Anti-collision monitoring method for crane on dock platform

Abstract

The invention belongs to the technical field of anti-collision monitoring of cranes, and particularly discloses an anti-collision monitoring method for a crane on a dock platform, which comprises the following steps: S1, monitoring the environment around the crane in real time through a sensor, and obtaining obstacle information around the crane; s2, performing data fusion and analysis on data acquired by different sensors; s3, calculating a collision risk according to the movement track of the crane and the position and speed of the obstacle, and giving an alarm when a potential collision risk is detected; s4, integrating S3 with a control system of the crane, and automatically controlling a starter to perform emergency braking; the anti-collision monitoring method can be operated more efficiently and reliably. The sensor provides environment sensing capability, the data processing module realizes intelligent analysis, the early warning and control module ensures safe operation, and the design can effectively reduce the collision risk of the crane on the dock platform and improve the operation safety.

Description

Technical Field

[0001] The present invention relates to the technical field of crane anti-collision monitoring, and in particular to a crane anti-collision monitoring method on a dock platform. Background Art

[0002] Cranes are essential equipment on dock platforms, used for lifting, transporting, and installing ship components. Due to the complex dock environment, cranes often need to operate within confined spaces, surrounded by other equipment, vessels, personnel, and temporary structures. This complex environment makes cranes prone to collisions during operation, resulting in equipment damage, casualties, and even production interruptions.

[0003] Traditionally, crane operations rely heavily on manual monitoring. Operators visually observe their surroundings to determine whether there is a collision risk. However, the human eye has a limited range and can easily miss obstacles. Long hours of work can lead to fatigue and misjudgment. Furthermore, collision avoidance systems typically rely on a single sensor (such as ultrasonic or lidar), which struggles to fully cover complex environments. Summary of the Invention

[0004] The object of the present invention is to provide a crane anti-collision monitoring method on a dock platform to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for monitoring the anti-collision of a crane on a dock platform, comprising the following steps:

[0006] S1. Use several sensors to monitor the environment around the crane in real time and obtain information about obstacles around the crane.

[0007] S2. Perform data fusion and analysis on the data collected by several sensors.

[0008] S3. Calculate the collision risk based on the crane's motion trajectory and the position and speed of the obstacle, and issue an alarm when a potential collision risk is detected.

[0009] S4, integrated with the crane's control system, automatically controls the starter for emergency braking.

[0010] Preferably, the sensor includes a laser radar, an ultrasonic sensor, an infrared sensor, a camera, an inertial measurement unit and a GPS.

[0011] The laser radar is used for high-precision three-dimensional environment scanning, detecting static and dynamic obstacles, and providing accurate distance and orientation information, and is suitable for medium and long-distance monitoring.

[0012] Ultrasonic sensors are used for close-range obstacle detection. They are low-cost and suitable for supplementing the blind spots of LiDAR, especially in low-visibility or complex environments.

[0013] Infrared sensors are used to detect obstacles at night or in low-light conditions, and can detect heat sources (such as people and vehicles).

[0014] The camera provides real-time images and video streams to identify the shape, color, and movement trajectory of obstacles. Combined with computer vision algorithms, it can identify specific targets (such as people, ships, and equipment).

[0015] The inertial measurement unit monitors the crane's attitude, acceleration, and angular velocity to predict its motion trajectory.

[0016] GPS provides global position information of the crane.

[0017] Preferably, the data processing module includes data fusion and environment modeling.

[0018] Data fusion is the use of multi-sensor fusion algorithms to fuse data from lidar, ultrasonic sensors, cameras and inertial measurement units; multi-sensor fusion algorithms include Kalman filtering, extended Kalman filtering or particle filtering.

[0019] LiDAR and camera data are used to build a three-dimensional environmental model, including the crane, platform, and obstacles, and the three-dimensional environmental model is transmitted to the crane's control system in real time. Point cloud compression algorithms and deep learning models are used to reduce data volume and improve processing efficiency.

[0020] Preferably, the collision risk includes distance calculation, trajectory prediction and collision warning.

[0021] The distance calculation calculates the Euclidean distance between the crane and the obstacle in real time based on the environment model; and dynamically adjusts the safety distance threshold according to the movement speed of the crane and the type of obstacle.

[0022] The trajectory prediction is based on IMU data and the crane kinematic model to predict the crane's motion trajectory in the next few seconds, using machine learning or physical models for prediction.

[0023] The collision warning is triggered when it is detected that the distance between the crane and the obstacle is less than a safety threshold or when the predicted trajectory may cause a collision.

[0024] Preferably, the warning method includes sound and light alarm and visual prompt; the sound and light alarm is to install warning lights and speakers around the cab and platform; the visual prompt is to display a red warning box and the location of the obstacle on the operation interface.

[0025] Preferably, the emergency braking includes automatic braking and path planning.

[0026] Automatic braking, when the collision risk reaches its highest level and the operator fails to respond promptly, automatically triggers the system to control the crane's hydraulic system or motor for smooth deceleration and stopping. This ensures a smooth braking process, preventing damage to the equipment or load sway caused by sudden stops.

[0027] The path planning uses the Dijkstra algorithm or the RRT algorithm to plan an obstacle avoidance path based on the environmental model and collision detection results, displays the suggested path on the operation interface, assists the operator in adjusting the crane movement, and dynamically updates the path according to real-time environmental changes (such as moving obstacles).

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The collision avoidance monitoring method of the present invention can operate more efficiently and reliably. Sensors provide environmental awareness, data processing modules perform intelligent analysis, and early warning and control modules ensure safe operation. This design can effectively reduce the collision risk of cranes on dock platforms, improve operational safety, ensure safe operation of cranes in complex environments, and reduce the possibility of accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] In the description of the present invention, it should be noted that the terms "vertical", "up", "down", "horizontal", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limiting the present invention.

[0033] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0034] See also Figure 1 The present invention provides a technical solution: a crane anti-collision monitoring method on a dock platform, comprising the following steps:

[0035] Step 1: Use a number of sensors to monitor the environment around the crane in real time and obtain information about obstacles around the crane.

[0036] The sensors include lidar, ultrasonic sensor, infrared sensor, camera, inertial measurement unit and GPS.

[0037] The LiDAR system is used for high-precision three-dimensional environmental scanning, detecting static and dynamic obstacles, and providing precise distance and orientation information, making it suitable for medium- and long-range monitoring. LiDARs are installed on the crane arm, on top of the cab, and at the four corners of the dock platform to ensure coverage of the crane's range of motion and the area surrounding the platform. Select a LiDAR with a high scanning frequency (e.g., 10-20Hz) and a long detection range (e.g., 50-100 meters) to accommodate the complex dock environment.

[0038] Ultrasonic sensors are low-cost and suitable for short-range obstacle detection, complementing LiDAR blind spots, particularly in low-visibility or complex environments. Install ultrasonic sensors at the end of the crane boom, near the hook, and on the edge of the platform. Choose ultrasonic sensors with strong anti-interference capabilities and high accuracy to withstand the humidity and temperature fluctuations found in dock environments.

[0039] Infrared sensors are used to detect obstacles at night or in low-light conditions, and can detect heat sources (such as people and vehicles).

[0040] Cameras provide real-time images and video streams to identify the shape, color, and movement of obstacles. Combined with computer vision algorithms, they can identify specific targets (such as people, ships, and equipment). Install high-definition cameras on the top of the crane cab and at the four corners of the platform to cover the crane's operating area. Choose cameras with wide-angle lenses, high resolutions (such as 1080p or 4K), and infrared capabilities for nighttime and low-light conditions.

[0041] Inertial measurement units monitor the crane's attitude, acceleration, and angular velocity to predict its trajectory. They are installed at key locations on the crane's boom and cab. High-precision, low-drift IMUs are selected to ensure data stability.

[0042] GPS provides global position information of the crane.

[0043] The data processing module includes data fusion and environment modeling.

[0044] Step 2: Fusion and analysis of data collected by several sensors.

[0045] Data fusion is the use of multi-sensor fusion algorithms to fuse data from lidar, ultrasonic sensors, cameras and inertial measurement units; multi-sensor fusion algorithms include Kalman filtering, extended Kalman filtering or particle filtering.

[0046] LiDAR and camera data are used to build a three-dimensional environmental model, including the crane, platform, and obstacles, and the three-dimensional environmental model is transmitted to the crane's control system in real time. Point cloud compression algorithms and deep learning models are used to reduce data volume and improve processing efficiency.

[0047] Step 3: Calculate the collision risk based on the crane's motion trajectory and the position and speed of the obstacle, and issue an alarm when a potential collision risk is detected.

[0048] The collision risk includes distance calculation, trajectory prediction and collision warning.

[0049] The distance calculation calculates the Euclidean distance between the crane and the obstacle in real time based on the environment model; and dynamically adjusts the safety distance threshold according to the movement speed of the crane and the type of obstacle.

[0050] The trajectory prediction is based on IMU data and the crane kinematic model to predict the crane's motion trajectory in the next few seconds, using machine learning or physical models for prediction.

[0051] The collision warning is triggered when it is detected that the distance between the crane and the obstacle is less than a safety threshold or when the predicted trajectory may cause a collision.

[0052] The warning methods include sound and light alarms and visual prompts; the sound and light alarms are warning lights and speakers installed around the cab and platform; the visual prompts are red warning boxes and obstacle locations displayed on the operation interface.

[0053] Step 4: Integrate with the crane's control system to automatically control the starter for emergency braking.

[0054] The emergency braking includes automatic braking and path planning.

[0055] Automatic braking, when the collision risk reaches its highest level and the operator fails to respond promptly, automatically triggers the system to control the crane's hydraulic system or motor for smooth deceleration and stopping. This ensures a smooth braking process, preventing damage to the equipment or load sway caused by sudden stops.

[0056] The path planning uses the Dijkstra algorithm or the RRT algorithm to plan an obstacle avoidance path based on the environmental model and collision detection results, displays the suggested path on the operation interface, assists the operator in adjusting the crane movement, and dynamically updates the path according to real-time environmental changes (such as moving obstacles).

[0057] In summary, the present invention monitors the status of the crane and its surrounding environment in real time, predicts potential collision risks, and prevents accidents through early warning or automatic intervention measures. It uses a variety of sensors such as lidar, ultrasonic sensors, cameras and IMU to collect environmental data. Data is integrated through data fusion algorithms (such as Kalman filtering and particle filtering) to build a high-precision environmental model. Based on sensor data, a three-dimensional model of the crane and its surrounding environment is constructed in real time. Static obstacles (such as platform structures) and dynamic obstacles (such as ships and personnel) are identified. Based on the motion state of the crane (such as speed, acceleration, angular velocity) and operating instructions, its future trajectory is predicted. Determine whether the predicted trajectory intersects with the obstacle. Calculate the distance between the crane and the obstacle in real time. Combined with the motion prediction results, the collision risk is evaluated and an early warning is triggered. According to the collision risk level, an audible and visual alarm or automatic braking is issued. Obstacle avoidance path planning is provided to assist the operator in adjusting the crane movement.

[0058] Multi-sensor data fusion improves the accuracy and robustness of obstacle detection. High-frequency data updates and fast algorithms ensure real-time system response. Through motion prediction and path planning, the system proactively identifies risks and assists in decision-making. Automatic braking and a graded warning mechanism effectively prevent collisions. The system can be customized and optimized for different dock environments and crane types.

[0059] It can be used to monitor collision risks between cranes and ships, platform structures, and other equipment. It can also be used for collision avoidance monitoring of large equipment such as container cranes and portal cranes. It can also be used for cranes and lifting equipment in factories and warehouses.

[0060] Through the above methods, the crane anti-collision monitoring system on the dock platform can significantly improve operational safety and efficiency, reduce the accident rate, and provide strong support for the intelligent management of ports and docks.

[0061] It is worth noting that the entire device is controlled by a master control button. Since the devices matched with the control button are commonly used devices and belong to existing mature technologies, their electrical connection relationships and specific circuit structures will not be described in detail here.

[0062] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A crane anti-collision monitoring method on a dock platform, characterized in that: The following steps are involved: S1. Use a number of sensors to monitor the environment around the crane in real time and obtain information about obstacles around the crane; S2, data fusion and analysis of data collected by several sensors; S3. Calculate the collision risk based on the crane's trajectory and the position and speed of the obstacle, and issue an alarm when a potential collision risk is detected; S4, integrated with the crane's control system, automatically controls the starter for emergency braking.

2. The method for monitoring crane collision avoidance on a dock platform according to claim 1, characterized in that: The sensors include lidar, ultrasonic sensor, infrared sensor, camera, inertial measurement unit and GPS; The laser radar is used for high-precision three-dimensional environment scanning and detection of static and dynamic obstacles; the ultrasonic sensor is used for close-range obstacle detection; and the infrared sensor is used for obstacle detection at night or in low-light conditions. The camera provides real-time images and video streams to identify the shape, color and movement trajectory of obstacles; the inertial measurement unit monitors the crane's posture, acceleration and angular velocity to predict its movement trajectory; and the GPS provides the crane's global position information.

3. The method for monitoring crane collision avoidance on a dock platform according to claim 1, characterized in that: The data processing module includes data fusion and environment modeling; Data fusion is the use of multi-sensor fusion algorithms to fuse data from lidar, ultrasonic sensors, cameras and inertial measurement units; multi-sensor fusion algorithms include Kalman filtering, extended Kalman filtering or particle filtering. Use lidar and camera data to build a three-dimensional environment model, including the crane, platform, and obstacles, and transmit the three-dimensional environment model to the crane's control system in real time, using point cloud compression algorithms and deep learning models to reduce the amount of data.

4. The method for monitoring crane collision avoidance on a dock platform according to claim 1, wherein: The collision risk includes distance calculation, trajectory prediction and collision warning; The distance calculation calculates the Euclidean distance between the crane and the obstacle in real time based on the environmental model; dynamically adjusts the safety distance threshold according to the crane's movement speed and the type of obstacle; The trajectory prediction, based on IMU data and the crane kinematic model, predicts the crane's motion trajectory in the next few seconds, using machine learning or physical models for prediction; The collision warning is triggered when it is detected that the distance between the crane and the obstacle is less than a safety threshold or when the predicted trajectory may cause a collision.

5. The method for monitoring crane collision avoidance on a dock platform according to claim 4, characterized in that: The warning methods include sound and light alarms and visual prompts; the sound and light alarms are warning lights and speakers installed around the cab and platform; the visual prompts are red warning boxes and obstacle locations displayed on the operation interface.

6. The method for anti-collision monitoring of a crane on a dock platform according to claim 1, characterized in that: The emergency braking includes automatic braking and path planning; Automatic braking, when the collision risk reaches its highest level and the operator fails to respond promptly, automatically triggers the system to control the crane's hydraulic system or motor for smooth deceleration and stopping. This ensures a smooth braking process, preventing damage to the equipment or load sway caused by sudden stops. The path planning uses the Dijkstra algorithm or the RRT algorithm to plan an obstacle avoidance path based on the environmental model and collision detection results, displays the suggested path on the operation interface, assists the operator in adjusting the crane movement, and dynamically updates the path according to real-time environmental changes.

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