A method for dynamic safety assessment of hoisting operations

Abstract

The present application relates to the technical field of hoisting construction dynamic safety evaluation, and specifically discloses a hoisting construction dynamic safety evaluation method, which acquires hoisting system operation parameters in real time through a multi-modal sensor array, generates a hoisting dynamic parameter set, and the hoisting dynamic parameter set contains pressure distribution data of each hoisting point, hoist attitude angle data and load space position data; based on the hoisting dynamic parameter set, force system dynamic balance analysis is performed, and the force system imbalance degree of the crane is obtained through analysis; when the force system imbalance degree of the crane is greater than a set threshold, an imbalance active correction mechanism is triggered, and an electro-hydraulic proportional valve is used to adjust and correct the imbalance of the crane. The safety and efficiency of hoisting operation can be significantly improved. The automatic imbalance correction mechanism not only reduces the risk of accidents, but also improves the flexibility and response speed of operation, providing strong technical support for modern hoisting operations.

Description

Technical Field

[0001] This invention belongs to the technical field of dynamic safety assessment of hoisting construction, and relates to a method for dynamic safety assessment of hoisting construction. Background Technology

[0002] With the increasing complexity of modern engineering construction, multi-point hoisting has become a common method for the installation and handling of large components. However, the coordinated operation of multiple hoisting points significantly increases the risk of force imbalance during the hoisting process. Imbalance at any one hoisting point can lead to instability of the entire hoisting system, thereby causing safety accidents. Therefore, real-time monitoring of the force system status at each hoisting point and timely identification and correction of imbalances are crucial for ensuring construction safety and improving work efficiency. Through effective monitoring methods, construction personnel can dynamically adjust the stress on each hoisting point during the hoisting process, ensuring even load distribution and avoiding serious consequences such as overturning, damage, or personal injury caused by force imbalance.

[0003] However, current technologies for monitoring force imbalance during multi-point lifting operations still have some shortcomings and drawbacks. First, existing monitoring systems typically rely on traditional sensors and manual monitoring methods, lacking real-time performance and automation. For example, many systems can only monitor a single lifting point, failing to provide comprehensive analysis and real-time feedback for multiple points. This limitation makes it difficult for managers to fully understand the stress state of each lifting point in complex lifting environments, potentially leading to a failure to detect imbalances in critical moments and increasing the risk of accidents. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention provides a dynamic safety assessment method for hoisting construction to solve the above-mentioned technical problems.

[0005] To achieve the above and other objectives, the technical solution adopted by the present invention is as follows:

[0006] This invention provides a method for dynamic safety assessment of hoisting operations, the method comprising the following steps:

[0007] S1: Real-time acquisition of lifting system operation parameters via a multi-modal sensor array generates a lifting dynamic parameter set. This set includes pressure distribution data, spreader attitude angle data, and load spatial position data for each lifting point. The pressure distribution data includes real-time three-dimensional force components (F_x, F_y, F_z) for each lifting point. The spreader attitude angle data includes the pitch angle θ and roll angle φ of the hook assembly. The load spatial position data includes the coordinate deviation (ΔX, ΔY, ΔZ) of the load's geometric center in the global coordinate system.

[0008] S2: Construct a multi-point three-dimensional mechanical feature dataset based on the hoisting dynamic parameter set, perform dynamic equilibrium analysis of the force system, synthesize the total axial load of each space based on the three-dimensional mechanical feature dataset, and generate the total hoisting load vector; then construct a three-dimensional moment synthesis matrix, perform mechanical equivalent solution based on the total hoisting load vector and the three-dimensional moment synthesis matrix, thereby generating the overall resultant force eccentricity of the crane, and finally analyze the force system imbalance of the crane.

[0009] S3: When the force imbalance of the crane exceeds the set threshold, the imbalance active correction mechanism is triggered, and the imbalance of the crane is corrected by adjusting the electro-hydraulic proportional valve.

[0010] For example, step S1 includes the following steps:

[0011] Step S11: Install a pressure sensor array on the inner side of the load-bearing beam at the lifting point to collect the instantaneous value of the vertical force at each lifting point in real time and generate an initial pressure dataset.

[0012] Step S12: Based on the initial pressure dataset, abnormal pressure fluctuations are detected at the lifting points. When the pressure difference between adjacent lifting points exceeds the preset tolerance threshold, the inertial measurement unit wake-up command is triggered to obtain the real-time pitch angle θ and roll angle φ of the spreader.

[0013] Step S13: Combining the pitch angle θ and roll angle φ, convert the vertical force values ​​of each lifting point into three-dimensional force components: X-axis component F_x = F_z × tanθ; Y-axis component F_y = F_z × tanφ; Z-axis component F_z retains the original pressure sensor data F_z, thereby generating a three-dimensional pressure distribution data set for each lifting point;

[0014] Step S14: Install a laser scanning module at the load end, calculate the spatial coordinate deviation of the load geometric center relative to the hoisting origin using a point cloud registration algorithm, optimize coordinate accuracy by fusing attitude angle data, and generate load spatial position data (ΔX, ΔY, ΔZ).

[0015] Step S15: Integrate and package the three-dimensional pressure distribution data set, pitch / roll angle data, and load space coordinate data to generate a set of hoisting dynamic parameters.

[0016] For example, the operation logic of step S14 is as follows:

[0017] Step S141: Preset a feature marker point array on the load surface, and collect real-time three-dimensional point cloud data of the load's outer contour through a laser scanning module;

[0018] Step S142: Perform point cloud registration and matching between the real-time 3D point cloud data and the preset load design model, and filter out the effective point cloud set corresponding to the feature marker points;

[0019] Step S143: Based on the effective point cloud set, calculate the real-time coordinate values ​​of each marker point in the hoisting global coordinate system, and take the arithmetic average of all marker point coordinates to obtain the current load geometric center coordinates (W_x, W_y, W_z);

[0020] Step S144: Read the pitch angle θ and roll angle φ from the spreader attitude angle data, and perform attitude correction and compensation on the geometric center coordinates using the coordinate rotation matrix:

[0021] The corrected X-axis coordinates are obtained as follows: ΔX1=W_x-L×sinθ, the corrected Y-axis coordinates are obtained as follows: ΔY1=W_y-L×sinφ, and the corrected Z-axis coordinates are obtained as follows: ΔZ1=W_z, where L is the length of the suspension chain;

[0022] Step S145: Verify the stability of the corrected coordinate data using analysis of variance. When the coordinate deviation of three consecutive scans is less than the set accuracy threshold, generate the final load spatial location data (ΔX, ΔY, ΔZ).

[0023] For example, step S12 includes the following steps:

[0024] Step S121: Extract the vertical pressure value F_i of each lifting point at the current moment from the initial pressure dataset, where i is the number of each lifting point, i=1,2,...,N; N represents the total number of lifting points;

[0025] Step S122: Calculate the real-time pressure difference between adjacent lifting points and take the absolute value to finally obtain the absolute value of the real-time pressure difference between each adjacent lifting point |ΔF_j|, where j is the number of each adjacent lifting point and the value of j ranges from 1 to N-1;

[0026] Step S123: Select the maximum value max(|ΔF_j|) from |ΔF_j|, compare it with the preset tolerance threshold F_th, and generate an abnormal fluctuation marker signal when max(|ΔF_j|) > F_th;

[0027] Step S124: Trigger the wake-up command of the inertial measurement unit according to the abnormal fluctuation marker signal, and synchronously collect the angular velocity ω_x, ω_y, ω_z and acceleration a_z data of the lifting device in three axes;

[0028] Step S125: Convert ω_x, ω_y, ω_z, and a_z into pitch angle θ and roll angle φ using a quaternion solving algorithm. The specific process is as follows:

[0029] Step S1251: Calculate the initial tilt angle θ_0=arctan(a_x / a_z) and the initial roll angle φ_0=arctan(a_y / a_z) using accelerometer data;

[0030] Step S1252: Integrate the gyroscope angular velocity data to obtain the angular change, which is divided into pitch angle change Δθ=∫ω_ydt and roll angle change Δφ=∫ω_xdt;

[0031] Step S1253: Use the complementary filtering algorithm to fuse θ_0 and Δθ to obtain the final pitch angle θ=α×θ_0+(1-α)(θ_prev+Δθ), where α is the filtering coefficient and θ_prev is the angle value of the previous cycle;

[0032] Step S1254: Similarly, calculate the roll angle φ = α × φ_0 + (1 - α)(φ_prev + Δφ).

[0033] For example, step S2 includes the following steps:

[0034] Step S21: Extract the real-time three-dimensional force components (F_x, F_y, F_z) and load spatial position data (ΔX, ΔY, ΔZ) of each lifting point in the lifting dynamic parameter set, and construct a multi-lifting-point three-dimensional mechanical feature dataset;

[0035] Step S22: Based on the three-dimensional mechanical feature dataset, synthesize the total load of each spatial axis to generate the total lifting load vector; at the same time, correct the spatial coordinate system transformation parameters according to the lifting device attitude angle (θ,φ) to construct a three-dimensional torque synthesis matrix;

[0036] Step S23: Perform mechanical equivalent calculation based on the total hoisting load vector and the three-dimensional moment synthesis matrix: extract the vertical total load component from the total hoisting load vector, where the vertical total load component is the sum of the absolute values ​​of the Z-axis force components of each hoisting point; calculate the lateral coordinate of the equivalent resultant force application point: lateral moment synthesis value / vertical total load component; calculate the longitudinal coordinate of the equivalent resultant force application point: longitudinal moment synthesis value / vertical total load component; wherein the lateral moment synthesis value comes from the moment component in the X-axis direction of the three-dimensional moment synthesis matrix, and the longitudinal moment synthesis value comes from the moment component in the Y-axis direction of the three-dimensional moment synthesis matrix;

[0037] Step S24: Calculate the Euclidean distance between the point of application of the equivalent resultant force and the spatial location data (ΔX, ΔY, ΔZ) of the load, and generate the overall resultant force eccentricity of the crane;

[0038] Step S25: Based on the sum of the absolute values ​​of the deviations between the three-dimensional force components (F_x, F_y, F_z) of each lifting point and their theoretical axial components, and in conjunction with the overall resultant force eccentricity of the crane, the force system imbalance of the crane is generated using the deviation weighted calculation formula.

[0039] For example, step S22 includes the following steps:

[0040] Step S221: Perform spatial axial mechanical decomposition on the three-dimensional mechanical feature dataset to separate the transverse force component set F_x, the longitudinal force component set F_y, and the vertical force component set F_z for each suspension point;

[0041] Step S222: Based on the spatial axial decomposition results, calculate the total lateral load ΣF_x, the total longitudinal load ΣF_y, and the total vertical load ΣF_z respectively, and synthesize the total hoisting load vector V=(ΣF_x,ΣF_y,ΣF_z);

[0042] Step S223: Based on the spreader attitude angle θ, perform pitch angle compensation correction on the lateral offset of the hook to generate the lateral coordinate system correction coefficient C_x=ΔX·cosθ; Based on the spreader roll angle φ, perform roll angle compensation correction on the longitudinal offset of the hook to generate the longitudinal coordinate system correction coefficient C_y=ΔY·sinφ;

[0043] Step S224: Construct the vertical coordinate compensation factor D_z=ΔZ using the load spatial position deviation ΔZ;

[0044] Step S225: Perform dynamic compensation calculation of the three-dimensional moment using coordinate correction parameters (C_x, C_y, D_z), where:

[0045] The total lateral moment M_x = Σ(F_y·D_z - F_z·C_y);

[0046] The total longitudinal torque My = Σ(F_z·C_x - F_x·D_z);

[0047] The total vertical moment M_z = Σ(F_x·C_y - F_y·C_x);

[0048] Step S226: Construct the three-dimensional moment synthesis matrix [M_x,My,M_z] by summing the compensated lateral, longitudinal, and vertical moments.

[0049] For example, step S25 includes the following steps:

[0050] Step S251: Perform axial theoretical force matching on the three-dimensional force components (F_x, F_y, F_z) of each lifting point, where the transverse theoretical force F'_x = total lifting load / total number of lifting points, the longitudinal theoretical force F'_y = 0, and the vertical theoretical force F'_z = total lifting load / total number of lifting points;

[0051] Step S252: Calculate the lateral force deviation value ΔF_x=|F_x-F'_x|, the longitudinal force deviation value ΔF_y=|F_y-F'_y|, and the vertical force deviation value ΔF_z=|F_z-F'_z| for each lifting point, and generate a three-dimensional force deviation feature set;

[0052] Step S253: Quantify the axial deviation contribution of the three-dimensional force deviation feature set; where the lateral deviation contribution Q_x=∑ΔF_x / (total number of lifting points × F'_x); the longitudinal deviation contribution Q_y=∑ΔF_y / (total number of lifting points × F'_z); and the vertical deviation contribution Q_z=∑ΔF_z / (total number of lifting points × F'_z);

[0053] Step S254: Based on the overall resultant force eccentricity E of the crane, the contribution of axial deviation is dynamically weighted, and the imbalance assessment coefficient of the crane is calculated as K=1+E×(Q_x+Q_y) / (Q_z+safety threshold);

[0054] Step S255: The crane's force system imbalance is finally generated using the formula: Crane force system imbalance = Q_z × K.

[0055] For example, step S3 includes the following process:

[0056] Step S31: Establish a force system imbalance threshold comparison mechanism. When the force system imbalance is greater than the set threshold, an electro-hydraulic adjustment trigger signal is generated.

[0057] Step S32: Extract the real-time deviation values ​​of the three-dimensional force components (F_x, F_y, F_z) of the lifting point according to the electro-hydraulic adjustment trigger signal, and calculate the hydraulic adjustment amount ΔL of each lifting point using the formula: Adjustment compensation amount = force system imbalance degree × (actual force component - theoretical force component) / theoretical force component;

[0058] Step S33: Based on the spreader attitude angle (θ,φ), perform spatial angle vector decomposition on the hydraulic adjustment amount ΔL to generate the pulse width modulation signal of the electro-hydraulic proportional valve: lateral adjustment component PWM_x=ΔL·cosθ; longitudinal adjustment component PWM_y=ΔL·sinφ;

[0059] Step S34: Drive the displacement compensation of the corresponding outrigger cylinder according to the pulse width modulation signal: lateral compensation execution time T_x = PWM_x / (maximum cylinder speed × safety factor); longitudinal compensation execution time T_y = PWM_y / (maximum cylinder speed × safety factor);

[0060] Step S35: After the compensation is completed, the dynamic parameter set of the hoisting is collected again to verify the imbalance of the secondary force system until the set threshold is met, at which point the correction cycle is terminated.

[0061] As described above, the dynamic safety assessment method for hoisting construction provided by the present invention has at least the following beneficial effects:

[0062] This invention utilizes a multimodal sensor array to collect real-time operational parameters of a lifting system and generate a dynamic parameter set for lifting. This has significant technical advantages and is essential, effectively improving the safety and efficiency of lifting operations. Firstly, the generation of the dynamic parameter set includes pressure distribution data at each lifting point, spreader attitude angle data, and load spatial position data, providing comprehensive real-time data support for lifting operations. Real-time monitoring of this data allows operators to clearly understand the working status of the lifting system and promptly identify potential safety hazards. For example, pressure distribution data helps determine the stress on the lifting points, preventing lifting accidents caused by uneven loads; while spreader attitude angle data helps ensure the stability of the spreader during operation, avoiding dangers caused by tilting or swaying.

[0063] Secondly, based on the dynamic parameter set of the lifting operation, dynamic balance analysis of the force system is performed, enabling real-time assessment of the crane's force system imbalance. This process is crucial for ensuring the safety of lifting operations. Force system imbalance can lead to serious accidents such as crane overturning and instability during operation, posing significant risks to personal and equipment safety. By monitoring and analyzing the crane's force system status in real time, the management system can react rapidly when the imbalance exceeds a set threshold, triggering an active imbalance correction mechanism. This automated response mechanism effectively reduces the need for manual intervention and improves the system's response speed and accuracy.

[0064] When the automatic imbalance correction mechanism is triggered, the electro-hydraulic proportional valve adjusts the crane's stress state to restore it to a balanced state. Compared to traditional manual adjustment methods, this automated correction method not only improves the accuracy of the adjustment but also significantly reduces the time required. Continuous operation of a crane in an unbalanced state can not only lead to equipment damage but also pose a safety threat to operators. The combination of real-time monitoring and the automatic correction mechanism can prevent accidents from occurring in the first instance, ensuring the safety of lifting operations.

[0065] In summary, by acquiring dynamic lifting parameters in real time using a multimodal sensor array and performing dynamic force balance analysis based on this data, the safety and efficiency of lifting operations can be significantly improved. The automated imbalance correction mechanism not only reduces the risk of accidents but also enhances operational flexibility and response speed, providing strong technical support for modern lifting operations. Attached Figure Description

[0066] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0067] Figure 1 This is a schematic diagram showing the connections between the steps of the method of the present invention. Detailed Implementation

[0068] The following description, in conjunction with the implementation of this invention, is merely an example and illustration of the concept of this invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the inventive concept or exceed the scope defined in these claims, all of which should fall within the protection scope of this invention. Example

[0069] Please see Figure 1 As shown, a dynamic safety assessment method for hoisting operations includes the following steps:

[0070] S1: Real-time acquisition of lifting system operation parameters via a multi-modal sensor array generates a lifting dynamic parameter set. This set includes pressure distribution data, spreader attitude angle data, and load spatial position data for each lifting point. The pressure distribution data includes real-time three-dimensional force components (F_x, F_y, F_z) for each lifting point. The spreader attitude angle data includes the pitch angle θ and roll angle φ of the hook assembly. The load spatial position data includes the coordinate deviation (ΔX, ΔY, ΔZ) of the load's geometric center in the global coordinate system.

[0071] Step S1 includes the following steps:

[0072] Step S11: Install a pressure sensor array on the inner side of the load-bearing beam at the lifting point to collect the instantaneous value of the vertical force at each lifting point in real time and generate an initial pressure dataset.

[0073] Step S12: Based on the initial pressure dataset, abnormal pressure fluctuations are detected at the lifting points. When the pressure difference between adjacent lifting points exceeds the preset tolerance threshold, the inertial measurement unit wake-up command is triggered to obtain the real-time pitch angle θ and roll angle φ of the spreader.

[0074] Step S13: Combining the pitch angle θ and roll angle φ, convert the vertical force values ​​of each lifting point into three-dimensional force components: X-axis component F_x = F_z × tanθ; Y-axis component F_y = F_z × tanφ; Z-axis component F_z retains the original pressure sensor data F_z, thereby generating a three-dimensional pressure distribution data set for each lifting point;

[0075] Step S14: Install a laser scanning module at the load end, calculate the spatial coordinate deviation of the load geometric center relative to the hoisting origin using a point cloud registration algorithm, optimize coordinate accuracy by fusing attitude angle data, and generate load spatial position data (ΔX, ΔY, ΔZ).

[0076] Step S15: Integrate and package the three-dimensional pressure distribution data set, pitch / roll angle data, and load space coordinate data to generate a set of hoisting dynamic parameters.

[0077] In this embodiment of the invention, high-precision resistance strain gauge pressure sensors are embedded at key stress locations on each load-bearing beam of the hoisting system to continuously collect raw pressure signals in the vertical direction of each hoisting point. After eliminating mechanical vibration noise using a Kalman filter algorithm, a time-series pressure dataset is generated. When the absolute value of the pressure difference between adjacent hoisting points continuously exceeds a preset threshold for three sampling cycles, the system automatically activates the miniature inertial measurement unit at the top of the hoisting device. A quaternion calculation algorithm is used to calculate the pitch angle θ and roll angle φ of the hook assembly in real time. The pitch angle θ is obtained by using the arctangent function to calculate the ratio of the Z-axis to X-axis components of the accelerometer, and the roll angle φ is obtained through dynamic analysis of the Y-axis and Z-axis components. Based on the acquired attitude angle data, each hoisting point... The vertical pressure F_z is transformed into a spatial coordinate system. The X-axis component F_x is calculated by multiplying F_z by the pitch tangent, and the Y-axis component F_y is obtained by multiplying F_z by the roll tangent. This constructs a pressure topology network containing three-dimensional force vectors. Simultaneously, a reflective marker array is arranged on the load surface, and the load contour point cloud data is acquired using a phase-matrix laser scanner. The real-time point cloud is registered with the design model using an iterative nearest-point algorithm. Combined with attitude angle data, the geometric center coordinates are rotated and compensated to calculate the actual spatial position deviation of the load. Finally, the three-dimensional pressure distribution, dual-axis attitude angles, and spatial deviation coordinates are aligned and encapsulated according to timestamps to form a lifting dynamic parameter set.

[0078] The operation logic for step S14 is as follows:

[0079] Step S141: Preset a feature marker point array on the load surface, and collect real-time three-dimensional point cloud data of the load's outer contour through a laser scanning module;

[0080] Step S142: Perform point cloud registration and matching between the real-time 3D point cloud data and the preset load design model, and filter out the effective point cloud set corresponding to the feature marker points;

[0081] Step S143: Based on the effective point cloud set, calculate the real-time coordinate values ​​of each marker point in the hoisting global coordinate system, and take the arithmetic average of all marker point coordinates to obtain the current load geometric center coordinates (W_x, W_y, W_z);

[0082] Step S144: Read the pitch angle θ and roll angle φ from the spreader attitude angle data, and perform attitude correction and compensation on the geometric center coordinates using the coordinate rotation matrix:

[0083] The corrected X-axis coordinates are obtained as follows: ΔX1=W_x-L×sinθ, the corrected Y-axis coordinates are obtained as follows: ΔY1=W_y-L×sinφ, and the corrected Z-axis coordinates are obtained as follows: ΔZ1=W_z, where L is the length of the suspension chain;

[0084] Step S145: Verify the stability of the corrected coordinate data using analysis of variance. When the coordinate deviation of three consecutive scans is less than the set accuracy threshold, generate the final load spatial location data (ΔX, ΔY, ΔZ).

[0085] In this embodiment of the invention, high-reflectivity marker spheres are affixed to the four corners and center of the load's outer surface to form a feature point array. A phase-type laser scanner is used to acquire three-dimensional point cloud data of the load contour. Point cloud clusters corresponding to the marker spheres are selected by filtering through a reflection intensity threshold. An iterative nearest-point algorithm is used to spatially register and align the real-time scanned point cloud with a preset load CAD model, automatically identifying and extracting the spatial coordinates of each marker sphere vertex. The arithmetic mean of the X / Y / Z coordinate values ​​of all valid marker points is calculated as the initial coordinates of the load's geometric center. Simultaneously, based on the pitch and roll angle data fed back in real time by the microelectromechanical inertial measurement unit installed at the top of the sling, combined with the sling chain length parameter, dynamic compensation calculation of geometric coordinates is performed. That is, the X-axis coordinate value needs to be subtracted from the horizontal displacement component caused by the sling tilt, the Y-axis coordinate is compensated for the lateral offset in the same way, and the Z-axis coordinate maintains the original vertical measurement value. Finally, the standard deviation of the coordinate data after three consecutive compensations is calculated and analyzed. When the fluctuation amplitude of all three axes is less than the set millimeter-level accuracy threshold, the final spatial position deviation data after attitude correction is generated.

[0086] Step S12 includes the following steps:

[0087] Step S121: Extract the vertical pressure value F_i of each lifting point at the current moment from the initial pressure dataset, where i is the number of each lifting point, i=1,2,...,N; N represents the total number of lifting points;

[0088] Step S122: Calculate the real-time pressure difference between adjacent lifting points and take the absolute value to finally obtain the absolute value of the real-time pressure difference between each adjacent lifting point |ΔF_j|, where j is the number of each adjacent lifting point and the value of j ranges from 1 to N-1;

[0089] Step S123: Select the maximum value max(|ΔF_j|) from |ΔF_j|, compare it with the preset tolerance threshold F_th, and generate an abnormal fluctuation marker signal when max(|ΔF_j|) > F_th;

[0090] Step S124: Trigger the wake-up command of the inertial measurement unit according to the abnormal fluctuation marker signal, and synchronously collect the angular velocity ω_x, ω_y, ω_z and acceleration a_z data of the lifting device in three axes;

[0091] Step S125: Convert ω_x, ω_y, ω_z, and a_z into pitch angle θ and roll angle φ using a quaternion solving algorithm. The specific process is as follows:

[0092] Step S1251: Calculate the initial tilt angle θ_0=arctan(a_x / a_z) and the initial roll angle φ_0=arctan(a_y / a_z) using accelerometer data;

[0093] Step S1252: Integrate the gyroscope angular velocity data to obtain the angular change, which is divided into pitch angle change Δθ=∫ω_ydt and roll angle change Δφ=∫ω_xdt;

[0094] Step S1253: Use the complementary filtering algorithm to fuse θ_0 and Δθ to obtain the final pitch angle θ=α×θ_0+(1-α)(θ_prev+Δθ), where α is the filtering coefficient and θ_prev is the angle value of the previous cycle;

[0095] Step S1254: Similarly, calculate the roll angle φ = α × φ_0 + (1 - α)(φ_prev + Δφ).

[0096] In this embodiment of the invention, vertical pressure data of each lifting point is acquired in real time through a piezoresistive sensor array. After eliminating instantaneous mechanical vibration noise using a moving average filtering algorithm, a pressure time sequence is formed. The pressure difference between adjacent lifting points is continuously calculated and its absolute value is taken. When the pressure difference between any pair of adjacent points exceeds a preset safety threshold and continues for three sampling cycles, the system automatically activates the miniature inertial navigation module on the top of the lifting device, and synchronously collects X / Y axis angular velocity and Z axis acceleration data. Based on the ratio of the Z-axis to X-axis components of the accelerometer, the initial pitch angle estimate is calculated using the arctangent function. At the same time, the initial roll angle is dynamically analyzed based on the Y-axis and Z-axis components. The gyroscope angular velocity data is integrated over time, and the change in the attitude angle of the lifting device in the current cycle is accumulated. A complementary filtering algorithm is used to fuse the low-frequency stable angle information measured by the accelerometer with the high-frequency dynamic data of the gyroscope. The weight of the acceleration data is set to 0.02, and the weight of the gyroscope integral data is set to 0.98, thereby eliminating the zero drift error of the gyroscope and suppressing the motion interference of the accelerometer. Finally, dynamically compensated pitch and roll angle data are output.

[0097] S2: Construct a multi-point three-dimensional mechanical feature dataset based on the hoisting dynamic parameter set, perform dynamic equilibrium analysis of the force system, synthesize the total axial load of each space based on the three-dimensional mechanical feature dataset, and generate the total hoisting load vector; then construct a three-dimensional moment synthesis matrix, perform mechanical equivalent solution based on the total hoisting load vector and the three-dimensional moment synthesis matrix, thereby generating the overall resultant force eccentricity of the crane, and finally analyze the force system imbalance of the crane.

[0098] Step S2 includes the following steps:

[0099] Step S21: Extract the real-time three-dimensional force components (F_x, F_y, F_z) and load spatial position data (ΔX, ΔY, ΔZ) of each lifting point in the lifting dynamic parameter set, and construct a multi-lifting-point three-dimensional mechanical feature dataset;

[0100] Step S22: Based on the three-dimensional mechanical feature dataset, synthesize the total load of each spatial axis to generate the total lifting load vector; at the same time, correct the spatial coordinate system transformation parameters according to the lifting device attitude angle (θ,φ) to construct a three-dimensional torque synthesis matrix;

[0101] Step S23: Perform mechanical equivalent calculation based on the total hoisting load vector and the three-dimensional moment synthesis matrix: extract the vertical total load component from the total hoisting load vector, where the vertical total load component is the sum of the absolute values ​​of the Z-axis force components of each hoisting point; calculate the lateral coordinate of the equivalent resultant force application point: lateral moment synthesis value / vertical total load component; calculate the longitudinal coordinate of the equivalent resultant force application point: longitudinal moment synthesis value / vertical total load component; wherein the lateral moment synthesis value comes from the moment component in the X-axis direction of the three-dimensional moment synthesis matrix, and the longitudinal moment synthesis value comes from the moment component in the Y-axis direction of the three-dimensional moment synthesis matrix;

[0102] Step S24: Calculate the Euclidean distance between the point of application of the equivalent resultant force and the spatial location data (ΔX, ΔY, ΔZ) of the load, and generate the overall resultant force eccentricity of the crane;

[0103] Step S25: Based on the sum of the absolute values ​​of the deviations between the three-dimensional force components (F_x, F_y, F_z) of each lifting point and their theoretical axial components, and in conjunction with the overall resultant force eccentricity of the crane, the force system imbalance of the crane is generated using the deviation weighted calculation formula.

[0104] In this embodiment of the invention, the hoisting dynamic parameter set is acquired in real time by a three-dimensional force sensor array installed at the hoisting points, simultaneously measuring the force components and load spatial offset along the x, y, and z axes at each hoisting point. The construction process of the three-dimensional mechanical feature dataset first performs spatiotemporal alignment processing on the mechanical parameters of each hoisting point, integrating the force data from different sampling times into a unified coordinate system through a timestamp synchronization mechanism, and eliminating acquisition delay errors between sensors through spatial interpolation compensation. When synthesizing the total spatial axial load, the system adopts the principle of static equilibrium, performing algebraic superposition calculations on the force components of each axis: the total lateral load is determined by the algebraic sum of the x-axis components of each hoisting point, the total longitudinal load is obtained by accumulating the y-axis components, and the total vertical load is the sum of the absolute values ​​of the z-axis components. The final hoisting total load vector represents the overall force state of the system in three-dimensional vector form. The lifting device attitude angle parameters are acquired in real time through an inertial measurement unit, and the original torque data is spatially compensated using a quaternion coordinate system transformation method. Specifically, a three-dimensional rotation matrix is ​​first constructed based on the hook pitch and roll angles. Then, the torque components in the local coordinate system of the lifting point are converted to components in the global coordinate system through matrix mapping. The lateral torque composite value is calculated by projecting the x-axis component of the rotation matrix, and the longitudinal torque is obtained by multiplying the y-axis component of the rotation matrix with the original torque. The constructed three-dimensional torque composite matrix thus fully characterizes the spatial torque distribution characteristics of the lifting system. During the mechanical equivalent calculation, the system uses the total vertical load as a reference quantity. This reference quantity is calculated from the absolute values ​​of the z-axis force components of each lifting point, ensuring the elimination of negative force interference when the lifting device attitude changes. Then, the accurate spatial coordinates of the equivalent resultant force application point are obtained by dividing the lateral and longitudinal torque composite values ​​by the total vertical load components, respectively. A sliding window mean filtering algorithm is used during the calculation to eliminate dynamic jitter errors. The overall resultant force eccentricity of the crane is generated using a Euclidean distance model. Three-dimensional spatial distance is calculated based on the coordinate deviation between the equivalent point of application and the geometric center of the load. An overshoot protection mechanism is incorporated into the calculation process; when a sudden positional shift is detected, a second-order low-pass filtering algorithm is automatically activated to smooth trajectory fluctuations. The final force imbalance assessment module is achieved by fusing multi-dimensional parameters. First, the absolute values ​​of the axial deviations of the three-dimensional force components at each lifting point from their theoretical values ​​are summed. The lateral theoretical value is taken as the uniform distribution value of the total lifting load, while the longitudinal theoretical value is set to zero to match the ideal force distribution. Then, through normalization, the ratio of the sum of axial deviations to the theoretical value is converted into a benchmark imbalance coefficient. Finally, considering the spatial amplification effect of the overall resultant force eccentricity, a linear weighted algorithm is used to generate the comprehensive force imbalance.

[0105] Step S22 includes the following steps:

[0106] Step S221: Perform spatial axial mechanical decomposition on the three-dimensional mechanical feature dataset to separate the transverse force component set F_x, the longitudinal force component set F_y, and the vertical force component set F_z for each suspension point;

[0107] Step S222: Based on the spatial axial decomposition results, calculate the total lateral load ΣF_x, the total longitudinal load ΣF_y, and the total vertical load ΣF_z respectively, and synthesize the total hoisting load vector V=(ΣF_x,ΣF_y,ΣF_z);

[0108] Step S223: Based on the spreader attitude angle θ, perform pitch angle compensation correction on the lateral offset of the hook to generate the lateral coordinate system correction coefficient C_x=ΔX·cosθ; Based on the spreader roll angle φ, perform roll angle compensation correction on the longitudinal offset of the hook to generate the longitudinal coordinate system correction coefficient C_y=ΔY·sinφ;

[0109] Step S224: Construct the vertical coordinate compensation factor D_z=ΔZ using the load spatial position deviation ΔZ;

[0110] Step S225: Perform dynamic compensation calculation of the three-dimensional moment using coordinate correction parameters (C_x, C_y, D_z), where:

[0111] The total lateral moment M_x = Σ(F_y·D_z-F_z·C_y) is the cumulative value of the product of the longitudinal force component and the vertical compensation factor at each lifting point minus the cumulative value of the product of the vertical force component and the longitudinal correction coefficient.

[0112] The total longitudinal torque My = Σ(F_z·C_x - F_x·D_z);

[0113] The total vertical moment M_z = Σ(F_x·C_y - F_y·C_x), which is the cumulative value of the interaction between the lateral force component and the longitudinal correction coefficient;

[0114] Step S226: Construct the three-dimensional moment synthesis matrix [M_x,My,M_z] by summing the compensated lateral, longitudinal, and vertical moments.

[0115] In this embodiment of the invention, the raw data collected by the three-dimensional force sensor array installed at the lifting points is first preprocessed with spatial alignment to eliminate the sampling time difference between different lifting points. Then, axial isolation technology is used to independently classify the three-dimensional force components of each lifting point according to the spatial coordinate direction: the transverse component set F_x corresponds to the force data in the x-axis direction of the lifting point, the longitudinal component set F_y represents the load distribution in the y-axis direction, and the vertical component set F_z reflects the load-bearing state of each lifting point in the z-axis direction. In the total load vector synthesis stage, the system obtains the transverse total load by horizontally superimposing and summing the transverse component sets using an algebraic accumulation method, obtains the longitudinal total load by vertically accumulating the longitudinal component sets, and calculates the total vertical load by absolute value accumulation of the vertical component sets. The final generated total lifting load vector completely represents the spatial mechanical distribution characteristics of the lifting system. To address the spatial coordinate system deviation caused by the spreader's attitude angle, the system employs an attitude compensation correction algorithm: the hook pitch angle θ, acquired in real-time by a high-precision tilt sensor, is used in the lateral offset correction calculation, with the specific correction coefficient determined by the product of the lateral position deviation ΔX and the cosine of the pitch angle. Similarly, the longitudinal correction coefficient is obtained by multiplying the sine of the hook roll angle φ and the longitudinal position deviation ΔY. This correction method effectively eliminates spatial coordinate projection errors caused by spreader sway. The vertical coordinate compensation factor directly uses the original value of the load spatial position deviation ΔZ, preserving the true physical characteristics of load height changes. In the dynamic compensation calculation of three-dimensional moment, the system adopts the principle of spatial vector orthogonal decomposition to handle the coupling relationship between the force components of each lifting point and the correction parameters: the sum of lateral moments is obtained by subtracting the cumulative value of the product of the longitudinal force component and the vertical compensation factor from the cumulative value of the product of the vertical force component and the longitudinal correction coefficient; the sum of longitudinal moments is obtained by subtracting the cumulative value of the interaction between the vertical force component and the lateral correction coefficient from the cumulative value of the interaction between the lateral force component and the vertical compensation factor; the sum of vertical moments is calculated by accumulating the spatial coupling effect between the lateral force component and the longitudinal correction coefficient. The final constructed three-dimensional moment synthesis matrix uses spatial multi-dimensional parameter fusion technology to standardize and encode the dynamically compensated and calibrated lateral, longitudinal, and vertical moment values, forming a resolvable matrix data structure.

[0116] Step S25 includes the following steps:

[0117] Step S251: Perform axial theoretical force matching on the three-dimensional force components (F_x, F_y, F_z) of each lifting point, where the transverse theoretical force F'_x = total lifting load / total number of lifting points, the longitudinal theoretical force F'_y = 0, and the vertical theoretical force F'_z = total lifting load / total number of lifting points;

[0118] Step S252: Calculate the lateral force deviation value ΔF_x=|F_x-F'_x|, the longitudinal force deviation value ΔF_y=|F_y-F'_y|, and the vertical force deviation value ΔF_z=|F_z-F'_z| for each lifting point, and generate a three-dimensional force deviation feature set;

[0119] Step S253: Quantify the axial deviation contribution of the three-dimensional force deviation feature set; where the lateral deviation contribution Q_x=∑ΔF_x / (total number of lifting points × F'_x); the longitudinal deviation contribution Q_y=∑ΔF_y / (total number of lifting points × F'_z); and the vertical deviation contribution Q_z=∑ΔF_z / (total number of lifting points × F'_z);

[0120] Step S254: Based on the overall resultant force eccentricity E of the crane, the contribution of axial deviation is dynamically weighted, and the imbalance assessment coefficient of the crane is calculated as K = 1 + E × (Q_x + Q_y) / (Q_z + safety threshold), where the safety threshold is 5% of the theoretical lifting load;

[0121] Step S255: The crane's force system imbalance is finally generated using the formula: Crane force system imbalance = Q_z × K.

[0122] In this embodiment of the invention, the actual stress state of each lifting point in the hoisting system is first idealized and modeled: the lateral theoretical stress is set according to the algebraic average distribution of the total hoisting load across the number of lifting points; the longitudinal theoretical stress is set to zero to eliminate the interference of lateral forces on the ideal stress; and the vertical theoretical stress is determined based on the vertical bearing capacity of the total hoisting load evenly distributed to each lifting point. The actual stress deviation analysis uses the absolute value cumulative difference method. The system calculates the absolute deviation between the actual stress and the theoretical value in each lifting point's three-dimensional direction. The lateral deviation reflects the uneven distribution of horizontal forces among the lifting points, the longitudinal deviation characterizes the abnormal lateral load of the system, and the vertical deviation reflects the hoisting load-bearing stability. In the deviation contribution quantification process, the contribution coefficients of the lateral, longitudinal, and vertical directions to the overall imbalance state are obtained by comparing the sum of each axial deviation with the product of the corresponding theoretical value and the number of lifting points. The calculation of the longitudinal deviation contribution uses the vertical theoretical value as a benchmark to ensure the data validity when the longitudinal theoretical stress is zero. The dynamic weighted processing innovatively introduces the overall resultant force eccentricity as a spatial amplification factor. The product of this eccentricity and the sum of the horizontal deviation contributions is proportionally calculated based on the theoretical lifting load's preset safety threshold, which is then superimposed on the vertical deviation contributions, thereby generating a comprehensive evaluation coefficient. The final force imbalance is obtained by multiplying the vertical contribution by the comprehensive evaluation coefficient.

[0123] S3: When the force imbalance of the crane exceeds the set threshold, the imbalance active correction mechanism is triggered, and the imbalance of the crane is corrected by adjusting the electro-hydraulic proportional valve.

[0124] Step S3 includes the following process:

[0125] Step S31: Establish a force system imbalance threshold comparison mechanism. When the force system imbalance is greater than the set threshold, an electro-hydraulic adjustment trigger signal is generated.

[0126] Step S32: Extract the real-time deviation values ​​of the three-dimensional force components (F_x, F_y, F_z) of the lifting point according to the electro-hydraulic adjustment trigger signal, and calculate the hydraulic adjustment amount ΔL of each lifting point using the formula: Adjustment compensation amount = force system imbalance degree × (actual force component - theoretical force component) / theoretical force component;

[0127] Step S33: Based on the spreader attitude angle (θ,φ), perform spatial angle vector decomposition on the hydraulic adjustment amount ΔL to generate the pulse width modulation signal of the electro-hydraulic proportional valve: lateral adjustment component PWM_x=ΔL·cosθ; longitudinal adjustment component PWM_y=ΔL·sinφ;

[0128] Step S34: Drive the displacement compensation of the corresponding outrigger cylinder according to the pulse width modulation signal: lateral compensation execution time T_x = PWM_x / (maximum cylinder speed × safety factor); longitudinal compensation execution time T_y = PWM_y / (maximum cylinder speed × safety factor);

[0129] Step S35: After the compensation is completed, the dynamic parameter set of the hoisting is collected again to verify the imbalance of the secondary force system until the set threshold is met, at which point the correction cycle is terminated.

[0130] In this embodiment of the invention, the force imbalance threshold comparison mechanism is implemented in real time through an embedded control system. When the intelligent monitoring module of the hoisting system detects that the force imbalance exceeds the preset safety threshold, the system immediately starts the electro-hydraulic adjustment trigger signal generation program. The electro-hydraulic adjustment trigger signal is transmitted to the hydraulic adjustment controller through an industrial fieldbus. During this process, the system synchronously reads the real-time three-dimensional force component data of each hoisting point. For each hoisting point, based on the deviation ratio between its actual force component and the theoretical force component, and combined with the current force imbalance, the hydraulic adjustment compensation amount is dynamically calculated. Specifically, the force imbalance is used as the adjustment gain coefficient, and the amplitude of the required cylinder displacement compensation amount for each hoisting point is linearly adjusted according to the percentage deviation of the actual force component from the theoretical value. In the spatial angle vector decomposition stage, the system acquires the pitch angle and roll angle data of the hoisting device in real time, and uses the triangular projection principle to decompose the axial compensation amount of the cylinder into lateral and longitudinal adjustment components. The lateral adjustment component is scaled by the cosine value of the pitch angle, and the longitudinal adjustment component is scaled by the sine value of the roll angle, generating the pulse width modulation waveform signal of the corresponding electro-hydraulic proportional valve. When the pulse width modulation signal drives the electro-hydraulic proportional valve through the PID control module, the system dynamically calculates the execution time of the lateral and longitudinal compensation actions based on the maximum cylinder speed parameter and a preset safety factor. The execution time calculation is set using the reciprocal relationship between the modulation signal amplitude and the cylinder speed, ensuring a smooth and controllable cylinder displacement compensation process. After a single adjustment is completed, the system automatically triggers the secondary acquisition of the hoisting dynamic parameter set and the force imbalance calculation module. Through a closed-loop control mechanism of iterative iteration, it continuously optimizes the force distribution at each hoisting point until the force imbalance falls back to within the safety threshold.

[0131] It should be noted that the interval and threshold sizes are set for ease of comparison. The size of the threshold depends on the amount of sample data and the base number set by those skilled in the art for each set of sample data, as long as it does not affect the proportional relationship between the parameter and the quantized value. Furthermore, the above formulas are all dimensionless calculations, and the formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0132] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0133] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0134] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0135] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic safety assessment of a hoisting construction, characterized by The method comprises the following steps: S1: Real-time acquisition of hoisting system operation parameters by a multi-modal sensor array, generation of a hoisting dynamic parameter set, the hoisting dynamic parameter set comprising pressure distribution data of each hoisting point, hoist attitude angle data and load space position data; S2: Construction of a multi-hoisting point three-dimensional mechanical characteristic data set based on the hoisting dynamic parameter set, execution of force system dynamic balance analysis, synthesis of each spatial axial total load based on the three-dimensional mechanical characteristic data set, generation of a hoisting total load vector; Further construction of a three-dimensional torque synthesis matrix, mechanical equivalent calculation based on the hoisting total load vector and the three-dimensional torque synthesis matrix, thereby generating a crane overall resultant force eccentricity, and finally obtaining a crane force system imbalance degree; Step S21: Extraction of real-time three-dimensional force components (F_x, F_y, F_z) of each hoisting point in the hoisting dynamic parameter set and load space position data (ΔX, ΔY, ΔZ), construction of a multi-hoisting point three-dimensional mechanical characteristic data set; Step S22: Synthesis of each spatial axial total load based on the three-dimensional mechanical characteristic data set, generation of a hoisting total load vector; At the same time, according to the hoist attitude angle (θ, φ), the spatial coordinate system transformation parameters are corrected, and a three-dimensional torque synthesis matrix is constructed; Step S23: Mechanical equivalent calculation based on the hoisting total load vector and the three-dimensional torque synthesis matrix: extraction of a vertical direction total load component from the hoisting total load vector, the vertical direction total load component being the sum of absolute values of Z-axis force components of each hoisting point; Calculation of the transverse coordinate of the equivalent resultant force action point: transverse torque synthesis value / vertical direction total load component; Calculation of the longitudinal coordinate of the equivalent resultant force action point: longitudinal torque synthesis value / vertical direction total load component; Wherein the transverse torque synthesis value is derived from the torque component in the X-axis direction in the three-dimensional torque synthesis matrix, and the longitudinal torque synthesis value is derived from the torque component in the Y-axis direction in the three-dimensional torque synthesis matrix; Step S24: Calculation of the Euclidean distance between the equivalent resultant force action point and the load space position data (ΔX, ΔY, ΔZ), generation of a crane overall resultant force eccentricity; Step S25: Generation of a crane force system imbalance degree through a deviation weighted calculation formula based on the sum of absolute values of deviations of three-dimensional force components (F_x, F_y, F_z) of each hoisting point from their theoretical axial components, and the crane overall resultant force eccentricity; The operation of step S22 comprises the following steps: Step S221: Spatial axial mechanical decomposition of the three-dimensional mechanical characteristic data set, separation of each hoisting point transverse force component set F_x, longitudinal force component set F_y and vertical force component set F_z; Step S222: According to the spatial axial decomposition result, the transverse total load ΣF_x, the longitudinal total load ΣF_y and the vertical total load ΣF_z are calculated respectively, and the hoisting total load vector V=(ΣF_x,ΣF_y,ΣF_z) is synthesized; Step S223: Based on the hoist attitude angle θ, the hook transverse offset is compensated and corrected by the pitch angle, generating a transverse coordinate system correction coefficient C_x=ΔX·cosθ; based on the hoist roll angle φ, the hook longitudinal offset is compensated and corrected by the roll angle, generating a longitudinal coordinate system correction coefficient C_y=ΔY·sinφ; Step S224: Construct the vertical direction coordinate compensation factor D_z = ΔZ using the load space position deviation amount ΔZ; Step S225: Perform dynamic compensation calculation on the three-dimensional torque through the coordinate correction parameters (C_x, C_y, D_z), wherein: The lateral torque sum M_x = Σ (F_y·D_z-F_z·C_y); The longitudinal torque sum M_y = Σ (F_z·C_x-F_x·D_z); The vertical torque sum M_z = Σ (F_x·C_y-F_y·C_x); Step S226: Construct the compensated lateral, longitudinal, and vertical torque sums into a three-dimensional torque synthesis matrix [M_x, M_y, M_z]; Step S25 includes the following steps: Step S251: Match the three-dimensional force components (F_x, F_y, F_z) of each lifting point with the axial theoretical force, wherein the lateral theoretical force F'_x = total lifting load / total number of lifting points, the longitudinal theoretical force F'_y = 0, and the vertical theoretical force F'_z = total lifting load / total number of lifting points; Step S252: Calculate the lateral force deviation value ΔF_x = |F_x-F'_x|, the longitudinal force deviation value ΔF_y = |F_y-F'_y|, and the vertical force deviation value ΔF_z = |F_z-F'_z| of each lifting point, and generate a three-dimensional force deviation feature set; Step S253: Quantify the axial deviation contribution of the three-dimensional force deviation feature set; wherein the lateral deviation contribution Q_x = ∑ΔF_x / (total number of lifting points × F'_x); the longitudinal deviation contribution Q_y = ∑ΔF_y / (total number of lifting points × F'_z); and the vertical deviation contribution Q_z = ∑ΔF_z / (total number of lifting points × F'_z); Step S254: Perform dynamic weighting processing on the axial deviation contribution based on the crane overall resultant force eccentricity E, and calculate the imbalance evaluation coefficient K of the crane = 1 + E × (Q_x + Q_y) / (Q_z + safety threshold); Step S255: Finally generate the force system imbalance degree of the crane through the formula: force system imbalance degree of the crane = Q_z × K; S3: When the force system imbalance degree of the crane is greater than the set threshold, trigger the imbalance active correction mechanism to adjust the crane through the electro-hydraulic proportional valve to correct the imbalance; Step S31: Establish a force system imbalance degree threshold comparison mechanism, and generate an electro-hydraulic adjustment trigger signal when the force system imbalance degree is greater than the set threshold; Step S32: Extract the real-time deviation value of the three-dimensional force components (F_x, F_y, F_z) of the lifting point according to the electro-hydraulic adjustment trigger signal, and calculate the hydraulic adjustment amount ΔL of each lifting point through the formula: adjustment compensation = force system imbalance degree × (actual force component - theoretical force component) / theoretical force component; Step S33: Based on the sling attitude angle (θ, φ), perform spatial angle vector decomposition on the hydraulic adjustment amount ΔL to generate the pulse width modulation signal of the electro-hydraulic proportional valve: lateral adjustment component PWM_x = ΔL·cosθ; longitudinal adjustment component PWM_y = ΔL·sinφ; Step S34: driving displacement compensation of corresponding outrigger oil cylinders according to the pulse width modulation signal: lateral compensation amount execution time T_x=PWM_x / (oil cylinder maximum speed x safety factor); longitudinal compensation amount execution time T_y=PWM_y / (oil cylinder maximum speed x safety factor); Step S35: after the compensation is completed, the hoisting dynamic parameter set is re-acquired to verify the secondary force system imbalance degree, and the correction cycle is terminated when the set threshold is met.

2. The dynamic safety assessment method for hoisting construction according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: arranging a pressure sensor array on the inside of the hoisting point load-bearing beam, collecting real-time vertical force instantaneous values of each hoisting point, and generating an initial pressure data set; Step S12: based on the initial pressure data set, detecting abnormal pressure fluctuations of the hoisting point, when the pressure difference between adjacent hoisting points exceeds the preset tolerance threshold, triggering an inertial measurement unit wake-up instruction to obtain the real-time pitch angle θ and roll angle φ of the hoist; Step S13: combining the pitch angle θ and the roll angle φ, converting the vertical force values of each hoisting point into three-dimensional force components: X-axis component F_x=F_z*tanθ; Y-axis component F_y=F_z*tanφ; Z-axis component F_z remains the original pressure sensor data F_z, thereby generating a three-dimensional pressure distribution data set for each hoisting point; Step S14: installing a laser scanning module at the load end, calculating the spatial coordinate deviation of the load geometric center relative to the hoisting origin through a point cloud registration algorithm, optimizing coordinate accuracy by fusing attitude angle data, and generating load space position data (ΔX, ΔY, ΔZ), which includes the coordinate deviation amount (ΔX, ΔY, ΔZ) of the load geometric center in the global coordinate system; Step S15: integrating and packaging the three-dimensional pressure distribution data set, pitch angle / roll angle data, and load space coordinate data to generate a hoisting dynamic parameter set.

3. The dynamic safety assessment method for hoisting construction according to claim 2, characterized in that, The operation logic of step S14 is as follows: Step S141: presetting an array of feature marker points on the load surface, and collecting real-time three-dimensional point cloud data of the load outer contour through the laser scanning module; Step S142: matching the real-time three-dimensional point cloud data with the preset load design model through point cloud registration, and selecting an effective point cloud set corresponding to the feature marker points; Step S143: based on the effective point cloud set, calculating the real-time coordinate values of each marker point in the hoisting global coordinate system, and taking the arithmetic average of all marker point coordinates to obtain the current load geometric center coordinates (W_x, W_y, W_z); Step S144: reading the pitch angle θ and roll angle φ in the hoist attitude angle data, and compensating the geometric center coordinates through a coordinate rotation matrix: respectively obtaining the corrected X-axis coordinate: ΔX1=W_x-L×sinθ, the corrected Y-axis coordinate: ΔY1=W_y-L×sinφ, and the corrected Z-axis coordinate ΔZ1=W_z, wherein L is the length of the sling chain; Step S145: verifying the stability of the corrected coordinate data through the variance analysis method, and when the coordinate deviation of three consecutive scans is less than the set precision threshold, generating the final load space position data (ΔX, ΔY, ΔZ).

4. The dynamic safety assessment method for hoisting construction according to claim 2, characterized in that, Step S12 includes the following steps: Step S121: Extract the vertical pressure value F_i of each lifting point at the current time from the initial pressure data set, where i is the number of each lifting point, i=1, 2,..., N; N represents the total number of lifting points; Step S122: Calculate the real-time pressure difference value between adjacent lifting points, and take the absolute value to finally obtain the real-time pressure difference absolute value |ΔF_j| between each adjacent lifting point, j is the number of each adjacent lifting point, j takes the value range of 1 to N-1; Step S123: Select the maximum value max(|ΔF_j|) from |ΔF_j|, compare it with the preset tolerance threshold F_th, and generate an abnormal fluctuation flag signal when max(|ΔF_j|) > F_th; Step S124: Trigger the inertial measurement unit wake-up instruction according to the abnormal fluctuation flag signal, and synchronously collect the angular velocity ω_x, ω_y, ω_z and acceleration a_z data of the lifting appliance in three axial directions; Step S125: Convert ω_x, ω_y, ω_z and a_z into pitch angle θ and roll angle φ through quaternion solution algorithm.

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