Intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback

Abstract

This invention relates to the field of intelligent monitoring technology for engineering hoisting, specifically an intelligent monitoring system for the segmented hoisting process of heavy-duty equipment based on BIM and force feedback. The system includes: a data synchronization module, a state fusion module, a BIM matching module, a trajectory optimization module, and a simulation monitoring module. The system acquires the hoisting state through multi-source sensor data, matches it with a detailed BIM model, and generates a theoretical motion trajectory. The trajectory optimization module generates a force feedback deviation field by comparing expected and actual force feedback data, inversely correcting the theoretical trajectory to achieve dynamic path optimization based on the mechanical state. The simulation monitoring module drives virtual simulation based on the optimized trajectory, updates the BIM model's attitude in real time, dynamically calculates interference areas and performs risk assessments, and automatically generates and issues control commands. This solution improves the response accuracy and proactive safety control capabilities of the hoisting process to changes in physical state.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for engineering hoisting, and in particular to an intelligent monitoring system for the process of heavy-duty segmented hoisting based on BIM and force feedback. Background Technology

[0002] Current monitoring of heavy-duty segmented hoisting operations primarily relies on tracking preset motion trajectories and visual monitoring. This method can only capture the geometric deviations in the spatial position and path of the hoisting unit, and cannot perceive changes in the mechanical state during the hoisting process, such as the load distribution at the lifting points, structural internal forces, and rigging stress. Due to the lack of real-time analysis and response to physical and mechanical signals, the system struggles to detect potential structural risks caused by uneven stress in a timely manner, resulting in delayed and inaccurate control decisions.

[0003] In current technologies, Building Information Modeling (BIM) is mostly used for static planning and visual simulation before hoisting. During dynamic hoisting, BIM models are usually used as background references and fail to be updated synchronously with the real-time physical state, lacking the ability to drive monitoring. For potential dynamic interference between the hoisting unit and the surrounding environment, existing methods rely on fixed-area detection or manual judgment, unable to calculate the dynamically changing interference area based on the real-time attitude of the hoisted body, and also unable to convert risk assessment results into automatically executable on-site control commands.

[0004] There is a need for a monitoring system that can deeply integrate real-time mechanical states for trajectory optimization and utilize high-precision dynamic models to achieve real-time interference warning and active control, in order to address the shortcomings of existing technologies in terms of insufficient response to physical states and weak dynamic risk prevention and control capabilities. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and propose an intelligent monitoring system for the heavy-duty segmented hoisting process based on BIM and force feedback.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent monitoring system for the heavy-duty segmented hoisting process based on BIM and force feedback, comprising:

[0007] The data synchronization module acquires multi-source sensor data in the heavy-duty segmented hoisting scenario, performs spatiotemporal alignment on the multi-source sensor data, and obtains a synchronized data stream.

[0008] The state fusion module extracts key hoisting features based on the synchronous data stream, and fuses the key hoisting features to obtain a hoisting state feature set;

[0009] The BIM matching module matches the hoisting status feature set with a preset BIM model library to obtain the corresponding high-weight segmented BIM fine model.

[0010] The trajectory optimization module generates a theoretical motion trajectory for the hoisting process based on the super-heavy segmented BIM fine model, calculates the expected force feedback data based on the theoretical motion trajectory, compares and analyzes the expected force feedback data with the actual force feedback data collected in real time from the force feedback sensor, generates a force feedback deviation field, and then reverse corrects the theoretical motion trajectory to obtain the optimized hoisting motion trajectory.

[0011] The simulation monitoring module drives virtual hoisting simulation based on the optimized hoisting motion trajectory and updates the attitude of the heavy-duty segmented BIM fine model in real time. It calculates the hoisting interference area based on the real-time attitude of the heavy-duty segmented BIM fine model, assesses the risk level of the hoisting interference area, generates a monitoring instruction set based on the risk level assessment result, and sends the monitoring instruction set to the on-site execution mechanism.

[0012] Preferably, the multi-source sensor data is spatiotemporally aligned to obtain a synchronized data stream, including:

[0013] Identify the timestamp and spatial coordinate system of each data source in the multi-source sensor data;

[0014] Establish a unified world coordinate system and a standard timeline, and map the timestamps of each data source to the standard timeline;

[0015] Based on the transformation relationship between the spatial coordinate system of each data source and the world coordinate system, the spatial data of each data source is transformed to the world coordinate system;

[0016] Interpolation is performed on the data converted to the same spatiotemporal reference to ensure that all data streams have a consistent sampling frequency on the time axis;

[0017] The processed multi-source data is packaged in chronological order to generate a synchronous data stream with spatiotemporal tags.

[0018] Preferably, key hoisting features are extracted from the synchronous data stream, and these key hoisting features are fused to obtain a hoisting state feature set, including:

[0019] Image data, mechanical data, and positioning data are separated from the synchronous data stream;

[0020] Identify the contour boundaries of the overweight segments and the connection points of the slings from the image data;

[0021] The real-time load distribution and pressure change trend of each lifting point were analyzed from the mechanical data.

[0022] The current position, speed, and orientation angle of the overweight segment are calculated from the positioning data.

[0023] The contour boundary, the connection point, the real-time load distribution, the pressure change trend, the current position, the moving speed, and the orientation angle are fused at the feature level.

[0024] Assign weight coefficients to the fused features to generate a hoisting state feature set describing the overall hoisting status.

[0025] Preferably, a corresponding high-weight segmented BIM fine model is obtained by matching the hoisting state feature set with a preset BIM model library, including:

[0026] The hoisting status feature set is analyzed to obtain the type identifier and geometric dimension parameters of the overweight segment;

[0027] Using the type identifier and the geometric dimension parameters as query conditions, a fuzzy match is performed in the preset BIM model library;

[0028] The model with the highest geometric accuracy and containing complete physical property information is selected from the matched candidate BIM models as the base model.

[0029] The basic model is fine-tuned according to the real-time geometric parameters in the hoisting state feature set to generate a high-weight segmented BIM fine model that is consistent with the actual object.

[0030] Preferably, the theoretical motion trajectory of the hoisting process is generated based on the high-weight segmented BIM fine model, and the expected force feedback data is calculated based on the theoretical motion trajectory, including:

[0031] Based on the physical properties and hoisting scheme of the aforementioned heavy-duty segmented BIM fine model, a collision-free path from the lifting point to the target point is planned as the theoretical motion trajectory.

[0032] The theoretical motion trajectory is discretized into a series of continuous trajectory points, and the theoretical attitude of the hypergravity segment at each trajectory point is calculated.

[0033] Based on the theoretical attitude and gravity of the overweight segment at each trajectory point, the theoretical tension value of each sling is calculated using the principle of static equilibrium.

[0034] The theoretical tension value sequence and the theoretical attitude change rate together constitute the expected force feedback data.

[0035] Preferably, the expected force feedback data is compared and analyzed with the actual force feedback data collected in real time from the force feedback sensor to generate a force feedback deviation field, including:

[0036] It receives real-time tension data from force feedback sensors deployed on hooks and slings;

[0037] The actual tension data at the same moment is compared point by point with the theoretical tension value in the expected force feedback data to calculate the tension deviation value.

[0038] At the same time, the actual collected overweight segment sway amplitude is compared with the theoretical attitude change rate to calculate the attitude deviation vector;

[0039] The tension deviation value and the attitude deviation vector are superimposed in three-dimensional space to generate a force feedback deviation field that evolves over time.

[0040] Preferably, the theoretical motion trajectory is then reverse-corrected to obtain an optimized hoisting motion trajectory, including:

[0041] Analyze the spatial distribution and intensity of the force feedback deviation field to identify areas where the deviation exceeds a preset threshold;

[0042] For each deviation exceeding the limit, the potential kinematic causes leading to the deviation are deduced in reverse, including excessive speed or sharp turning;

[0043] Based on the deduction results, the motion parameters of the corresponding segments in the theoretical motion trajectory are adjusted to generate a smooth trajectory correction scheme;

[0044] The trajectory correction scheme is applied to the entire theoretical motion trajectory to obtain the optimized hoisting motion trajectory.

[0045] Preferably, the hoisting interference area is calculated in real time using the attitude of the high-weight segmented BIM fine model, and a risk level assessment is performed on the hoisting interference area, including:

[0046] Based on the real-time attitude of the super-heavy segmented BIM fine model, the position of its outer contour envelope in three-dimensional space is calculated.

[0047] The outer contour envelope is subjected to real-time Boolean operation with the pre-imported surrounding environment BIM model to detect whether there is an intersection area. The intersection area is the hoisting interference area.

[0048] Calculate the volume of the hoisting interference region and its distance relative to the center of gravity of the overweight segment;

[0049] Based on the volume and distance, and combined with the current hoisting speed, a preset risk mapping table is consulted to determine the risk level of the current hoisting interference area.

[0050] Preferably, a monitoring instruction set is generated based on the risk level assessment results, and the monitoring instruction set is sent to the field execution agency, including:

[0051] If the risk level is low, an early warning instruction will be generated to remind operators to pay attention.

[0052] If the risk level is medium, a speed limit command will be generated, requiring a reduction in the hoisting speed.

[0053] If the risk level is high, a pause command is generated, requiring the hoisting movement to stop immediately and the current position to be maintained;

[0054] If the risk level is critical, an emergency avoidance command is generated to control the hoisting system to execute preset emergency avoidance actions;

[0055] All generated instructions are sorted by priority, packaged into a monitoring instruction set, and sent to the corresponding field actuators via the industrial network.

[0056] Preferably, the system further includes:

[0057] The archive generation module records the monitoring data stream, force feedback data sequence, and system command logs of the entire hoisting process, forming a digital archive of the hoisting process, specifically including:

[0058] Using the time axis as an index, the synchronous data stream, the hoisting status feature set, the force feedback deviation field, the optimized hoisting motion trajectory, the risk level assessment results, and the monitoring instruction set are stored continuously.

[0059] Attach timestamps, data source identifiers, and data quality tags to all stored data;

[0060] The stored data is compressed and encrypted, and a data integrity check code is generated.

[0061] The processed data packets are associated with and stored with the verification code to form an unalterable digital archive of the hoisting process.

[0062] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0063] The expected force feedback data is calculated based on the theoretical motion trajectory and compared with the actual data collected by the force feedback sensor to generate a force feedback deviation field reflecting the differences in spatial forces. This deviation field is then used to inversely correct the theoretical motion trajectory, resulting in an optimized hoisting trajectory. This technology allows the trajectory planning and optimization process to not only rely on geometric spatial information but also deeply integrate real-time physical and mechanical states. The hoisting path can be dynamically adjusted according to the actual load distribution, torque changes, and other mechanical feedback, effectively adapting to force changes caused by structural deformation, wind loads, or operational fluctuations during hoisting, thus improving the trajectory's adaptability and control accuracy in complex actual working conditions.

[0064] The system drives virtual simulation based on the optimized hoisting motion trajectory and simultaneously updates the attitude of the high-weight segmented BIM fine model in real time. Based on this real-time attitude, the system dynamically calculates the hoisting interference area, conducts risk level assessments, and generates a monitoring command set based on the assessment results, which is then sent to the on-site execution mechanism. This technology transforms the BIM model from a static design reference to a dynamic monitoring core, enabling interference detection and risk assessment based on instantaneously updated three-dimensional states. The system can automatically identify potential collision areas newly generated due to changes in motion trajectory and attitude, and quickly generate executable control commands, constructing a closed-loop control process of perception, analysis, decision-making, and execution. This shortens the risk response cycle and enhances the proactive prevention and control capabilities for process safety. Attached Figure Description

[0065] Figure 1 This is a timing diagram of the intelligent monitoring system for the heavy-duty segmented hoisting process based on BIM and force feedback described in this invention.

[0066] Figure 2 A flowchart for spatiotemporal alignment of multi-source sensor data;

[0067] Figure 3 A flowchart for matching the fine-grained BIM model of the overweight segmentation;

[0068] Figure 4 A bar chart showing the distribution of hoisting interference risk levels;

[0069] Figure 5 A bar chart comparing the effects before and after optimizing the hoisting trajectory. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0071] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention 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 the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0072] See Figure 1The data synchronization module first acquires multi-source sensor data, consisting of images, mechanical information, and positional information, from various sensors deployed in the hoisting scenario. This data is then spatiotemporally aligned to form a synchronized data stream. The state fusion module then extracts key features closely related to the hoisting process from this synchronized data stream and merges these features into a unified hoisting state feature set. The BIM matching module searches and matches this feature set in a pre-set BIM model library, calling and fine-tuning to generate a refined BIM model of the overweight segment that matches the actual overweight segment on-site. The trajectory optimization module plans the theoretical motion trajectory based on this refined model and calculates the expected force feedback data. By comparing and analyzing the expected data with the actual data collected in real-time by the force feedback sensors, a force feedback deviation field is generated, and the theoretical motion trajectory is inversely corrected accordingly to obtain the optimized hoisting motion trajectory. Finally, the simulation monitoring module drives the refined model to perform virtual simulation according to the optimized trajectory, calculates the hoisting interference area in real-time, assesses the risk level, generates corresponding monitoring command sets based on the assessment results, and sends them to the on-site actuators, thereby achieving intelligent monitoring of the hoisting process.

[0073] In one embodiment of the present invention, see [reference] Figure 2 The data synchronization module acquires multi-source sensor data for heavy-duty segmented hoisting scenarios. This data includes image data from a vision sensor, load data from a mechanical sensor, and spatial coordinate data from a positioning sensor. When performing spatiotemporal alignment on this multi-source sensor data, the module identifies the timestamp and spatial coordinate system of each data source, establishes a unified world coordinate system and a standard time axis as a spatiotemporal reference, maps the timestamp of each data source to the standard time axis, and transforms the spatial data of each data source to the world coordinate system based on the transformation relationship between the spatial coordinate system of each data source and the world coordinate system. Interpolation is then performed on the data transformed to the same spatiotemporal reference to ensure that all data streams have a consistent sampling frequency on the time axis. Finally, the processed multi-source data is packaged in chronological order to generate a synchronized data stream with spatiotemporal tags. The state fusion module extracts key hoisting features based on the synchronous data stream. The module separates image data, mechanical data, and positioning data from the synchronous data stream. It identifies the contour boundary of the overloaded segment and the connection points of the slings from the image data. It parses the real-time load distribution and pressure change trend of each hoisting point from the mechanical data. It calculates the current position, moving speed, and orientation angle of the overloaded segment from the positioning data. The module performs feature-level fusion of the contour boundary, the connection points, the real-time load distribution, the pressure change trend, the current position, the moving speed, and the orientation angle, and assigns weight coefficients to the fused features to generate a hoisting state feature set describing the overall hoisting state.

[0074] In practical implementation, the data synchronization module acquires multi-source sensor data from the heavy-duty segmented hoisting scenario. This multi-source sensor data includes image data from two industrial cameras, mechanical data from tension sensors at four hooks, and positioning data from three ultra-wideband positioning tags installed on the heavy-duty segment. The data synchronization module identifies the timestamp and spatial coordinate system of each data source. The timestamp of the vision sensor is generated based on the camera's internal clock, and the spatial coordinate system is established with the camera's optical center as the origin. The timestamp of the mechanical sensor originates from the system clock of the data acquisition card, and the spatial coordinate system is defined at the sensor's own force center. The timestamp of the positioning sensor is generated by the positioning base station based on satellite synchronization signals, and the spatial coordinate system uses the global geodetic coordinate system. The data synchronization module establishes a unified world coordinate system and a standard time axis. The world coordinate system uses a pre-set control point on the ground in the hoisting area as its origin, and the standard time axis is based on the master clock of the monitoring system. Mapping the timestamp of each data source to the standard time axis requires clock offset compensation; for example, the timestamp of the vision sensor is subtracted by a fixed image transmission and processing delay before being aligned to the standard time axis. Based on the transformation relationship between the spatial coordinate system and the world coordinate system of each data source, the spatial data of each data source is transformed to the world coordinate system. Mechanical sensor readings are transformed to the local coordinate system of the hook using a known installation matrix, and then further transformed to the world coordinate system using the known pose of the hook in the world coordinate system. Interpolation is performed on the data transformed to the same spatiotemporal reference, using linear interpolation to fill in temporarily missing positioning data due to transmission delays, ensuring that all data streams have a consistent 100 Hz sampling frequency on the time axis. The processed multi-source data is packaged chronologically to generate a synchronous data stream with spatiotemporal tags. Each data packet contains a precise timestamp, a set of coordinate data in the world coordinate system, and a data source identifier.

[0075] The state fusion module extracts key features of the hoisting process from the synchronous data stream, separating image data, mechanical data, and positioning data. From the image data, it identifies the contour boundaries of the overloaded segment and the connection points of the slings. Based on an edge detection algorithm, it extracts the outer contour pixel set of the overloaded segment from the binocular camera images, and based on a feature matching algorithm, it identifies the pixel coordinates of the sling connection rings in the image. From the mechanical data, it analyzes the real-time load distribution and pressure change trends at each hoisting point. It analyzes the readings of four tension sensors to form a four-dimensional vector representing the real-time load distribution, and calculates the difference in tension readings between two adjacent time steps to form a four-dimensional vector representing the pressure change trend. From the positioning data, it calculates the current position, moving speed, and orientation angle of the overloaded segment. It uses the coordinates of three positioning beacons on the overloaded segment to calculate the coordinates of its center of gravity in the world coordinate system as the current position. It calculates the moving speed by performing time difference analysis on the center of gravity coordinate sequence, and obtains the orientation angle by fitting a plane to the coordinates of the three beacons and calculating its normal vector direction angle. The contour boundary, connection points, real-time load distribution, pressure change trend, current position, movement speed, and orientation angle are fused at the feature level. The contour boundary feature is represented as a 3D point cloud of a series of boundary points, the connection point feature is represented as a set of coordinates in the image pixel coordinate system, the real-time load distribution feature is represented as a four-dimensional vector, the pressure change trend feature is represented as a four-dimensional difference vector, the current position feature is represented as a three-dimensional coordinate, the movement speed feature is represented as a three-dimensional vector, and the orientation angle feature is represented as a set of Euler angles. Weight coefficients are assigned to the fused features, dynamically based on the historical accuracy calibration results of each sensor, to generate a hoisting state feature set describing the current overall hoisting state.

[0076] In some embodiments, the feature-level fusion process involves the normalization and concatenation of feature vectors. Specifically, all numerical features are uniformly scaled to a range of 0 to 1, and then concatenated into a high-dimensional feature vector according to a preset order. This high-dimensional feature vector serves as the core data representation of the hoisting state feature set, for use by subsequent modules.

[0077] In practice, the current position of the overweight segment is calculated using the coordinates of three positioning beacons. The calculation formula is as follows:

[0078]

[0079] in: This represents the three-dimensional coordinate vector of the centroid of the overweight segment in the world coordinate system. Let represent the three-dimensional coordinate vector of the i-th positioning beacon in the world coordinate system. Optionally, the weighting coefficients can be dynamically updated based on the online estimated sensor confidence. In some embodiments, the weighting coefficients of the mechanical sensors are temporarily reduced when the system detects a drastic change in readings to avoid abnormal data having an excessive impact on the hoisting state feature set. Understandably, this dynamic adjustment mechanism enhances the system's robustness to transient sensor failures.

[0080] In one embodiment of the present invention, see [reference] Figure 3 The BIM matching module matches the hoisting state feature set with a preset BIM model library. The module parses the hoisting state feature set to obtain the type identifier and geometric dimension parameters of the overweight segment. Using the type identifier and geometric dimension parameters as query conditions, it performs fuzzy matching in the preset BIM model library. From the matched candidate BIM models, it selects the model with the highest geometric accuracy and containing complete physical attribute information as the base model. Based on the real-time geometric parameters in the hoisting state feature set, it performs scale fine-tuning on the base model to generate a fine BIM model of the overweight segment that is consistent with the actual object. The trajectory optimization module generates the theoretical motion trajectory of the hoisting process based on the high-weight segment BIM fine model. Based on the physical properties of the high-weight segment BIM fine model and the hoisting scheme, it plans a collision-free path from the lifting point to the target point as the theoretical motion trajectory. The theoretical motion trajectory is discretized into a series of continuous trajectory points, and the theoretical attitude of the high-weight segment at each trajectory point is calculated. Based on the theoretical attitude and gravity of the high-weight segment at each trajectory point, the theoretical tension value of each sling is calculated using the principle of static equilibrium. The theoretical tension value sequence and the theoretical attitude change rate together constitute the expected force feedback data.

[0081] In practical implementation, the BIM matching module matches a pre-set BIM model library based on the hoisting state feature set. The module parses the hoisting state feature set, which includes geometric information of the overweight segment contour boundaries identified from image data and the overweight segment dimensions calculated from positioning data. The module obtains the type identifier and geometric dimension parameters of the overweight segment from the hoisting state feature set. The type identifier is a classification code output by a contour shape classification algorithm, and the geometric dimension parameters include the length, width, and height measurements of the overweight segment in the world coordinate system. Using the type identifier and geometric dimension parameters as query conditions, a fuzzy match is performed in the pre-set BIM model library, which is stored in a relational database. Each BIM model record contains a unique model code, model type identifier, theoretical geometric dimensions, geometric accuracy level, and physical attribute integrity marker fields. The fuzzy matching process calculates the matching degree between the query conditions and each record in the model library, considering both the complete consistency of the type identifier and the similarity of the geometric dimension parameters. The model with the highest geometric accuracy and complete physical property information is selected from the matched candidate BIM models as the base model. Highest geometric accuracy means the model has the largest number of detail levels recorded in the model library. Complete physical property information means the model's attributes include mass, center of gravity, and moment of inertia data, and are marked as verified. The base model is then fine-tuned based on real-time geometric parameters from the hoisting state feature set. These real-time geometric parameters are precise dimensions obtained through real-time sensor measurements, generating a high-weight segmented BIM model that closely matches the actual object.

[0082] In some embodiments, the fuzzy matching performed by the BIM matching module involves a quantified matching degree calculation. In a specific implementation, the matching degree M of a candidate model in the model library is calculated using a formula. The formula is defined as:

[0083]

[0084] in: Indicates the matching score. It is an indicator function that takes the value 1 when the type identifier of the query is exactly the same as the type identifier of the candidate model, and takes the value 0 otherwise. This represents the j-th geometric dimension parameter in the query conditions (length, width, and height in that order). This represents the j-th theoretical geometric dimension parameter recorded in the candidate model. The matching degree M has a range of [0,1], and the closer the value is to 1, the higher the matching degree.

[0085] The trajectory optimization module generates the theoretical motion trajectory for the hoisting process based on the high-weight segmented BIM fine model. This trajectory is based on the physical properties of the high-weight segmented BIM fine model and the hoisting scheme. The physical properties include the mass, center of gravity, and dimensions of the high-weight segmented BIM fine model. The hoisting scheme includes the coordinates of the lifting point, the target point, the coordinates of critical path points, and the spatial range of environmental obstacles. A collision-free path from the lifting point to the target point is planned as the theoretical motion trajectory. Collision-free path planning employs a sampling-based path planning algorithm, searching for a smooth spatial curve within the known obstacle space that does not interfere with the obstacle model. The theoretical motion trajectory is discretized into a series of continuous trajectory points. The theoretical motion trajectory is sampled at fixed time intervals, with each sampling time corresponding to the spatial coordinates of a trajectory point. The theoretical attitude of the high-weight segment at each trajectory point is calculated. The theoretical attitude includes the spatial position of the high-weight segmented BIM fine model at that point and its rotation angle around the three coordinate axes, derived from the geometric characteristics of the path curve and preset kinematic constraints. Based on the theoretical attitude and gravity of the segmented BIM model at each trajectory point, the gravity of the segmented BIM model is calculated using its mass and gravitational acceleration constant. The theoretical tension values ​​of each sling are calculated using the principle of static equilibrium. The principle of static equilibrium states that under the theoretical attitude, the resultant force of the tension in each sling in space is equal in magnitude and opposite in direction to the gravity of the segmented BIM model, and the resultant torque is zero. The theoretical tension values ​​at each sling point are obtained by solving a system of linear equations. The theoretical tension value sequence and the theoretical attitude change rate together constitute the expected force feedback data. The theoretical tension value sequence is a data set of theoretical tension at each sling point arranged in chronological order. The theoretical attitude change rate is an angular velocity sequence obtained by differential calculation of the theoretical attitude angles between continuous trajectory points.

[0086] Optionally, the theoretical attitude of a trajectory point can be determined by the tangential and normal vectors of the path curve. In some embodiments, for a trajectory point on the theoretical motion trajectory, its position coordinates are directly given by the path curve parametric equation, and its orientation is calculated based on the tangential direction of the path curve at that point and a preset global reference vector. This calculation method ensures that the overweight segmented BIM fine model has continuous and reasonable attitude changes as it moves along the path. In specific implementations, the calculation of the theoretical tension values ​​of each sling requires establishing mechanical equilibrium equations. The geometric shape, center of gravity position, sling position, and theoretical attitude of the overweight segmented BIM fine model are known quantities. Based on the equilibrium conditions of the spatial force system, a set of equations concerning the tension of the four slings is listed and solved. The set of equations includes three force equilibrium equations and three moment equilibrium equations. For statically determinate or statically indeterminate lifting systems, this set of equations has a unique solution or a least-squares solution, and the solution result is the expected theoretical tension value.

[0087] In one embodiment of the present invention, the trajectory optimization module compares and analyzes the expected force feedback data with the actual force feedback data collected in real time from the force feedback sensor. The module receives the actual tension data from the force feedback sensor deployed on the hook and sling in real time, compares the actual tension data at the same moment with the theoretical tension value in the expected force feedback data point by point, calculates the tension deviation value, and compares the actual collected overload segment sway amplitude with the theoretical attitude change rate to calculate the attitude deviation vector. The tension deviation value and the attitude deviation vector are superimposed in three-dimensional space to generate a force feedback deviation field that evolves over time. The module then performs reverse correction on the theoretical motion trajectory, analyzes the spatial distribution and intensity of the force feedback deviation field, identifies areas where the deviation exceeds a preset threshold, and for each deviation exceeding the limit area, reversely deduces the potential kinematic causes of the deviation, including excessive speed or sharp turning. Based on the deduction results, the motion parameters of the corresponding segments in the theoretical motion trajectory are adjusted to generate a smooth trajectory correction scheme. The trajectory correction scheme is applied to the entire theoretical motion trajectory to obtain the optimized hoisting motion trajectory.

[0088] In practical implementation, the trajectory optimization module compares and analyzes the expected force feedback data with the actual force feedback data collected in real time from the force feedback sensors. The module receives real-time actual tension data from the force feedback sensors deployed on the hook and sling. These sensors collect tension values ​​at four lifting points at a frequency of 100 times per second and transmit this data to the trajectory optimization module via an industrial network, forming a real-time data stream. The actual tension data at the same moment is compared point-by-point with the theoretical tension value in the expected force feedback data to calculate the tension deviation value. The tension deviation value is defined as the algebraic difference between the actual and theoretical tension values. For each sampling time t and each lifting point i, a deviation value is calculated. Simultaneously, the actual collected sway amplitude of the overweight segment is compared with the theoretical attitude change rate to calculate the attitude deviation vector. The actual sway amplitude is measured by an inertial measurement unit installed on the overweight segment and expressed as angular velocity; the theoretical attitude change rate is directly read from the expected force feedback data; the attitude deviation vector is obtained by calculating the vector difference between the actual angular velocity vector and the theoretical angular velocity vector. The tension deviation value and the attitude deviation vector are superimposed in three-dimensional space to generate a force feedback deviation field that evolves over time. The tension deviation value is mapped to a spatial vector in each cable direction, and the attitude deviation vector itself is a spatial vector. Within each time slice, these vectors are vector-synthesized in a three-dimensional coordinate system to form a spatial field representing the magnitude and direction of the overall deviation at that moment.

[0089] In some embodiments, the calculation of the tension deviation value can incorporate a normalization process. Specifically, for a suspension point i at time t, the tension deviation value... The following formula is used for calculation:

[0090]

[0091] in: This represents the normalized tension deviation value of suspension point i at time t. This indicates the suspension point data collected in real time from the force feedback sensor. At any moment Actual tension data, This indicates the suspension point obtained from the expected force feedback data. At any moment The theoretical tension value. Normalized tension deviation value. It is a dimensionless scalar, and its positive or negative sign indicates the direction of deviation of the actual tension from the theoretical value.

[0092] The trajectory optimization module then reverse-corrects the theoretical motion trajectory, analyzes the spatial distribution and intensity of the force feedback deviation field, and identifies regions where the deviation exceeds a preset threshold. The preset threshold is set for the normalized tension deviation value and the magnitude of the attitude deviation vector, respectively. When the absolute value of the normalized tension deviation value at any suspension point at a certain sampling moment is greater than 0.15, or the magnitude of the attitude deviation vector is greater than 0.1 radians per second, the trajectory region at that moment is determined to be a deviation-exceeding region. For each deviation-exceeding region, the potential kinematic causes of the deviation are reverse-engineered, including excessive speed or sharp turning. The deduction process is based on a rigid body dynamics model, using the observed tension and attitude deviations as system outputs. The inverse solution may cause changes in the system inputs, i.e., motion parameters, that lead to these outputs; for example, continuous negative tension deviations accompanied by positive pitch angle deviations may be deduced as excessive acceleration along the forward direction. Based on the simulation results, the motion parameters of corresponding segments in the theoretical trajectory are adjusted to generate a smooth trajectory correction scheme. The motion parameter adjustments include reducing the preset moving speed of the corresponding segments in the trajectory point sequence, increasing the radius of curvature at turns, or inserting brief pauses at key points. The smooth trajectory correction scheme uses a spline interpolation algorithm to refit the adjusted discrete trajectory points to generate a new continuous path. Applying the trajectory correction scheme to the entire theoretical trajectory yields the optimized hoisting trajectory, which is represented by a series of new trajectory point sequences with timestamps, spatial coordinates, desired speeds, and desired attitude angles.

[0093] Optionally, the identification of deviation exceeding the limit region can be smoothed using a sliding time window. In some embodiments, the system uses data from five consecutive sampling times as a window, calculates the average value of the deviation index within the window, and triggers an over-limit warning when the average value exceeds 80% of the threshold, marking the trajectory segment covered by the time window as a region to be corrected. This time window-based smoothing process helps filter out instantaneous false alarms caused by sensor noise, making deviation identification more robust. In a specific implementation, the reverse deduction process can be established as an optimization problem. Using the original motion parameters of the theoretical trajectory within the deviation exceeding the limit region as the initial guess, and minimizing the cumulative intensity of the force feedback deviation field in that region as the objective function, a set of corrected motion parameters is solved through an iterative algorithm. The result of solving this optimization problem is the correction amount for the local kinematic parameters of the original theoretical trajectory, thereby deriving the potential kinematic causes.

[0094] In one embodiment of the present invention, the simulation monitoring module calculates the hoisting interference region based on the real-time attitude of the high-weight segment BIM fine model. Based on the real-time attitude of the high-weight segment BIM fine model, the module calculates the position of its outer contour envelope in three-dimensional space, performs real-time Boolean operations on the outer contour envelope and the pre-imported surrounding environment BIM model, and detects whether there is an intersection region. The intersection region is the hoisting interference region. The module calculates the volume of the hoisting interference region and its distance relative to the center of gravity of the high-weight segment. Based on the volume and distance, combined with the current hoisting speed, the module queries a preset risk mapping table to determine the risk level of the current hoisting interference region. The module generates a monitoring instruction set based on the risk level assessment results. If the risk level is low, an early warning instruction is generated to remind the operator to pay attention; if the risk level is medium, a speed limit instruction is generated to require the hoisting movement speed to be reduced; if the risk level is high, a pause instruction is generated to require the hoisting movement to be stopped immediately and the current position to be maintained; if the risk level is critical, an emergency avoidance instruction is generated to control the hoisting system to execute preset emergency avoidance actions; all generated instructions are sorted according to priority, packaged into a monitoring instruction set, and sent to the corresponding field actuators through the industrial network.

[0095] In practical implementation, the simulation monitoring module calculates the hoisting interference area based on the real-time attitude of the high-weight segmented BIM fine model. The real-time attitude of the high-weight segmented BIM fine model is defined by its current spatial coordinates and rotation angles around three coordinate axes. The module calculates the position of its outer contour envelope in three-dimensional space, represented by an axial bounding box. The size and orientation of this bounding box are updated in real-time based on the vertex coordinates of the high-weight segmented BIM fine model in its current attitude. Real-time Boolean operations are performed between the outer contour envelope and a pre-imported surrounding environment BIM model. This pre-imported surrounding environment BIM model includes three-dimensional geometric models of permanent structures, temporary facilities, and other obstacles near the hoisting path. The real-time Boolean operation uses the separating axis theorem for rapid collision detection, checking for any intersection between the outer contour envelope and any entity model in the surrounding environment BIM model. This intersection is the hoisting interference area. The volume of the hoisting interference zone and its distance relative to the center of gravity of the overweight segment are calculated. When an interference zone is detected, it is a three-dimensional geometric volume, and its volume is approximated using a voxelization method. The distance relative to the center of gravity of the overweight segment is defined as the Euclidean distance from the center of gravity of the overweight segment to the geometric center of the interference zone. Based on the volume and distance, combined with the current hoisting speed, a preset risk mapping table is consulted to determine the risk level of the current hoisting interference zone.

[0096] A pre-defined risk mapping table defines the corresponding risk levels under different parameter combinations. In implementation, this risk mapping table is stored in the system as a data structure; its logical relationships are shown in Table 1.

[0097] Table 1: Mapping Table for Determining the Risk Level of Lifting Interference

[0098]

[0099] In some embodiments, the volume of the interference region can be calculated using a discrete approximation method. In a specific implementation, when Boolean operations detect interference, the system divides the interference region into a three-dimensional mesh and estimates the total volume by counting the number of mesh cells belonging to the interference region. The estimation formula is expressed as:

[0100]

[0101] in: This indicates the estimated volume of the hoisting interference region. This represents the total number of 3D mesh cells identified as belonging to the interference region, and the preset mesh cell side length. It can be understood that by adjusting the mesh cell side length, a trade-off can be struck between computational accuracy and computational efficiency.

[0102] The simulation monitoring module generates a set of monitoring instructions based on the risk level assessment results. If the risk level is low, an early warning instruction is generated, which sends a text prompt to the operator's console and triggers an audio prompt, reminding the operator to observe the interference area. If the risk level is medium, a speed limit instruction is generated, which sends a reduction coefficient to the hoisting equipment's motion controller, requiring the hoisting speed to be reduced to 50% of the current speed. If the risk level is high, a pause instruction is generated, which sends an emergency stop signal to the hoisting equipment's motion controller, requiring the immediate cessation of hoisting movement and maintenance of the current position, while simultaneously locking all drive devices. If the risk level is critical, an emergency avoidance instruction is generated, which sends a set of preset reverse motion trajectory points to the hoisting equipment's motion controller, controlling the hoisting system to execute preset emergency avoidance actions, such as causing the overloaded segment to move a safe distance in the opposite direction along the original path. All generated instructions are sorted by priority and packaged into a monitoring instruction set. The instruction priority order is: critical instructions are higher than high-level instructions, high-level instructions are higher than medium-level instructions, and medium-level instructions are higher than low-level instructions. The instructions are then sent to the corresponding field actuators via the industrial network. The field actuators include operator consoles, crane motion controllers, and alarm indicator lights.

[0103] See Figure 4 This is a bar chart showing the distribution of interference risk levels during hoisting operations, clearly illustrating the number of stages corresponding to different risk levels. Throughout the hoisting process, low and medium risks are the primary risk types, indicating that the overall risk of the hoisting process is within a controllable range. However, the high-risk aspects of the translation stage require close monitoring. The translation stage is the only stage where high-risk situations occur because the overloaded section moves the longest distance and at the fastest speed during this stage, resulting in the highest probability of interference with the surrounding environment. This chart can help on-site personnel quickly identify high-risk stages, enabling them to implement stricter monitoring measures during the translation stage, such as reducing movement speed and increasing the frequency of interference detection.

[0104] In one embodiment of the present invention, the system includes an archive generation module for recording the monitoring data stream, force feedback data sequence, and system command log of the entire hoisting process, forming a digital archive of the hoisting process. The module uses a timeline as an index to continuously store the synchronous data stream, the hoisting state feature set, the force feedback deviation field, the optimized hoisting motion trajectory, the risk level assessment results, and the monitoring command set. It adds timestamps, data source identifiers, and data quality tags to all stored data, compresses and encrypts the stored data, generates a data integrity check code, and associates the processed data packets with the check code for storage, forming an immutable digital archive of the hoisting process.

[0105] In practical implementation, the archive generation module records the monitoring data stream, force feedback data sequence, and system command log of the entire hoisting process to form a digital archive of the hoisting process. The archive generation module uses a timeline as an index and continuously stores synchronous data streams, hoisting status feature sets, force feedback deviation fields, optimized hoisting motion trajectories, risk level assessment results, and monitoring command sets. The storage process writes the above data sequentially to a high-speed buffer queue, using millisecond-level timestamps as keys, according to their generation time order, and then persistently stores them in non-volatile storage media. All stored data is appended with timestamps, data source identifiers, and data quality tags. The timestamps are accurate to milliseconds and synchronized with the system master clock; the data source identifier indicates the specific module or sensor number that generated the data; the data quality tag is a score value generated based on data verification algorithms and signal-to-noise ratio analysis. The stored data undergoes compression and encryption. Compression uses a lossless compression algorithm to process batches of historical data to reduce storage space usage. Encryption uses a symmetric encryption algorithm to encrypt each data packet to ensure data confidentiality and generates a data integrity check code. The data integrity check code is calculated based on the encrypted data packet content using a hash function. The processed data packets are associated with and stored with the verification codes to form an immutable digital archive of the hoisting process. This associated storage is achieved by writing the data packets and their corresponding verification codes together into a digitally signed data block, which is then added to a blockchain-like data structure linked in chronological order.

[0106] In some embodiments, the data integrity check code is generated using hash operations. Specifically, for an encrypted data packet D to be stored, its corresponding data integrity check code H is calculated using the following formula:

[0107]

[0108] in: This represents the final generated data integrity check code. This represents the total number of sub-blocks after dividing the encrypted data packet D. This represents the data content of the k-th sub-block. This represents a cryptographic hash function. This function converts the output value of a hash function to a decimal value. In essence, this formula calculates the hash values ​​of each block of a data packet to generate a comprehensive checksum, which is then used to verify the integrity of the data.

[0109] The archive generation module continuously stores a synchronous data stream containing raw spatiotemporally aligned data from all sensors. The hoisting status feature set includes fused feature vectors and their weighting coefficients. The force feedback deviation field contains tension deviation values ​​and attitude deviation vectors for each calculation cycle. The optimized hoisting motion trajectory includes a corrected sequence of trajectory points. The risk level assessment results include the risk level code for each assessment moment and the parameters that trigger that level. The monitoring instruction set includes the content, timestamps, and target actuator identifiers of all issued instructions. Timestamps, data source identifiers, and data quality tags are packaged together with the master data in the form of extended metadata, forming a complete data record unit.

[0110] Optionally, data quality labels can be generated based on multi-dimensional indicators. In some embodiments, the score of a data quality label is jointly determined by the verification results of the data itself, the recent self-diagnostic status of the sensor that collected the data, and the statistical reasonableness of the data, ultimately outputting an integer score between 0 and 100. It can be understood that this multi-dimensional evaluation method can more comprehensively reflect the reliability and availability level of the stored data.

[0111] In practice, the encryption and associated storage process is sequential and irreversible. Encryption uses a session key generated by the system hardware security module, which is valid only within a single hoisting task cycle. The processed data packet and its checksum are submitted together to an archiving service, which writes the data into a structured archive file and adds a digital signature and timestamp authentication to that file. The resulting digital archive of the hoisting process is stored as a single encrypted archive file on a dedicated storage server. It can be understood that by combining encryption, hash verification, digital signatures, and a chained storage structure, the integrity of the archive content and the immutability of the generation process are effectively ensured.

[0112] See Figure 5 This is a bar chart comparing the effects of hoisting trajectory optimization before and after, clearly demonstrating the performance improvements brought about by the optimization scheme across four core indicators. The force feedback stability improvement reached approximately 95% after optimization, indicating a significant enhancement in the system's ability to perceive and control changes in sling tension and attitude—a core indicator for ensuring hoisting safety. The trajectory smoothness improvement reached approximately 92%, indicating a smoother hoisting path, effectively reducing swaying and impact during heavy-duty sections and lowering structural stress. The risk reduction rate reached approximately 75% after optimization, demonstrating that trajectory optimization effectively avoids interference risks with the surrounding environment. While the energy consumption saving rate was only about 15%, a smaller increase than other indicators, it still reflects the practical value of the optimization scheme in energy saving.

[0113] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. An intelligent monitoring system for the segmented lifting process of heavy-duty equipment based on BIM and force feedback, characterized in that, The system includes: The data synchronization module acquires multi-source sensor data in the heavy-duty segmented hoisting scenario, performs spatiotemporal alignment on the multi-source sensor data, and obtains a synchronized data stream. The state fusion module extracts key hoisting features based on the synchronous data stream, and fuses the key hoisting features to obtain a hoisting state feature set; The BIM matching module matches the hoisting status feature set with a preset BIM model library to obtain the corresponding high-weight segmented BIM fine model. The trajectory optimization module generates a theoretical motion trajectory for the hoisting process based on the super-heavy segmented BIM fine model, calculates the expected force feedback data based on the theoretical motion trajectory, compares and analyzes the expected force feedback data with the actual force feedback data collected in real time from the force feedback sensor, generates a force feedback deviation field, and then reverse corrects the theoretical motion trajectory to obtain the optimized hoisting motion trajectory. The simulation monitoring module drives virtual hoisting simulation based on the optimized hoisting motion trajectory and updates the attitude of the heavy-duty segmented BIM fine model in real time. It calculates the hoisting interference area based on the real-time attitude of the heavy-duty segmented BIM fine model, assesses the risk level of the hoisting interference area, generates a monitoring instruction set based on the risk level assessment result, and sends the monitoring instruction set to the on-site execution mechanism.

2. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, The multi-source sensor data is spatiotemporally aligned to obtain a synchronized data stream, including: Identify the timestamp and spatial coordinate system of each data source in the multi-source sensor data; Establish a unified world coordinate system and a standard timeline, and map the timestamps of each data source to the standard timeline; Based on the transformation relationship between the spatial coordinate system of each data source and the world coordinate system, the spatial data of each data source is transformed to the world coordinate system; Interpolation is performed on the data converted to the same spatiotemporal reference to ensure that all data streams have a consistent sampling frequency on the time axis; The processed multi-source data is packaged in chronological order to generate a synchronous data stream with spatiotemporal tags.

3. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, Based on the synchronous data stream, key hoisting features are extracted and fused to obtain a hoisting state feature set, including: Image data, mechanical data, and positioning data are separated from the synchronous data stream; Identify the contour boundaries of the overweight segments and the connection points of the slings from the image data; The real-time load distribution and pressure change trend of each lifting point were analyzed from the mechanical data. The current position, speed, and orientation angle of the overweight segment are calculated from the positioning data. The contour boundary, the connection point, the real-time load distribution, the pressure change trend, the current position, the moving speed, and the orientation angle are fused at the feature level. Assign weight coefficients to the fused features to generate a hoisting state feature set describing the overall hoisting status.

4. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, Based on the hoisting state feature set, a preset BIM model library is matched to obtain the corresponding high-weight segmented BIM fine model, including: The hoisting status feature set is analyzed to obtain the type identifier and geometric dimension parameters of the overweight segment; Using the type identifier and the geometric dimension parameters as query conditions, a fuzzy match is performed in the preset BIM model library; The model with the highest geometric accuracy and containing complete physical property information is selected from the matched candidate BIM models as the base model. The basic model is fine-tuned according to the real-time geometric parameters in the hoisting state feature set to generate a high-weight segmented BIM fine model that is consistent with the actual object.

5. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, Based on the aforementioned high-weight segmented BIM fine model, a theoretical motion trajectory for the hoisting process is generated. Based on this theoretical motion trajectory, the expected force feedback data is calculated, including: Based on the physical properties and hoisting scheme of the aforementioned heavy-duty segmented BIM fine model, a collision-free path from the lifting point to the target point is planned as the theoretical motion trajectory. The theoretical motion trajectory is discretized into a series of continuous trajectory points, and the theoretical attitude of the hypergravity segment at each trajectory point is calculated. Based on the theoretical attitude and gravity of the overweight segment at each trajectory point, the theoretical tension value of each sling is calculated using the principle of static equilibrium. The theoretical tension value sequence and the theoretical attitude change rate together constitute the expected force feedback data.

6. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, The expected force feedback data is compared and analyzed with the actual force feedback data collected in real time from the force feedback sensor to generate a force feedback deviation field, including: It receives real-time tension data from force feedback sensors deployed on hooks and slings; The actual tension data at the same moment is compared point by point with the theoretical tension value in the expected force feedback data to calculate the tension deviation value. At the same time, the actual collected overweight segment sway amplitude is compared with the theoretical attitude change rate to calculate the attitude deviation vector; The tension deviation value and the attitude deviation vector are superimposed in three-dimensional space to generate a force feedback deviation field that evolves over time.

7. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, Then, the theoretical motion trajectory is reverse-corrected to obtain the optimized hoisting motion trajectory, including: Analyze the spatial distribution and intensity of the force feedback deviation field to identify areas where the deviation exceeds a preset threshold; For each deviation exceeding the limit, the potential kinematic causes leading to the deviation are deduced in reverse, including excessive speed or sharp turning; Based on the deduction results, the motion parameters of the corresponding segments in the theoretical motion trajectory are adjusted to generate a smooth trajectory correction scheme; The trajectory correction scheme is applied to the entire theoretical motion trajectory to obtain the optimized hoisting motion trajectory.

8. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, The hoisting interference area is calculated in real time using the high-weight segmented BIM fine model, and a risk level assessment is performed on the hoisting interference area, including: Based on the real-time attitude of the super-heavy segmented BIM fine model, the position of its outer contour envelope in three-dimensional space is calculated. The outer contour envelope is subjected to real-time Boolean operation with the pre-imported surrounding environment BIM model to detect whether there is an intersection area. The intersection area is the hoisting interference area. Calculate the volume of the hoisting interference region and its distance relative to the center of gravity of the overweight segment; Based on the volume and distance, and combined with the current hoisting speed, a preset risk mapping table is consulted to determine the risk level of the current hoisting interference area.

9. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, Based on the risk level assessment results, a monitoring instruction set is generated and issued to the field execution agency, including: If the risk level is low, an early warning instruction will be generated to remind operators to pay attention. If the risk level is medium, a speed limit command will be generated, requiring a reduction in the hoisting speed. If the risk level is high, a pause command is generated, requiring the hoisting movement to stop immediately and the current position to be maintained; If the risk level is critical, an emergency avoidance command is generated to control the hoisting system to execute preset emergency avoidance actions; All generated instructions are sorted by priority, packaged into a monitoring instruction set, and sent to the corresponding field actuators via the industrial network.

10. The intelligent monitoring system for heavy-duty segmented hoisting process based on BIM and force feedback as described in claim 1, characterized in that, The system also includes: The archive generation module records the monitoring data stream, force feedback data sequence, and system command logs of the entire hoisting process, forming a digital archive of the hoisting process, specifically including: Using the time axis as an index, the synchronous data stream, the hoisting status feature set, the force feedback deviation field, the optimized hoisting motion trajectory, the risk level assessment results, and the monitoring instruction set are stored continuously. Attach timestamps, data source identifiers, and data quality tags to all stored data; The stored data is compressed and encrypted, and a data integrity check code is generated. The processed data packets are associated with and stored with the verification code to form an unalterable digital archive of the hoisting process.

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