516 results about "Collision risk" patented technology

The invention relates to the technical field of equipment control, and provides an autonomous tracking anti-interference control method and system.The confidence coefficient of a sensor is determined by combining the historical precision of the sensor and the current signal quality through a sensing recognition module, and the fusion weight is dynamically adjusted based on the confidence coefficient, so that the target tracking state can accurately reflect the actual motion characteristics of a target; the trajectory planning module takes an interference type, an interference degree and a target tracking state as input parameters cooperatively, and combines an extended state space and a reward function containing an anti-interference reward, so that an explored tracking trajectory can actively adapt to an interference scene; meanwhile, the predicted collision probability is compared with a probability threshold value, so that the global updating triggering opportunity is ensured to be accurate, and the collision risk caused by an unreasonable track is effectively avoided; and the control optimization module generates a targeted feed-forward compensation amount according to the interference type and the interference degree, and generates a control instruction after superposing the basic control amount, thereby realizing cooperation of anti-interference compensation and trajectory tracking control.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention discloses a bridge ship collision early warning system based on artificial intelligence, and relates to the field of intelligent traffic infrastructure safety protection. The method is used for solving the problems of difficulty in multi-source data fusion and low risk identification accuracy in a complex water area. The method comprises the following steps: firstly, uniformly mapping a radar point cloud, an AIS signal and a visual trajectory to a coordinate system taking a pier as a center through a Lie group transformation algorithm, and constructing a ship motion state tensor; then, ship behavior characteristics are extracted through a space-time diagram convolutional network, and intention probability distribution and behavior deviation degree are output; thirdly, an orthogonal constraint variational auto-encoder is adopted to decouple the autonomous control behavior and the environment disturbance factor, and a collision risk quantized value is generated according to the norm ratio of the autonomous control behavior and the environment disturbance factor; and finally, constructing a dynamic alarm threshold value according to the behavior deviation degree and the disturbance characteristics, realizing graded early warning, and driving a behavior pattern library to update through a feedback mechanism to form a self-adaptive closed-loop early warning system.

The invention discloses an intelligent ship autonomous collision avoidance method based on COLREGs and a DDPG algorithm, and relates to the technical field of an intelligent ship technology and an autonomous collision avoidance algorithm, and the method comprises the steps: building an intelligent ship kinematics model based on the motion parameters of a ship in a north-east coordinate system; aIS, radar and visual information are fused, COLREGs rule constraints are embedded, an environment model is constructed, and a ship size safety threshold value is calculated. According to the method, COLREGs and a DDPG algorithm are combined, a high-precision environment model is constructed by utilizing multi-source sensing data, and an algorithm structure is optimized aiming at a typical collision avoidance scene, so that the intelligent ship can automatically identify the meeting situation type, determine the way-giving responsibility or the direct navigation obligation and generate an optimal collision avoidance path in a complex marine environment; compared with a traditional collision avoidance method depending on manual driving and fixed rules, the autonomous collision avoidance capability of the ship in dynamic and complex ocean traffic scenes is remarkably improved, and the collision risk caused by human factors is reduced.

A navigation route planning apparatus includes processing circuitry configured to receive a planned route indicating a route and a current course direction of a movable body, receive movable body information that includes a position, a travelling direction, and a speed of the own ship, receive obstacle information that includes a position, a traveling direction, and a speed of an obstacle, determine a collision risk value associated with the planned route along the current course direction based on the movable body information and the obstacle information, and determine whether the movable body requires to at least one of evade the planned route and continue traversing on the planned route based on the collision risk value.

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 discloses a diffusion model trajectory planning method based on historical guidance and collision suppression. The method comprises the following steps: collecting own vehicle information, environment information and routing lane information in normal vehicle driving data; constructing negative sample scene data containing vehicle collision; during model training, encoding static obstacle states, dynamic obstacle historical tracks and surrounding lane structure information in a current environment; training a diffusion model decoder based on the future trajectory of the own vehicle, inputting the environmental condition information, the routing lane information and the own vehicle planning result of the model at the previous moment as condition information into the decoder, and enhancing the time continuity of the planning result; and training a classifier based on the collision data, and applying negative guidance to a collision category by using a classifier guidance mechanism during reasoning and sampling of the diffusion model so as to suppress a trajectory generation result with a high collision risk. According to the invention, efficient, safe and stable planning track generation can be realized in a real road scene.

The invention discloses a ship collaborative collision avoidance decision-making method based on a multilayer coding genetic algorithm. The method comprises the following steps: obtaining and processing related information of each ship in an encounter situation based on a sensor; based on a collision risk degree model, constructing a dynamic early warning mechanism in a multi-ship encounter scene: constructing a multi-stage ship collision avoidance strategy based on a queuing theory theoretical framework, establishing a multi-ship collision avoidance decision queuing system with specific space-time coupling characteristics, introducing a dynamic time window distribution mechanism and a space-time constraint coupling network modeling method, and establishing a multi-ship collision avoidance decision queuing system; multi-objective optimization is carried out on the ship decision scheme of the first priority gradient, and a ship intelligent collision avoidance strategy with dynamic adaptability is provided; a multi-ship collaborative collision avoidance decision optimization model is constructed on the basis of a genetic evolution mechanism, a multi-target adaptation function is integrated, a decision scheme when multiple groups of ships meet in a first priority is determined through the synergistic effect of dynamic priority management and multi-layer coding optimization, and hierarchical analysis of decisions in a multi-ship meeting situation is achieved.

The invention discloses an equipment disassembly sequence automatic generation and verification method based on a physical engine, which belongs to the technical field of equipment disassembly, and realizes component physical attribute and dynamic connection constraint loading by acquiring an equipment three-dimensional model and initializing a physical engine simulation environment. And in the physical engine simulation environment, iteratively evaluating the feasibility of the disassembly operation, accurately capturing physical behaviors such as gravity and collision, and identifying disassembly obstruction and preset operation to generate candidate operation. A path search algorithm is utilized to intelligently explore and generate a creative disassembly sequence, and the limitation of traditional artificial experience and a static method is overcome. Through a multi-dimensional nonlinear total cost function, factors such as the number of disassembly steps, collision risks, tool replacement and accessibility and the like are comprehensively quantified, and an optimal solution considering efficiency, safety and operation convenience is verified and selected.

The invention relates to the technical field of dredging engineering, in particular to a dredger autonomous path planning method based on reinforcement learning, which comprises the following steps: acquiring a multi-dimensional state space vector in real time; constructing a digital twinborn scene, training a strategy network by adopting a three-stage course learning strategy through increasing environment complexity, and dynamically adjusting a path planning priority through a layered reward function; migrating the trained strategy network to a ship entity; recognizing an obstacle based on a target multi-mode sensing system, evaluating a collision risk through a dynamic probability model, and performing local path reconstruction when the collision risk exists; and in a multi-ship collaborative operation scene, constructing a global optimization model, performing dynamic task redistribution on each ship by adopting a distributed iterative solution and incremental updating strategy until convergence, and outputting a collaborative optimization scheme of each ship. According to the method, the path planning efficiency in a multi-ship collaborative operation scene can be improved, and the training cost is reduced.

The invention relates to the technical field of fishing port safety monitoring, and discloses a fishing port fishing boat berthing safety monitoring system. The system comprises a fishing boat dynamic sensing module which collects dynamic position and attitude data of each fishing boat in a fishing port in real time; the berth state analysis module generates berth occupation state distribution information based on the data; the environmental parameter acquisition module acquires fishing port hydro-meteorological parameters in real time; the safety constraint initialization module outputs an initial safety monitoring constraint including an allowable yaw threshold value and a maximum allowable collision risk value in combination with a berth occupancy state and hydro-meteorological parameters; the parking risk assessment module executes risk quantitative analysis and generates risk level identification data by taking the initial safety monitoring constraint as a condition; a scheduling parameter optimization module optimizes berth resource scheduling according to the risk level identification data to obtain safety scheduling control parameters; and the alarm signal generation module converts an alarm dimension according to the safety scheduling control parameter and the dynamic position data updated in real time, and outputs a multi-stage acoustic alarm signal.

The invention discloses a ship dynamic collision avoidance decision-making method based on an improved DWA algorithm. The ship dynamic collision avoidance decision-making method comprises the steps of constructing sea chart environment data containing obstacle density through a grid method; establishing a dynamic elliptical ship domain based on a quaternion ship domain (QSD) model, and calculating a distance collision risk degree (NCRI) between ships based on the dynamic elliptical ship domain; improving a DWA algorithm evaluation function, and adding a path yaw evaluation function and a collision avoidance rule evaluation function on the basis of optimizing a speed and azimuth angle evaluation function; fusing the multiple evaluation functions by adopting a weight distribution method; and finally, generating a local collision avoidance optimization route based on environment data, NCRI and an improved DWA algorithm. According to the method, safety boundary dynamic adjustment, rule compliance constraint and ship control characteristic cooperation are realized, and the robustness and practicability of collision avoidance decision making under complex sea conditions are remarkably improved.

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 relates to the technical field of intelligent driving assistance systems, in particular to a vision-assisted high-precision lane-level navigation system which comprises a visual perception module, a geometric modeling module, a trajectory optimization module, a dynamic obstacle avoidance module and an execution control module. Establishing a lane local coordinate system based on arc length parameterization, and accurately capturing road geometric characteristics; state representation is constructed in a Riemannian metric space, an optimal track is selected by calculating geodesic line deviation measurement, and the generated track is made to better conform to road geometric characteristics; compared with the prior art, the method has the advantages that the lane-level positioning with centimeter-level precision can be realized, the driving track with smoother curvature change can be generated, and the driving comfort can be obviously improved.

The invention provides a port mobile machinery intelligent decision-making method, device and equipment based on multiple modes, and relates to the technical field of artificial intelligence. Comprising the steps of capturing a causal chain of wind speed sudden change-lifting appliance swing amplitude increase-AGV path offset through a multi-modal causal embedding layer, mapping heterogeneous data into uniform potential space representation, eliminating data modal difference, and adaptively enhancing the response capability to key risks by a model by dynamically adjusting a causal edge weight, so as to improve the stability of the AGV path. And furthermore, an AGV speed limiting or lifting appliance braking instruction is triggered through a preset condition. Therefore, according to the embodiment of the invention, the problems of multi-modal data fusion and dynamic causal modeling can be solved, the decision-making speed and accuracy are improved, and the collision risk is greatly reduced.

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 buoy ship collision early warning method based on artificial intelligence, a medium and a system, and belongs to the technical field of buoy ship collision early warning. A ship trajectory prediction model is utilized to dynamically adjust neuron time constant parameters through an adaptive adjustment function to output a ship future position prediction sequence, and the prediction sequence is input into a greedy snake path verification algorithm to carry out gridding processing and continuity inspection to delete abnormal path segments. Based on the verified prediction sequence, calculating a collision probability value comprehensively considering a distance parameter relative speed parameter course angle difference value and a sea condition complexity coefficient, determining a collision risk level according to the collision probability value and a preset threshold value, and calculating estimated collision time; and the collision early warning information including the risk level estimation time and the emergency degree is output, so that the technical problems of low accuracy and poor timeliness of collision early warning of ships in a sea area are solved.

The invention relates to the field of automation, and particularly provides a crane boom anti-collision method and system based on dynamic detection, and the method comprises the following steps: obtaining power parameters, motion data and structural data of a main engine boom and an auxiliary engine boom; determining the current position information of a main engine boom, and determining the current position information of an auxiliary engine boom; acquiring predicted deceleration position information and predicted stop position information of the main engine arm support based on the motion data, the power parameters and the structural data of the main engine arm support; based on the current position information of the main machine arm frame, the current position information of the auxiliary machine arm frame and the predicted deceleration position information and the predicted stop position information of the main machine arm frame, judging a collision risk assessment result of the main machine arm frame and the auxiliary machine arm frame; and when the collision risk assessment result is early warning, the main engine boom is decelerated, or when the collision risk assessment result is dangerous, the main engine boom is stopped. According to the anti-collision method, collision of the crane boom can be avoided.

The invention discloses an intelligent driving control method and system for an unmanned ship, and belongs to the field of unmanned driving control. Firstly, the position and speed of the unmanned ship and the positions and speeds of surrounding obstacles are obtained through a sensor and a positioning device, then dynamic path tracking is carried out in combination with global path planning and local path planning, and finally the unmanned ship is driven to sail along an optimized path by controlling the course and an accelerator. Local path planning adopts an improved dynamic window method, and an unmanned ship trajectory prediction evaluation function is modified by introducing a dynamic risk evaluation layer and a rule compliance layer; the dynamic risk evaluation layer is used for designing a collision risk evaluation function to dynamically screen a speed sampling space and rejecting a speed vector conflicting with a predicted trajectory of a moving obstacle; and the rule compliance layer classifies the meeting situation, introduces a rule evaluation function to perform weight correction on the candidate velocity vector, and drives the unmanned ship to generate a course adjustment strategy conforming to the collision avoidance rule. The unmanned ship realizes full-autonomous cruise, high-precision obstacle avoidance and path tracking in a complex marine environment.

The invention provides an automatic berthing method and device for a single boat in the new quality field, and relates to the technical field of automatic control of boats in the new quality field. Static berth information and a sensor sensing result are fused to construct a task situation package containing a multi-moving-obstacle trajectory prediction set and a dynamic prohibited area set, dynamic collision avoidance guidance is executed in a first-stage tracking path, and a staged deceleration strategy of vector motion and hovering is switched to obtain a berthing point state set when conflicts occur. Performing in-situ heading adjustment by using the state set of the berthing point, outputting consistent heading, zero navigational speed and position states, executing target point type vector approach guidance in the second stage, hovering when a collision risk is predicted, and recovering vector motion control in a safe time window to complete lateral positioning welt berthing. And finally, precise thruster control instruction output is achieved through thrust distribution of the double water-jet thrusters. According to the invention, automatic berthing of the new-quality field single boat under the condition of multiple moving obstacles can be realized.

The invention provides an intelligent ship decision-making system, and relates to the technical field of ship automation, and the system comprises the following modules: a multi-source data collection module which is provided with a plurality of sensors inside and outside a ship; the data fusion processing module is used for preprocessing, fusing and analyzing the original data acquired by the multi-source data acquisition module; the intelligent decision-making module is used for generating an optimal navigation path from the data input by the data fusion processing module through an A * algorithm, a Dijkstra algorithm, a deep learning algorithm and a collision avoidance algorithm; the execution module is used for transmitting the instruction to each corresponding equipment neutralization module; the man-machine interaction module and the communication module realize information interaction and sharing between the ship and an external related system; through the arrangement of the multi-source data acquisition module, the data fusion processing module and the intelligent decision-making module, various factors in ship operation can be fully considered, the optimal navigation route can be quickly and accurately planned, the collision risk is avoided, and the navigation time and cost are saved.

The invention discloses a large ship autonomous berthing path planning method, and relates to the technical field of channel control. The method comprises the steps of collecting and processing data, establishing a ship motion model, setting a path planning target, selecting and solving an optimization algorithm, verifying and adjusting a path, obtaining hydro meteorological historical data and real-time sensing data of port wind, wave and flow, ship basic parameters and under-actuated dynamic characteristic data, and preprocessing the data. Obtaining a ship motion model; minimum berthing time, minimum energy consumption and maximum safety are set, and a comprehensive objective function is obtained; defining constraint conditions of kinematics, dynamics, port environment and collision avoidance; solving by using an optimization algorithm, performing dynamics simulation verification and collision detection verification on the planned path, adjusting parameters of the optimization algorithm, and modifying a target function or a constraint condition; according to the method, the optimal berthing path can be planned, the collision risk is effectively avoided, the safety and efficiency of large ship berthing are improved, and the method has universality and flexibility.

The invention relates to the technical field of ship navigation safety, in particular to a real-time ship trajectory tracking and early warning method, which comprises the steps of multi-source data acquisition and preprocessing, trajectory prediction model construction, collision risk assessment, early warning triggering and the like. By integrating AIS, radar and meteorological data, accurate trajectory prediction is realized by adopting Kalman filtering and extended Kalman filtering algorithms, and the method adapts to a complex environment based on dynamic weight factors and fuzzy logic control. Meanwhile, a multi-level early warning mechanism and redundant communication design are provided, and timely transmission of early warning information is ensured. The collision risk assessment model is optimized by using the deep neural network, and the early warning precision is improved. The real-time performance, accuracy and dynamic adaptability of ship trajectory tracking and early warning can be improved, and the efficient and safe requirements of modern shipping are met.

The invention discloses a drag suction dredger sailing collision risk dynamic assessment and collision avoidance decision-making method, and relates to the technical field of sailing collision risk assessment. According to the method, a precise evaluation foundation is laid through all-dimensional basic data standardization processing, a potential risk area is dynamically determined in combination with ship manipulation and environment characteristics, and the collision risk degree and the response level are quantified to improve the timeliness and the accuracy of risk early warning; a personalized operation strategy library is constructed based on an LSTM model, so that a collision avoidance instruction fits an artificial habit, the man-machine adaptation cost is reduced, and the anti-interference capability and the real-time performance of the instruction are enhanced through feedforward-feedback dual-path control and parallel DQN dynamic optimization weight. The problems that in the prior art, due to the fact that multi-source data are disordered and free of standards, evaluation bases are not unified, potential collision risk area recognition is not combined with ship handling characteristics and lacks dynamics, collision risk quantization is fuzzy, collision avoidance instructions break away from manual operation habits, and due to the fact that traditional control is poor in anti-interference capacity and fixed in weight, robustness is insufficient are solved.

The invention belongs to the technical field of intelligent navigation situation sensing, and particularly relates to an intelligent navigation situation multi-dimensional sensing terminal, which generates a forward sensing domain covering a water surface and an underwater area in a preset space-time range in front of a route based on a target ship route trajectory, identifies candidate obstacles in the domain through multi-sensor fusion, and obtains a target navigation situation. Constructing an obstacle original data set containing position, size and medium type, performing confidence cross validation with collaborative perception data of ships in an overlapped perception domain, removing low-confidence candidate obstacles, generating a verified obstacle list, and combining real-time draught, attitude and tide data of the ships to obtain an obstacle data set; a three-dimensional electronic fence is constructed for each obstacle in the list, whether the collision risk exists in the navigation relative to the target ship or not is judged, finally, the electronic fences and the risk state are superposed to an electronic chart for visual display, high-reliability environment sensing data are provided for the autonomous navigation of the intelligent ship, and the obstacle avoidance efficiency in the navigation environment is remarkably improved.

The invention discloses a moored ship motion state prediction and anti-collision early warning system, particularly relates to the field of traffic control and anti-collision early warning, and is used for solving the problems that ship motion state accurate prediction and anti-collision scheduling response lag cannot be realized in existing port mooring operation. According to the system, ship trajectory, port operation and environment monitoring data are acquired in real time through a data acquisition module, and a fusion data stream with consistent time-space attributes is generated; constructing a space-time diagram structure by using a feature extraction module, extracting traffic flow dynamic features, and calculating a short-term conflict probability, a trajectory convergence risk value and a mooring risk value; the risk early warning module generates a collision risk index based on risk level mapping and triggers graded early warning; the mooring decision-making module sets a key control leg according to the early warning level and generates a dynamic decision-making scheme; the anti-collision scheduling module executes actual channel passing, speed adjustment and track change, and dynamic coordination and safety management and control of port mooring anti-collision are achieved.

The invention discloses a low-altitude unmanned aerial vehicle dynamic scene autonomous navigation adaptation method and system, and relates to the field of intelligent navigation, and the method comprises the following steps: 1, continuously obtaining real-time image data in a flight process through an image collection part carried on an unmanned aerial vehicle, analyzing the real-time image data based on predetermined route information of the unmanned aerial vehicle and a preset route attention area, and judging and identifying whether one or more dynamic targets exist in the route attention area; 2, if the existence of the dynamic target is identified, calling a motion risk prediction model which is constructed and pre-trained based on a deep learning algorithm; the motion risk prediction model integrating the relative speed, the motion direction, the target type and the trajectory prediction uncertainty is introduced, and the potential collision risk is quantitatively evaluated, so that the unmanned aerial vehicle can recognize the danger caused by the sudden dynamic target in time, and the safety guarantee in the densely populated area and airspace mixed environment is enhanced.

The invention discloses a marine unmanned ship formation obstacle avoidance and cooperation method based on an improved artificial potential field method, and the method comprises the steps: obtaining a control resultant force according to the attraction force, repulsive force, cooperation force and a speed correction term of a ship body; updating the velocity vector of the boat body by using the control resultant force so as to optimize the path; the space position of the boat body is divided into two areas, the minimum value of the minimum distance of the two areas serves as the minimum distance, and the distance between the boat bodies is monitored and adjusted; and dynamically adjusting the path by using the expected path direction of the hull, the path updating step factor and the direction disturbance suppression correction term. According to the embodiment of the invention, multiple intelligent mechanisms such as nonlinear potential field adjustment, inter-boat collaborative modeling and division and conquering real-time calculation are fused, and the safety, stability and adaptability of unmanned boat formation are effectively improved in a template-free formation scene. The method has high adaptive capacity in multi-task, multi-obstacle and autonomous coordination tasks, and path conflict and collision risks can be remarkably reduced.

The embodiment of the invention provides a collision avoidance method and device, equipment and a storage medium, which can be applied to the technical field of space debris, and the method comprises the steps: determining a plurality of time intervals for a spacecraft to execute collision avoidance on at least one dangerous object within a set time period; for any time interval, determining at least one avoidance strategy in the time interval based on the initial orbit of the spacecraft and the operation orbit of the at least one dangerous object; the evasion strategy comprises a target pulse moment and a target evasion speed pulse applied to the spacecraft at the target pulse moment; predicting whether a dangerous object with a new collision risk exists or not after the at least one avoidance strategy is executed; and the avoidance strategy without the new collision risk is determined as the target avoidance strategy, so that accurate avoidance of at least one dangerous object by executing the avoidance strategy once is realized, and the collision avoidance calculation automation and intelligence are improved.

The invention discloses an adaptive tower crane anti-collision early warning method based on data fusion. The method comprises the following steps: step 1, acquiring attitude data of equipment, surrounding environment data and interaction data between tower cranes through data acquisition equipment; performing time synchronization and coordinate unification; 2, according to the relative space relation between the tower cranes and the relative space relation between the tower cranes and the environment, multiple collision risk scenes are divided, key risk features in all the scenes are extracted, and fusion processing is conducted on the features through a data fusion algorithm; 3, according to the real-time working condition parameters, dynamically adjusting the weight of the key risk characteristics in each scene; carrying out the normalization processing of the fused risk features, and carrying out the weighted fusion according to the weight, and obtaining a global risk value; 4, setting a third-level threshold value, and triggering a corresponding early warning action according to an interval to which the global risk value belongs; and collecting risk characteristic data feedback in real time, and if the risk is not reduced to a safety range and meets a time interval condition, repeatedly executing early warning evaluation and instruction sending.