8838results 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 discloses a large container crane intelligent early warning system based on personnel behavior analysis, and the system comprises the following steps: collecting operator behaviors, equipment operation states and environment parameters in real time through a multi-mode sensor network, constructing a dynamic data set, extracting behavior characteristics through a time difference embedding algorithm, and carrying out the early warning of the operator behaviors, the equipment operation states and the environment parameters; a multi-level spatial-temporal characteristic matrix is generated in combination with dynamic scene influence factors, regional risk factors are dynamically calculated by using a Bayesian inference model, comprehensive risk values are generated through an adaptive multi-objective optimization algorithm, risk levels are divided into low, medium and high classes, and a system triggers graded early warning responses according to the risk levels. And an optimized behavior adjustment suggestion is generated in combination with a quantum probability behavior prediction algorithm, and a feature extraction rule and risk assessment logic are dynamically optimized through a progressive learning algorithm to form a closed-loop optimization process. According to the invention, intelligent analysis and real-time early warning of personnel behaviors are realized, and the safety and adaptability of the crane in an operation environment are 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 the technical field of data analysis, and discloses a crane intelligent safety analysis and early warning system which comprises a behavior pattern capturing module, an operation abnormity analysis module, a load monitoring module, a dynamic early warning adjustment module, a risk assessment module and a real-time early warning transmitting module. According to the invention, by analyzing the operation data and operation behaviors of the crane in real time, the accuracy and response speed of the safety early warning system are effectively enhanced, the strategy of dynamically adjusting the operation limitation and speed allows the system to adapt to the changes of different working environments, and the safety threshold is adjusted in time to cope with the instant operation conditions and environmental factors; furthermore, through comparison with historical accident data, behavior modes similar to historical accidents are accurately recognized, and possible risks are warned in advance, so that safety accidents caused by misoperation or crane faults can be remarkably reduced, the safety of construction site personnel is guaranteed, and meanwhile possible property loss is also avoided.

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.

According to the bridge crane intelligent safety monitoring method and device based on multi-source data fusion, by integrating the lateral pressure type pressure sensor, the intelligent camera, the displacement sensor and the electromagnetic sensor, multi-dimensional collection of weight, image, position and broken wire data is achieved. A multi-source data stream is processed by adopting frequency division sampling and a time synchronization mechanism, and accurate detection of personnel in a dangerous area is realized by combining a convolutional neural network configured by a self-adaptive residual block. The system monitors multi-dimensional safety risks such as weight overrun of a hoisted object, breakage of a steel wire rope and deviation of a lifting hook track at the same time, and all-dimensional intelligent monitoring of the operation process of the crane is achieved. The problem that a traditional single monitoring scheme is insufficient in perception capability is effectively solved, and the accuracy of safety early warning is improved.

The invention discloses a cooperative control method for double-crane lifting of a large-span steel structure, which comprises the following steps of: pre-installing sensor modules on a crane hook and a steel structure key node before construction, acquiring load distribution and structural deformation data in real time through a multi-source sensor fusion technology, and establishing a unified coordinate system; and the cooperative control system is started to control the hoisting process. The invention further discloses a safety monitoring system for double-crane lifting of the large-span steel structure. According to the invention, the action matching height precision of the two cranes is ensured, the construction precision and the hoisting efficiency are improved, the safety of hoisting operation is guaranteed in all directions, and the accident rate is reduced.

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 provides a movement track control method and system of a container reach stacker, and relates to the technical field of intelligent control, and the method comprises the following steps: executing data acquisition, building a lifting area into a three-dimensional fitting scene, then executing three-dimensional scene identification, establishing an article sensitive factor, and performing sensitive monitoring through a static sensor group, and updating a three-dimensional fitting scene in real time, then carrying out path fitting optimization to establish an optimization path, establishing M levels of early warning limits, controlling the crane to execute trajectory control fitting, then executing real-time trajectory tracking, generating following compensation and carrying out motion trajectory control. The technical problems that in the prior art, control over the movement track is not accurate, real-time data information interaction is lacked, safety limiting cannot be accurately conducted in time according to the track position, and the control precision and efficiency are further influenced can be solved, accurate control and optimization of the movement track of the crane are achieved, and the control efficiency is improved. And the working efficiency, the safety and the reliability are improved.

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 invention relates to the field of hoisting equipment, in particular to a crane monitoring system and a crane monitoring method, and the operation efficiency, the safety and the reliability of the system are remarkably improved through real-time monitoring analysis, trend analysis and fault analysis. Real-time monitoring and analysis can respond to abnormal conditions in time, operation parameters are optimized, and the equipment utilization rate is improved. Trend analysis supports predictive maintenance and resource optimization, and downtime is reduced. Fault reasons are accurately diagnosed through fault analysis, detailed reports and prevention measures are provided, and safety is guaranteed. Data quality and analysis accuracy are improved through data preprocessing, and long-term stable operation of the system is ensured through historical data analysis and trend prediction. And the comprehensive report and data visualization assist in management decision making, and the operation cost is reduced. On the whole, the data analysis module brings comprehensive optimization and remarkable benefit improvement to the crane monitoring system.

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 relates to the technical field of crane control, in particular to an intelligent tower crane anti-collision accurate hoisting system based on multi-sensor fusion, which comprises a data acquisition module, a data processing module, a safety early warning module, a control execution module, a data processing module, a data processing module and a control module, an alarm module electrically connected with the safety early warning module and the data processing module and used for receiving the alarm signal sent by the safety early warning module; a hoisting control instruction is generated based on the alarm signal and the real-time three-dimensional position coordinates of the hoisted object sent by the data processing module; according to the method, the lifting control instruction is executed, the lifting arm angle, the rotation angle and the lifting height of the tower crane are adjusted, accurate lifting and anti-collision control are achieved, and the safety, accuracy and efficiency of the tower crane are comprehensively improved through organic combination of advanced technologies such as multi-sensor fusion, an intelligent algorithm and self-adaptive control.

The invention discloses a fault diagnosis method for a bridge crane, relates to the technical field of fault diagnosis, and solves the technical problem in the prior art that the operation of the bridge crane can be influenced by no consideration of environmental factors so as to cause crane faults. The method comprises the following steps: acquiring operation data of the bridge crane and surrounding environment data; calculating an environment early warning evaluation coefficient based on the environment data; judging whether early warning information is generated or not based on the environment early warning evaluation coefficient and a preset environment threshold value; if yes, early warning information is sent to the user side; if not, continuously monitoring and judging; training an artificial intelligence model based on the historical operation data and the corresponding environment data to obtain a fault recognition model; and performing fault prediction identification on the operation data of the bridge crane and the environment early warning evaluation coefficient corresponding to the environment data based on the fault identification model to obtain an identification result, thereby solving the technical problem.

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 provides a crane hoisting monitoring method and system based on multi-normal-form visual fusion and a medium, and relates to the technical field of computer vision. Acquiring an image of the target acquisition area through an image acquisition device, and obtaining a real-time image acquisition video stream; preprocessing the real-time image acquisition video stream to obtain real-time image frame data; performing real-time image frame data identification based on the visual detection model to generate detection information; inputting the detection information into a visual tracking module, and tracking the identification object to obtain tracking information; acquiring a fixed dangerous area or a dynamic dangerous area based on the detection information and the tracking information; and identifying a dangerous condition in the video stream according to the dynamic dangerous area or the fixed dangerous area, obtaining dangerous condition accumulated data and triggering an alarm. The technical problems that in the prior art, the monitoring means is single, and dynamic dangers are difficult to accurately recognize are solved, and the technical effect of accurately recognizing potential dangers of hoisting operation in real time is achieved.

The invention provides a hoisting mechanism for water conservancy and hydropower pipe fitting installation, and relates to the technical field of pipe fitting hoisting. Comprising a hoisting plate, the top of the hoisting plate is connected with a crane through a steel cable, first motors are arranged on the two sides of the hoisting plate correspondingly, a pair of sliding blocks are symmetrically arranged at the bottom of the hoisting plate, the sliding blocks are slidably connected to the hoisting plate through the first motors, and the bottoms of the two sliding blocks are fixedly connected with a first connecting rod and a second connecting rod correspondingly; a first hoisting frame is fixedly connected to the bottom of the first connecting rod, and a first protection part is arranged on one side of the first hoisting frame and used for protecting one end of the water conservancy and hydropower pipe fitting. By arranging the first protection part, the adjusting assembly and the second protection part, when an operator hoists a pipe fitting, not only can the port of the pipe fitting be protected, but also a hanging piece with a right-angle bent pipe can be hoisted, so that the pipe fitting is safer in the hoisting process.

The invention discloses a wind generating set cabin crane safety control system, and relates to the technical field of wind power generation, the wind generating set cabin crane safety control system comprises a monitoring center, the monitoring center is in communication connection with a monitoring module, a data processing module, a judgment module, a crane motor control module and an alarm and prompt module, all the modules are in electric signal connection, and the monitoring module is in communication connection with the data processing module. The monitoring module is used for monitoring environment data and working state data of the crane in real time, and the environment data comprises wind speed, wind direction, temperature and humidity; the working state information of the crane comprises the speed, the lifting rope position, the load weight and the like. By integrating various sensors and a real-time monitoring technology, the operation state and the surrounding environment of the crane are accurately sensed, and when abnormal conditions are detected, the system immediately triggers an early warning or emergency shutdown mechanism, so that accidents are effectively avoided, and the safety accident risk caused by man-made misjudgment or equipment failure is reduced.

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 relates to the field of control, in particular to a hoisting risk early warning method and system for a hoisting unmanned aerial vehicle, and the method comprises the steps: calculating a load balance index of the hoisting unmanned aerial vehicle; when the load balance index is smaller than a preset balance threshold value, executing primary early warning; marking the hoisting unmanned aerial vehicle with the minimum load as an abnormal unmanned aerial vehicle, and marking other hoisting unmanned aerial vehicles as normal unmanned aerial vehicles; when the primary early warning is executed, constructing a time sequence, and calculating an angular velocity change coefficient of the abnormal unmanned aerial vehicle at each moment; calculating difference values of relative positions of the abnormal unmanned aerial vehicle and the normal unmanned aerial vehicle corresponding to two adjacent moments, and summing all the difference values to obtain a relative movement value of the abnormal unmanned aerial vehicle corresponding to the moment; and calculating a rollover early warning index of the abnormal unmanned aerial vehicle, and when the rollover early warning index is greater than a preset rollover threshold value, executing secondary early warning. The abnormal condition of the unmanned aerial vehicle is effectively monitored, and the safety and efficiency of hoisting operation are improved.

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.