation model;

[0048] According to the time efficiency score, the energy consumption score, the risk score, the first weight, the second weight, and the third weight, the trajectory evaluation model outputs a trajectory score corresponding to the predicted hoisting trajectory;

[0049] The trajectory with the highest trajectory score is selected from the plurality of predicted hoisting trajectories as the real-time hoisting trajectory of the hoisting device.

[0050] The above scheme realizes intelligent optimization of the hoisting trajectory through a multi-dimensional dynamic weight distribution mechanism. The predicted trajectory and device parameters are input into an evaluation model to extract scores of three core indicators, namely time efficiency, energy consumption, and risk, thereby breaking through the limitations of traditional single indicator evaluation. The weight self-attention distribution mechanism is used to dynamically adjust the weights of each indicator, thereby solving the problem that fixed weights cannot adapt to different construction scenarios. The optimal trajectory is selected based on the weighted score results distributed by the self-attention mechanism, which avoids subjective bias of artificial experience and realizes a balance between efficiency, energy consumption, and safety through quantitative evaluation model, thereby improving the safety of hoisting.

[0051] As a preferred example, the hoisting device is controlled according to the real-time hoisting trajectory to perform the hoisting operation of the to-be-hoisted reinforcement cage, and hoisting data of the to-be-hoisted reinforcement cage is collected in real time to correct hoisting deviation based on the hoisting data, and the hoisting operation is completed, including:

[0052] Real-time collection of inclination data, stress data, and environmental data of the to-be-hoisted reinforcement cage during the hoisting process to obtain a hoisting posture of the to-be-hoisted reinforcement cage;

[0053] Comparison of the hoisting posture with an expected state in the real-time hoisting trajectory to calculate a pose deviation and a load deviation of the to-be-hoisted reinforcement cage, and generation of adjustment device parameters of the hoisting device according to the pose deviation and the load deviation;

[0054] Control of the hoisting device according to the adjustment device parameters to correct the deviation, and completion of the hoisting operation of the to-be-hoisted reinforcement cage by the hoisting device after correction.

[0055] The above scheme realizes precise regulation and control of the hoisting process through dynamic closed-loop control. Real-time collection of multi-dimensional data such as inclination, stress, and environment is used to construct a real-time digital mapping of the hoisting posture, thereby breaking through the lagging defect of traditional manual observation. The actual hoisting posture is compared with the expected trajectory generated by the model, and the hoisting deviation is accurately quantified through coupling calculation of the pose deviation and the load deviation. The device adjustment parameters generated based on the deviation data can dynamically correct key control variables such as hoist arm angle and lifting speed, effectively suppress the cumulative error in the hoisting process, ensure stable controlled motion trajectory of the reinforcement cage in complex construction environment, and improve the safety of hoisting.

[0056] In another aspect, the present application discloses a reinforcement cage hoisting control system based on digital twinning, which includes a device optimization module, a hoisting point selection module, a work point selection module, an environment recognition module, a trajectory prediction module, a trajectory optimization module, and a hoisting control module.

[0057] The device selection module is configured to calculate a hoisting radius according to the boom information of the hoisting device to be selected and the hoisting operation information of the steel reinforcement cage to be hoisted, and perform collision analysis according to the hoisting radius, so as to select the hoisting device from a plurality of hoisting devices to be selected;

[0058] The hoisting point selection module is configured to determine a plurality of hoisting prediction points of the steel reinforcement cage to be hoisted according to the preset construction coordinates, the boom information and the hoisting operation information.

[0059] The operation point selection module is configured to obtain hoisting coordinates of the steel reinforcement cage to be hoisted, and select a hoisting operation point from a plurality of hoisting prediction points according to the hoisting coordinates, the hoisting operation information and a preset stress deformation analysis model.

[0060] The environment recognition module is configured to obtain laser point cloud data and image data of a construction area, and generate an environment model corresponding to the construction area according to the laser point cloud data and the image data.

[0061] The trajectory prediction module is configured to extract a hoisting constraint condition corresponding to the steel reinforcement cage to be hoisted from the hoisting operation information, and generate a plurality of predicted hoisting trajectories corresponding to the hoisting device according to the hoisting constraint condition, the environment model and the hoisting operation point.

[0062] The trajectory selection module is configured to input the predicted hoisting trajectories and device parameters of the hoisting device into a pre-constructed trajectory evaluation model to obtain a trajectory risk of each predicted hoisting trajectory, and select a real-time hoisting trajectory from a plurality of predicted hoisting trajectories according to the trajectory risk.

[0063] The hoisting control module is configured to control the hoisting device to perform the hoisting operation of the steel reinforcement cage to be hoisted according to the real-time hoisting trajectory, and collect hoisting data of the steel reinforcement cage to be hoisted in real time, so as to perform hoisting deviation correction according to the hoisting data and complete the hoisting operation.

[0064] The application discloses a steel reinforcement cage hoisting control system based on digital twinning, which realizes intelligent control of hoisting operation by constructing virtual-real mapping of construction environment and hoisting process through digital twinning technology. By calculating the hoisting radius and selecting the hoisting equipment combined with collision analysis, the collision risk that may exist when the traditional manual equipment selection is avoided; the hoisting prediction point is determined based on the construction coordinates and hoisting operation information, and the hoisting operation point meeting the mechanical properties is selected combined with the stress deformation analysis model, so as to ensure the structural stability of the steel reinforcement cage in the hoisting process; the environment model is generated by using the laser point cloud and image data, which provides accurate three-dimensional space constraints for subsequent trajectory planning; a plurality of predicted hoisting trajectories are generated by the hoisting constraint conditions and the environment model, and the optimal trajectory is selected based on the trajectory evaluation model to reduce the probability of operation failure; finally, the hoisting process is dynamically adjusted through real-time data acquisition and deviation correction, and the hoisting precision and safety are improved.

[0065] As a preferred example, the device selection module includes a radius identification unit, a collision warning unit and a device selection unit;

[0066] The radius identification unit is used to obtain the length, height and hoisting radius of the hoist arm of the hoisting equipment to be selected, and to extract the hoisting height and hoisting weight of the steel reinforcement cage to be hoisted according to the hoisting operation information; according to the hoist arm height, the hoist arm hoisting radius and the hoisting height, the hoisting radius between the hoisting equipment to be selected and the steel reinforcement cage to be hoisted is obtained through a preset collision radius calculation function;

[0067] The collision warning unit is used to match the maximum lifting radius corresponding to the hoisting equipment to be selected according to the hoisting weight, and when the hoisting radius is less than or equal to the maximum lifting radius, the coordinates of the intersection point of the hoist arm and the main body of the hoisting equipment to be selected are obtained according to the preset construction coordinates; the collision area corresponding to the hoisting equipment to be selected is determined according to the intersection point coordinates, the hoisting radius and the hoisting height.

[0068] The device selection unit is used to obtain the planar intersection point of the hoist arm and the steel reinforcement cage to be hoisted, so as to select the hoisting equipment from a plurality of hoisting equipment to be selected according to the overlap detection result of the planar intersection point and the collision area.

[0069] The above scheme realizes accurate selection of hoisting equipment through multi-dimensional parameter fusion and dynamic analysis of collision area. First, by obtaining the geometric parameters of the hoist arm and the hoisting parameters of the reinforcement cage, a spatial relationship model between the equipment and the reinforcement cage is established by combining the collision radius calculation function, solving the problem of unable to quantify the hoisting radius in traditional experience judgment. Second, based on the selection mechanism of matching the maximum lifting radius with the hoisting weight, the equipment with insufficient lifting capacity is excluded in the equipment selection stage, ensuring the safety of the foundation. Then, the three-dimensional collision area is constructed by the hoist arm intersection point coordinates, which converts the abstract hoisting radius into a specific space range, providing visual judgment basis for subsequent collision detection. Finally, through the overlap detection of the plane intersection point and the collision area, the dynamic interference analysis of the equipment motion trajectory and the spatial position of the reinforcement cage is realized, improving the safety of hoisting. BRIEF DESCRIPTION OF DRAWINGS

[0070] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings described in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0071] Figure 1 is a flowchart of a reinforcement cage hoisting control method based on digital twinning disclosed by an embodiment of the present application;

[0072] Figure 2 is a structural diagram of a reinforcement cage hoisting control system based on digital twinning disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0074] REFERENCE Figure 1 In order to improve the safety in the hoisting process, the present embodiment discloses a reinforcement cage hoisting control method based on digital twinning, mainly including:

[0075] Step 101: Calculate the hoisting radius according to the hoist arm information of the hoisting equipment to be selected and the hoisting operation information of the reinforcement cage to be hoisted, and perform collision analysis according to the hoisting radius, to select the hoisting equipment from a plurality of hoisting equipment to be selected.

[0076] In the embodiment, the step mainly includes: obtaining the boom length, boom height and boom lifting radius of the to-be-selected lifting device, and extracting the lifting height and lifting weight of the to-be-lifted reinforcement cage according to the lifting operation information; obtaining the lifting radius between the to-be-selected lifting device and the to-be-lifted reinforcement cage according to the boom height, the boom lifting radius and the lifting height by a preset collision radius calculation function; matching the maximum lifting radius corresponding to the to-be-selected lifting device according to the lifting weight, and when the lifting radius is less than or equal to the maximum lifting radius, obtaining the intersection point coordinates of the boom and the device body in the to-be-selected lifting device according to a preset construction coordinate; determining the collision area corresponding to the to-be-selected lifting device according to the intersection point coordinates, the lifting radius and the lifting height; obtaining the planar intersection point of the boom and the to-be-lifted reinforcement cage, so as to select the lifting device from the plurality of to-be-selected lifting devices according to the overlap detection result of the planar intersection point and the collision area.

[0077] In some embodiments of the embodiment, first, a spatial relationship model is established by the boom geometric parameters and the reinforcement cage lifting parameters, and the boom height, the lifting height and the boom lifting radius are converted into quantitative safety distances by a collision radius calculation function, so as to solve the problem that the traditional experience judgment cannot accurately calculate. The boom length refers to the extension range of the boom from the device body to the end of the hook, which can be obtained by a parameter manual of the lifting device or a sensor measurement, and is used to determine the operation range of the lifting device. The boom lifting radius refers to the maximum operation distance of the boom in the horizontal direction, which can be calculated by the trigonometric function relationship of the boom elevation angle and the boom length, and is used to quantify the spatial distance between the device and the reinforcement cage. The collision radius calculation function refers to a mathematical model established based on the boom height, the lifting height and the boom lifting radius, for example, a minimum safety distance between the lifting device and the reinforcement cage is calculated by geometric projection relationship, which is used to dynamically evaluate the collision risk.

[0078] Subsequently, the maximum lifting radius of the equipment is matched based on the lifting weight, and the equipment with insufficient lifting capacity is excluded. Then, the three-dimensional coordinates of the boom intersection point are determined according to the construction coordinates, and the collision area is constructed combining the lifting radius and the lifting height, so as to convert the abstract safety distance into a specific spatial range. Finally, through the overlap detection of the plane intersection point and the collision area, it is judged whether the boom movement trajectory interferes with the reinforcement cage, and the dynamic collision prediction is realized. For example, when the plane intersection point is located inside the collision area, it indicates that the equipment has a collision risk and needs to be excluded; if all intersection points are located outside the collision area, it is determined that the equipment meets the safety requirements. The maximum lifting radius refers to the limit operation radius of the lifting equipment under a specific lifting weight, which can be obtained through the equipment performance curve or database matching, and is used for screening the equipment that meets the lifting capacity. The collision area refers to the cylindrical spatial range formed with the intersection point coordinates as the origin, the lifting radius as the radius and the lifting height as the axial direction, which can be generated through three-dimensional coordinate system modeling and is used for visualizing the potential interference area of the equipment movement trajectory. The plane intersection point refers to the intersection coordinates of the boom on the horizontal projection plane and the reinforcement cage contour, which can be obtained through two-dimensional geometric calculation or image recognition technology, and is used for judging the position relationship between the lifting path and the reinforcement cage.

[0079] In the embodiment, the above steps realize the accurate screening of the lifting equipment through multi-dimensional parameter fusion and dynamic analysis of the collision area. First, by obtaining the boom geometric parameters and the reinforcement cage lifting parameters, a spatial relationship model between the equipment and the reinforcement cage is established combining the collision radius calculation function, which solves the problem that the lifting radius cannot be quantified in traditional experience judgment. Second, based on the screening mechanism of matching the maximum lifting radius with the lifting weight, the equipment with insufficient lifting capacity is excluded in the equipment selection stage, ensuring the basic safety. Then, the three-dimensional collision area is constructed through the boom intersection point coordinates, which converts the abstract lifting radius into a specific spatial range, providing a visual judgment basis for subsequent collision detection. Finally, through the overlap detection of the plane intersection point and the collision area, the dynamic interference analysis of the equipment movement trajectory and the spatial position of the reinforcement cage is realized, improving the safety of lifting.

[0080] Step 102: Determine a plurality of lifting prediction points of the reinforcement cage to be lifted according to the preset construction coordinates, the boom information and the lifting operation information.

[0081] In this embodiment, the step mainly includes: obtaining the center rotation coordinates of the hoisting equipment based on the construction coordinates, and determining multiple hoisting points corresponding to the steel cage to be hoisted based on the center rotation coordinates and the boom length of the hoisting equipment; for any hoisting point: determining a first distance based on the center rotation coordinates, the boom length, the hoisting height, and the hoisting point; determining a second distance based on the hoisting point, the three-dimensional dimension information of the steel cage to be hoisted, the boom length, and the hoisting height; when the second distance is greater than the first distance, determining the hoisting point as the predicted hoisting point of the steel cage to be hoisted.

[0082] In some embodiments of this example, after determining the center rotation coordinates of the hoisting equipment using construction coordinates, multiple candidate hoisting points are generated on the horizontal plane based on the boom length. For each candidate hoisting point, a first distance from the end of the boom to that point is first calculated. This distance must be less than or equal to the boom length to ensure equipment accessibility. Subsequently, combining the three-dimensional dimensions of the reinforcing cage and the hoisting height, a second distance between that point and the edge of the reinforcing cage is calculated. This second distance must be greater than the first distance to prevent deformation or collision of the reinforcing cage due to insufficient space during hoisting. When the second distance exceeds the first distance, it indicates that the hoisting point meets the dual constraints of the equipment's operating range and the space occupied by the reinforcing cage, and is thus selected as a predicted hoisting point.

[0083] The central rotation coordinates refer to the coordinates of the rotation center of the hoisting equipment's boom, which can be determined using a GPS positioning system or a construction coordinate system to ensure that the hoisting point is within the operating range of the hoisting equipment. The first distance refers to the straight-line distance from the end of the hoisting equipment's boom to the hoisting point, which can be calculated using geometric projection and reflects the actual coverage area of ​​the hoisting equipment at that point. The second distance refers to the maximum horizontal distance between the hoisting point and the edge of the reinforcing cage, which can be calculated using trigonometric functions based on the three-dimensional dimensions of the reinforcing cage and the hoisting height, and represents the space requirement of the reinforcing cage at that hoisting point.

[0084] In the embodiment, the above steps establish a lifting point screening mechanism by combining the construction coordinates and the lifting equipment parameters, to solve the collision risk and structural deformation problem caused by unreasonable lifting point selection. Specifically, first, the initial lifting point is determined based on the center rotation coordinates of the lifting equipment and the length of the lifting arm, to ensure that the lifting point is within the operating range of the equipment; then, for each lifting point, the first distance reflecting the reachable range of the lifting equipment and the second distance reflecting the restriction of the size of the reinforcement cage and the lifting height on the space occupation are calculated, and the two are compared and screened. When the second distance is greater than the first distance, it indicates that the space occupation of the reinforcement cage under the lifting point exceeds the actual coverage range of the equipment, at which point the lifting point is marked as a lifting prediction point, thereby excluding the lifting points that may cause collision or exceed the carrying capacity of the equipment, and finally realizing the dual constraints of safety and feasibility of the lifting point and improving the safety of lifting.

[0085] Step 103: acquiring the lifting coordinates of the reinforcement cage to be lifted, and selecting a lifting operation point from the plurality of lifting prediction points according to the lifting coordinates, the lifting operation information and a preset stress deformation analysis model.

[0086] In the embodiment, this step mainly includes: acquiring the prediction point coordinates corresponding to each of the lifting prediction points according to the lifting coordinates of the reinforcement cage to be lifted; acquiring the lifting stress corresponding to each of the lifting prediction points according to the prediction point coordinates, the lifting weight and a pre-marked parameter constant; acquiring the horizontal deformation amount of the reinforcement cage corresponding to each of the lifting prediction points according to the lifting stress and a preset stress deformation analysis model; and selecting a lifting operation point from the plurality of lifting prediction points according to the horizontal deformation amount of the reinforcement cage and a preset deformation amount threshold.

[0087] In some embodiments of the embodiment, the spatial positions of the prediction points are acquired through the lifting coordinates, to provide a geometric data basis for subsequent mechanical analysis. The relative positional relationship between the lifting point and the center of gravity of the reinforcement cage is determined based on the prediction point coordinates, and the tension or pressure borne by each point is calculated in combination with the lifting weight. The pre-marked parameter constant is introduced to include the material properties in the calculation, to ensure the physical accuracy of the stress analysis. The stress deformation analysis model is used to simulate the structural response at different lifting points, to output the horizontal deformation amount data of the reinforcement cage. By comparing the deformation amount with the preset threshold, the lifting points with excessive deformation are automatically excluded, and the candidate positions that meet the structural safety requirements are retained. Thus, the optimal lifting operation point is selected from the spatial distribution and mechanical performance dimensions.

[0088] The lifting coordinates refer to the spatial position data of the reinforcement cage in the construction coordinate system, which can be obtained by using GPS positioning or laser scanning technology, and are used to determine the three-dimensional coordinates of the lifting prediction point. The lifting weight refers to the total mass data of the reinforcement cage, which can be obtained by using design drawing parameters or on-site weighing equipment, and is used to calculate the lifting stress. The pre-marked parameter constant includes physical property parameters such as material elastic modulus and cross-sectional moment of inertia, which can be matched based on a steel bar model and specification database, and is used to establish a mechanical calculation model. The stress deformation analysis model refers to a simulation model of a cantilever beam or a truss structure based on the theory of material mechanics, which can be realized by using a finite element analysis method, and is used to convert the stress data into a deformation prediction value. The deformation threshold refers to the maximum horizontal displacement limit value allowed by the reinforcement cage, which can be set according to engineering specifications or structural safety standards, and is used to screen the lifting points that meet the safety requirements.

[0089] In the embodiment, the above steps realize the safety screening of the lifting points by quantitatively analyzing the mechanical response of the lifting points, thereby improving the safety of lifting. First, the spatial positions of the prediction points are obtained by using the lifting coordinates, which provides basic data for subsequent mechanical analysis. The lifting stress is calculated based on the prediction point coordinates, lifting weight and pre-calibrated parameter constant, which combines the actual physical parameters with the theoretical model to ensure the accuracy of the stress calculation. Then, the stress deformation analysis model is used to convert the stress data into the horizontal deformation of the reinforcement cage, and the structural deformation of different lifting points is predicted by model simulation. Finally, the lifting operation points with deformation meeting the safety requirements are screened by comparing the deformation with the preset threshold, which not only avoids the errors of manual experience judgment, but also ensures the structural stability of the reinforcement cage during lifting. The introduction of the pre-marked parameter constant can adapt to the material properties of different engineering scenarios, and the setting of the deformation threshold is directly related to the structural safety standards, which improves the accuracy of evaluation.

[0090] Step 104: Obtain laser point cloud data and image data of the construction area to generate an environment model corresponding to the construction area according to the laser point cloud data and the image data.

[0091] In the embodiment, this step mainly includes: projecting the laser point cloud data of the construction area onto the image data to obtain first depth environment image data corresponding to the construction area; performing bilateral filtering on the first depth environment image data and the image data to obtain second depth environment image data; converting the second depth environment image data into point cloud depth image data, and inputting the image coordinates of the point cloud depth image data into a pre-constructed environment construction model to obtain an environment model corresponding to the construction area.

[0092] In some embodiments of the present embodiment, first, multi-modal data of the construction area is collected synchronously by laser radar and camera, laser point cloud data and image data are spatially registered, and first depth environment image data with fused depth and texture information is formed. Then, the fused data is processed by using a bilateral filtering algorithm, and by adjusting the standard deviation parameters of the spatial domain and the color domain, noise is suppressed while edge blurring is avoided. Subsequently, the filtered second depth environment image data is converted into point cloud format, and the point cloud data is three-dimensionally reconstructed by using a pre-trained environment construction model, and finally an environment model containing obstacle geometric shape, position and size parameters is output. The model optimizes the spatial perception accuracy through multi-source data complementation, and provides reliable environmental constraints for hoisting trajectory planning.

[0093] The laser point cloud data refers to a set of three-dimensional spatial coordinates of the construction area obtained by laser radar scanning, which can be implemented by using pulse laser ranging technology, and is used to construct high-precision spatial geometric information. The image data refers to two-dimensional visual information of the construction area collected by an optical camera, which can be implemented by using an RGB or grayscale image sensor, and is used to supplement texture and color features. The bilateral filtering refers to a nonlinear filtering method combining spatial proximity and pixel value similarity, which can be implemented by using a Gaussian kernel function to process image data, and is used to retain edge features while reducing noise. The environment construction model refers to a three-dimensional scene reconstruction algorithm based on deep learning, which can be implemented by using a convolutional neural network to extract features from point cloud depth images, and is used to generate a three-dimensional environment model containing obstacle distribution.

[0094] In the present embodiment, the above steps realize high-precision environment modeling through multi-source data fusion and optimization processing, and then use high-precision modeling to improve the safety of hoisting. By projecting and fusing laser point cloud data and image data, first depth environment image data with depth information is formed, solving the problem of missing data dimension of a single sensor. Then, the fused data is denoised by using bilateral filtering, retaining edge features while eliminating data noise and improving the signal-to-noise ratio of environment data. Finally, by converting point cloud depth images and using a pre-constructed environment construction model, two-dimensional image coordinates are converted into a three-dimensional space model, and an environment model containing obstacle spatial distribution features is constructed, which not only guarantees the geometric accuracy of the environment model, but also realizes digital reconstruction of the construction scene through modeling processing, providing reliable environmental constraints for subsequent hoisting trajectory planning and improving the safety of hoisting.

[0095] Step 105: Extracting the hoisting constraint conditions corresponding to the to-be-hoisted reinforcement cage from the hoisting operation information, to generate a plurality of predicted hoisting trajectories corresponding to the hoisting equipment according to the hoisting constraint conditions, the environment model and the hoisting operation point.

[0096] In the embodiment, the step mainly includes: processing the environment model to obtain a three-dimensional geometric space constraint representing a static obstacle space distribution of the construction area; obtaining design parameters and attribute parameters of the to-be-lifted reinforcement cage from the lifting operation information, so as to obtain a mechanical property constraint of the to-be-lifted reinforcement cage according to the design parameters and the attribute parameters; wherein the mechanical property constraint includes maximum stress and maximum deflection; constructing a configuration space containing the geometric space constraint and the mechanical property constraint, and performing parallel solving in the configuration space through a preset path planning algorithm, a predicted point coordinate and a construction coordinate, to obtain a plurality of predicted lifting trajectories.

[0097] In some embodiments of the present embodiment, the environment model generates three-dimensional space distribution data of the tower crane, the temporary support and the existing building after point cloud data processing, and the geometric constraint range of the static obstacle is determined through a boundary extraction algorithm. At the same time, the cross-sectional size, reinforcement density and material elastic modulus of the reinforcement cage are input into a mechanical analysis model to calculate the stress distribution and deflection change threshold under different lifting postures. After the geometric constraint and the mechanical constraint are mapped to the same configuration space, the path planning algorithm simultaneously considers the space obstacle avoidance requirement and the structural strength limitation in the space, and generates a candidate trajectory set through multi-objective optimization. For example, when the lifting trajectory causes the reinforcement cage to exceed the allowable value of the mid-span bending moment when passing around the obstacle, the algorithm will automatically adjust the lifting point position or the movement path to reduce the structural stress, so as to ensure that the generated trajectory meets the requirements of space safety and structural safety.

[0098] The three-dimensional geometric space constraint is a quantifiable avoidance area obtained by converting the spatial position and volume information of the fixed obstacle in the construction area into a three-dimensional modeling technology, which can be realized by laser scanning and image fusion technology, and is used to avoid physical collision between the lifting equipment and the surrounding structure during the trajectory planning stage. The mechanical property constraint is the maximum bearing capacity limit calculated based on the material strength and structural stiffness of the reinforcement cage, which can be realized by finite element analysis or material mechanics formula derivation, and is used to prevent plastic deformation or fracture of the reinforcement cage due to overloading during lifting. The configuration space is a multi-dimensional constraint space formed by integrating the geometric obstacle avoidance condition and the structural strength limitation, which can be realized by a space mapping algorithm to unify the constraint parameters of different dimensions to the same coordinate system to provide comprehensive constraint conditions for path planning. The path planning algorithm is a calculation method for generating a feasible movement trajectory under multi-dimensional constraint conditions, which can be realized by an improved A* algorithm or RRT algorithm, and is used to search the optimal path under the premise of meeting the dual constraints.

[0099] In the embodiment, the above steps achieve intelligent planning of a safe hoisting trajectory by establishing a configuration space with dual constraints of space and mechanics. Firstly, three-dimensional geometric space constraints are extracted by processing an environment model to accurately identify the spatial distribution characteristics of static obstacles such as tower cranes and buildings in the construction area, ensuring that the hoisting trajectory avoids all physical obstacles. Secondly, based on the design parameters and material attribute parameters of the reinforcement cage, the maximum stress and maximum deflection and other mechanical performance constraints are derived to prevent structural deformation or fracture caused by excessive stress during hoisting. The geometric constraints and mechanical constraints are fused to construct a multi-dimensional configuration space, breaking through the limitations of traditional path planning which only considers spatial obstacle avoidance. A path planning algorithm is used to perform parallel solving in the configuration space, which can simultaneously meet the requirements of spatial safety and structural safety. A candidate trajectory set that avoids obstacles and meets material strength requirements is generated through multi-objective optimization, improving the safety of hoisting.

[0100] Step 106: inputting the predicted hoisting trajectory and the device parameters of the hoisting device into a pre-constructed trajectory evaluation model to obtain a trajectory risk of each predicted hoisting trajectory, so as to select a real-time hoisting trajectory from the multiple predicted hoisting trajectories according to the trajectory risk.

[0101] In the embodiment, this step mainly includes: for any one of the predicted hoisting trajectories: inputting the predicted hoisting trajectory and the device parameters into a pre-constructed trajectory evaluation model to extract a time efficiency score, an energy consumption score and a risk score corresponding to the predicted hoisting trajectory through the trajectory evaluation model; obtaining a first weight of the time efficiency score, a second weight of the energy consumption score and a third weight of the risk score through a pre-set weight self-attention distribution mechanism in the trajectory evaluation model; outputting a trajectory score corresponding to the predicted hoisting trajectory through the trajectory evaluation model according to the time efficiency score, the energy consumption score, the risk score, the first weight, the second weight and the third weight; selecting a trajectory corresponding to the highest trajectory score from the multiple predicted hoisting trajectories as the real-time hoisting trajectory of the hoisting device.

[0102] In some embodiments of the present embodiment, in the hoisting trajectory optimization process, first, the coordinate sequence of the predicted trajectory and the device parameter are input into the trajectory evaluation model. The model extracts the spatial features of the trajectory through the convolution layer, and generates the basic scores of time efficiency, energy consumption and risk by using the fully connected layer to associate the device parameters. Subsequently, the weight self-attention distribution mechanism dynamically adjusts the weight proportion of each score through the attention weight calculation module based on the device power, the obstacle density of the construction area and the reinforcement cage weight parameters. For example, when the device power is lower than the preset threshold, the weight of the energy consumption score is automatically reduced to avoid the decline of the trajectory safety due to excessive restriction of energy consumption; when there are dense obstacles in the construction area, the weight of the risk score is increased to strengthen the obstacle avoidance ability. Finally, the model takes the weighted comprehensive score as the basis for trajectory optimization, and selects the trajectory with the highest score as the real-time hoisting trajectory.

[0103] The trajectory evaluation model refers to a mathematical model for quantitatively evaluating the comprehensive performance of the hoisting trajectory, which can be implemented by a regression model based on a neural network. By inputting the spatial coordinate sequence of the trajectory and the device parameters, it outputs multi-dimensional score indicators. The weight self-attention distribution mechanism refers to a calculation module that dynamically adjusts the score weights according to the device parameters and trajectory features. It can be implemented by a multi-head attention mechanism combined with a fully connected layer, which generates weight distribution coefficients by analyzing the correlation between device power, construction area complexity and trajectory shape. The time efficiency score refers to the quantitative evaluation of the operation time required for the hoisting trajectory, which can be calculated by the ratio of the trajectory length to the preset hoisting speed, and is used to measure the timeliness of the hoisting operation. The energy consumption score refers to the energy consumption estimate when the hoisting device executes the trajectory, which can be obtained by multiplying the motor power and the hoisting time and correcting it by the number of trajectory turns, and is used to evaluate the energy utilization efficiency. The risk score refers to the comprehensive evaluation of the safety of the trajectory, which can be output by a classification model trained by the minimum distance between the trajectory and the obstacle, the hoisting posture stability parameters and historical accident data, and is used to identify potential collision and structural failure risks.

[0104] In the present embodiment, the above steps achieve intelligent optimization of hoisting trajectories through a multi-dimensional dynamic weight distribution mechanism. By inputting the predicted trajectory and device parameters into the evaluation model, the time efficiency, energy consumption and risk scores are extracted, breaking through the limitations of traditional single indicator evaluation. By dynamically adjusting the weights of each indicator through the weight self-attention distribution mechanism, the problem of fixed weights not being able to adapt to different construction scenarios is solved. By selecting the optimal trajectory based on the weighted score results distributed by the self-attention mechanism, subjective bias of manual experience is avoided, and the comprehensive balance of efficiency, energy consumption and safety is achieved through the quantitative evaluation model, improving the safety of hoisting.

[0105] Step 107: controlling the hoisting equipment to perform the hoisting operation of the to-be-hoisted reinforcement cage according to the real-time hoisting trajectory and collecting hoisting data of the to-be-hoisted reinforcement cage in real time, so as to correct hoisting deviation according to the hoisting data and complete the hoisting operation.

[0106] In this embodiment, this step mainly includes: collecting inclination data, stress data and environmental data of the to-be-hoisted reinforcement cage in the hoisting process in real time to obtain a hoisting posture of the to-be-hoisted reinforcement cage; comparing the hoisting posture with an expected state in the real-time hoisting trajectory to calculate a pose deviation and a load deviation of the to-be-hoisted reinforcement cage, and generating adjustment equipment parameters of the hoisting equipment according to the pose deviation and the load deviation; controlling the hoisting equipment to correct the deviation according to the adjustment equipment parameters, and completing the hoisting operation of the to-be-hoisted reinforcement cage according to the hoisting equipment after correction.

[0107] In some embodiments of this embodiment, during hoisting, the inclination, stress and surrounding environmental data of the reinforcement cage are synchronously collected by a multi-source sensor to construct a real-time updated hoisting posture model. The model is matched with a preset expected trajectory in a digital twin system, and a coordinate transformation algorithm is used to convert the actual measurement value into a parameter in the same coordinate system as the expected trajectory. When the pose deviation is detected to exceed a preset threshold, a deviation correction mechanism is triggered, and the motion compensation amount of the hoisting equipment is calculated by a mechanical model inversion. For example, if the reinforcement cage deviates in the horizontal direction, the system will generate an adjustment instruction of the rotation angular velocity of the hoisting arm, so that the hoisting equipment gradually eliminates the deviation in a dynamic adjustment manner. At the same time, the load deviation data is used to correct the output power of the hoisting motor to avoid overloading and rupture of the steel wire rope due to sudden load change.

[0108] The inclination data refers to the inclination angle of the reinforcement cage in the three-dimensional space, which can be collected in real time by a gyroscope or an inertial measurement unit, and is used to monitor whether the reinforcement cage deviates unexpectedly. The stress data refers to the stress state of the key nodes of the reinforcement cage, which can be obtained by a strain sensor or a distributed optical fiber sensor, and is used to determine whether the structure exceeds the safe bearing range. The environmental data includes wind speed and obstacle distance information, which can be collected in real time by a laser radar or an ultrasonic sensor, and is used to evaluate external interference factors. The pose deviation refers to the deviation of the actual position of the reinforcement cage from the target trajectory, which can be calculated by a coordinate conversion algorithm to quantify the hoisting deviation. The load deviation refers to the difference between the actual load of the hoisting equipment and the theoretical value, which can be measured by a pressure sensor to measure the change of the tension of the steel wire rope, and is used to judge the overload risk. The adjustment equipment parameters include the correction amount of the arm angle, the hoisting speed and the rotation angular velocity, which can be generated by a PID control algorithm, and are used to dynamically adjust the motion state of the hoisting equipment.

[0109] In the embodiment, the above steps are realized by dynamic closed-loop control to accurately regulate the lifting process. By collecting real-time multi-dimensional data of inclination, stress and environment, a real-time digital mapping of the lifting posture is constructed, breaking through the lagging defect of traditional manual observation. The actual lifting posture is compared with the expected trajectory generated by the model, and the lifting deviation is accurately quantified through the coupling calculation of the pose deviation and the load deviation. The device adjustment parameters generated based on the deviation data can dynamically correct the key control variables such as the lifting arm angle and the lifting speed, effectively suppress the cumulative error in the lifting process, ensure the stable controlled motion trajectory of the reinforcement cage in the complex construction environment, and improve the safety of lifting.

[0110] On the other hand, with reference to Figure 2 The embodiment also discloses a reinforcement cage lifting control system based on digital twinning, mainly comprising a device optimization module 201, a lifting point selection module 202, a work point selection module 203, an environment recognition module 204, a trajectory prediction module 205, a trajectory optimization module 206 and a lifting control module 207.

[0111] The device optimization module 201 is used to calculate a lifting radius according to the lifting arm information of the lifting device to be selected and the lifting work information of the reinforcement cage to be lifted, and perform collision analysis according to the lifting radius, so as to select the lifting device from a plurality of lifting devices to be selected.

[0112] The lifting point selection module 202 is used to determine a plurality of lifting prediction points of the reinforcement cage to be lifted according to a preset construction coordinate, the lifting arm information and the lifting work information.

[0113] The work point selection module 203 is used to obtain lifting coordinates of the reinforcement cage to be lifted, and select a lifting work point from a plurality of the lifting prediction points according to the lifting coordinates, the lifting work information and a preset stress deformation analysis model.

[0114] The environment recognition module 204 is used to obtain laser point cloud data and image data of a construction area, and generate an environment model corresponding to the construction area according to the laser point cloud data and the image data.

[0115] The trajectory prediction module 205 is used to extract a lifting constraint condition corresponding to the reinforcement cage to be lifted from the lifting work information, and generate a plurality of predicted lifting trajectories corresponding to the lifting device according to the lifting constraint condition, the environment model and the lifting work point.

[0116] The trajectory optimization module 206 is used to input the predicted lifting trajectory and the device parameters of the lifting device into a pre-constructed trajectory evaluation model to obtain a trajectory risk of each of the predicted lifting trajectories, and select a real-time lifting trajectory from a plurality of the predicted lifting trajectories according to the trajectory risk.

[0117] The hoisting control module 207 is configured to control the hoisting equipment to perform the hoisting operation of the to-be-hoisted reinforcement cage according to the real-time hoisting trajectory and collect hoisting data of the to-be-hoisted reinforcement cage in real time, correct hoisting deviation according to the hoisting data, and complete the hoisting operation.

[0118] In this embodiment, the device selection module 201 includes a radius identification unit, a collision warning unit, and a device selection unit.

[0119] The radius identification unit is configured to obtain a length, a height, and a hoisting radius of a hoisting arm of the to-be-selected hoisting equipment, and extract a hoisting height and a hoisting weight of the to-be-hoisted reinforcement cage according to the hoisting operation information; and obtain a hoisting radius between the to-be-selected hoisting equipment and the to-be-hoisted reinforcement cage by a preset collision radius calculation function according to the height of the hoisting arm, the hoisting radius of the hoisting arm, and the hoisting height.

[0120] The collision warning unit is configured to match a maximum lifting radius corresponding to the to-be-selected hoisting equipment according to the hoisting weight, and when the hoisting radius is less than or equal to the maximum lifting radius, obtain coordinates of a joint point of a hoisting arm and a device main body in the to-be-selected hoisting equipment according to a preset construction coordinate; and determine a collision area corresponding to the to-be-selected hoisting equipment according to the joint point coordinates, the hoisting radius, and the hoisting height.

[0121] The device selection unit is configured to obtain a planar intersection point of the hoisting arm and the to-be-hoisted reinforcement cage, and select a hoisting equipment from a plurality of to-be-selected hoisting equipments according to a coincidence detection result of the planar intersection point and the collision area.

[0122] The steel reinforcement cage hoisting control method and system based on digital twinning disclosed in this embodiment realize intelligent control of the hoisting operation by constructing a virtual-real mapping of the construction environment and the hoisting process through the digital twinning technology. The hoisting equipment is selected by calculating the hoisting radius and combining the collision analysis, which avoids the collision risk that may exist when the equipment is selected manually. The hoisting prediction point is determined based on the construction coordinate and the hoisting operation information, and the hoisting operation point meeting the mechanical properties is selected by combining the stress deformation analysis model, which ensures the structural stability of the reinforcement cage in the hoisting process. The environment model is generated by using the laser point cloud and the image data, which provides accurate three-dimensional space constraints for subsequent trajectory planning. A plurality of predicted hoisting trajectories are generated by the hoisting constraint condition and the environment model, the risk is quantified based on the trajectory evaluation model, the optimal trajectory is selected, and the probability of operation failure is reduced. Finally, the hoisting process is dynamically adjusted through real-time data collection and deviation correction, which improves the hoisting precision and safety.

[0123] The above-described specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above-described specific embodiments are merely examples of the present application and are not intended to limit the protection scope of the present application. It is particularly pointed out that any modification, equivalent replacement, improvement, etc. made by those skilled in the art within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A steel cage hoisting control method based on digital twinning, characterized in that, include: The lifting radius is calculated based on the boom information of the lifting equipment to be selected and the lifting operation information of the steel cage to be lifted. Collision analysis is then performed based on the lifting radius to select the lifting equipment from multiple options. Multiple lifting prediction points for the steel cage to be lifted are determined based on the preset construction coordinates, the boom information, and the lifting operation information. Obtain the lifting coordinates of the steel cage to be lifted, and select the lifting operation point from multiple lifting prediction points based on the lifting coordinates, the lifting operation information, and the preset stress deformation analysis model; Acquire laser point cloud data and image data of the construction area, and generate an environmental model corresponding to the construction area based on the laser point cloud data and the image data; Extract the hoisting constraints corresponding to the steel cage to be hoisted from the hoisting operation information, and generate multiple predicted hoisting trajectories corresponding to the hoisting equipment based on the hoisting constraints, the environment model and the hoisting operation point; The predicted hoisting trajectory and the equipment parameters of the hoisting equipment are input into a pre-built trajectory evaluation model to obtain the trajectory risk of each predicted hoisting trajectory, so as to select a real-time hoisting trajectory from multiple predicted hoisting trajectories based on the trajectory risk; The hoisting equipment is controlled to perform the hoisting operation of the steel cage to be hoisted according to the real-time hoisting trajectory and the hoisting data of the steel cage to be hoisted is collected in real time so as to correct the hoisting deviation according to the hoisting data and complete the hoisting operation. The process of calculating the lifting radius based on the boom information of the lifting equipment to be selected and the lifting operation information of the steel cage to be lifted, and performing collision analysis based on the lifting radius to select lifting equipment from multiple options, includes: Obtain the boom length, boom height, and boom lifting radius of the lifting equipment to be selected, and extract the lifting height and lifting weight of the steel cage to be lifted based on the lifting operation information; Based on the boom height, the boom lifting radius, and the lifting height, the lifting radius between the selected lifting equipment and the steel cage to be lifted is obtained through a preset collision radius calculation function; The maximum lifting radius of the selected lifting equipment is matched according to the lifting weight. When the lifting radius is less than or equal to the maximum lifting radius, the coordinates of the intersection point between the boom and the main body of the lifting equipment are obtained according to the preset construction coordinates. The collision area corresponding to the selected hoisting equipment is determined based on the coordinates of the intersection point, the hoisting radius, and the hoisting height. Obtain the planar intersection point between the boom and the steel cage to be lifted, and select a lifting device from a plurality of lifting devices to be selected based on the overlap detection result of the planar intersection point and the collision area; The step of determining multiple predicted lifting points for the steel cage to be lifted based on preset construction coordinates, boom information, and lifting operation information includes: The center rotation coordinates of the hoisting equipment are obtained based on the construction coordinates, and multiple hoisting points corresponding to the steel cage to be hoisted are determined based on the center rotation coordinates and the boom length of the hoisting equipment. For any one of the aforementioned lifting points: determine a first distance according to the central rotation coordinates, the boom length, the lifting height, and the lifting point; determine a second distance according to the lifting point, the three-dimensional size information of the to-be-lifted reinforcement cage, the boom length, and the lifting height; when the second distance is greater than the first distance, determine the lifting point as a lifting prediction point of the to-be-lifted reinforcement cage; the obtaining of the lifting coordinates of the to-be-lifted reinforcement cage and the selection of a lifting operation point from multiple lifting prediction points according to the lifting coordinates, the lifting operation information, and a preset stress deformation analysis model, comprises: obtaining a prediction point coordinate corresponding to each lifting prediction point according to the lifting coordinates of the to-be-lifted reinforcement cage; obtaining a lifting stress corresponding to each lifting prediction point according to the prediction point coordinate, the lifting weight, and a pre-marked parameter constant; obtaining a reinforcement cage horizontal deformation amount corresponding to each lifting prediction point according to the lifting stress and a preset stress deformation analysis model; selecting a lifting operation point from multiple lifting prediction points according to the reinforcement cage horizontal deformation amount and a preset deformation amount threshold.

2. The steel bar cage hoisting control method based on digital twinning according to claim 1, characterized in that, the obtaining of laser point cloud data and image data of a construction area, and the generation of an environment model corresponding to the construction area according to the laser point cloud data and the image data, comprises: projecting the laser point cloud data of the construction area onto the image data to obtain first depth environment image data corresponding to the construction area; performing bilateral filtering on the first depth environment image data and the image data to obtain second depth environment image data; converting the second depth environment image data into point cloud depth image data, and inputting image coordinates of the point cloud depth image data into a pre-constructed environment construction model to obtain the environment model corresponding to the construction area.

3. The steel bar cage hoisting control method based on digital twinning according to claim 2, characterized in that, the extraction of a lifting constraint condition corresponding to the to-be-lifted reinforcement cage from the lifting operation information, and the generation of multiple predicted lifting trajectories corresponding to the lifting equipment according to the lifting constraint condition, the environment model, and the lifting operation point, comprises: processing the environment model to obtain a three-dimensional geometric space constraint representing a static obstacle space distribution of the construction area; obtaining a design parameter and an attribute parameter of the to-be-lifted reinforcement cage from the lifting operation information to obtain a mechanical property constraint of the to-be-lifted reinforcement cage according to the design parameter and the attribute parameter; wherein the mechanical property constraint comprises a maximum stress and a maximum deflection; constructing a configuration space containing the geometric space constraint and the mechanical property constraint, and performing parallel solving in the configuration space through a preset path planning algorithm, a prediction point coordinate, and a construction coordinate to obtain multiple predicted lifting trajectories.

4. The steel bar cage hoisting control method based on digital twinning according to claim 3, characterized in that, the inputting of the predicted lifting trajectory and a device parameter of the lifting equipment into a pre-constructed trajectory evaluation model to obtain a trajectory risk of each predicted lifting trajectory, and the selection of a real-time lifting trajectory from multiple predicted lifting trajectories according to the trajectory risk, comprises: for any one of the predicted lifting trajectories: inputting the predicted hoisting trajectory and the equipment parameter into a pre-constructed trajectory evaluation model to extract a time efficiency score, an energy consumption score and a risk score corresponding to the predicted hoisting trajectory through the trajectory evaluation model; obtaining a first weight of the time efficiency score, a second weight of the energy consumption score and a third weight of the risk score through a preset weight self-attention distribution mechanism in the trajectory evaluation model; outputting a trajectory score corresponding to the predicted hoisting trajectory through the trajectory evaluation model according to the time efficiency score, the energy consumption score, the risk score, the first weight, the second weight and the third weight; selecting a trajectory corresponding to the highest trajectory score from the multiple predicted hoisting trajectories as a real-time hoisting trajectory of the hoisting equipment.

5. The steel bar cage hoisting control method based on digital twinning according to claim 4, characterized in that, controlling the hoisting equipment to perform the hoisting operation of the to-be-hoisted reinforcement cage according to the real-time hoisting trajectory and collecting hoisting data of the to-be-hoisted reinforcement cage in real time to correct hoisting deviation according to the hoisting data and complete the hoisting operation, comprising: collecting inclination data, stress data and environmental data of the to-be-hoisted reinforcement cage in the hoisting process in real time to obtain a hoisting posture of the to-be-hoisted reinforcement cage; comparing the hoisting posture with an expected state in the real-time hoisting trajectory to calculate a pose deviation and a load deviation of the to-be-hoisted reinforcement cage, and generating adjustment equipment parameters of the hoisting equipment according to the pose deviation and the load deviation; controlling the hoisting equipment to correct the deviation according to the adjustment equipment parameters, and completing the hoisting operation of the to-be-hoisted reinforcement cage according to the hoisting equipment after correction.

6. A digital-twin-based reinforcement cage hoisting control system for implementing the digital-twin-based reinforcement cage hoisting control method according to claim 1, characterized by, comprising an equipment optimization module, a hoisting point selection module, a job point selection module, an environment recognition module, a trajectory prediction module, a trajectory optimization module and a hoisting control module; the equipment optimization module is used to calculate a hoisting radius according to hoisting arm information of a to-be-selected hoisting equipment and hoisting operation information of a to-be-hoisted reinforcement cage, and select a hoisting equipment from multiple to-be-selected hoisting equipments according to the hoisting radius; the hoisting point selection module is used to determine multiple hoisting prediction points of the to-be-hoisted reinforcement cage according to a preset construction coordinate, the hoisting arm information and the hoisting operation information; the job point selection module is used to obtain hoisting coordinates of the to-be-hoisted reinforcement cage, and select a hoisting operation point from the multiple hoisting prediction points according to the hoisting coordinates, the hoisting operation information and a preset stress deformation analysis model; the environment recognition module is used to obtain laser point cloud data and image data of a construction area, and generate an environment model corresponding to the construction area according to the laser point cloud data and the image data; the trajectory prediction module is used to extract hoisting constraint conditions corresponding to the to-be-hoisted reinforcement cage from the hoisting operation information, and generate multiple predicted hoisting trajectories corresponding to the hoisting equipment according to the hoisting constraint conditions, the environment model and the hoisting operation point; The trajectory optimization module is configured to input the predicted hoisting trajectories and device parameters of the hoisting device into a pre-constructed trajectory evaluation model to obtain trajectory risks of each of the predicted hoisting trajectories, and select a real-time hoisting trajectory from the predicted hoisting trajectories according to the trajectory risks. The hoisting control module is configured to control the hoisting device to perform hoisting of the to-be-hoisted reinforcement cage according to the real-time hoisting trajectory, and collect hoisting data of the to-be-hoisted reinforcement cage in real time, so as to correct hoisting deviation according to the hoisting data and complete the hoisting operation.

7. The steel cage hoisting control system based on digital twinning of claim 6, wherein, The device optimization module comprises a radius identification unit, a collision warning unit and a device selection unit. The radius identification unit is configured to obtain a length, a height and a hoisting radius of a hoisting arm of the to-be-selected hoisting device, and extract a hoisting height and a hoisting weight of the to-be-hoisted reinforcement cage according to the hoisting operation information; and obtain a hoisting radius between the to-be-selected hoisting device and the to-be-hoisted reinforcement cage according to the hoisting arm height, the hoisting arm hoisting radius and the hoisting height by using a pre-set collision radius calculation function. The collision warning unit is configured to match a maximum lifting radius corresponding to the to-be-selected hoisting device according to the hoisting weight, and obtain coordinates of a joint point of a hoisting arm and a hoisting device main body in the to-be-selected hoisting device according to pre-set construction coordinates when the hoisting radius is less than or equal to the maximum lifting radius; and determine a collision area corresponding to the to-be-selected hoisting device according to the joint point coordinates, the hoisting radius and the hoisting height. The device selection unit is configured to obtain a planar intersection point of the hoisting arm and the to-be-hoisted reinforcement cage, and select a hoisting device from the to-be-selected hoisting devices according to a coincidence detection result of the planar intersection point and the collision area.

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