265 results about "Collision risk" patented technology

The invention belongs to the technical field of ship navigation and port automation, and particularly discloses a dock ship guiding path planning and dynamic obstacle avoidance system, which is characterized in that a data acquisition module acquires ship parameters, environmental data and obstacle information through a ship end sensing unit and a dock end monitoring unit and standardizes the ship parameters, the environmental data and the obstacle information; the initial path planning module generates an optimal initial ship guiding path based on an improved A * algorithm in combination with ship physical constraints, environmental constraints and operation rules; the dynamic obstacle avoidance module monitors the movement of an obstacle in real time, predicts the trajectory of a ship and the obstacle, and triggers local path re-planning and smoothing processing when a collision risk occurs; and the control output module converts the path into a control instruction, issues and receives ship feedback through two-way communication, and forms closed-loop control according to a deviation fine adjustment instruction or re-planning. The system realizes full-process automation of ship guiding, can dynamically adapt to environmental changes, improve path precision and operation safety, adapt to different docks and ship types, and ensure that ships are accurately and safely parked to target berths.

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

The invention discloses a mobile robot accurate docking method based on edge calculation, and aims to solve the problems that the dynamic docking precision is reduced and the collision risk is increased due to micro-motion or drifting of a target station. According to the method, unified time reference alignment is carried out on data of a camera, a laser radar, an inertial measurement unit, an ultra-wideband range finder, a station encoder and a programmable logic controller at an edge node, and a three-dimensional special Euclidean group equivariant multi-source fusion network is used for outputting relative pose estimation and covariance; the estimation in the time window is further used as a condition to be input into a conditional diffusion short-time prediction model to obtain a time-varying mean value and a time-varying covariance, an anisotropic probability tube is constructed, and prediction-measurement joint correction is carried out based on a score function; scenarized opportunity constraints are constructed under correction probability tube constraints, a tubular nonlinear model is adopted to predict, control and solve a reference trajectory, an actuator command is generated in combination with depth visual servo and compliance control, and the technical effects of high precision, robustness and safe docking under the station dynamic disturbance condition are achieved.

The invention provides a model training method, a trajectory planning method, electronic equipment and a program product, which can be used in the technical field of aided driving. The model training method comprises the steps of obtaining training data; inputting the training data into a pre-constructed neural network model, and obtaining a prediction trajectory output by the neural network model; constructing a comprehensive loss function of the neural network model based on the predicted trajectory and the real trajectory; wherein the comprehensive loss function comprises an imitation learning loss function and a composite collision loss function; training the neural network model by using the comprehensive loss function to obtain a trajectory planning model; wherein the trajectory planning model is used for obtaining a corresponding planning trajectory based on the driving data of the vehicle. According to the method, the neural network model is trained by introducing the composite collision loss function, so that the trained trajectory planning model can fully consider the collision risk between the vehicle and the obstacle, and the safety of trajectory planning is remarkably improved.

The invention discloses a vehicle obstacle avoidance method and device, computer equipment and a storage medium, and the method comprises the steps: screening an obstacle meeting a collision condition as a target obstacle when there is an obstacle behind a vehicle, and obtaining a first collision risk value of the target obstacle and the vehicle in a current driving state; acquiring a target avoidance track of the vehicle for the target obstacle, and acquiring a second collision risk value between the target obstacle and the vehicle when the vehicle finishes running according to the target avoidance track; and generating an obstacle avoidance strategy for the target obstacle based on the first collision risk value and the second collision risk value. The coherent obstacle avoidance strategy is generated by evaluating the current and future collision risks of the vehicle and the rear obstacle, so that when the vehicle avoids the front obstacle, the real-time risk of the vehicle behind the adjacent lane is judged, and the collision risk of the vehicle is judged. It is ensured that the situation that in the avoiding process, due to the dangerous target appearing behind, interruption or unnecessary exit is caused is avoided, and therefore the continuity and safety of avoiding decision making are improved.

The invention relates to the technical field of probe testing, in particular to a PCB flying probe test path optimization method based on electrical characteristics, which comprises the following steps: calculating accessibility scores of test points according to the distribution of board surface test points and elements to determine risk types of the test points; determining a risk area grade corresponding to the grid unit according to the accessibility score in combination with the potential shadow area; optimizing the test path of the probe according to the risk area grade corresponding to the grid unit, and adjusting the initial moving speed of the test path of the probe in different grid units; for the same moving time sequence of different probe groups, determining overlapped grids according to the initial probe angles and the test paths corresponding to the probes, and determining whether there is a collision risk between the probes in combination with the initial moving speeds of the probes in different grid units; and when a collision risk exists between the probes, corresponding adjustment measures are taken according to the number of the overlapped grids. According to the invention, the test path is optimized in combination with multi-dimensional design based on the electrical characteristics and testability of the board.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and device fusing lane dynamic evaluation and safety indexes and a medium. Real-time collision risks are calculated by collecting dynamic data of a vehicle and surrounding vehicles, multi-lane traffic flow data are collected under triggering of specific requirements, and characteristics such as density, speed and flow are extracted; and a lane basic passage score is calculated by fusing the flow and the large vehicle influence, and correction is carried out by combining the lane changing intention of surrounding vehicles to obtain a dynamic lane evaluation result. And constructing a multi-objective decision function based on the safety index and the lane evaluation result, and finally generating and selecting a trajectory with the minimum comprehensive cost value in a decision period for execution. According to the invention, collaborative evaluation of the vehicle close-range safety risk and the lane passing potential is realized, and the lane evaluation can reflect the traffic flow interaction influence. The driving efficiency and the lane adaptability are optimized, and the decision-making ability of the automatic driving system in the traffic environment is improved.

The invention discloses a cascade operation state real-time monitoring and anti-collision risk intelligent early warning method and system, and the method comprises the steps: obtaining multi-sensor data, determining a key monitoring node, and extracting parameter changes to form an operation state trend sample; carrying out edge calculation processing on the trend sample, reversely analyzing and reconstructing component state parameters from the vibration spectrum signal, generating a step gap change curve, matching with a standard model to obtain a risk characteristic probability value, and generating a state traction chain according to the risk characteristic probability value; obtaining a displacement distribution coefficient and a vibration speed index from the state drag chain, predicting the collision probability distribution of the displacement distribution coefficient through cloud deep learning, determining an early warning level in combination with the vibration speed index, and generating early warning optimization configuration; emergency distribution is determined through linkage control analysis, differentiated response areas are identified, and linkage optimization factors are extracted from the differentiated response areas; finally, differential control parameters are generated and matched with the state traction chain to generate an early warning signal, and intelligent abnormal recognition and self-adaptive early warning of the step equipment are achieved.

The invention provides a low-altitude equipment state information processing method and system, and belongs to the technical field of low-altitude equipment state information processing, and the method comprises the steps: receiving the real-time state data of each low-altitude equipment state through a multi-protocol data access network; based on position data in the real-time state data, dividing a geographic space into grid units with set levels by using an airspace grid engine, and encoding the grid units to generate an airspace grid model; based on the airspace grid model, airspace occupation calculation and conflict detection are carried out, and early warning information is generated; and pushing the collision risk early warning information to a monitoring terminal for three-dimensional visual display through the message middleware, and / or sending the collision risk early warning information to a low-altitude equipment control system to generate a flight control instruction. According to the invention, real-time data is acquired through multiple protocols, the dynamic airspace grid model is constructed, conflicts are accurately detected, early warning is generated, the data are pushed to the monitoring terminal for visual display, and the flight safety and the operation efficiency are improved.

The invention discloses a dam upstream navigation management prediction method based on multi-feature factor research, and relates to the technical field of dam upstream navigation management. According to the method, multi-dimensional data of ship foundation, traffic flow, scheduling management and environment are collected, and key feature factors are extracted after preprocessing; constructing a navigation efficiency evaluation model fusing the cargo load, the ex-warehouse flow and the waiting time, and determining a weight coefficient by adopting a maximum variance method; a navigation state prediction model is constructed based on a random forest algorithm, and prediction of traffic flow density, collision risk and navigation efficiency is realized; and finally, generating an air route planning, scheduling adjustment and emergency management optimization scheme. According to the method, the limitation of single factor consideration of a traditional method is overcome, the comprehensiveness of navigation efficiency evaluation and the accuracy of navigation state prediction are improved, scientific decision support can be provided for navigation management of the upstream water area of the dam, the collision risk is effectively reduced, and the navigation efficiency is improved.

The embodiment of the invention discloses a river-crossing bridge safety management method and device based on multi-source data, and relates to the technical field of bridge construction safety management, and the method comprises the steps: obtaining bridge construction design data to construct a bridge BIM model and a construction finite element model, and constructing a dynamic digital twinborn body in combination with a safety risk knowledge graph; performing real-time deduction calculation by using the construction finite element model to realize graded early warning pushing; and identifying the ship target according to the video stream data by using the lightweight ship target detection model, predicting the navigation trajectory of the ship target, and performing collision risk prediction. A dynamic digital twinborn body fusing a BIM model, a finite element model and a risk knowledge graph is constructed, and deduction calculation is performed by using real-time data of each physical component, so that safety risk grading early warning is realized. And collision risk early warning is carried out on a ship target in a bridge water area through video analysis in combination with a lightweight ship target identification model. Existing hardware resources can be effectively utilized to reduce the cost, and collision risks are accurately warned in real time.

The invention relates to a multi-mechanical-arm path planning method, a storage medium and detection equipment, and the multi-mechanical-arm path planning method comprises the following steps: region division: dividing an operation region into a plurality of sub-regions according to the number of mechanical arms, configuring one mechanical arm for each sub-region, and synchronously starting operation by all the mechanical arms; starting point selection: acquiring coordinate information of all target points in each sub-region, and determining an operation starting point of the mechanical arm in the sub-region based on the position relationship of the target points; and generating a path. According to the method, the collision risk of the path can be avoided, the global path optimization effect and the calculation efficiency are considered, and the precision and the efficiency of path planning can be effectively balanced.

The invention discloses a ship path planning method and system, a storage medium and a processor, and belongs to the field of marine ship sailing, and the method comprises the following steps: S1, constructing a dynamic environment model; s2, optimizing a node combination by adopting a DE algorithm, and outputting a global optimization path; s3, based on the real-time sensing data and the global optimization path, evaluating a collision risk through a multi-dimensional index, and outputting a local collision avoidance path; s4, in combination with the dynamic flow field model and the ship energy consumption model, calculating an energy consumption gradient, backtracking along the gradient through a Level Set algorithm, and optimizing and generating an energy optimal path on the basis of the collision avoidance path; and S5, performing safety compliance verification. By adopting the ship path planning method and system, the storage medium and the processor, ship path planning with COLREGs compliance, multi-objective optimization, high energy efficiency and real-time collision avoidance is realized, and the ship path planning method and system are suitable for a complex ocean dynamic environment.

The invention discloses a ship autonomous collision avoidance decision-making method and system based on cognitive entropy near-end strategy optimization, and belongs to the field of intelligent ship control. The method comprises the following steps: acquiring ship and environment information, and constructing a state vector containing a collision risk index; the method comprises the following steps of: inputting a cognitive entropy into a cognitive entropy near-end strategy optimization decision model, introducing a cognitive entropy regular term under a near-end strategy optimization framework to regulate and control exploration and balance of a strategy, realizing smooth transition from high exploration to high utilization, and outputting a propulsive force / rudder angle instruction; after the instruction is executed, calculating a multi-component reward based on target proximity, track deviation, fused / collision risk and COLREGs compliance; and iteratively optimizing network parameters by utilizing rewards and state feedback. The problems that exploration and utilization of reinforcement learning in ship collision avoidance are difficult to balance and convergence efficiency is low are solved, and decision safety, compliance and training stability are improved.

The invention discloses a ship pier anti-collision early warning method and system based on a laser radar, and relates to the field of ship collision early warning, and the method comprises the steps: judging the movement condition of a ship based on GPS data and imu data, and collecting the three-dimensional point cloud data in front of a ship driving route when detecting that the ship moves towards a collision object; converting the three-dimensional point cloud data from a radar coordinate system to a ned coordinate system, and filtering and segmenting the three-dimensional point cloud data after coordinate conversion to obtain a point cloud cluster of the collision object; based on the distance from each point in the point cloud cluster to the ship contour, a preset distance threshold value and a preset number threshold value, executing multi-level collision risk assessment so as to output an early warning signal of a corresponding level; according to the method, the three-dimensional point cloud data is filtered and segmented, noise point clouds are effectively eliminated, the point cloud cluster representing the collision object is accurately extracted, and refined and graded early warning of the collision risk is further realized through a multi-stage risk assessment mechanism.

The invention discloses a PV-RCNN network-based dead zone early warning method, system and equipment for a heavy truck and a medium, mainly relates to the technical field of dead zone early warning, and aims to solve the problem that function accuracy is difficult to meet by using a single sensor in the existing scheme. Comprising the steps that when a preset tracking target is detected, the preset tracking target detected at the current moment and the preset tracking target detected at the last moment are compared, and a tracking target list is updated; detecting whether a preset tracking target involved in the tracking target list exists in a preset sight blind area or not, and when the preset tracking target exists in the preset sight blind area, performing information prompt on a driver; and detecting whether a preset tracking target existing in the preset sight blind area has a collision risk, and when the collision risk exists, performing preset early warning.

The invention provides an intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller. The intelligent ship digital twinning motion modeling and parallel anti-interference path tracking controller comprises a guidance signal module, a speed optimization module, a parallel path tracking controller module, a data-driven learning predictor module and an actual controller module. The data-driven learning predictor module receives position information, course angle information and speed information from the actual intelligent ship system module, and the data-driven learning predictor module receives parallel path tracking control force and torque from the parallel path tracking controller module; the data driving learning predictor module sends estimation information of the position and course angle of a virtual intelligent ship and estimation information of the speed of the virtual intelligent ship to the guidance signal module and the parallel path tracking controller module. According to the method, the self-adaptive capacity of the ship under the complex sea condition is enhanced, the collision risk is reduced fundamentally, and an effective solution is provided for the navigation scene with the high safety requirement.

The invention relates to the technical field of intelligent channel information, in particular to a coastal channel operation state real-time monitoring and early warning method based on digital twinning. Building a digital twin architecture and fusing multi-source operation data to realize real-time synchronous mapping from a physical channel to a virtual channel; introducing a multi-dimensional mechanism model and a historical mechanism function thereof to drive an AI model to perform function mapping, and constructing a back-silting dynamic prediction model to obtain a navigation risk index of a future leg; a space-time occupation matrix and space-time safety envelope model is constructed based on the navigational speed and course data of nearby ships and the ship, the future ship collision risk is predicted, studied and judged, and a navigation safety index is generated; performing coupling evaluation on the navigation risk and the navigation risk by using a marginal dependence distribution model to form a comprehensive risk and trigger dynamic early warning; and issuing early warning information and auxiliary decisions through the shipborne terminal and the VTS center. According to the invention, a complete ship-shore-cloud cooperative digital twin system is constructed, and global digital and intelligent management of coastal channels is realized.

The invention provides an intelligent obstacle avoidance and path planning method based on a ship, and relates to the technical field of intelligent control of small leisure ships. S1, the state and surrounding environment information of the ship are obtained through a multi-sensor group deployed on the ship, and a distortion compensation algorithm combining Kalman filtering with a sea condition coefficient is adopted; outputting the compensated obstacle information, sea condition parameters and ship task types; s2, the local end uploads the compensated obstacle information and the sea condition parameters output in the step S1 to a server end, the server end introduces sea condition correction factors to construct a CPA model, collision risk indexes are calculated through the CPA model, and the path conflict probability between ship pairs in the water area is predicted according to a path conflict probability calculation formula. According to the invention, accurate risk judgment can be ensured through collision risk correction, and multi-ship ordered avoidance is realized through scenarized priority, so that the problems of path conflict, low decision-making efficiency and increased navigation risk caused by lack of collaboration in a multi-ship environment are solved.

The utility model relates to the technical field of mold storage equipment, and discloses a glue pouring mold storage rack which comprises an outer frame, a taking and placing mechanism is arranged in the outer frame, and two fixing mechanisms are arranged at the bottom of the taking and placing mechanism. The taking and placing mechanism comprises a plurality of steel plates, the exteriors of the multiple steel plates are fixedly connected to the interior of the outer frame, the tops of the multiple steel plates are fixedly connected with two positioning seats, taking and placing grooves are formed in the interiors of the multiple positioning seats, and multiple positioning grooves are formed in the tops of the multiple positioning seats; and a plurality of molds are arranged in the multiple positioning seats correspondingly, and a plurality of moving assemblies are fixedly connected to the bottom of one steel plate. According to the mold taking and placing device, millimeter-level accurate positioning of molds is achieved, the molds in the multiple positioning bases are arranged in order and are convenient to manage and find, meanwhile, the collision risk in the taking and placing process is reduced, and compared with a traditional storage mode, the taking and placing efficiency is improved by 40%.

The application provides a crane group boom anti-collision early warning method and system based on digital twinning, which comprises the following steps: based on the data collected by a rotary encoder, an angle sensor, an inertial measurement unit, a laser radar and a ship body attitude measuring device, a dynamic three-dimensional digital twinning model is constructed and updated in real time; in the model, real-time path planning is performed on each crane boom, the spatial interference risk with the bulkhead, hatch coaming and other booms is evaluated, and a collision-free optimal action path is generated; the path nodes are dynamically risk evaluated, the minimum distance, relative speed, motion direction angle and ship body swing disturbance are comprehensively considered, and a collision risk score is generated; according to the score, hierarchical intervention control is implemented, the collision is avoided, and the attitude stability of the suspended goods is maintained. The application realizes the safety early warning and intelligent regulation and control of multi-machine cooperative operation under high dynamic sea conditions, and effectively improves the operation efficiency and safety.

This invention discloses a method and system for amphibious vehicle navigation perception fusion in complex environments, relating to the field of amphibious vehicle waterborne perception technology. The method includes the following steps: acquiring point cloud data in different multimodal regions; preprocessing the point cloud data, with different regions including the water surface region, the above-water region, and the below-water region; performing feature-level fusion on the multimodal point cloud data and assigning comprehensive credibility to the fused feature-level targets; using a fuzzy logic algorithm to perform decision-level fusion on the feature-level targets to generate the final comprehensive environmental perception result; and performing environmental modeling, path analysis, and collision risk assessment based on the final comprehensive environmental perception result. This invention utilizes the complementary advantages of deep collaborative sonar and millimeter-wave radar to achieve stable and accurate detection of obstacles on, below, and above water in complex environments.

The invention discloses a space target collision early warning method based on GPU parallel computing and a J2 perturbation item, aims to solve the problems of high computing complexity and poor real-time performance of existing CPU processing space target collision risk assessment, and provides a multi-stage screening method based on GPU parallel computing and the J2 perturbation item. The method comprises the following steps: reading a TLE and calculating near / distant points and a reference check point at a GPU (Graphics Processing Unit); combining according to C (N, 2) and screening according to epoch, height and geocentric distance; taking a check point state as an initial value in a check interval, and adopting single-precision J2-RK iterative screening to obtain events NJ2pre and tJ2pre; the events are screened by using double-precision J2-RK in front and back check sections, and minimum distances distJ2 and tJ2 are output; and finally, SGP4 / SDP4 is used for parallel actuarial calculation to obtain a minimum distance distDC, a relative speed, a collision probability and the like. The method gives consideration to real-time performance and precision, significantly reduces the O (n) calculation cost, and is suitable for large-scale in-orbit target early warning.

The invention provides a dangerous scene rehearsal and avoidance decision-making system and method based on anti-fact reasoning, and the system comprises a perception data obtaining module which is used for obtaining the perception data of a current scene, and the current scene comprises a vehicle, other vehicles and pedestrians; the risk perception module is used for constructing a dynamic structure causal model based on the perception data and determining a potential collision risk of the current scene in a preset future time window based on the dynamic structure causal model; the anti-fact scene generation module is used for driving a condition generation model to generate a plurality of anti-fact intervention sequences when the potential collision risk is greater than a risk threshold value; the risk assessment module is used for determining a risk score of each anti-fact intervention sequence based on a preset assessment index; and the evasion decision module is used for determining an optimal evasion action from the plurality of anti-fact intervention sequences based on the risk score and executing the optimal evasion action. According to the invention, a prospective and safer risk management closed loop is realized.

The application discloses a remote wireless control system of a pipe automatic lifting appliance, relates to the technical field of remote control of hoisting equipment, and comprises an appliance body, a remote control terminal and a sensing module, a control module and an execution module arranged on the appliance body. The sensing module comprises an appliance state sensor, a pipe posture sensor, an environment sensor and a digital wind speed and direction instrument, and is used for collecting the appliance state, the pipe posture, the environment obstacles and the wind speed and direction data. After the control module receives the data, a dynamic safety envelope is constructed, the collision risk is predicted through continuous collision detection, and a hierarchical obstacle avoidance instruction is generated. The execution module drives the lifting, walking and amplitude changing mechanism of the appliance to execute the obstacle avoidance action. The system is also integrated with a fault prediction and health management module and a digital twin visualization function. The application can realize real-time sensing of the dynamic posture of the pipe, avoid collision risks, realize equipment health prediction and operation transparency, and improve the safety, reliability and intelligent level of pipe hoisting.

The application discloses a kind of based on Internet of Things's hook remote intelligent control method, it is related to remote intelligent control technical field, the present application is applied in the hooking of this method, the self-acting cooperation of multiple hooking hoist is applied in tower crane, tire crane, gate machine operation The self-acting level of level is significantly improved;In efficiency dimension, dynamic priority arbitration and local path planning eliminate the operation delay caused by traditional centralization scheduling, make multiple hooks seamless cooperation in cross operation, reduce task interruption;At the same time, the real-time fuse response of sudden obstacle avoids the hysteresis of artificial intervention, maintains operation continuity;Safety level, motion envelope prediction is combined with wind speed compensation, enhances the robustness of path to dynamic interference, still maintains millimeter level obstacle avoidance precision under strong wind or hook mutation movement scene, greatly reduces collision risk.

The present application relates to a kind of water surface robot safety guidance control method based on dynamics constraint, comprising the following steps: S1, based on the construction of candidate control space of propulsion angle and rotating speed, generate multiple candidate control quantity combinations;S2, according to dynamics model, trajectory prediction is carried out to each candidate control quantity combination, and obtain feasible trajectory that satisfies dynamics constraint within preset prediction period;S3, collision risk assessment is carried out using closest approach point method, determine whether there is collision risk;S4, construct safety guidance control evaluation function, and select optimal control quantity combination from candidate control quantity combination by normalization weighted sum;S5, when there is collision risk, encounter situation is judged, and corresponding rule constraint is applied to candidate control quantity combination, and the final safety guidance control instruction is generated.The present application can quickly identify potential encounter threat in real-time scene, improve overall guidance control success rate and operation reliability.

The present application relates to the technical field of power transmission line construction simulation, and discloses a GIS-based power transmission line pay-off simulation construction visual simulation method, comprising: acquiring line elevation data and calculating a terrain fingerprint sequence, and dividing a risk section according to a fingerprint value; generating a basic dynamic envelope for a medium-risk area, and superimposing an asymmetric compensation envelope driven by the fingerprint on a high-risk area; and performing spatial overlay analysis in GIS to identify collision risks, wherein the present application realizes accurate optimization configuration of computing resources through a hierarchical response mechanism driven by the terrain fingerprint, simultaneously fuses multi-source data features to construct an adaptive safety boundary, and improves the accuracy and reliability of construction safety prediction under complex terrain.

The utility model relates to the field of transfer equipment, concretely to a safe circuit breaker transfer trolley based on control system convenient to dismouting maintenance, including transfer trolley body, transfer trolley body includes two stand, and the lateral wall fixed mounting of two stand has two side edge frame, and the installation of two side edge frames has the carrying platform, and the below of carrying platform is provided with two fixed links, and fixed link penetrates the corresponding stand and is located the side of corresponding side edge frame, and the end of two side edge frames away from stand is all fixed mounting has laser radar. Compared with prior art, the patent realizes intelligent navigation and obstacle avoidance through the setting laser radar, sensor, PLC, rotating assembly, significantly reduces the risk of manual operation failure and the collision risk in complex environment. The adjustment assembly provides active mechanical parking lock, greatly enhances the stability of the trolley during carrying and overhauling, prevents safety accidents caused by accidental movement.

This invention relates to the field of intelligent waterway information technology, specifically a method for real-time monitoring and early warning of coastal waterway operation status based on digital twins. It establishes a digital twin architecture and integrates multi-source operational data to achieve real-time synchronous mapping from physical waterways to virtual waterways; it introduces a multi-dimensional mechanism model and its historical mechanism functions to drive an AI model for function mapping, constructing a dynamic siltation prediction model to obtain navigation risk indicators for future waterways; based on the speed and heading data of nearby vessels and the vessel itself, it constructs a spatiotemporal occupancy matrix and a spatiotemporal safety envelope model to predict and assess future vessel collision risks and generate navigation safety indicators; it uses a marginal dependency distribution model to couple and assess navigation risks, forming a comprehensive risk and triggering dynamic early warnings; and it disseminates early warning information and assists in decision-making through shipborne terminals and the VTS center. This invention constructs a complete ship-shore-cloud collaborative digital twin system, realizing full-domain digital and intelligent management of coastal waterways.