Hoisting posture prediction method and device, electronic equipment and storage medium

Abstract

The application discloses a hoisting posture prediction method and device, electronic equipment and a storage medium, relates to the technical field of hoisting posture and the like, and the hoisting posture prediction method comprises the following steps: constructing a three-dimensional model of a target object based on region division of the target object; extracting a key mode based on the three-dimensional model and constructing a posture reduction model; obtaining a gravity parameter based on a current hoisting path; and solving the posture reduction model based on the gravity parameter and a first constraint condition to obtain a hoisting prediction posture. The hoisting posture prediction method adaptively establishes the three-dimensional model of the target object with consideration of accuracy and calculation amount through region division of the target object, and the posture in the hoisting process is predicted in real time through solving of the posture reduction model, so that real-time risk early warning and active protection are realized.

Description

Technical Field

[0001] This application relates to the technical field of hoisting attitude, and in particular to a hoisting attitude prediction method, device, electronic device and storage medium. Background Technology

[0002] For attitude monitoring during the hoisting of large, irregular three-dimensional structural components, such as the vacuum chamber sector of a tokamak nuclear fusion device, the following challenges arise. These components are characterized by their large mass, significant center of gravity offset, and susceptibility to shape errors during processing and transportation. During final assembly and hoisting, they typically undergo multiple attitude transformations, including spatial rotation, attitude adjustment, and multi-point hoisting coupling. The evolution of their gravitational attitude directly impacts assembly accuracy and construction safety.

[0003] However, in related technologies, the evaluation of the hoisting attitude of such large components mainly relies on a combination of static finite element simulation and empirical verification. Although the finite element method can obtain high-precision deformation and stress prediction results, it has the following disadvantages: (1) The full-fidelity model is huge and computationally expensive: The geometry of large components is complex, and all lifting tools, connectors, temporary supports, etc. need to be modeled, with the number of elements often reaching tens of millions; the calculation of contact and large displacement seriously affects the stability and real-time performance of the solution. (2) Traditional models cannot adapt to the needs of continuous attitude changes: During the hoisting process, the direction of gravity, load transfer path and contact state change dynamically, and the static modal reduced-order model cannot guarantee the accuracy of continuous prediction of multiple attitudes. (3) The center of gravity and rotational inertia change with the adjustment of the hoisting point, but there is a lack of real-time update mechanism: Existing simulations usually fix the inertia parameters, and cannot predict the instability torsion or dangerous attitude that may occur in the hoisting path. (4) There is a lack of effective closed loop between on-site measurement information and simulation: Although local deformation or attitude information can be obtained through laser trackers during construction, there is a lack of mechanism to feed the measurement data back to the simulation model for correction in real time. (5) Safety control and simulation are disconnected: The system is usually only used for pre-construction analysis and cannot drive control strategies such as lifting point leveling and collision avoidance in real time, making it difficult to achieve dynamic risk warning. Summary of the Invention

[0004] Therefore, the purpose of this application is to propose a lifting attitude prediction method, device, electronic device and storage medium. By dividing the target object into regions, a three-dimensional model of the target object is adaptively established, taking into account both accuracy and computational load. By solving the attitude reduction model, the attitude during the lifting process is predicted in real time, thereby realizing real-time risk warning and active protection.

[0005] This application provides a method for predicting hoisting attitude, the method comprising: constructing a three-dimensional model of the target object based on the region division of the target object; extracting key modes based on the three-dimensional model and constructing a reduced-order attitude model; obtaining gravity parameters based on the current hoisting path; and solving the reduced-order attitude model based on the gravity parameters and a first constraint condition to obtain the predicted hoisting attitude.

[0006] For example, the step of constructing a three-dimensional model of the target object based on the region division of the target object includes: constructing finite element mass elements for the key regions of the target object; constructing virtual mass elements for the non-key regions of the target object; and constructing a three-dimensional model of the target object based on the finite element elements and the virtual mass elements.

[0007] For example, the attitude reduction model includes a structural displacement field, and the step of extracting key modes and constructing the attitude reduction model based on the three-dimensional model includes: extracting key modes based on key regions of the three-dimensional model; and constructing the structural displacement field based on the product of the modal coefficients and the key modes.

[0008] For example, the method further includes: obtaining the current pose; and weighting the modal coefficients or the key modes based on the current pose and a preset weight change function.

[0009] For example, the method further includes: obtaining the moment of inertia tensor relative to the center of gravity based on the current hoisting path; determining the angular acceleration vector based on the gravity parameters and the moment of inertia tensor; and determining the attitude rollover or instability based on the angular acceleration vector.

[0010] For example, the current hoisting path includes the current hoisting point position, the gravity parameter includes the gravitational moment, and the step of obtaining the gravity parameter based on the current hoisting path includes: determining a first difference between the current hoisting point position and the coordinates of the center of gravity; determining a first product between the total mass and the gravitational acceleration; and obtaining the gravitational moment based on the vector cross product between the first difference and the first product.

[0011] For example, the moment of inertia tensor is obtained by the following formula:

[0012]

[0013] in, This represents the rotational inertia tensor. Indicates the first The mass of a mass unit, where n represents the number of mass units. Indicates the first The distance between the center of mass and the center of gravity of each mass unit It is the identity matrix. For the first The outer product matrix of the coordinate difference vectors between the centroid and the center of mass of each mass unit.

[0014] For example, the gravity parameter includes gravitational torque, and the step of determining the angular acceleration vector based on the gravity parameter and the moment of inertia tensor includes: determining the product between the inverse tensor of the moment of inertia tensor and the gravitational torque as the angular acceleration vector.

[0015] For example, determining the attitude flip or instability based on the angular acceleration vector includes: determining that there is an attitude flip or instability when the angular acceleration vector is greater than an angular acceleration threshold.

[0016] For example, the hoisting predicted attitude includes a predicted displacement field. The step of solving the attitude reduction model based on the gravity parameters and the first constraint to obtain the hoisting predicted attitude includes: constructing a structural equilibrium equation based on the gravity parameters and the first constraint, wherein the structural equilibrium equation characterizes the relationship between the structural displacement field and the equivalent gravity load; and solving the attitude reduction model based on the structural equilibrium equation to obtain the predicted displacement field.

[0017] For example, the method further includes: determining the stress distribution in the key region based on the predicted displacement field; and making index judgments based on the predicted displacement field, the stress distribution, the current attitude offset angle, and the local deformation.

[0018] For example, the method further includes: determining a spreader adjustment strategy based on the current attitude offset angle, the local deformation, and the angular acceleration vector.

[0019] For example, the method further includes: acquiring the actual hoisting posture; adjusting the parameters of the posture reduction model based on the difference between the actual hoisting posture and the predicted hoisting posture; wherein, adjusting the parameters of the posture reduction model includes at least one of adjusting the virtual mass units in non-critical areas and adjusting the stiffness parameters of the posture reduction model.

[0020] For example, the method further includes updating the critical region and the non-critical region based on the influence factor of the quality unit.

[0021] For example, the influence factor includes a shape sensitivity index, and the update of the critical region and the non-critical region based on the influence factor of the mass unit includes: calculating the shape sensitivity index of each mass unit; determining the mass unit whose shape sensitivity index is greater than or equal to a preset sensitivity threshold as the critical region, and determining the mass unit whose shape sensitivity index is less than the preset sensitivity threshold as the non-critical region.

[0022] For example, the influence factor includes the energy contribution ratio, and the updating of the critical region and the non-critical region based on the influence factor of the quality unit includes: calculating the energy contribution ratio of each quality unit; determining the quality unit whose energy contribution ratio is greater than or equal to a preset contribution ratio as the critical region, and determining the quality unit whose energy contribution ratio is less than the preset contribution ratio as the non-critical region.

[0023] For example, the method further includes: constructing a multi-suspension point force model; and solving the multi-suspension point force model based on a second constraint condition with the goal of minimizing the angular acceleration vector to obtain the target load of the suspension point.

[0024] For example, the method further includes: determining the length adjustment amount of the lifting point based on the target load of the lifting point; and adjusting the length of the lifting point based on the length adjustment amount.

[0025] For example, the three-dimensional model of the target object includes the total mass and centroid coordinates of the target object. The step of constructing the three-dimensional model of the target object based on the finite element mass unit and the virtual mass unit includes: summing the masses corresponding to the finite element mass unit and the virtual mass unit to obtain the total mass of the target object; determining the first product between the mass and the spatial coordinates of the mass point corresponding to each mass unit; and obtaining the centroid coordinates based on the ratio between the first product and the total mass of the target object.

[0026] For example, the method further includes: dividing the region of the target object into states based on the current attitude offset angle, the safety factor of the local deformation, the angular acceleration vector and the interference gap; wherein the safety factor of the local deformation includes the ratio between the local deformation and the maximum allowable deformation.

[0027] Another embodiment of this application provides a hoisting attitude prediction device, the device comprising: a first construction module for constructing a three-dimensional model of the target object based on the region division of the target object; a second construction module for extracting key modes and constructing a reduced-order attitude model based on the three-dimensional model; an acquisition module for acquiring gravity parameters based on the current hoisting path; and a solution module for solving the reduced-order attitude model based on the gravity parameters and a first constraint condition to obtain the predicted hoisting attitude.

[0028] Another embodiment of this application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described hoisting attitude prediction method.

[0029] Another embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described hoisting attitude prediction method.

[0030] In the above embodiments, the hoisting attitude prediction method includes: constructing a three-dimensional model of the target object based on the region division of the target object; extracting key modes based on the three-dimensional model and constructing a reduced-order attitude model; obtaining gravity parameters based on the current hoisting path; and solving the reduced-order attitude model based on the gravity parameters and the first constraint condition to obtain the predicted hoisting attitude. The hoisting attitude prediction method of this invention adaptively establishes a three-dimensional model of the target object that balances accuracy and computational complexity by dividing the target object into regions, and by solving the reduced-order attitude model, it performs real-time prediction of the attitude during hoisting, achieving real-time risk warning and proactive protection. Attached Figure Description

[0031] Figure 1 A flowchart illustrating the hoisting attitude prediction method provided in this application's embodiments;

[0032] Figure 2 A flowchart for constructing a three-dimensional model of the target object provided in this application embodiment;

[0033] Figure 3 A flowchart for calculating the total mass and center-of-gravity coordinates of a target object, provided for an embodiment of this application;

[0034] Figure 4 A flowchart for extracting key modes and constructing an attitude reduction model provided in this application embodiment;

[0035] Figure 5 A flowchart for adaptive adjustment of modal coefficients or key modes provided for embodiments of this application;

[0036] Figure 6 A flowchart for obtaining gravity parameters provided in this application embodiment;

[0037] Figure 7 A flowchart for calculating the angular acceleration vector provided in this application embodiment;

[0038] Figure 8 A flowchart for obtaining the predicted hoisting posture is provided for an embodiment of this application;

[0039] Figure 9 A flowchart for index judgment provided for the implementation of this application;

[0040] Figure 10 A flowchart illustrating the adjustment of parameters in an attitude reduction model provided for an embodiment of this application;

[0041] Figure 11 Flowcharts for updating critical and non-critical regions provided for embodiments of this application;

[0042] Figure 12 A flowchart illustrating the updating of critical and non-critical regions provided in another embodiment of this application;

[0043] Figure 13 A flowchart illustrating the optimized force distribution of the lifting device provided in this application embodiment;

[0044] Figure 14 A flowchart of a method for rapid prediction and solution of Sector hoisting gravity attitude combined with virtual mass method provided for the implementation of this application;

[0045] Figure 15 A schematic diagram of the hoisting attitude prediction device provided in the embodiments of this application;

[0046] Figure 16 A block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0047] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0048] The hoisting attitude prediction method of this application can be applied to the hoisting attitude prediction of large nuclear fusion device components, such as the attitude prediction of ultra-large, heterogeneous, and center-of-gravity-offset structures like vacuum chamber sectors under complex hoisting paths. Of course, the target application of the hoisting attitude prediction method of this application is not limited to vacuum chamber sectors; it can be used in multiple fields, such as real-time attitude prediction and risk control during the assembly of heavy engineering equipment, including tokamak main unit assembly, nuclear power equipment hoisting, and attitude adjustment of large aerospace components.

[0049] In some examples, various types of models can be used for modeling during the hoisting or significant attitude changes of large and complex structures, such as simulation, analysis, digital twins, and reduced-order models. For instance, a full-order finite element model combined with static / quasi-static finite element analysis can be used, employing the Finite Element Method (FEM) to perform full-geometric, full-mesh discretization modeling of the structure, including hoisting equipment, connectors, temporary supports, and the complex geometry and material distribution of the components themselves. Load states during the hoisting process (such as gravity, hoisting point reactions, constraints, and contact) are applied, and static or quasi-static analyses are performed to obtain the stress, deformation, and displacement responses of the components. Many engineering structural strength and safety checks rely on this method. However, this method has the following drawbacks: ① Large computational scale: For geometrically complex and large-sized components (such as vacuum chamber sectors), the number of degrees of freedom in the full-mesh model is extremely high, potentially reaching millions or even tens of millions, resulting in enormous computational resource and time consumption. ② Difficulty in real-time and rapid response: Due to the large amount of solution, nonlinear contact, multi-point lifting tools / constraints, and complex working conditions such as large displacement / rotation, full-order FEM is difficult to meet the needs of "real-time" or "near real-time" attitude prediction and safety verification during the lifting process. ③ Lack of dynamic attitude adaptability: Typical static analysis assumes that the lifting attitude and load state are fixed, which is not suitable for continuous prediction and control after repeated adjustments to lifting points, attitude, and path changes.

[0050] In some examples, a rigid-body / simplified dynamic model can also be used. For large structures, a rigid body model is applied, ignoring elastic deformation and treating the structure as a rigid body. Only macroscopic parameters such as mass, center of gravity, and moment of inertia are considered for rapid kinematic / dynamic analysis (e.g., hoisting, rotation, and transport path planning). This method is similar to rigid body dynamics simulation. However, this method has the following drawbacks: ① It ignores structural deformation and stress distribution: The rigid body model cannot reflect the elastic deformation, flexible response, local bending / torsion, stress concentration, etc., of the component itself, making it insufficient for hoisting operations requiring strength verification or deformation limitations. ② The accuracy is insufficient to guarantee assembly safety / positional accuracy: For high-precision alignment requirements such as butt joints, mating surfaces, and sealing surfaces, the rigid body model struggles to predict minute elastic deformations or tilt deviations caused by gravity, thus failing to meet strict alignment requirements. ③ It is difficult to capture complex behaviors such as contact, friction, and support deformation. Especially in cases involving complex lifting equipment, contact interfaces, lifting point distribution, and temporary supports, rigid body models are overly simplified and lack the ability to reflect the actual force-deformation coupling.

[0051] In some examples, a reduced-order model or a hybrid "Stick / FE" model (multi-fidelity / multi-resolution model) can be used, combining a lightweight (stick / rod / simplified rod-beam / skeleton) model with a full-order FE model. FE is still used to describe stress / deformation sensitive areas of the structure, while simplified rod / member / "stick" models are used for non-critical areas, achieving a balance between overall and local considerations. This multifidelity or hybrid model method improves computational efficiency by using simplified models while retaining detailed analysis of critical areas, resulting in faster responses in some lightly loaded / dynamic conditions. However, this method has the following drawbacks: ① Limited applicability: It is only suitable for mast / lifting systems of self-elevating units (SEUs), where the geometry and connections are relatively regular, the corresponding structure is simple, and the simplified / rod-beam approximation is reasonable. It is not suitable for geometrically complex, heterogeneous, thick-walled, or irregularly shaped welded structures (such as vacuum chamber sectors). ② Poor compatibility with dynamic response and large displacement / rotation: When components undergo large attitude changes, large rotations, and large center of gravity shifts, models based on simplified linear rods / rod-beams struggle to accurately capture the coupling effects of real deformation, contact, inertia, and gravity redistribution. This is especially problematic when the rigid and flexible parts of the structure are complexly coupled, potentially leading to significant errors. ③ Subjective selection of reduced / simplified regions and simplification assumptions make it difficult to guarantee model reliability: Determining which regions can be simplified and which must be represented by high-precision FE requires engineering experience or manual judgment. For complex, large components, inappropriate simplification may miss critical deformation / stress pathways.

[0052] In summary, existing technical solutions (full-order FEM, rigid body models, reduced-order / hybrid models, etc.) all share the following common challenges: ① Difficulty in balancing simulation accuracy with computational speed / real-time performance. Full-order FEM offers high accuracy but is slow; simplified / rigid body models are fast but lack accuracy and applicability. ② Insufficient applicability and model reliability for complex, large, irregularly shaped components (such as vacuum chamber sectors), especially those with complex geometry, non-uniform materials, welds / joints / thick walls / irregular structures. ③ Difficulty adapting to changes in lifting points / lifting paths / center of gravity / inertia / load states. Most methods assume fixed geometric / connection / contact / constraint relationships, which are unsuitable for the variable conditions during lifting. ④ Lack of on-site measurement and model closed-loop calibration mechanisms, making it impossible to achieve consistency calibration between simulation and reality, and difficult to correct in a timely manner as construction errors accumulate. ⑤ Difficulty in linking with lifting equipment control or safety decisions. Simulation is only used for preliminary analysis and cannot be used for real-time on-site control (such as lifting point adjustment, collision avoidance, automatic deceleration, etc.), thus resulting in weak safety warning and active control functions.

[0053] To address the aforementioned issues, this application proposes a rapid prediction and solution method for lifting gravity attitude using the virtual mass method. By decoupling and modeling mass effects and mechanical effects, introducing an adaptive reduced-order attitude model, updating inertia and torsional trends in real time, integrating on-site measurement data for closed-loop correction, and linking with the lifting gear leveling strategy, this method achieves high-precision and rapid prediction and safety decision-making for complex lifting processes. It is more suitable for high-requirement lifting scenarios with severe center of gravity offset and sensitivity to attitude, center of gravity, and inertia, such as geometrically complex, non-homogeneous, thick-walled, and irregularly shaped welded large components (e.g., vacuum chamber sectors).

[0054] Figure 1 This is a flowchart of a hoisting attitude prediction method according to an embodiment of this application.

[0055] As an example, such as Figure 1 As shown, the hoisting attitude prediction method includes:

[0056] S101, Construct a three-dimensional model of the target object based on the region division of the target object.

[0057] S102, extract key modes based on the 3D model and construct a reduced-order attitude model.

[0058] S103, obtain gravity parameters based on the current hoisting path.

[0059] S104, based on gravity parameters and the first constraint condition, solve the attitude reduction model to obtain the predicted hoisting attitude.

[0060] For example, the target object can be a vacuum chamber sector, or other large components with complex geometry. This application divides the target object into critical and non-critical regions, constructing different 3D models for each region, ultimately obtaining a 3D model of the entire target object. This 3D model balances accuracy and computational complexity. Based on the 3D model, key modes are extracted and a reduced-order attitude model is constructed. Key modes are understood to be modes with high shape sensitivity, used to simulate the deformation of a specific region. The reduced-order attitude model is then constructed based on the key modes. During the hoisting process, the current hoisting path is acquired in real-time. As gravity parameters change continuously during hoisting, this application continuously updates the gravity parameters based on the real-time hoisting path, providing a data foundation for subsequent attitude prediction. The reduced-order attitude model is solved based on the gravity parameters and the first constraint condition to obtain the predicted hoisting attitude.

[0061] The hoisting attitude prediction method of this application divides the target object into regions, adaptively establishes a three-dimensional model of the target object that takes into account both accuracy and computational load, and predicts the attitude in real time during the hoisting process by solving the attitude reduction model, thereby realizing real-time risk warning and active protection.

[0062] This application takes the standard sector of the vacuum chamber of a tokamak device as an example for illustration. The following is a detailed description of the construction of the three-dimensional model of the target object.

[0063] As an example, such as Figure 2 As shown, a 3D model of the target object is constructed based on the region division of the target object, including:

[0064] S201: For the critical areas of the target object, construct finite element mass elements for the critical areas; for the non-critical areas of the target object, construct virtual mass elements for the non-critical areas.

[0065] S202, constructing a three-dimensional model of the target object based on finite element units and virtual mass units.

[0066] For example, a virtual mass method model is used to model the structural characteristics of the target object. The overall structure is divided into critical and non-critical regions. For the critical regions of the target object, finite element mass elements are constructed for the critical regions. That is, the critical regions are still expressed by real finite element mass elements to bear the transmission of stress and deformation. For the non-critical regions of the target object, virtual mass elements are constructed for the non-critical regions. That is, the non-critical regions can be replaced by rigid blocks or concentrated mass elements, which only participate in the calculation of mass distribution and gravitational moment without bearing the mechanical response.

[0067] Specifically, critical areas include, for example, the lifting lugs and their surrounding reinforcing structures, interface areas connecting with other components, and weak areas where significant stress or deformation may occur. These areas are modeled using solid elements, such as the ten-node quadratic tetrahedral element C3D10 or the eight-node linear hexahedral element C3D8R. Non-critical areas include, for example, large-volume, small-deformation main shells or solid blocks that are not sensitive to stress distribution. In this application, these areas are simplified into several virtual mass blocks or concentrated mass points.

[0068] As an example, such as Figure 3 As shown, the three-dimensional model of the target object includes the total mass and center of gravity coordinates of the target object. The three-dimensional model of the target object is constructed based on finite element mass elements and virtual mass elements, including:

[0069] S301, sum the masses corresponding to the finite element mass elements and the virtual mass elements to obtain the total mass of the target object.

[0070] S302, determine the first product between the mass and the spatial coordinates of the mass point corresponding to each mass unit, and obtain the centroid coordinates based on the ratio between the first product and the total mass of the target object.

[0071] For example, when simplifying the virtual mass block, its mass and spatial position are preserved so that its contribution to the total mass, center of gravity, and moment of inertia of the structure remains unchanged, but it no longer participates in the transmission of stress and deformation. Assume the entire structure is discretized as... If there are multiple mass units, then the total mass of the whole is... Represented as:

[0072]

[0073] in, For the first The mass of a single mass element (including real finite element mass elements and virtual mass elements with lumped mass), in kg.

[0074] The centroid coordinates of the entire structure This is represented as:

[0075]

[0076] in, Here are the spatial coordinates of the mass point, in meters (m). They are typically represented in the global coordinate system as follows:

[0077]

[0078] The coordinates of the structure's center of gravity in the global coordinate system, in meters, are expressed as:

[0079]

[0080] This application significantly reduces the number of finite element elements and contact surfaces by replacing non-critical areas with virtual mass blocks, thereby reducing the computational scale. On the other hand, it still accurately represents the overall mass and center of gravity of the structure, providing a reliable foundation for subsequent calculations of gravitational moment and inertial characteristics.

[0081] Traditional full-fidelity finite element models, under complex modeling conditions including lifting equipment, connectors, temporary supports, and contact relationships, are prone to computational explosion when solving large displacements and multiple contacts, and cannot meet the requirements of rapid prediction and timely verification of gravity attitude in the assembly site.

[0082] The modeling method in this application replaces non-critical areas with concentrated mass or rigid blocks using the virtual mass method, thereby achieving model lightweighting and improving the attitude prediction solution speed by 1 to 2 orders of magnitude. This method is more suitable for real-time or near-real-time analysis needs at hoisting sites and overcomes the shortcomings of traditional full-fidelity finite element models, which are large in scale and have long solution times.

[0083] As an example, such as Figure 4As shown, the attitude reduction model includes the structural displacement field. Key modes are extracted based on the 3D model, and the attitude reduction model is constructed, including:

[0084] S401, extract key modes based on key regions of a 3D model.

[0085] S402 is based on the product of modal coefficients and key modes to construct the structural displacement field.

[0086] For example, after completing the virtual mass method modeling, modal analysis or characteristic deformation analysis is performed on the key regions that retain the true finite element description to extract key modes. The number of key modes is not limited; they can be understood as modes or eigenvectors sensitive to gravity deformation, forming a reduced-order solution space. Then, the structural displacement field is constructed based on the product between the modal coefficients and the key modes.

[0087] For example, this application uses a linear combination of a finite number of modal vectors to approximate the structural displacement field, which can be denoted as u. The expression for the structural displacement field is:

[0088]

[0089] in, For the first The modal vector represents the unit modal shape of the structure in that mode, and is usually a dimensionless vector. These are modal coefficients, which could be, for example, generalized coordinates. For the number of modes ( (Much smaller than the total number of degrees of freedom in a complete finite element model).

[0090] As an example, such as Figure 5 As shown, the hoisting attitude prediction method also includes:

[0091] S501, obtain the current attitude.

[0092] S502 performs weighted processing on modal coefficients or key modes based on the current attitude and a preset weight change function.

[0093] For example, during the hoisting process, the target object will rotate and change its posture around a certain axis. Let the current posture be determined by the rotation angle. Description. The dominant deformation region of a structure may change under different orientations. Therefore, this application uses a preset weighting function to adaptively adjust the modal coefficients or key modes. This adaptive adjustment can be a weighted process. It can be understood that the modal coefficients or key modes are weighted to form a new structural displacement field. The preset weighting function can be a weighting function based on shape sensitivity or energy contribution ratio, adaptively adjusting the modal coefficients or modal participation level so that, under new orientations, the modes most sensitive to the current loading direction are still primarily used for combination.

[0094] For example, modal weights can be introduced. Parameters, modal weights Modal weights are adjusted in real time based on attitude changes, as shown in the following expression:

[0095]

[0096] in, Indicates the initial modal weights. The current orientation (which can be a rotation angle, etc.). It can be a weight change function obtained based on shape sensitivity analysis. For Hadamard, multiply element-wise.

[0097] As the attitude changes continuously during the hoisting process, this application uses shape sensitivity factors to dynamically update the modal weights, enabling the model to maintain its ability to express key deformations in different orientations, thereby overcoming the shortcomings of traditional fixed modal bases that cannot adapt to large attitude changes.

[0098] The reduced-order model in this application can dynamically adjust the modal contribution according to the attitude change. Through this adaptive reduction method, even under the condition of large-scale rotation and translation of the structure, the accuracy of displacement and stress prediction can be maintained, while significantly reducing the amount of computation.

[0099] As an example, such as Figure 6 As shown, the current lifting path includes the current lifting point position, and the gravity parameters include the gravitational moment. The gravity parameters are obtained based on the current lifting path, including:

[0100] S601, determine the first difference between the current lifting point position and the center of gravity coordinates.

[0101] S602, determine the first product between the total mass and the gravitational acceleration.

[0102] S603, the gravitational torque is obtained based on the vector cross product between the first difference and the first product.

[0103] For example, during hoisting, changes in the position of the hoisting point or the support status can lead to a shift in the center of gravity and a redistribution of torsional loads. Existing simulation methods mostly calculate the gravitational moment based on static inertial properties, which cannot reflect the instability and torsional trend in real time. This application dynamically adjusts the gravitational moment according to the current position of the hoisting point to realistically reflect changes in the gravitational moment.

[0104] For example, under the driving force of the lifting path, the dynamic positional changes of the lifting points cause the center of gravity offset direction and torsional load to continuously evolve. The current lifting point position is obtained; when there are multiple lifting points, an equivalent lifting point position can be used as the current lifting point position.

[0105] For example, the lifting device employs a dual-lifting-point arrangement, and the coordinates of each lifting point in the global coordinate system can be represented as follows: The unit is meters. Number the lifting points. The resultant force of the two lifting points can be equivalent to the equivalent force and torque acting on a certain reference point. When analyzing the torsional tendency caused by gravity, a certain equivalent lifting point position can be selected. With center of gravity Together they are used to calculate gravitational torque.

[0106] For example, a first difference is determined between the current lifting point position and the center of gravity coordinates, i.e., the first difference is... Determine the first product between total mass and gravitational acceleration. The vector cross product of the first difference and the first product yields the gravitational torque, which is expressed as follows:

[0107]

[0108] in, The torque is the gravitational torque, in N·m. This is the current lifting point position, in meters (m). These are the coordinates of the structure's center of gravity, in meters (m). For vector cross product, The total weight of the structure, in kg. This is the vector of gravitational acceleration, in m / s².

[0109] In addition to updating the gravitational moment based on the current hoisting path, the moment of inertia tensor and angular acceleration vector are also updated based on the current hoisting path.

[0110] As an example, such as Figure 7 As shown, the hoisting attitude prediction method also includes:

[0111] S701, obtain the rotational inertia tensor relative to the center of gravity based on the current hoisting path.

[0112] S702 determines the angular acceleration vector based on gravity parameters and the moment of inertia tensor.

[0113] S703 determines attitude rollover or instability based on angular acceleration vectors.

[0114] For example, the current lifting path includes the current lifting point location, such as the equivalent lifting point location. The rotational inertia tensor relative to the center of gravity is obtained based on the current hoisting path.

[0115] As an example, the moment of inertia tensor is obtained using the following formula:

[0116]

[0117] in, Represents the moment of inertia tensor. Indicates the first The mass of a mass unit, where n represents the number of mass units. Indicates the first The distance between the center of mass and the center of gravity of each mass unit It is the identity matrix. For the first The outer product matrix of the coordinate difference vectors between the centroid and the center of mass of each mass unit.

[0118] This application analyzes the rotational response of a structure under gravity by using the rotational inertia tensor of the structure relative to its center of gravity. The analysis was conducted under a given gravitational torque. and rotational inertia tensor Then, the angular acceleration trend of the structure under this working condition can be obtained.

[0119] As an example, the gravity parameters include the gravitational torque. The angular acceleration vector is determined based on the gravity parameters and the moment of inertia tensor, including: determining the product between the inverse tensor of the moment of inertia tensor and the gravitational torque as the angular acceleration vector.

[0120] For example, let the angular acceleration vector be . The unit is rad / s² (or deg / s² in engineering descriptions), and its direction and magnitude reflect the torsional acceleration trend of the structure under the current attitude. The expression for the angular acceleration vector is shown below:

[0121]

[0122] in, This is the moment of inertia tensor, with units of kg·m². This is the gravitational torque.

[0123] By monitoring the angular acceleration vector The size and orientation of the structure can be used to determine whether it is prone to flipping or becoming unstable in a dangerous posture.

[0124] As an example, determining attitude rollover or instability based on angular acceleration vectors includes: determining the presence of attitude rollover or instability when the angular acceleration vector is greater than an angular acceleration threshold.

[0125] For example, in hoisting path planning, as time or path parameters change... Changes in the position of the lifting point Continuous updates occur, and this application repeatedly calculates at each path step. , and This allows for a continuous torsional risk assessment of the entire lifting path. An angular acceleration threshold can be used to judge the angular acceleration vector. If the angular acceleration vector exceeds the threshold, it indicates an attitude rollover or instability, thus enabling real-time torsional risk assessment of the target object.

[0126] The following section provides a detailed explanation of how to solve the attitude reduction model.

[0127] As an example, hoisting attitude prediction includes predicting the displacement field, such as Figure 8 As shown, the attitude reduction model is solved based on gravity parameters and the first constraint condition to obtain the predicted hoisting attitude, including:

[0128] S801, based on gravity parameters and the first constraint condition, constructs structural equilibrium equations, where structural equilibrium equations characterize the relationship between structural displacement field and equivalent gravity load.

[0129] S802, based on the structural equilibrium equation, solves the attitude reduction model to obtain the predicted displacement field.

[0130] For example, based on gravity parameters and the first constraint condition, the structural equilibrium equations are constructed. The equivalent nodal load vector generated by gravity can be obtained through the gravity parameters, and the equivalent reaction load vectors caused by the suspension points, temporary supports, and constraint conditions can be obtained through the first constraint condition. The resulting structural equilibrium equations are shown below:

[0131]

[0132] in, This is the reduced-order equivalent stiffness matrix, in N / m; The displacement field of the structure is expressed in meters (m). This is the equivalent nodal load vector generated by gravity, in units of N. This is the equivalent reaction force load vector caused by the lifting point, temporary support, and constraints, in N.

[0133] For example, after obtaining the load and attitude parameters, the updated torsional load is input into the structural equilibrium equations to solve the reduced-order model. Solving the linear system of structural equilibrium equations yields the predicted displacement field. Predicting the displacement field This represents the displacement information of each mass element obtained from the prediction.

[0134] As an example, such as Figure 9 As shown, the hoisting attitude prediction method also includes:

[0135] S901 determines the stress distribution in key areas based on the predicted displacement field.

[0136] S902 makes index judgments based on predicted displacement field, stress distribution, current attitude offset angle, and local deformation.

[0137] For example, the structural displacement field constructed as described above By combining the predicted displacement field with modal expansion, the modal information of the key region can be obtained, which can be represented as the stress distribution in the key region. For example, the strain distribution in the key region can be obtained by solving the partial derivatives of the displacement information. Furthermore, the strain-stress relationship can be used... , Stress, expressed in Pa or MPa; For dimensionless strain; The constitutive matrix of the material is given in Pa. The stress distribution in the critical region is obtained.

[0138] For example, after obtaining the stress distribution, indicators can be judged based on the stress distribution. For instance, strength safety assessments can be performed on indicators such as equivalent stress, maximum principal stress, and stress of critical weld sections to assess whether the stress in the critical area meets the stress requirements of the critical area.

[0139] For example, index judgments can also be made based on the predicted displacement field, the current attitude offset angle, and local deformation. For instance, assembly tolerance checks can be performed based on the predicted displacement field, the attitude deviation (offset angle) of key interfaces, and local deformation (which can be obtained through strain). For example, judging the attitude offset angle... If the installation angle difference exceeds the allowable angle difference, determine the trend of angular acceleration. Whether it exceeds the controllable leveling range, determine the local deformation. To determine if there is a conflict with the alignment tolerance, and whether the lifting lug exceeds the allowable limit of the material design. .

[0140] Traditional prediction models lack real-time coupling between the dynamic changes in center of gravity position, moment of inertia, and force path. Changes in the position of the lifting point or the support state can lead to center of gravity shift and redistribution of torsional loads. Existing simulation methods mostly calculate gravitational torque based on static inertial properties, which cannot reflect the instability and torsional trend in real time. In contrast, this application can predict center of gravity shift and dangerous torsional trends in real time by incorporating changes in the gravity path caused by changes in the lifting point into the real-time calculation. This allows for early identification of instability and rollover risks, providing a basis for avoiding dangerous postures.

[0141] As an example, the lifting attitude prediction method also includes: determining the lifting device adjustment strategy based on the current attitude offset angle, local deformation, and angular acceleration vector.

[0142] Traditional hoisting simulation results cannot directly serve safety control decisions. They lack linkage interfaces for control strategies such as hoisting point leveling and collision avoidance. Simulations are often limited to offline analysis before construction and cannot support real-time risk warnings and proactive protection during the hoisting process.

[0143] Based on rapid prediction, this application outputs information such as the current attitude offset angle, local deformation, and angular acceleration vector to the spreader control system. Based on these inputs, the control system can calculate the spreader adjustment strategy, which can be the adjustment amount of the lifting point displacement or the adjustment amount of the wire rope length, to achieve automatic leveling and deceleration in dangerous areas.

[0144] The expression for the spreader adjustment strategy is as follows:

[0145]

[0146] in, This is a lifting point adjustment command, in mm, which can be the adjustment amount of lifting point displacement or wire rope length. This is a hoisting control strategy function that can be customized according to project requirements.

[0147] The simulation results of this application can directly drive the leveling and obstacle avoidance operations of the lifting equipment, realizing the linkage from prediction to control, and improving the lifting safety guarantee from passive response to active intervention, thus significantly improving the reliability of operation.

[0148] As an example, such as Figure 10 As shown, the hoisting attitude prediction method also includes:

[0149] S1001, collects the actual hoisting posture.

[0150] S1002, the parameters of the attitude reduction model are adjusted based on the difference between the actual hoisting attitude and the predicted hoisting attitude.

[0151] The adjustment of parameters in the attitude reduction model includes at least one of adjusting the virtual mass elements in non-critical regions and adjusting the stiffness parameters of the attitude reduction model.

[0152] For example, this application also introduces model closed-loop correction driven by on-site measurement information. To offset the model-object discrepancy caused by manufacturing errors, welding deformation, and clamping deviations, the actual hoisting posture can be acquired in real time by introducing measurement equipment such as laser trackers. The parameters of the posture reduction model are adjusted according to the difference between the actual hoisting posture and the predicted hoisting posture. For example, the virtual mass distribution and local mechanical properties are corrected in reverse, so that the prediction error converges successively. The geometric error correction is expressed as:

[0153]

[0154] in, These are measured values. These are predicted values. Adjusting the parameters of the attitude reduction model includes adjusting the virtual mass units in non-critical regions, for example, by adjusting the deviation. As input, update the virtual mass block distribution according to the correction rule:

[0155]

[0156] in, For virtual mass distribution parameters, This is the correction factor, with a value ranging from 0 to 1.

[0157] This allows the model to be continuously corrected during the construction process, effectively suppressing the accumulation of long-term errors caused by manufacturing errors and clamping deviations.

[0158] This application uses measurement data from laser trackers and other sources to perform closed-loop correction on the model, ensuring that the simulation results are consistent with actual working conditions. It is suitable for large-scale assembly engineering environments with long cycles, and can be continuously corrected as the construction progresses, resulting in higher reliability of attitude model predictions.

[0159] The above embodiments fully demonstrate that, compared with traditional full-scale finite element analysis, the method of this application significantly reduces the computational scale and significantly improves the simulation speed while ensuring the accuracy of stress and deformation prediction. Furthermore, it can achieve closed-loop correction of the model by combining on-site measurements and can also be linked with the lifting control system to realize the transition from "simulation analysis" to "real-time safety control".

[0160] As an example, the hoisting attitude prediction method also includes updating critical and non-critical regions based on the influence factor of mass units.

[0161] For example, this application can also introduce an automatic identification algorithm for key deformation zones. The influence factors of mass elements scan the entire model, determine in real time which regions may transform into new local weak regions under different postures, and automatically adjust the mechanical modeling accuracy and virtual mass substitution strategy for these regions. This approach enables dynamic updates to the boundary between the virtual mass region and the real finite element region, thereby further improving the reliability of structural posture prediction and the lightweight performance of the model.

[0162] For example, the entire model can be scanned through finite element sensitivity analysis or stress gradient analysis to update in real time which regions can be transformed into critical regions and which regions can be transformed into non-critical regions.

[0163] As an example, such as Figure 11 As shown, the impact factor includes a shape sensitivity index. The impact factor, based on the mass unit, updates critical and non-critical regions, including:

[0164] S1101, calculate the shape sensitivity index for each mass unit.

[0165] S1102, determine the mass units with shape sensitivity index greater than or equal to the preset sensitivity threshold as critical regions, and / or determine the mass units with shape sensitivity index less than the preset sensitivity threshold as non-critical regions.

[0166] For example, an initial virtual mass model is constructed, using the same initial modeling method as the three-dimensional model described above. The structure is divided into critical regions and non-critical regions of virtual mass, a reduced-order model is established, and the total mass in the initial state is determined. Center of gravity This involves solving for attitude modes. Under path-varying gravity loads, the deformation sensitivity of different parts of the structure changes. This can be achieved by calculating the shape sensitivity indices of nodes or elements. This is used to determine its contribution to the total displacement energy.

[0167]

[0168] in, Total strain energy (unit: J); For the first Stiffness parameters of the element (unit: N / m); This is a dimensionless sensitivity value used to assess whether the model fidelity needs to be improved in this region.

[0169] For example, if a certain mass unit Higher than or equal to the preset sensitivity threshold If so, then the region is determined to be the new dominant stress-deformation zone, and is identified as a critical region. And / or, if a certain mass element... Less than the preset sensitivity threshold If so, the area is determined to be a non-critical area.

[0170] As an example, such as Figure 12 As shown, the impact factor includes the energy contribution ratio. The impact factor is updated based on quality units for both critical and non-critical regions, including:

[0171] S1201, calculate the energy contribution ratio of each mass unit.

[0172] S1202, determine the mass units whose energy contribution ratio is greater than or equal to the preset contribution ratio as critical regions, and / or determine the mass units whose energy contribution ratio is less than the preset contribution ratio as non-critical regions.

[0173] For example, the energy contribution ratio of the mass unit can also be calculated. The energy contribution ratio is updated for critical and non-critical areas. The expression is shown in the following formula:

[0174]

[0175]

[0176] in, The energy contribution ratio of the i-th element is a dimensionless quantity used to characterize the relative importance of the node or element in the overall structural deformation. The strain energy absorbed by the i-th element under the current working condition is expressed in joules (J), reflecting the amount of energy stored by the node or element under elastic deformation under external load; U is the total strain energy of the entire structure under the current working condition, expressed in joules (J), and its value is the sum of the strain energies of all elements. Let be the displacement vector of the i-th element, in meters (m), used to describe the displacement state of the node or element under load. The equivalent stiffness matrix or stiffness parameter corresponding to the i-th node or element, in units of Newtons per meter (N / m), is used to characterize the resistance of the node or element to deformation. displacement vector The transpose of .

[0177] For example, if the energy contribution ratio of a certain mass unit If the energy contribution ratio is higher than or equal to the preset contribution ratio, then the region is determined to be a new dominant stress-deformation zone and identified as a critical region. And / or, if the energy contribution ratio of a certain mass element... If the contribution ratio is less than the preset contribution ratio, the area is determined to be a non-critical area. Preset contribution ratio The preset energy contribution ratio threshold is a dimensionless empirical parameter used to distinguish between critical stress deformation areas and non-critical areas. Its value can be set according to engineering experience or accuracy requirements; for example, it can be 5%.

[0178] As an example, after updating critical and / or non-critical regions, high-sensitivity regions can be upgraded from virtual mass regions to real finite element regions, or the original real finite element regions can be downgraded to virtual mass regions. For example, for the selected high-sensitivity regions, perform the following operations: ① Delete the virtual mass block description: retain the real geometry and material properties; ② Remesh: replace the original lumped mass representation with finite element elements (such as C3D8R or C3D10); ③ Update mechanical properties: add local stiffness and material constants (elastic modulus). Poisson's ratio (etc.). After completing the above operations, the region can transmit stress and deformation, significantly improving response accuracy.

[0179] Because the model zoning has changed, the structural mass and center of gravity need to be recalculated:

[0180]

[0181]

[0182] in, This represents the number of newly added real units.

[0183] The updated mass distribution affects the torsional load trend, requiring a recalculation of the inertia tensor. And update the angular acceleration trend:

[0184]

[0185] This makes the prediction of reversal risks more closely reflect the actual situation.

[0186] As the real finite element region expands, its modal contribution will increase, requiring a reordering of the modal vectors:

[0187] Increase the weight of high-energy participating modes and decrease the participation of insensitive modes, so that:

[0188]

[0189] Ensure that prediction accuracy is continuously optimized as attitude changes.

[0190] It is understandable that after the 3D model of the target object is changed, subsequent steps based on the 3D model will all be updated.

[0191] This application introduces an automatic identification algorithm, enabling the model to update key deformation regions in real time as attitude and load change, thereby dynamically improving the accuracy of local structural solutions and avoiding accuracy degradation due to unreasonable fixed virtual mass region range.

[0192] As an example, such as Figure 13 As shown, the hoisting attitude prediction method also includes:

[0193] S1301, Construct a multi-suspension point force model.

[0194] S1302, with the goal of minimizing the angular acceleration vector, solves the multi-suspension point force model based on the second constraint condition to obtain the target load of the suspension point.

[0195] For example, this application further optimizes the force distribution of the lifting equipment by adjusting the force ratio of each lifting point in real time, thereby minimizing structural posture deviation, reducing dangerous torsional tendencies, and significantly improving lifting safety. This embodiment is particularly suitable for working conditions where the lifting points are asymmetrically distributed, the center of gravity shifts drastically, there is a risk of rollover, or high alignment accuracy is required.

[0196] For example, firstly, a multi-point load-bearing model is constructed, which includes multi-point load-bearing equilibrium equations and torque equilibrium equations. The multi-point load-bearing equilibrium equations are then established as follows:

[0197] This embodiment uses a dual-point lifting device as an example, assuming the two lifting points are as follows: and The loads at their lifting points are respectively , The unit is N. The overall force equilibrium condition is:

[0198]

[0199] in, The total mass of the structure is expressed in kg. The acceleration due to gravity is 9.81 m / s². The coordinates of the suspension points are as follows: , Unit: m, barycentric coordinates: , unit m.

[0200] The torque balance expression is:

[0201]

[0202] in, Let be a unit vector in the direction of gravity, dimensionless. If this equation cannot be strictly satisfied, then a tendency for torsional loading must exist.

[0203] For example, a torsional trend optimization objective function is then constructed, which can minimize the angular acceleration vector as the objective.

[0204] According to the trend reversal calculation formula in the above embodiments: The control objective is to reduce the structural torsional tendency, therefore the objective function is set as follows:

[0205]

[0206] Seek a force distribution scheme for the suspension point that minimizes angular acceleration.

[0207] Combined with the second constraint, the second constraint may include, for example, at least one of the following: force balance constraint, spreader capacity constraint, and safety deviation limit.

[0208] ① Force balance constraints

[0209]

[0210] ② Lifting equipment capacity constraints

[0211]

[0212]

[0213] in, , This represents the maximum load that each lifting point can withstand, expressed in N.

[0214] ③ Safety deviation limit (alignment angle limit)

[0215]

[0216] The multiplier method or adaptive linear programming can be used to iteratively solve the multi-suspension point stress model to obtain:

[0217]

[0218] To meet the need for real-time updates.

[0219] As an example, the hoisting attitude prediction method also includes: determining the length adjustment amount of the hoisting point based on the target load of the hoisting point; and adjusting the length of the hoisting point based on the length adjustment amount.

[0220] For example, the target load of the lifting point and the length adjustment of the lifting point can be calculated based on an iterative algorithm, for example, by solving... , Converted to wire rope length adjustment (obtained from the known force-displacement conversion relationship of the lifting device system):

[0221]

[0222] in, For lifting points Rope length adjustment amount, in mm. This is a control function, related to the characteristics of the spreading gear. Number the lifting points. After adjustment, reconstruct the multi-lifting-point force model to form a closed-loop iterative control.

[0223] This invention improves leveling performance through optimized lifting point layout and adaptive allocation strategies. Based on real-time predicted center of gravity shift, torsional load, and attitude deflection angle, the force distribution coefficient of each lifting point is calculated, automatically adjusting the load ratio at each point to maintain overall attitude deviation within a controllable range. Simultaneously, this strategy can incorporate dangerous attitude criteria for priority response, proactively altering lifting point operation methods when risks increase, thereby enhancing proactive safety capabilities during the lifting process.

[0224] As an example, the hoisting attitude prediction method also includes: dividing the target object's region into states based on the current attitude offset angle, the safety factor of local deformation, the angular acceleration vector, and the interference gap; wherein, the safety factor of local deformation includes the ratio between local deformation and the maximum allowable deformation.

[0225] For example, this application can present the analysis results in a three-dimensional visualization, enabling on-site operators to identify dangerous posture areas, reversal trends, and near-collision risks in real time through a graphical interface, thereby improving the interpretability of hoisting operations and the efficiency of safety decision-making. This method can be implemented in a human-machine interface, a digital twin system, or a construction digital monitoring platform.

[0226] For example, a pose parameter space is constructed, whereby the pose of the target object in the global coordinate system is determined by the Euler angle set. express:

[0227]

[0228] in, The rotation angle around the X-axis (unit: deg or rad); The rotation angle around the Y-axis (unit: deg or rad); The rotation angle around the Z-axis (unit: deg or rad).

[0229] Define the pose search space :

[0230]

[0231] in, , , This is a set of rotation angle ranges, and this space will serve as the parameter domain for constructing the safe zone.

[0232] Then, an attitude safety evaluation index system is established, and key indicators under the above embodiments are calculated, including:

[0233] ① Attitude deviation index The unit is deg, which represents the deviation from the target pose. The target posture.

[0234] ② Reversal risk indicators , unit: rad / s², represents the trend of angular acceleration.

[0235] ③ Local stress safety factor , dimensionless, represents the safety factor of the structure; the larger the value, the safer the structure.

[0236] ④ Minimum interference gap Unit: mm, indicating dangerous approach distance, below the safe distance. This could pose a risk of interference.

[0237] Attitude safety partitions can be defined using the above metrics, as shown in Table 1 below:

[0238] Table 1 - Attitude Safety Zoning Table

[0239]

[0240] As an example, attitude risk cloud maps can be calculated and rendered in 3D visualization, for example, based on parameter space. For internal attitude sampling points, perform fast order reduction prediction for each sampling point:

[0241]

[0242] Different color codes are assigned based on risk level: for example, green represents a safe zone, yellow represents a warning zone, and red represents a danger zone. This is plotted as a 3D risk situation cloud map and displayed as an interactive attitude safety boundary surface. Users can drag the angle in real time. View the corresponding risk assessment, and see the arrow pointing in the direction of the reversal trend and the minimum interference area.

[0243] As an example, the attitude risk cloud map can also be linked to spreader leveling and path decision-making, based on real-time attitude points. The system provides action suggestions based on the location of the risk zone. For example, if entering a warning zone, it automatically slows down and alerts the operator; if approaching a danger zone, it outputs a hoisting point correction command; if there is a tendency to cross a danger zone, it blocks control commands and prompts the operator to replan the route. This forms a control logic that extends from simulation to decision-making to regulation, further developing from information display to intelligent safety intervention.

[0244] This application enables efficient information transmission from simulation prediction to on-site operation by constructing a posture safety zone cloud map and a construction visualization interface. The prediction results of this invention are not only output in data form, but also distinguish and label the feasible and dangerous areas of the structure in posture space, displaying them graphically in real time to on-site operators and the hoisting control system. This allows for intuitive identification of center of gravity drift trends, collision risk locations, and the probability of dangerous postures, thereby assisting construction decisions and reducing the risk of operational errors.

[0245] Figure 14 This is a flowchart of a method for rapid prediction and solution of Sector lifting gravity attitude combining virtual mass method according to an embodiment of this application, as shown below. Figure 14 As shown, a 3D model of the sector and a simplified model using the virtual mass method are first established. Through the dual separation of structure and mechanics modeling using the virtual mass method, the size of the finite element model is significantly reduced and the computational speed is improved while maintaining accuracy. Then, key modes are extracted and an attitude-adaptive reduced-order model is established. This model is adaptively updated as the attitude changes, maintaining response prediction accuracy. Based on the current lifting point position and attitude information, the center of gravity, moment of inertia, and torsional trend are calculated in real time, enabling real-time updates of the center of gravity, moment of inertia, and torsional trend predictions. Attitude deviation, stress, and clearance are also calculated to determine if the indicators meet safety standards. An automated leveling and collision avoidance strategy is also introduced, automatically generating lifting point adjustment amounts and performing leveling or obstacle avoidance operations. Model closed-loop correction is driven by on-site measurement information. An automatic identification algorithm for key deformation zones is introduced, using finite element sensitivity analysis or stress gradient analysis to scan the entire model in real time, determining which areas may transform into new local weak points under different attitudes, and automatically adjusting the mechanical modeling accuracy and virtual mass substitution strategy for these areas, further improving the reliability of structural attitude prediction and the lightweight performance of the model. Leveling performance is improved through optimized lifting point layout and adaptive allocation strategies. Efficient information transmission from simulation prediction to on-site operation is achieved by constructing a posture safety zone cloud map and a construction visualization interface. The prediction results of this application are not only output in data form, but also distinguish and label the feasible and dangerous areas of the structure in posture space, displaying them graphically in real time to on-site operators and the lifting control system. This allows for intuitive identification of center of gravity drift trends, collision risk locations, and the probability of dangerous postures, thereby assisting construction decisions and reducing the risk of operational errors.

[0246] This application also proposes a hoisting attitude prediction device.

[0247] As an example, such as Figure 15As shown, the hoisting attitude prediction device includes: a first construction module 1501, used to construct a three-dimensional model of the target object based on the region division of the target object; a second construction module 1502, used to extract key modes based on the three-dimensional model and construct a reduced-order attitude model; an acquisition module 1503, used to acquire gravity parameters based on the current hoisting path; and a solution module 1504, used to solve the reduced-order attitude model based on the gravity parameters and the first constraint condition to obtain the predicted hoisting attitude.

[0248] This application also proposes a computer-readable storage medium.

[0249] In this embodiment, a computer program is stored on a computer-readable storage medium, and when the computer program is executed by a processor, it implements the steps of the above-described hoisting attitude prediction method.

[0250] Figure 16 A block diagram of an electronic device provided in an embodiment of this application.

[0251] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described hoisting attitude prediction method.

[0252] like Figure 16 As shown, for ease of understanding, embodiments of this application illustrate a specific electronic device.

[0253] Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0254] like Figure 16 As shown, the device includes a computing unit 1601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 1602 or a computer program loaded into random access memory (RAM) 1603 from storage unit 1608. The RAM 1603 may also store various programs and data required for the operation of the electronic device. The computing unit 1601, ROM 1602, and RAM 1603 are interconnected via bus 1604. An input / output (I / O) interface 1605 is also connected to bus 1604.

[0255] Multiple components in the electronic device are connected to the I / O interface 1605. These components include: an input unit 1606, such as a keyboard or mouse; an output unit 1607, such as various types of displays or speakers; a storage unit 1608, such as a hard disk or optical disk; and a communication unit 1609, such as a network interface card (NIC), a modem, or a wireless transceiver. The communication unit 1609 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0256] The computing unit 1601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 1601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1601 performs the various methods described above, such as the hoisting attitude prediction method. For example, in some embodiments, the hoisting attitude prediction method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 1608. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 1602 and / or communication unit 1609. When the computer program is loaded into RAM 1603 and executed by the computing unit 1601, the hoisting attitude prediction method described above can be executed. Alternatively, in other embodiments, the computing unit 1601 may be configured to perform the hoisting attitude prediction method by any other suitable means (e.g., by means of firmware).

[0257] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this application, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0258] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0259] In the description of this application, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this application, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0260] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0261] Furthermore, the terms "first," "second," etc., used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this application can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this application, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly and specifically defined in the embodiments.

[0262] In this application, unless otherwise explicitly specified or limited in the embodiments, the terms "installation," "connection," "joining," and "fixing" appearing in the embodiments should be interpreted broadly. For example, a connection can be a fixed connection, a detachable connection, or an integral part; it can also be a mechanical connection, an electrical connection, etc. Of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication between two components, or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific implementation.

[0263] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0264] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for predicting hoisting attitude, characterized in that, The method includes: Construct a 3D model of the target object based on the region division of the target object; Key modes are extracted based on the 3D model, and a reduced-order attitude model is constructed. Gravity parameters are obtained based on the current hoisting path; The attitude reduction model is solved based on the gravity parameters and the first constraint condition to obtain the predicted hoisting attitude; The construction of a 3D model of the target object based on the region division of the target object includes: For the key areas of the target object, finite element mass elements are constructed for the key areas, and virtual mass elements are constructed for the non-key areas of the target object. The virtual mass elements represent virtual mass blocks or concentrated mass points that only participate in the calculation of mass distribution and gravitational torque. A three-dimensional model of the target object is constructed based on the finite element mass element and the virtual mass element.

2. The hoisting attitude prediction method according to claim 1, characterized in that, The attitude reduction model includes a structural displacement field. The extraction of key modes and construction of the attitude reduction model based on the three-dimensional model includes: Based on the key regions of the 3D model, key modes are extracted; The structural displacement field is constructed based on the product of the modal coefficients and the key modes.

3. The hoisting attitude prediction method according to claim 2, characterized in that, The method further includes: Get the current pose; The modal coefficients or key modes are weighted based on the current posture and a preset weight change function.

4. The hoisting attitude prediction method according to claim 1, characterized in that, The method further includes: Obtain the rotational inertia tensor relative to the center of gravity based on the current hoisting path; The angular acceleration vector is determined based on the gravity parameters and the moment of inertia tensor. The attitude rollover or instability is determined based on the angular acceleration vector.

5. The hoisting attitude prediction method according to claim 1, characterized in that, The current hoisting path includes the current hoisting point position, and the gravity parameters include the gravitational moment. Obtaining the gravity parameters based on the current hoisting path includes: Determine the first difference between the current lifting point position and the center of gravity coordinates; Determine the first product between the total mass and the acceleration due to gravity; The gravitational torque is obtained based on the vector cross product between the first difference and the first product.

6. The hoisting attitude prediction method according to claim 4, characterized in that, The moment of inertia tensor is obtained using the following formula: Where I represents the moment of inertia tensor, Let n represent the mass of the i-th mass unit, and n represent the number of mass units. E represents the distance between the centroid and the center of mass of the i-th mass unit, and E is the identity matrix. It is the outer product matrix of the coordinate difference vectors between the centroid and the center of mass of the i-th mass unit.

7. The hoisting attitude prediction method according to claim 6, characterized in that, The gravity parameters include gravitational torque, and the determination of the angular acceleration vector based on the gravity parameters and the moment of inertia tensor includes: The product of the inverse tensor of the moment of inertia tensor and the gravitational torque is determined as the angular acceleration vector.

8. The hoisting attitude prediction method according to claim 6, characterized in that, The determination of attitude rollover or instability based on the angular acceleration vector includes: If the angular acceleration vector is greater than the angular acceleration threshold, it is determined that there is an attitude rollover or instability.

9. The hoisting attitude prediction method according to claim 2, characterized in that, The predicted hoisting attitude includes a predicted displacement field. Solving the reduced-order attitude model based on the gravity parameters and the first constraint condition to obtain the predicted hoisting attitude includes: Based on the gravity parameters and the first constraint condition, a structural equilibrium equation is constructed, wherein the structural equilibrium equation characterizes the relationship between the structural displacement field and the equivalent gravity load. The predicted displacement field is obtained by solving the attitude reduction model based on the structural equilibrium equation.

10. The hoisting attitude prediction method according to claim 9, characterized in that, The method further includes: The stress distribution in key areas is determined based on the predicted displacement field. The index is judged based on the predicted displacement field, the stress distribution, the current attitude offset angle, and the local deformation.

11. The hoisting attitude prediction method according to claim 10, characterized in that, The method further includes: The lifting device adjustment strategy is determined based on the current attitude offset angle, the local deformation, and the angular acceleration vector.

12. The hoisting attitude prediction method according to claim 1, characterized in that, The method further includes: Collect data on the actual hoisting posture; The parameters of the attitude reduction model are adjusted based on the difference between the actual hoisting attitude and the predicted hoisting attitude. The adjustment of the parameters of the attitude reduction model includes at least one of adjusting the virtual mass elements in non-critical regions and adjusting the stiffness parameters of the attitude reduction model.

13. The hoisting attitude prediction method according to claim 1, characterized in that, The method further includes: The critical region and the non-critical region are updated based on the influence factor of the quality unit.

14. The hoisting attitude prediction method according to claim 13, characterized in that, The influence factor includes a shape sensitivity index, and the updating of the critical region and the non-critical region based on the influence factor of the mass unit includes: Calculate the shape sensitivity index for each mass unit; The mass units whose shape sensitivity index is greater than or equal to a preset sensitivity threshold are identified as the critical regions, and / or the mass units whose shape sensitivity index is less than the preset sensitivity threshold are identified as the non-critical regions.

15. The hoisting attitude prediction method according to claim 13, characterized in that, The impact factor includes the energy contribution ratio, and the updating of the critical region and the non-critical region based on the impact factor of the quality unit includes: Calculate the energy contribution ratio of each mass unit; The mass units whose energy contribution ratio is greater than or equal to a preset contribution ratio are identified as the critical regions, and / or the mass units whose energy contribution ratio is less than the preset contribution ratio are identified as the non-critical regions.

16. The hoisting attitude prediction method according to claim 4, characterized in that, The method further includes: Construct a multi-suspension-point stress model; With the goal of minimizing the angular acceleration vector, the multi-suspension point force model is solved based on the second constraint condition to obtain the target load of the suspension point.

17. The hoisting attitude prediction method according to claim 16, characterized in that, The method further includes: The length adjustment amount of the lifting point is determined based on the target load of the lifting point; The length of the suspension point is adjusted based on the stated length adjustment amount.

18. The hoisting attitude prediction method according to claim 1, characterized in that, The three-dimensional model of the target object includes the total mass and center of gravity coordinates of the target object. The construction of the three-dimensional model of the target object based on the finite element mass element and the virtual mass element includes: The total mass of the target object is obtained by summing the masses corresponding to the finite element mass units and the virtual mass units. The first product between the mass and the spatial coordinates of the mass point corresponding to each mass unit is determined, and the centroid coordinates are obtained based on the ratio between the first product and the total mass of the target object.

19. The hoisting attitude prediction method according to claim 11, characterized in that, The method further includes: The target object's region is divided into states based on the current attitude offset angle, the safety factor of the local deformation, the angular acceleration vector, and the interference gap; The safety factor for local deformation includes the ratio between the local deformation and the maximum permissible deformation.

20. A hoisting attitude prediction device, characterized in that, The device: The first construction module is used to construct a 3D model of the target object based on the region division of the target object; The second construction module is used to extract key modes based on the three-dimensional model and construct a reduced-order attitude model. The acquisition module is used to obtain gravity parameters based on the current hoisting path; The solver module is used to solve the attitude reduction model based on the gravity parameters and the first constraint condition to obtain the hoisting prediction attitude; The first construction module is further configured to: construct finite element mass elements for the key regions of the target object, construct virtual mass elements for the non-key regions of the target object, wherein the virtual mass elements represent virtual mass blocks or concentrated mass points that only participate in the calculation of mass distribution and gravitational torque; and construct a three-dimensional model of the target object based on the finite element mass elements and the virtual mass elements.

21. An electronic device, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the hoisting attitude prediction method according to any one of claims 1-19.

22. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps of the hoisting attitude prediction method according to any one of claims 1-19.

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