Loading arm full-automatic alignment loading monitoring system and method based on laser radar

Abstract

The invention discloses a laser radar-based loading arm full-automatic alignment loading monitoring system and method, and the system comprises an industrial personal computer and a first network switch which are located in a central control room, a full-automatic loading arm, a laser radar and a drive motor which are located in a loading site, and a PLC and a second network switch which are located in a field control cabinet. In the invention, the first network switch and the second network switch form an intelligent double-layer switching network with edge computing capability, safe, efficient, accurate and low-cost automatic loading and unloading are realized through point cloud data processing and dynamic scheduling distribution, and the requirements of the petrochemical industry are met.

Description

Technical Field

[0001] This invention relates to the field of petrochemical technology, and more specifically to a fully automated loading arm alignment and monitoring system and method based on lidar. Background Technology

[0002] Loading arms, specialized equipment used for liquid loading and unloading in the petrochemical industry, are primarily applied to the liquid transfer process between storage tanks or transport vehicles (such as tank trucks, trains, or ships). Their structure typically includes a column, inner arm, outer arm, vertical pipe, and hydraulic drive system to achieve flexible positioning and sealed connections. In existing technologies, to improve loading and unloading efficiency and safety, some loading arm systems have incorporated remote visual management capabilities. For example, a visible light camera mounted on the vertical pipe, combined with a small lamp, allows for real-time observation of the interior of the loading / unloading container, facilitating remote monitoring of liquid flow by operators and preventing the risk of overflow or leakage.

[0003] For example, Chinese utility model patent CN218579639U (application number: 202221765065.1) discloses an loading arm with remote visual management function, including a column, a liquid connection port, an inner arm, an outer arm, a vertical pipe, and a sealing block. The outer arm is driven by a telescopic hydraulic cylinder, and a monitoring structure is set below the sealing block, including a visible light camera on the mounting ring and a lamp head in the fixing block, which is used to improve the lighting inside the container and realize video detection. This device emphasizes passive observation of the inside of the container during loading and unloading, which improves work efficiency. However, the monitoring is limited to two-dimensional images and is greatly affected by environmental factors such as light, fog, and dust, and cannot provide three-dimensional spatial data to support accurate positioning.

[0004] Similarly, Chinese invention patent CN111994865B (application number: 202010983689.X) describes a video alignment system for loading arms, which uses a remote control device and a camera system to achieve visually assisted alignment between the loading arm and the tanker opening. However, its monitoring function still relies on a basic camera system, which is only used for two-dimensional video feedback. Furthermore, the movement control of the loading arm still requires personnel to refer to the images transmitted back from the camera system on the remote control device. Although this patent introduces video alignment technology to assist in positioning, the monitoring method is limited to visible light video, which is susceptible to weather interference. Moreover, it does not integrate 3D scanning technology, resulting in alignment accuracy relying on manual adjustment, which poses a safety hazard.

[0005] While the aforementioned existing technologies have made some progress in mechanical structure and remote video monitoring, they have significant shortcomings: First, monitoring relies on visible light cameras, providing only two-dimensional images. The accuracy is insufficient to achieve millimeter-level automatic alignment of the loading arm and the tank truck opening, especially under low light or inclement weather conditions, easily leading to misalignment or overflow accidents. Second, the lack of three-dimensional sensing equipment such as lidar prevents the generation of point cloud data for autonomous navigation and real-time calibration, requiring manual intervention during loading, resulting in low efficiency and high safety risks. Third, the simple network architecture, typically a single-layer switch connection, cannot support parallel operation of multiple devices and fault isolation, leading to large data transmission delays and hindering large-scale industrial applications. These problems restrict the intelligence and automation level of loading arm loading systems, necessitating a fully automated loading arm alignment monitoring system capable of high precision, high efficiency, and reliable safety to achieve high-precision three-dimensional positioning, real-time data fusion, and reliable transmission, thereby improving loading and unloading efficiency and safety. Summary of the Invention

[0006] In view of this, the present invention provides a fully automatic alignment and loading monitoring system and method for loading arms based on lidar, so as to solve at least some of the technical problems existing in the background art.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: First, a fully automatic loading arm alignment and monitoring system based on lidar is disclosed, including an industrial control computer and a first network switch located in the central control room, a fully automatic loading arm, lidar, and drive motor located at the loading site, and a PLC controller and a second network switch located in the field control cabinet. The fully automatic loading arm is installed on a column and located above the tanker to be loaded. The drive motor is installed on the fully automatic loading arm. The industrial control computer is connected to the second network switch through the first network switch and then to the PLC controller. The lidar is fixed above the tanker and is connected to the first network switch through the second network switch and then to the industrial control computer. The industrial control computer is connected to two displays, which are used to display the upper-level configuration interface and the remote monitoring interface, respectively. It is configured to display the status information of the fully automatic loading arm. It receives externally input control commands through its upper-level configuration interface and sends them to the PLC controller. The PLC controller is used to respond to the control commands and control the movement of the fully automatic loading arm to achieve automatic alignment.

[0008] Preferably, the first network switch and the second network switch constitute an intelligent two-layer switching network with edge computing capabilities. The second network switch has a built-in point cloud preprocessing unit, an instruction caching and retransmission module, and a device self-diagnosis module. It is used to filter, extract features, and calculate coordinates of the point cloud data collected by the LiDAR locally, and only upload key coordinate information and anomaly indicators to the first network switch. The first network switch dynamically adjusts the VLAN bandwidth allocation according to the operation stage through its built-in industrial protocol parsing engine and traffic self-learning scheduling module, and provides a dynamic priority promotion mechanism for real-time control commands.

[0009] Preferably, the first network switch, through its built-in industrial protocol parsing engine and traffic self-learning scheduling module, can dynamically adjust VLAN bandwidth allocation according to the operational phase, specifically including: During the initial operation phase, data traffic of each VLAN is collected in real time at different operation stages, including alignment, filling, and homing. Information such as timestamp, packet size, packet type, source and destination addresses are recorded, and the current operation stage identifier is also recorded. For each operation stage, traffic characteristics are extracted, including average bandwidth utilization, peak bandwidth utilization, data packet arrival interval distribution, data packet size distribution, and the proportion of data packets with different priorities. The traffic characteristics of each operation stage are clustered to form typical traffic patterns for each stage, and a traffic template for each operation stage is established, including: Template phase = {VLAN10: expected bandwidth ratio, VLAN20: expected bandwidth ratio, VLAN30: expected bandwidth ratio, others: expected bandwidth ratio}; The system monitors the difference between the actual traffic and the pre-allocated bandwidth of each VLAN in real time. If the traffic of a certain VLAN continues to exceed the threshold of the pre-allocated bandwidth, dynamic adjustment is triggered to dynamically allocate VLAN bandwidth.

[0010] As a preferred method, VLAN bandwidth allocation is dynamically performed, specifically including the following adjustment formula: New bandwidth = Current bandwidth × (1 + α × (Actual traffic - Pre-allocated bandwidth) / Pre-allocated bandwidth); Where α is an adjustment coefficient, with a value ranging from 0.1 to 0.5, set according to the degree of network congestion.

[0011] Preferably, the switch has a built-in device power consumption monitoring unit, which is used to monitor the power consumption status of each access device in real time, and automatically perform power supply protection and report to the industrial control computer when overload or abnormality occurs.

[0012] As a preferred option, the point cloud preprocessing unit's specific processing flow includes: Step 1: Data Reception and Buffering The lidar transmits raw point cloud data to the point cloud preprocessing unit of the second network switch at a fixed frequency; The preprocessing unit allocates a buffer to receive and temporarily store the raw point cloud data; Step 2: Outlier Filtering A statistical outlier filtering algorithm is used to calculate the average distance from each point cloud point to all its neighboring points. Assuming that the resulting distribution is Gaussian, points whose mean distance exceeds the standard deviation σ are removed. Step 3: Remove ground spots The random sampling consensus algorithm is used to fit the ground plane, and ground points are removed; Step 4: Extraction of edge features at the can opening For the point cloud after removing the ground, the Euclidean clustering algorithm is used to divide the point cloud into multiple clusters, each cluster representing a potential object. For each cluster, principal component analysis is used to calculate its eigenvalues. If the ratio of the largest eigenvalue to the second largest eigenvalue is greater than the threshold, it is determined to be a tank opening. For clusters identified as can openings, the least squares method is used to fit a cylindrical model to obtain the direction of the can opening's central axis, the coordinates of the center point, and the radius.

[0013] Step 5: Data Compression and Upload The center coordinates (x, y, z) of the tank opening, the fitting radius r, and the confidence score (interior point ratio) are used as results, packaged into a data packet, and uploaded to the industrial control computer.

[0014] Preferably, the industrial control computer is equipped with a host monitoring interface for displaying loading arm status information, point cloud processing results, receiving control commands, and displaying alarm information.

[0015] Preferably, the system also includes a UPS power supply, which is in a charging state during normal system operation and supplies power to the system in the event of an emergency power outage, so as to maintain the normal operation of the system for a period of time.

[0016] Preferably, the system also includes supplementary lighting and a monitoring camera.

[0017] This invention also discloses a fully automatic alignment and loading monitoring method for loading arms based on the above system, comprising the following steps: Drive the tanker truck to be loaded to the area below the fully automatic loading arm; Clicking the "Automatic Alignment" button triggers the lidar to collect point cloud data from the tank opening. The second network switch's built-in point cloud preprocessing unit performs local filtering, feature extraction, and coordinate calculation. Only key coordinate information is transmitted to the industrial control computer via the first network switch. The industrial control computer generates tank opening coordinate information based on the key coordinate information and transmits it to the PLC controller. The PLC controller calculates the rotation angles of the three drive motors based on the coordinate information and drives the inner arm, outer arm, and vertical pipe of the fully automatic loading arm to move, ensuring that the vertical pipe is precisely aligned below the tank opening. After alignment is completed and safety is confirmed, click the "Fill" button. The system will then perform the filling and monitor the liquid level and flow rate in real time through the anti-overflow controller. The filling will automatically stop when the preset liquid level is reached. After filling, let it stand for a period of time, and then drive the loading arm to return to its original position.

[0018] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a fully automatic loading and positioning monitoring system and method for loading arms based on lidar, which has the following beneficial effects: The fully automated loading arm alignment and monitoring system disclosed in this invention includes an industrial control computer and a first network switch located in the central control room, a fully automated loading arm, a lidar, and a drive motor located at the loading site, and a PLC controller and a second network switch located in the field control cabinet. The first network switch and the second network switch constitute an intelligent two-layer switching network with edge computing capabilities. Through point cloud data processing and dynamic scheduling and allocation, it achieves safe, efficient, accurate, and low-cost automated loading and unloading, meeting the needs of the petrochemical industry. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the fully automatic loading and positioning monitoring system for loading arms provided by the present invention.

[0021] In the diagram: 1-Tank truck, 2-Fully automatic loading arm, 3-Industrial control computer, 4-First network switch, 5-LiDAR, 6-Drive motor, 7-PLC controller, 8-Second network switch, 9-Servo driver, 10-Display of upper-level configuration interface, 11-Display of remote monitoring interface, 12-UPS power supply, 13-Supplemental lighting, 14-Monitoring camera, 15-Overflow prevention controller, 16-Liquid receiving hopper, 17-Canopy, 18-Column, 19-Anti-slip device, 20-Anti-static device, 21-Key management area, 22-Field control cabinet, 23-Central control room. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] This invention discloses a fully automated loading arm alignment and monitoring system based on lidar, such as... Figure 1As shown, the system includes an industrial control computer 3 and a first network switch 4 located in the central control room 23; a tank truck 1, a fully automatic loading arm 2, a lidar 5, and a drive motor 6 located on-site; and a PLC controller 7 and a second network switch 8 located in the on-site control cabinet 22. The fully automatic loading arm 2 is installed on a column 18 and located above the tank truck 1. The drive motor 6 is installed on the fully automatic loading arm 2. The industrial control computer 3 is connected to the second network switch 8 via the first network switch 4, and then to the PLC controller 7. The lidar 5 is fixed above the tank truck 1, connected to the first network switch 4 via the second network switch 8, and then to the industrial control computer 3. The industrial control computer 3 is configured to display the fully automatic loading arm's... The system receives status information and external control commands through its upper-level configuration interface 10, which are then sent to the PLC controller 7. The PLC controller 7 responds to the control commands and controls the movement of the fully automatic loading arm 2 to achieve automatic alignment. Three-dimensional point cloud data of the tank opening is collected by the lidar 5. The second network switch 8 has a built-in point cloud preprocessing unit that performs local filtering, feature extraction, and coordinate calculation, uploading only key coordinate information to the industrial control computer 3 through the first network switch 4. The industrial control computer 3 generates tank opening coordinate information based on the key coordinate information and sends it to the PLC controller 7. The PLC controller 7 calculates the rotation angle of the drive motor 6 based on the coordinate information and controls the movement of the inner arm, outer arm, and vertical tube 16 of the fully automatic loading arm 2 to achieve precise alignment. The industrial control computer 3 runs the upper-level configuration interface 10 and the remote monitoring interface 11 to display the loading arm status information, receive external control commands and transmit them to the PLC controller, and also supports data storage and remote monitoring. In the figure, M1, M2, and M3 all represent drive motors; 9 represents a servo driver. 12 represents the UPS power supply, which is in a charging state when the system is running normally and provides power to the system in the event of an emergency power outage to maintain the normal operation of the system for a period of time; 13 represents the supplementary lighting, 14 represents the monitoring camera, 15 represents the overflow controller, 17 represents the canopy, 19 represents the anti-slip device, 20 represents the anti-static device, and 21 represents the key management area, which is used to store the tanker keys.

[0024] In this invention, the first network switch and the second network switch constitute an intelligent two-layer switching network with edge computing capabilities. The second network switch has a built-in point cloud preprocessing unit, an instruction caching and retransmission module, and a device self-diagnosis module. It is used to filter, extract features, and calculate coordinates of the point cloud data collected by the LiDAR locally, and only upload key coordinate information and anomaly identifiers to the first network switch. The first network switch dynamically adjusts the VLAN bandwidth allocation according to the operation stage through its built-in industrial protocol parsing engine and traffic self-learning scheduling module, and provides a dynamic priority enhancement mechanism for real-time control commands.

[0025] Specifically, the first network switch, through its built-in industrial protocol parsing engine and traffic self-learning scheduling module, can dynamically adjust VLAN bandwidth allocation according to the operational phase, including the following steps: During the initial operation phase, data traffic of each VLAN is collected in real time at different operation stages, including alignment, filling, and homing. Information such as timestamp, packet size, packet type, source and destination addresses are recorded, and the current operation stage identifier is also recorded. For each operation stage, traffic characteristics are extracted, including average bandwidth utilization, peak bandwidth utilization, data packet arrival interval distribution, data packet size distribution, and the proportion of data packets with different priorities. The traffic characteristics of each operation stage are clustered to form typical traffic patterns for each stage, and a traffic template for each operation stage is established, including: Template phase = {VLAN10: expected bandwidth ratio, VLAN20: expected bandwidth ratio, VLAN30: expected bandwidth ratio, others: expected bandwidth ratio}; The system monitors the difference between the actual traffic and the pre-allocated bandwidth of each VLAN in real time. If the traffic of a certain VLAN continues to exceed the threshold of the pre-allocated bandwidth, dynamic adjustment is triggered to dynamically allocate VLAN bandwidth.

[0026] As a preferred method, VLAN bandwidth allocation is dynamically performed, specifically including the following adjustment formula: New bandwidth = Current bandwidth × (1 + α × (Actual traffic - Pre-allocated bandwidth) / Pre-allocated bandwidth); Where α is an adjustment coefficient, with a value ranging from 0.1 to 0.5, set according to the degree of network congestion.

[0027] Preferably, the switch has a built-in device power consumption monitoring unit, which is used to monitor the power consumption status of each access device in real time, and automatically perform power supply protection and report to the industrial control computer when overload or abnormality occurs.

[0028] As a preferred option, the point cloud preprocessing unit's specific processing flow includes: Step 1: Data Reception and Buffering The lidar transmits raw point cloud data to the point cloud preprocessing unit of the second network switch at a fixed frequency; The preprocessing unit allocates a buffer to receive and temporarily store the raw point cloud data; Step 2: Outlier Filtering A statistical outlier filtering algorithm is used to calculate the average distance from each point cloud point to all its neighboring points. Assuming that the resulting distribution is Gaussian, points whose mean distance exceeds the standard deviation σ are removed. Step 3: Remove ground spots The random sampling consensus algorithm is used to fit the ground plane, and ground points are removed; Step 4: Extraction of edge features of the can opening For the point cloud after removing the ground, the Euclidean clustering algorithm is used to divide the point cloud into multiple clusters, each cluster representing a potential object. For each cluster, principal component analysis is used to calculate its eigenvalues. If the ratio of the largest eigenvalue to the second largest eigenvalue is greater than the threshold, it is determined to be a tank opening. For clusters identified as can openings, the least squares method is used to fit a cylindrical model to obtain the direction of the can opening's central axis, the coordinates of the center point, and the radius.

[0029] Step 5: Data Compression and Upload The center coordinates (x, y, z) of the tank opening, the fitting radius r, and the confidence score (interior point ratio) are used as results, packaged into a data packet, and uploaded to the industrial control computer.

[0030] Preferably, the industrial control computer is equipped with a host monitoring interface for displaying loading arm status information, point cloud processing results, receiving control commands, and displaying alarm information.

[0031] Based on the above system, this invention also discloses a fully automated loading and unloading monitoring method for loading arms based on lidar, including the following steps: Drive the tanker truck to be loaded to the area below the fully automatic loading arm; Clicking the "Automatic Alignment" button triggers the lidar to collect point cloud data from the tank opening. The second network switch's built-in point cloud preprocessing unit performs local filtering, feature extraction, and coordinate calculation. Only key coordinate information is transmitted to the industrial control computer via the first network switch. The industrial control computer generates tank opening coordinate information based on the key coordinate information and transmits it to the PLC controller. The PLC controller calculates the rotation angles of the three drive motors based on the coordinate information and drives the inner arm, outer arm, and vertical pipe of the fully automatic loading arm to move, ensuring that the vertical pipe is precisely aligned below the tank opening. After alignment is completed and safety is confirmed, click the "Fill" button. The system will then perform the filling and monitor the liquid level and flow rate in real time through the anti-overflow controller. The filling will automatically stop when the preset liquid level is reached. After filling, let it stand for a period of time, and then drive the loading arm to return to its original position.

[0032] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0033] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A fully automated positioning and loading monitoring system for loading arms based on lidar, characterized in that, It includes an industrial control computer and a first network switch located in the central control room, a fully automatic loading arm, lidar, and drive motor located at the loading site, and a PLC controller and a second network switch located in the field control cabinet. The fully automatic loading arm is installed on a column and located above the tanker to be loaded. The drive motor is installed on the fully automatic loading arm. The industrial control computer is connected to the second network switch through the first network switch and then to the PLC controller. The lidar is fixed above the tanker and is connected to the first network switch through the second network switch and then to the industrial control computer. The industrial control computer is connected to two displays, which are used to display the upper-level configuration interface and the remote monitoring interface, respectively. It is configured to display the status information of the fully automatic loading arm. It receives externally input control commands through its upper-level configuration interface and sends them to the PLC controller. The PLC controller is used to respond to the control commands and control the movement of the fully automatic loading arm to achieve automatic alignment.

2. The fully automatic alignment and loading monitoring system for loading arms based on lidar according to claim 1, characterized in that, The first network switch and the second network switch constitute an intelligent two-layer switching network with edge computing capabilities. The second network switch has a built-in point cloud preprocessing unit, an instruction caching and retransmission module, and a device self-diagnosis module. It is used to filter, extract features, and calculate coordinates of the point cloud data collected by the LiDAR locally, and only upload key coordinate information and anomaly indicators to the first network switch. The first network switch dynamically adjusts the VLAN bandwidth allocation according to the operation stage through its built-in industrial protocol parsing engine and traffic self-learning scheduling module, and provides a dynamic priority promotion mechanism for real-time control commands.

3. The fully automatic alignment and loading monitoring system for loading arms based on lidar according to claim 2, characterized in that, The first network switch, through its built-in industrial protocol parsing engine and traffic self-learning scheduling module, can dynamically adjust VLAN bandwidth allocation according to the operational phase, specifically including: During the initial operation phase, data traffic of each VLAN is collected in real time at different operation stages, including alignment, filling, and homing. Information such as timestamp, packet size, packet type, source and destination addresses are recorded, and the current operation stage identifier is also recorded. For each operation stage, traffic characteristics are extracted, including average bandwidth utilization, peak bandwidth utilization, data packet arrival interval distribution, data packet size distribution, and the proportion of data packets with different priorities. The traffic characteristics of each operation stage are clustered to form typical traffic patterns for each stage, and a traffic template for each operation stage is established, including: Template phase = {VLAN10: expected bandwidth ratio, VLAN20: expected bandwidth ratio, VLAN30: expected bandwidth ratio, others: expected bandwidth ratio}; The system monitors the difference between the actual traffic and the pre-allocated bandwidth of each VLAN in real time. If the traffic of a certain VLAN continues to exceed the threshold of the pre-allocated bandwidth, dynamic adjustment is triggered to dynamically allocate VLAN bandwidth.

4. The fully automatic alignment and loading monitoring system for loading arms based on lidar according to claim 3, characterized in that, Dynamic allocation of VLAN bandwidth includes the following adjustment formulas: New bandwidth = Current bandwidth × (1 + α × (Actual traffic - Pre-allocated bandwidth) / Pre-allocated bandwidth); Where α is the adjustment coefficient.

5. A fully automated loading arm alignment and monitoring system based on lidar as described in claim 1, characterized in that, The switch has a built-in device power consumption monitoring unit, which is used to monitor the power consumption status of each access device in real time, and automatically perform power supply protection and report to the industrial control computer when overload or abnormality occurs.

6. A fully automated loading arm alignment and monitoring system based on lidar according to claim 2, characterized in that, The point cloud preprocessing unit's specific processing flow includes: Step 1: Data Reception and Buffering The lidar transmits raw point cloud data to the point cloud preprocessing unit of the second network switch at a fixed frequency; The preprocessing unit allocates a buffer to receive and temporarily store the raw point cloud data; Step 2: Outlier Filtering A statistical outlier filtering algorithm is used to calculate the average distance from each point cloud point to all its neighboring points. Assuming that the resulting distribution is Gaussian, points whose mean distance exceeds the standard deviation σ are removed. Step 3: Remove ground spots The random sampling consensus algorithm is used to fit the ground plane, and ground points are removed; Step 4: Extraction of edge features at the can opening For the point cloud after removing the ground, the Euclidean clustering algorithm is used to divide the point cloud into multiple clusters, each cluster representing a potential object. For each cluster, principal component analysis is used to calculate its eigenvalues. If the ratio of the largest eigenvalue to the second largest eigenvalue is greater than the threshold, it is determined to be a tank opening. For clusters identified as can openings, the least squares method is used to fit a cylindrical model to obtain the direction of the can opening's central axis, the coordinates of the center point, and the radius. Step 5: Data Compression and Upload The center coordinates (x, y, z) of the tank opening, the fitting radius r, and the confidence score are used as results, packaged into a data packet, and uploaded to the industrial control computer.

7. A fully automatic loading and unloading monitoring system for loading arms based on lidar according to claim 1, characterized in that, The industrial control computer is equipped with a host monitoring interface, which is used to display the status information of the loading arm, the point cloud processing results, receive control commands, and display alarm information.

8. A fully automated positioning and loading monitoring method for loading arms based on lidar, characterized in that, The system implementation based on any one of claims 1-7 includes the following steps: Drive the tanker truck to be loaded to the area below the fully automatic loading arm; Clicking the "Automatic Alignment" button triggers the lidar to collect point cloud data from the tank opening. The second network switch's built-in point cloud preprocessing unit performs local filtering, feature extraction, and coordinate calculation. Only key coordinate information is transmitted to the industrial control computer via the first network switch. The industrial control computer generates tank opening coordinate information based on the key coordinate information and transmits it to the PLC controller. The PLC controller calculates the rotation angles of the three drive motors based on the coordinate information, driving the inner arm, outer arm, and vertical pipe of the fully automatic loading arm to move, ensuring the vertical pipe is precisely aligned below the tank opening. After alignment is completed and safety is confirmed, click the "Fill" button. The system will then perform the filling and monitor the liquid level and flow rate in real time through the anti-overflow controller. The filling will automatically stop when the preset liquid level is reached. After filling, let it stand for a period of time, and then drive the loading arm to return to its original position.

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