5760results about "Safety gear" patented technology

The invention discloses a portal crane grab bucket control system and method based on multi-sensor fusion, and particularly relates to the technical field of portal crane grab bucket control. Image acquisition equipment and multiple types of sensors are arranged in a portal crane operation area, a multi-dimensional input data set under a unified time sequence is constructed, an optimized visual detection model and an attention mechanism are combined, high-precision recognition and abnormity judgment of the grab bucket state are achieved, and the system not only can automatically plan a grabbing path and a posture adjusting instruction, but also can automatically adjust the grabbing posture of the grab bucket. And control parameters can be dynamically optimized according to the grabbing effect and pose feedback, the grab bucket recognition accuracy, the control stability and the system intelligence level are remarkably improved, and particularly higher robustness and reliability are shown in the small-size, complex-pose or multi-target operation environment.

The invention discloses a wind turbine generator hoisting construction tower drum operation system and a construction method thereof, and relates to the technical field of intelligent control. The problems that in the prior art, a static tension balance mechanism cannot restrain bending moment abrupt change, steel-concrete interface stress concentration causes microcrack propagation, the wave dynamic load compensation capacity is insufficient, and dynamic rigidity attenuation early warning is lacked are solved. Comprising a dynamic load prediction module, a multi-mode vibration suppression module, an offset compensation module and a digital twinborn decision module, a hoisting load is solved in real time through a multi-physics field coupling model and an improved time sequence deep learning algorithm, and interface crack propagation is suppressed in combination with traveling wave offset control and sweep frequency vibration. An improved Morison equation is adopted to drive a two-stage hydraulic servo to compensate a wave dynamic load, and a digital twin closed-loop correction mechanism is constructed based on a 5G URLLC protocol; the tower drum hoisting precision, the structural safety and the operation reliability under the complex working condition are remarkably improved.

The invention relates to the technical field of engineering control, in particular to a hoisting control method of a safe and portable hoisting cage, which comprises a hoisting pre-stage, data pre-acquisition and analysis before hoisting, a hoisting control stage and hoisting landing control. In the prior art, position closed-loop control mainly depends on a stroke encoder and a position sensor, dynamic swing deviation caused by wind disturbance is difficult to accurately pre-judge and counteract in real time, and the defects of control lag and insufficient response exist. According to the scheme, the motion trail and posture of the cage are predicted in advance based on simple pendulum dynamic model numerical solution and the Runge-Kutta method, high-precision deviation detection is carried out in combination with a virtual-real vision calibration technology and AR enhanced display, a cage motion state control model based on feedforward prediction is established, trail prediction errors are greatly compressed, and the accuracy of the motion state of the cage is improved. The forward-looking inhibition of the movement of the cage, especially wind-induced swinging, is realized, and the accuracy and the response speed of an active swinging inhibition measure are obviously improved.

The invention discloses an intelligent port crane operation monitoring method and system, and relates to the technical field of crane systems.The system comprises a data acquisition unit, an edge computing node, a cloud processor and an early warning monitoring unit, through deep fusion and efficient treatment of multi-source data, a dynamic twinborn data pool architecture is established, and the dynamic twinborn data pool architecture is established; cross-data-type space-time alignment is realized through multi-modal coding, and the feature fusion accuracy is improved; the dynamic twin data pool is intelligently analyzed, and the crane operation path is optimized in real time, so that the track of the crane operation path is dynamically adjusted, and the working efficiency is improved; through a digital twin model and integration of a multi-modal physical engine, the operation physical state deviation of the crane is evaluated, an operation plan is corrected in real time, and the operation precision is improved; video streams are deeply analyzed, virtual-real comparison is carried out, and model prediction video deviation is monitored, so that the analogue simulation precision is improved; by establishing a closed-loop early warning mechanism, full-process digital management and control are realized, and intelligent upgrading of a port is promoted.

The invention discloses a BIM (Building Information Modeling)-based hoisting construction safety monitoring and early warning system. The system comprises a terminal sensing layer which is used for collecting environmental parameters and personnel behavior data in a closed space in real time; the edge computing layer is used for carrying out cleaning, compression and encrypted transmission on original data by utilizing an explosion-proof edge computing gateway; the cloud collaboration layer is used for storing full data based on a BIM digital twinborn platform, constructing a'danger mode-construction feature-disposal measure 'three-dimensional meta-knowledge graph by adopting an MAML + + algorithm, meanwhile, coupling a physical mechanism data enhancement engine with a multi-physics field coupling model and a physical constraint generative adversarial network, generating virtual data conforming to mass conservation and energy conservation, and sending the virtual data to the cloud collaboration layer; performing mixed training with real data; according to the intelligent decision-making layer, a space-time adaptive threshold evolutionary algorithm encodes a space-time context through a graph attention network and Transform, an alarm threshold is dynamically optimized through deep reinforcement learning, meanwhile, a digital twin deduction engine calculates a shortest safety path in real time, and rescue resource allocation is optimized.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention discloses a crane motion track prediction method based on an RGBD image, and belongs to the technical field of crane intelligent control, and the crane motion track prediction method comprises the following steps: S1, synchronously collecting multi-modal data, deploying a binocular RGBD camera array, and obtaining an RGB image and depth point cloud data of a crane operation area in real time; s2, cross-modal feature alignment and fusion are carried out; s3, track prediction and physical constraint correction; and S4, calculating the minimum safety distance between the crane and the dynamic object in real time. According to the method, a cross-modal space-time alignment mechanism is constructed by integrating the binocular RGBD camera array, the millimeter wave radar, the laser range finder and IMU data, the deformable convolutional network is adopted to dynamically compensate sensor data offset, multi-source data conflicts are eliminated, and compared with a traditional single sensor scheme, the method has the advantages that the method is simple in structure and convenient to operate. The multi-mode cooperative mechanism effectively solves the problem of sensing failure in a sensor blind area and a severe environment.

The invention relates to the technical field of machine vision and perception, in particular to a ton bag lifting unmanned control system based on a binocular vision camera and a laser radar, which comprises an intelligent control unit, a lifting appliance executing mechanism, a sensing unit and a special ton bag, the sensing unit comprises a binocular vision camera and a laser radar and is used for collecting depth vision and three-dimensional point cloud information of an operation area; the intelligent control unit fuses multi-source data, locates a lifting lug by improving a weighted multi-feature fusion algorithm, plans a safety path and generates a staged instruction; the lifting appliance executing mechanism lifts and pulls a collapsed lifting lug through an electromagnetic adsorption module, a mechanical gripper module clamps the lifting lug, and reliable operation is achieved in cooperation with a verification mechanism; the special ton bag is matched with a sensing and executing module through a high-contrast color and a pre-embedded metal piece. The full-process unmanned operation is achieved, the robustness and operation safety of the complex environment are improved, and the ton bag hoisting requirements of multiple industries are met.

The invention relates to a bridge crane operation dangerous area personnel intrusion detection method and system, and the method comprises the steps: obtaining the real-time position and speed data of a hoisted object through a sensor, analyzing the motion trail of the hoisted object through a deep learning algorithm, and obtaining the prediction result of the motion trail of the hoisted object; calculating a dangerous area boundary by adopting a dynamic model according to a hoisting object motion trail prediction result in combination with load mass and rope length data, and determining a dynamic dangerous area range; acquiring image data of an operation area through an image acquisition device, and identifying personnel positions in the image to obtain personnel position data; and when it is judged that the personnel enter the dynamic dangerous area range according to the personnel position data, an early warning signal is generated, a three-dimensional visualization technology is adopted to superpose the early warning signal to the field real-time image, visual early warning information is obtained, and the visual early warning information is displayed through a display device. According to the invention, dynamic and accurate prevention and control of the dangerous area are realized through multi-dimensional technology fusion.

The invention relates to the technical field of steel corrugated plate hoisting, and discloses an unsupported hoisting installation system applied to a large-span arched steel corrugated plate, which comprises a lifting hook, auxiliary mechanisms are symmetrically connected below the lifting hook, and the auxiliary mechanisms are used for being connected with haunches of the steel corrugated plate so as to avoid deformation of the steel corrugated plate in the hoisting process. An adjusting mechanism which is connected with the arch crown of the steel corrugated plate and can adjust the span of the steel corrugated plate is further connected below the lifting hook through a hand-drive block, and the adjusting mechanism comprises a lifting lug. The support-free hoisting installation system applied to the large-span arched steel corrugated plate can effectively solve the problems that in the prior art, before hoisting of the steel corrugated plate, the span of the steel corrugated plate is mainly adjusted by manually and repeatedly shaking a hoist chain, climbing to the top of the steel corrugated plate for multiple times is needed for high-altitude operation, the labor intensity is large, the risk is high, and the working efficiency is high. And repeated debugging is often needed, so that the adjustment time of a single steel corrugated plate is relatively long, and the overall hoisting progress is slowed down.

The embodiment of the invention discloses a double-truck-crane cooperative hoisting monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that component parameters and operation environment parameters of hoisting operation are collected, a reference data model is constructed, and an action expected value and a safety control threshold value are set; state acquisition devices are arranged on the two truck cranes to acquire multi-source data in real time and transmit the multi-source data to a central control system so as to realize synchronous sensing of the two trucks; processing multi-source data based on a data fusion algorithm, and constructing a dynamic attitude model and a collaborative operation state model; a state prediction correction value in the model is compared with the reference data model in real time, deviation is judged, and safety early warning is triggered; and generating a feedback control instruction, dynamically adjusting the hoisting operation, correcting the coordination deviation, and realizing the coordination control of the hoisting posture. In this way, through multi-source data fusion and real-time feedback control, the safety and coordination of cooperative lifting operation of the double truck cranes are remarkably improved.

The invention provides a three-dimensional space collision avoidance system based on multiple tower cranes, and relates to the technical field of port hoisting operation, a three-dimensional voxel grid VoxelGrid space map is constructed, and the three-dimensional space collision avoidance system comprises a first horizontal coverage map, a second descending coverage map, a third fan-shaped area occupation map, a fourth track occupation area map and a fifth interference area map; the system can identify the space overlapping area of the tower crane operation path at the voxel level, breaks through the limitation of the traditional path planning method on the dimension and precision of space modeling, and enhances the fine granularity and accuracy of collision detection. A dynamic acquisition module and a dynamic interference module are introduced, dynamic disturbance caused by factors such as stormy waves and tides on a ship in a port operation environment can be sensed in real time, the deviation trend of the tail end of the lifting appliance is predicted, target correction and track compensation are carried out before track execution, and the situation that the lifting appliance is accidentally touched or falls off due to track drifting is effectively promoted to be reduced.

The invention relates to the field of crane management, and discloses an intelligent agent control method and system.The method comprises the steps that operation parameters and operation environment data are collected in real time through multiple sensors arranged on a crane and subjected to standardization processing; transmitting the processed data to an operation state analysis model, and outputting an operation state evaluation value so as to analyze the current operation state of the crane and predict potential risks; based on the analysis result, an optimization control instruction is generated and issued to each execution mechanism controller, and corresponding mechanisms are driven to execute precise actions; through multi-source data fusion and intelligent analysis, real-time monitoring and risk assessment of the running state of the crane are achieved, the self-adaptive capacity and safety of the control system are improved, the manual intervention requirement is effectively reduced, and the method is suitable for intelligent crane control in the complex working environment.

The invention relates to the technical field of hoisting equipment monitoring, and discloses a hoisting equipment digital real-time monitoring system based on the Internet of Things. A dynamic load analysis module of the system collects multi-dimensional operation parameters in real time through distributed edge computing nodes; the risk situation assessment module executes tensor decomposition operation on the parameters, extracts feature vectors and generates a three-dimensional risk map; the self-adaptive safety control module dynamically adjusts the working state of the equipment according to the risk map; the digital twin mapping module is used for realizing time-space synchronous mapping of real-time parameters and a three-dimensional model and outputting holographic running state projection; and the cloud collaborative diagnosis module fuses the historical fault case library to generate a preventive maintenance strategy and returns the preventive maintenance strategy. The system can comprehensively monitor the equipment state, accurately assess the risk, realize dynamic safety control and preventive maintenance, and improve the safety and reliability of the operation of the hoisting equipment.

The invention discloses a BIM-based hoisting construction supervision optimization management system. The system comprises a sensing layer which collects multi-source heterogeneous data in real time; according to the decision-making layer, a bottom layer utilizes an incremental RRT # algorithm to generate candidate paths meeting crane kinematics constraints, a distributed Q-learning framework is embedded in an upper layer, all cranes serve as independent agents, collaborative learning is carried out through a shared experience pool, a path planning strategy is dynamically optimized according to environment sensing data and construction progress requirements, and a path planning strategy is established. Meanwhile, a fuzzy sliding mode control algorithm is developed to be combined with an LSTM-Transformer crane cart and trolley walking speed, a hook crane cart and trolley walking speed, a hook lifting speed and a steel wire rope disturbance prediction model to calculate crane motion compensation parameters, and an optimal control instruction is generated in advance based on predicted crane moving walking and lifting data; the execution layer is used for issuing the instruction generated by the decision-making layer to construction equipment; and the optimization layer is used for constructing a BIM model and feeding back an equipment execution result and structure safety monitoring data to the decision-making layer.

The invention discloses a method, a system and a device for predicting hoisting of a balance beam of a crane. The method comprises the following steps: acquiring operation data of the balance beam in an operation space; establishing a measurement range model according to the position data; calculating an adjustment value of each lifting hook according to the inclination angle data and the spatial position data; according to the data of the horizontal distance between the balance beam and the surrounding space object, judging and determining a safety area of the operation of the balance beam; calculating the stress and strain of the balance beam by using the structural parameters of the balance beam; calculating an attitude angle in the lifting operation process by using acceleration data of the lifting operation of the balance beam; and analyzing the calculated data by using a fusion algorithm to obtain a balance beam hoisting operation model, a monitoring index and a prediction result. The system comprises modules corresponding to the method, the device comprises an ultrasonic distance measuring sensor, an inclinometer, an acceleration sensor, a processing module and a display device, the operation efficiency can be guaranteed, and the operation safety can be improved.

The invention provides an overhead and portal crane lifting appliance control method based on multi-stage model predictive control, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring a control parameter and an initial state variable so as to define an operation boundary and an initial state, and establishing a dynamic model containing a coupling relationship among a trolley acceleration, a sling rope length acceleration and a sling swing angle; acquiring obstacle distribution by using space sensing equipment, and calculating the dynamic safety height of the lifting appliance by combining obstacle parameters and control parameters; judging whether the lifting appliance is in an obstacle avoidance stage or a target tracking stage according to the initial state variable and the dynamic safety height; constructing a model prediction controller and dynamically adjusting a state weight matrix and a control input weight matrix; finally, a control input sequence is generated through an optimization algorithm, and track safety and attitude stability control of the lifting appliance is achieved. According to the technical scheme, the swing angle of the lifting appliance is restrained, meanwhile, the obstacles are effectively avoided, and the safety of the lifting appliance in the whole operation process is guaranteed.

The invention discloses an anti-collision detection method, system, equipment and medium for hoisting machinery, and relates to the technical field of computer platform load balancing, and the method comprises the following steps: collecting multi-modal data of the hoisting machinery, and preprocessing the multi-modal data; fusing the collected multi-modal data through Kalman filtering, constructing an LSTM-GRU prediction model, and inputting the fused data into the LSTM-GRU prediction model to predict the moving trajectory of the hoisting machinery; and according to the trajectory prediction result, an anti-collision control instruction is dynamically generated through the AI edge computing device, and the PLC receives and executes the anti-collision control instruction. According to the method, on the basis of multi-modal data fusion, the crane operation state sensing precision is improved, on the basis of LSTM-GRU trajectory prediction, accurate future trajectory calculation is achieved, on the basis of AI edge calculation equipment, dynamic anti-collision control is achieved, and the intelligent level of anti-collision detection is effectively improved.

The invention discloses an automatic cargo hooking and unhooking method and system based on a machine vision technology. The method comprises the following steps: collecting data; preprocessing data, and respectively establishing disturbance terms of each factor according to each related data of the hook and the related data of the environment; constructing a hook coefficient model and a pressure coefficient model fusing related disturbance terms; generating an instruction; setting a hook coefficient threshold value of each level, a pressure coefficient threshold value and a double-coefficient joint threshold value, judging whether an unhooking condition is met or not by adopting a double-coefficient joint decision-making mechanism, and sending an early warning signal if the unhooking condition is not met; different processing tasks are triggered according to the unhooking instruction or the early warning signal; according to the method and the system, the unhooking composite risk can be comprehensively evaluated, the dependence of traditional mechanical positioning on standard cargo types and operation experience is broken through, intelligent unhooking decision under a complex environment and multi-dimensional disturbance can be realized, the expansion rate of adaptive cargo types is high, and the unhooking safety is guaranteed; according to the unhooking method, automatic unhooking can be achieved, and the'last kilometer 'breakpoint of port intelligent construction is broken through.

The invention provides a large-diameter long-pipe-section pipe pile hoisting path planning method and device, and relates to the technical field of cranes. According to the method, motion state parameters of pipe sections and pipe piles are obtained in real time, and in combination with three-dimensional environment sensing data of a hoisting area, a space operation diagram containing obstacles and a safety buffer area is constructed; generating a plurality of groups of candidate paths combined by speeds and steering angles in the fan-shaped region according to a set angle interval by taking the current motion direction as a reference; removing paths conflicting with obstacles through collision detection, and reserving an effective path set; based on multi-dimensional feature analysis of a security item index, a smoothness index and a progress index, a scoring function is constructed by presetting a weight, and an optimal path is dynamically selected; path planning is carried out through a fixed time interval until the pipe pile stably arrives at a target point; through real-time environment modeling and dynamic path planning, the problems of obstacle avoidance and efficiency of large-diameter pipe pile hoisting in a complex scene are solved, and hoisting safety and operation accuracy are remarkably improved.

The invention belongs to the field of bridge engineering construction monitoring and intelligent control, and provides a Beidou digital twinning system for bridge steel member precision hoisting and a hoisting method aiming at the problems of low efficiency and poor real-time performance of on-site hoisting of a segment steel member in a large-span bridge structure. The system comprises a multi-source data acquisition module, a coordinate conversion module, an attitude calculation module, a three-dimensional virtual model construction module, a hoisting motion state monitoring module and a digital twinning module. The attitude resolving module calculates a standard attitude vector by using a comprehensive deep learning model according to the degree-of-freedom data, the attitude information and the movement speed, and performs attitude resolving based on the standard attitude vector to obtain an attitude angle of the bridge steel member; the hoisting motion state monitoring module can perform safety early warning and posture adjustment when the bridge steel member is abnormal; and the digital twin module can synchronously update the attitude information and the three-dimensional virtual model of the bridge steel member to obtain the current space trajectory and historical state of the bridge steel member.

The invention discloses a hoisting device and method for casting production. The hoisting device comprises a hoisting plate, two clamping pincers and an automatic telescopic piece, and two sets of inserting claws are arranged at the bottoms of the two clamping pincers in a staggered mode; two pulleys are connected below the lifting plate in a left-right sliding mode, the top ends of the clamping pincers are each fixedly connected with a set of lifting ropes, and the two sets of lifting ropes are crossed, then bypass the pulleys in the opposite side directions and are connected with the inserting claws; a rectangular ring is arranged at the crossed position of the lifting ropes, the upper end of the rectangular ring is connected with an automatic telescopic piece, a rhombic four-bar linkage is hinged to the lower end of the rectangular ring, and the left and right sides of the rhombic four-bar linkage are hinged to power arms of the clamping pincers; through the arrangement of the automatic telescopic piece, the lifting plate, the clamping pincers, the pulley, the inserting claw, the rectangular ring and the lifting rope, the automatic telescopic piece stretches out and draws back up and down, the two clamping pincers can be driven to be opened or closed through the rhombic four-bar linkage, and therefore a casting is automatically clamped or released, and time and labor are saved; in addition, through coordination and cooperation of a lifting rope, a rhombic four-bar linkage and an inserting claw, the clamping effect on the casting is improved.

The invention relates to the technical field of safety monitoring of hoisting equipment, and discloses a safety detection and early warning system and method for an intelligent portal crane, and the system comprises a front-end sensing module which is used for collecting the operation data and operation pictures of the portal crane in real time, and carrying out the fusion; the voiceprint recognition module is used for assisting in judging whether internal parts make abnormal sounds or not; the data processing module is used for monitoring operation data of the portal crane through a machine learning algorithm, identifying abnormity and evaluating a health state; and the terminal display module is used for displaying the operation state and the operation data of the portal crane and displaying the monitoring result and the alarm information to a manager. According to various operation data, the integrated machine learning algorithm is utilized, abnormal modes are accurately recognized, and whether the equipment has potential safety hazards or not is rapidly judged, so that early potential faults of the equipment are found in time, the accuracy of fault early warning is improved, and the accidental shutdown time and the maintenance cost are reduced.

The invention relates to the technical field of crane equipment health monitoring, in particular to a crane running state health monitoring system and method. A sensing data acquisition unit is used for acquiring a winding drum vibration harmonic component, a steel wire rope leakage magnetic field gradient and a pulley block real-time load; the data analysis unit performs rotating speed synchronous variational mode decomposition on the vibration component to extract resonance characteristics, performs temperature and stress double-compensation correction on a leakage magnetic field gradient, quantifies an energy entropy attenuation rate through wavelet packet decomposition, constructs a phase difference model of a load and vibration to output a slip risk phase offset, calculates a load spectrum damage cumulant, and calculates a load spectrum damage cumulant; and after the four heterogeneous features are fused, a residual life coefficient is output through a dual-channel convolution-long and short-term memory hybrid neural network, and an execution unit triggers crane speed reduction control when the residual life coefficient is lower than a threshold value, so that the problems of insufficient multi-source data fusion and lack of dynamic compensation in the traditional technology are solved, and the fault early warning accuracy is improved.

The invention belongs to the technical field of industrial crane control, and provides a bridge crane motion path planning method and system, and the method comprises the steps: uniformly dividing the motion environment of a bridge crane into grid units, recognizing a dynamic obstacle, endowing each grid unit with a state value, carrying out the track prediction of the dynamic obstacle, and carrying out the prediction of the motion path of the bridge crane. The method comprises the following steps: generating a time grid state matrix, constructing a time sequence passing grid set sequence, exploring a shortest path by adopting an improved A * algorithm, exploring multiple feasible paths by adopting an RRT algorithm, marking change nodes, screening the multiple feasible paths based on the shortest path, marking an initial path, and sorting an initial path set and a path node sequence; and quantitatively calculating the path tortuosity of the initial path, screening based on the path tortuosity, marking an optimal path, analyzing the load swing of the load at the change node, calculating the swing envelope range of the load, marking a risk change node, and screening to obtain an optimal path.

The invention relates to the technical field of tower crane safety monitoring, and discloses a tower crane construction safety guarantee method and system.The method comprises the steps that a multi-tower crane cross operation area is dynamically recognized based on tower crane parameter data and real-time operation state data, and a dynamic distribution diagram of the cross operation area is obtained; based on the tower crane load data and the environment parameter data, tower crane operation stability evaluation is conducted, and an operation stability evaluation value is obtained; performing dynamic collision risk analysis based on the dynamic distribution map and the operation stability evaluation value to obtain a dynamic collision risk index; setting an adaptive safety distance threshold based on the dynamic collision risk index; and grading early warning and intervention control are carried out based on a self-adaptive safety distance threshold. Through collection and fusion analysis of multi-dimensional data, comprehensive perception and dynamic monitoring of a multi-tower crane operation environment are realized, and dispersed tower crane safety monitoring can be integrated into a cooperative system, so that the safety management level of a construction site is remarkably enhanced.

The invention relates to the technical field of hoisting construction dynamic safety assessment, and particularly discloses a hoisting construction dynamic safety assessment method, which comprises the following steps of: acquiring operation parameters of a hoisting system in real time through a multi-modal sensor array, and generating a hoisting dynamic parameter set; the lifting dynamic parameter set comprises pressure distribution data of each lifting point, lifting appliance attitude angle data and load space position data; based on the hoisting dynamic parameter set, force system dynamic balance analysis is executed, and the force system unbalance degree of the crane is obtained through analysis; and when the force system unbalance degree of the crane is larger than a set threshold value, an unbalance active correction mechanism is triggered, and unbalance correction is conducted on the crane through adjustment of the electro-hydraulic proportional valve. And the safety and the efficiency of hoisting operation can be obviously improved. The automatic unbalance correction mechanism not only reduces the accident risk, but also improves the operation flexibility and response speed, and provides powerful technical support for modern hoisting operation.

The invention provides a steel box girder hoisting device for building construction, and relates to the technical field of building construction, slings are arranged at the tail parts of a main hook and an auxiliary hook, and during hoisting, under the action of the gravity of a hoisted steel box girder, the slings move from the tail parts of the main hook and the auxiliary hook to the middle parts to drive the auxiliary hook to rotate relative to the main hook; the middle part of the auxiliary hook is gradually close to the middle part of the main hook, namely the auxiliary hook is gradually changed from a state of being separated from the main hook to a state of being combined with the main hook. The auxiliary hook is limited by the damping mechanism in the rotating process, the speed of the lifting appliance moving from the tails of the main hook and the auxiliary hook to the middle is reduced, and the damping effect is achieved, that is, the damping mechanism absorbs impact stress generated between the auxiliary hook and the lifting appliance when the lifting appliance is subjected to instantaneous impact force at the moment of lifting load; and when the lifting appliance moves from the tail parts of the main hook and the auxiliary hook to the middle part, the auxiliary hook provides lateral support and limit for the lifting appliance, so that the unstable shaking of the lifting appliance is reduced, and the influence on the lifting stability and the construction safety of the steel box girder is reduced.

The invention provides a method, a system, equipment and a medium for alarming personnel invasion in a hoisting working area of an automobile crane, and relates to the technical field of safety guarantee in the construction process of the automobile crane, which comprises the following steps: setting the radius of the hoisting working area of the automobile crane, and acquiring suspension arm state information of the automobile crane in real time; calculating the position of a lifting hook in real time according to the state information of the lifting arm; calculating the position of a lifting working area in real time according to the position of the lifting hook and the radius of the lifting working area; acquiring a monitoring image in the lifting working area in real time; identifying the monitoring image in the lifting working area; and an alarm is given out when a person intrudes into the hoisting working area. The method has the advantages of strong pertinence of monitoring image acquisition, small limitation of use scenes, high working efficiency, strong environmental adaptability and high identification accuracy, and can meet the real-time safety guarantee requirement of the operation of the automobile crane.

The invention relates to the technical field of ship cranes, in particular to a ship crane hoisting assembly which comprises a ship crane, a sling connecting and fixing assembly is arranged below the ship crane, a hoisting frame assembly is arranged below the sling connecting and fixing assembly, and an object fixing frame is arranged below the hoisting frame assembly. The container lifting device has the beneficial effects that the lifting frame assembly is designed to be matched with the gravity center adjusting assembly and the auxiliary buffer frame assembly, and the first position sensor and the second position sensor are carried on the two sides of the object fixing frame, so that when a container is initially lifted, the gravity center adjusting assembly and the auxiliary buffer frame assembly are matched, and the gravity center adjusting assembly and the auxiliary buffer frame assembly are matched; whether the gravity center of the container deviates excessively or not is fed back, the independent sling winding roller set I and the sling winding roller set II are used for controlling the connecting sling to achieve winding or loosening of the winding roller adjusting sliding rail in the displacement process, the gravity center of the container is adjusted in the direction opposite to the deviation direction, and the stability of the container in the lifting process is guaranteed.