Hoisting and positioning remote control method and system for port container crane

Abstract

The present application relates to the technical field of remote control, in particular to a hoisting positioning remote control method and system for port container cranes, comprising the following steps: collecting the spatial coordinate sequence of the lifting appliance, analyzing the vector angle and displacement length, generating the trajectory curvature sequence, generating the risk point based on the comparison of curvature and spatial distance, calculating the collision time advance, constructing the three-dimensional envelope surface based on the prediction value, calculating the stacking boundary error, projecting the calculation coverage ratio, and generating the hoisting risk area. In the present application, the trajectory curvature and spatial position of the lifting appliance are accurately calculated, the lifting appliance movement is monitored in real time and potential risk points are identified, the collision time is predicted based on the ratio of tangential velocity to spatial distance, the risk prediction accuracy is improved, three-dimensional pose analysis and error control enable fine management of the spatial error distribution of the lifting appliance, the risk area is promptly calibrated, operation interruption is avoided, and the accuracy, safety and remote control capability of hoisting operation are overall improved.

Description

Technical Field

[0001] This invention relates to the field of remote control technology, and in particular to a method and system for remote control of lifting and positioning of port container cranes. Background Technology

[0002] The field of remote control technology encompasses technologies for operating and managing target equipment via remote signals. Its core components include transmitting control commands through wired or wireless communication networks, combined with position detection, status monitoring, and feedback mechanisms to achieve precise control of the equipment. The overall technological framework systematically involves command generation, signal encoding and transmission, actuator driving, real-time status monitoring, and feedback control.

[0003] Among them, the remote control method and system for lifting and positioning of port container cranes refers to the method and system that, for port container crane lifting operation scenarios, achieves precise positioning of the equipment spreader in the horizontal and vertical directions through preset control strategies, collects data on the relative position of the spreader and the container using sensors, and transmits the monitoring data to the remote control platform based on a real-time communication link.

[0004] While existing technologies enable remote control and monitoring of equipment, they have certain limitations in practical operation. First, traditional control methods mostly rely on simple command generation and feedback mechanisms, lacking detailed analysis and accurate prediction of equipment motion states. In crane lifting operations, although equipment status feedback can be obtained, the real-time monitoring accuracy of the relative positions of the spreader and container is low, especially in dynamic environments, failing to effectively predict potential collision risks. Second, existing technologies suffer from delays in signal transmission and execution response, causing the system to be unable to adjust equipment actions in a timely manner when facing complex operating environments, potentially delaying the occurrence of risks. Furthermore, existing monitoring and control methods are relatively rudimentary, unable to promptly identify and process error accumulation and deviation changes during equipment operation, making it difficult to achieve high-precision dynamic control of the equipment's attitude and position in real-time operations, thus affecting operational efficiency and safety. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a remote control method for lifting and positioning of port container cranes, comprising the following steps:

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a remote control method for lifting and positioning of port container cranes, comprising the following steps:

[0007] S1: Collect the spatial position coordinate sequence of the spreader during operation, analyze the vector angle and displacement length of adjacent points of the spreader and calculate the local curvature value to generate the spreader trajectory curvature sequence;

[0008] S2: Based on the comparison between the curvature sequence of the spreader trajectory and the collected spatial distance from the leading edge of the spreader to the container stacking boundary, when the curvature sequence of the spreader trajectory is rising and the spatial distance does not reach the spatial distance threshold, potential risk points are generated;

[0009] S3: Based on the potential risk points, collect the tangential velocity of the lifting device in the corresponding direction, calculate the ratio of spatial distance to tangential velocity as the collision time advance, perform time-series prediction, and generate risk time prediction values;

[0010] S4: Based on the predicted risk time value, collect the three-dimensional pose parameters of the spreader for the corresponding time period and construct a three-dimensional outer envelope surface, calculate the error of the spatial distance with the container stacking boundary coordinates, and generate the spreader spatial error distribution;

[0011] S5: Based on the spatial error distribution of the spreader, project it onto the vertical plane of the container stacking boundary and calculate the coverage ratio of the projected area. When the coverage ratio exceeds the coverage ratio threshold and the risk time prediction value is earlier than the time threshold, generate the lifting positioning risk area.

[0012] As a further aspect of the present invention, the curvature sequence of the lifting device trajectory specifically includes a local curvature sequence, an angle sequence between adjacent points, and a trajectory point index; the potential risk points include position coordinates, trajectory curvature state, and spatial distance state; the risk time prediction value specifically refers to the collision time advance, time-series prediction result, and risk point index; the spatial error distribution of the lifting device includes pose parameters, outer envelope boundary, and spatial error distance value; and the lifting positioning risk area specifically includes a risk area index, coverage ratio parameters, and time threshold state.

[0013] As a further aspect of the present invention, the specific steps of S1 are as follows:

[0014] S101: Collect the spatial position coordinate sequence of the lifting device during operation, and vectorize the three-dimensional coordinate difference between adjacent points. Then, calculate the angle between the direction difference of adjacent vectors based on the magnitude of each vector to generate the angle sequence between adjacent points.

[0015] S102: Based on the adjacent point angle sequence, call the modulus data of the adjacent point vectors, perform joint operation on the angle value and modulus value, calculate the local curvature value of each point, and arrange them according to the point order to obtain the local curvature sequence.

[0016] S103: Based on the local curvature sequence, concatenate all curvature values ​​in the sequence according to the time index and maintain the correspondence with the position points to generate the curvature sequence of the lifting device trajectory.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Based on the curvature sequence of the lifting device trajectory, perform time sequence detection on the continuous curvature values ​​in the sequence, determine whether the curvature is in an upward state and mark it, and generate a curvature rising identifier sequence.

[0019] S202: Call the curvature rise identifier sequence and obtain the spatial distance data from the front edge of the spreader to the container stacking boundary. Compare each spatial distance value with a preset spatial distance threshold, filter the distance points that do not exceed the spatial distance threshold, and obtain the set of distances that do not reach the threshold.

[0020] S203: Based on the correspondence between the curvature rise identifier sequence and the distance set that does not reach the threshold, retrieve the points that simultaneously satisfy the curvature rise state and the spatial distance not reaching the threshold, and aggregate and mark the points to generate potential risk points.

[0021] As a further aspect of the present invention, the spatial distance threshold is determined by statistically analyzing the spatial distance data from the leading edge of the spreader to the container stacking boundary, extracting the minimum safe operating interval value, and then weighting and correcting it in conjunction with the dynamic offset margin generated during equipment operation.

[0022] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0023] S301: Collect tangential velocity data of the spreader in the corresponding direction based on the potential risk points, and record the index position of each risk point and the instantaneous tangential velocity value to obtain a tangential velocity sequence;

[0024] S302: Call the tangential velocity sequence, and according to the spatial distance value corresponding to the potential risk point, perform a ratio calculation on each spatial distance and the corresponding tangential velocity, use the ratio result as the collision time advance and summarize it to generate a collision time advance sequence;

[0025] S303: Based on the numerical distribution of the collision time advance sequence, perform time sequence prediction processing, and correlate the predicted time sequence results with the locations of potential risk points to obtain the risk time prediction value.

[0026] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0027] S401: Based on the predicted risk time value, collect the three-dimensional pose parameters of the lifting device for the corresponding time period, aggregate the three-dimensional pose data of multiple time periods according to the time index, and call the spatial geometric construction operation to form the three-dimensional outer envelope surface of the lifting device in the three-dimensional coordinate system.

[0028] S402: Call the three-dimensional outer envelope surface of the spreader, and according to the coordinate system position of the container stacking boundary, perform difference calculation on the spatial distance from multiple points on the outer envelope surface to the boundary coordinate point to obtain a spatial distance error sequence;

[0029] S403: Based on the spatial distance error sequence, aggregate and calibrate all error points, and map and encode their distribution in three-dimensional space to generate the spatial error distribution of the lifting device.

[0030] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0031] S501: Based on the spatial error distribution of the spreader, a projection is established in the vertical direction corresponding to the container stacking boundary surface, and the displacement data of multiple coordinate points in the spatial error distribution is converted to the vertical plane to generate the error projection area;

[0032] S502: Call the error projection area, and calculate the ratio of the projection area to the total area of ​​the boundary surface based on the current planar area parameters of the container stacking boundary surface to obtain the projection coverage ratio;

[0033] S503: Based on the value of the projection coverage ratio and the risk time prediction value, compare the coverage ratio with the coverage ratio threshold, and at the same time judge the time prediction value with the time threshold. If both conditions are met, mark the area on the stack boundary surface to generate the hoisting positioning risk area.

[0034] The coverage ratio threshold is set based on the coverage of the error projection area and the stacking boundary surface during the operation of the lifting device.

[0035] As a further aspect of the present invention, the time threshold is set by statistically analyzing the time distribution of the risk event occurrence period and the normal operation period during the operation of the lifting equipment, taking the shortest warning time before the risk event as a reference benchmark, and then combining it with the allowable safety buffer time in the operation process.

[0036] Remote control system for lifting and positioning of container cranes in ports, including:

[0037] The trajectory generation module collects the spatial position coordinate sequence of the spreader during operation, analyzes the vector angle and displacement length of adjacent points of the spreader and calculates the local curvature value, generates the spreader trajectory curvature sequence and transmits it to the risk identification module;

[0038] The risk identification module compares the curvature sequence of the spreader trajectory with the collected spatial distance from the leading edge of the spreader to the container stacking boundary. When the curvature sequence of the spreader trajectory is rising and the spatial distance does not reach the spatial distance threshold, a potential risk point is generated and transmitted to the collision prediction module.

[0039] The collision prediction module collects the tangential velocity of the lifting device in the corresponding direction based on the potential risk points, calculates the ratio of spatial distance to tangential velocity as the collision time advance and performs time-series prediction, generates risk time prediction values ​​and transmits them to the error analysis module.

[0040] The error analysis module, based on the risk time prediction value, collects the three-dimensional pose parameters of the spreader for the corresponding time period and constructs a three-dimensional outer envelope surface, calculates the error of the spatial distance with the container stacking boundary coordinates, generates the spreader spatial error distribution and transmits it to the risk assessment module.

[0041] The risk assessment module calculates the coverage ratio of the projection area based on the spatial error distribution of the spreader and its projection onto the vertical plane of the container stacking boundary. When the coverage ratio exceeds the coverage ratio threshold and the risk time prediction value is earlier than the time threshold, a lifting positioning risk area is generated.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0043] This invention incorporates the calculation of spatial position coordinate sequences and curvature values ​​in the monitoring and analysis of spreader trajectories, enabling a refined description of the spreader's motion trajectory and obtaining more accurate motion dynamics. By dynamically comparing the spatial relationship between the spreader trajectory curvature sequence and the container stacking boundary, potential risk points can be detected in real time, providing early warnings and preventing accidents such as collisions. The ratio of the tangential velocity to the optimal spatial distance during spreader movement, combined with the prediction of collision lead time, further improves the accuracy of risk prediction. Through monitoring and error analysis of the spreader's three-dimensional pose, the spatial error distribution of the spreader can be meticulously modeled during operation, effectively controlling the relative error between the spreader and the container stacking boundary. When the error exceeds a set coverage threshold, the risk area can be quickly identified, allowing for timely measures to prevent equipment damage or operational interruption. Overall, this innovative solution, through comprehensive monitoring of the spreader's motion trajectory, speed, attitude, and spatial errors, not only improves the accuracy and safety of lifting operations but also provides stronger remote control capabilities for port operations. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the steps of the present invention;

[0046] Figure 2This is a detailed schematic diagram of S1 of the present invention;

[0047] Figure 3 This is a detailed schematic diagram of S2 of the present invention;

[0048] Figure 4 This is a detailed schematic diagram of S3 of the present invention;

[0049] Figure 5 This is a detailed schematic diagram of S4 of the present invention;

[0050] Figure 6 This is a detailed schematic diagram of S5 of the present invention;

[0051] Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0053] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0054] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0055] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0056] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0057] Please see Figure 1 This invention provides a remote control method for lifting and positioning of a port container crane, comprising the following steps:

[0058] S1: Collect the spatial position coordinate sequence of the spreader during operation, analyze the vector angle and displacement length of adjacent points of the spreader and calculate the local curvature value to generate the spreader trajectory curvature sequence;

[0059] S2: Based on the comparison between the spreader trajectory curvature sequence and the collected spatial distance from the leading edge of the spreader to the container stacking boundary, potential risk points are generated when the spreader trajectory curvature sequence is rising and the spatial distance does not reach the spatial distance threshold.

[0060] S3: Based on the tangential velocity of the lifting device in the corresponding direction at potential risk points, calculate the ratio of spatial distance to tangential velocity as the collision time lead and perform time-series prediction to generate risk time prediction values;

[0061] S4: Based on the risk time prediction value, collect the three-dimensional pose parameters of the spreader for the corresponding time period and construct the three-dimensional outer envelope surface, calculate the error of the spatial distance with the container stacking boundary coordinates, and generate the spreader spatial error distribution;

[0062] S5: Based on the spatial error distribution of the spreader, project it onto the vertical plane of the container stacking boundary and calculate the coverage ratio of the projected area. When the coverage ratio exceeds the coverage ratio threshold and the risk time prediction value is earlier than the time threshold, generate the lifting positioning risk area.

[0063] The curvature sequence of the lifting device trajectory specifically includes a local curvature sequence, an angle sequence between adjacent points, and a trajectory point index. Potential risk points include position coordinates, trajectory curvature status, and spatial distance status. The risk time prediction value specifically refers to the collision time lead, time-series prediction results, and risk point index. The spatial error distribution of the lifting device includes pose parameters, outer envelope boundary, and spatial error distance value. The lifting positioning risk area specifically includes a risk area index, coverage ratio parameters, and time threshold status.

[0064] Please see Figure 2 The specific steps of S1 are as follows:

[0065] S101: Collect the spatial position coordinate sequence of the lifting device during operation, and vectorize the three-dimensional coordinate difference between adjacent points. Then, calculate the angle between the direction difference of adjacent vectors based on the magnitude of each vector to generate the angle sequence between adjacent points.

[0066] A high-frequency three-dimensional position sensor mounted on the spreading device collects the continuous spatial coordinates of the spreading device during operation at a sampling frequency of 50Hz. For example, the coordinates of five consecutive position points (in meters) are collected as follows: P1 (10.00, 25.00, 15.00), P2 (10.10, 25.15, 14.90), P3 (10.25, 25.32, 14.78), P4 (10.42, 25.45, 14.65), and P5 (10.65, 25.55, 14.50). Then, the difference in three-dimensional coordinates between adjacent points is vectorized. The vector V1 pointing from P1 to P2 is obtained by subtracting the coordinates of the two points, resulting in (0.10, 0.15, -0.10). Similarly, vector V2 is (0.15, 0.17, -0.12), vector V3 is (0.17, 0.13, -0.13), and vector V4 is (0.23, 0.10, -0.15). Next, the magnitudes of each vector are calculated. The magnitude L1 of vector V1 is the square root of the sum of the squares of its components, calculated to be 0.2062 meters. Similarly, L2 is calculated to be 0.2565 meters, L3 to be 0.2504 meters, and L4 to be 0.2922 meters. Then, the angle difference between adjacent vectors is calculated based on their magnitudes. The dot product of vectors V1 and V2 is 0.0525, and the included angle θ12 is calculated by dividing the dot product by the inverse cosine function of the product of the two magnitudes, yielding 6.98 degrees. The dot product of vectors V2 and V3 is 0.0632, and the included angle θ23 is calculated to be 10.18 degrees. The dot product of vectors V3 and V4 is 0.0716, and the included angle θ34 is calculated to be 11.87 degrees. Finally, the calculated included angles are arranged in order to generate a sequence of included angles between adjacent points: [6.98, 10.18, 11.87].

[0067] S102: Based on the sequence of angles between adjacent points, call the modulus data of the vectors of adjacent points, perform joint calculation on the angle value and the modulus value, calculate the local curvature value of each point, and arrange them according to the order of the points to obtain the local curvature sequence.

[0068] Based on the generated sequence of angles between adjacent points [6.98, 10.18, 11.87] and the vector magnitude data L1=0.2062 m, L2=0.2565 m, L3=0.2504 m, L4=0.2922 m, a joint operation is performed on the angle values ​​and magnitude values ​​to calculate the local curvature of each location point. This operation involves converting the angle value to radians and then dividing it by the arithmetic mean of the two vector magnitudes that constitute the angle. Taking location point P2 as an example, the calculation process for the local curvature K2 is as follows: the angle of 6.98 degrees is converted to 0.1218 radians, the average value of L1 and L2 is calculated to be 0.2314 m, and the two are divided to obtain the value of K2, which is 0.5264. Taking point P3 as an example, the calculation process for its local curvature K3 is as follows: the included angle of 10.18 degrees is converted to 0.1777 radians, the average value of L2 and L3 is calculated to be 0.2535 meters, and the two are divided to obtain the value of K3, which is 0.7010. Taking point P4 as an example, the calculation process for its local curvature K4 is as follows: the included angle of 11.87 degrees is converted to 0.2072 radians, the average value of L3 and L4 is calculated to be 0.2713 meters, and the two are divided to obtain the value of K4, which is 0.7637. To evaluate the magnitude of curvature, a "high curvature" judgment threshold of 0.45 is set. The threshold is based on the analysis of experimental data of the spreader running on standard circular tracks (radii of 10 meters, 5 meters, and 2.5 meters). Turns with a radius of less than 5 meters are defined as sharp turns with a curvature greater than 0.20. The threshold of 0.45 is set on this basis with an added safety margin. The calculated curvature values ​​were compared with the threshold. K2, K3, and K4 were all greater than 0.45, and were therefore determined to be high curvature. Finally, the calculated local curvature values ​​were arranged in order of location to obtain the local curvature sequence: [0.5264, 0.7010, 0.7637].

[0069] S103: Based on the local curvature sequence, concatenate all curvature values ​​in the sequence according to the time index and maintain the correspondence with the position points to generate the curvature sequence of the spreader trajectory;

[0070] Based on the obtained local curvature sequence [0.5264, 0.7010, 0.7637], all curvature values ​​in the sequence are concatenated according to their time indices, maintaining a one-to-one correspondence with the location points. The sampling frequency of the acquisition system is 50Hz, therefore the timestamp interval between adjacent points is 0.02 seconds. Setting the timestamp of point P1 to t1=0.00s, then the timestamps for P2, P3, P3, and P4 are t2=0.02s, t3=0.04s, and t4=0.06s, respectively. The local curvature calculation is based on three consecutive points, therefore the calculation result is associated with the intermediate point. Specifically, the local curvature value 0.5264 calculated from P1, P2, and P3 is associated with the intermediate point P2 and the timestamp 0.02s. The local curvature value 0.7010 calculated from P2, P3, and P4 is associated with the intermediate point P3 and the timestamp 0.04s. The local curvature value of 0.7637, calculated from P3, P4, and P5, is associated with the intermediate point P4 and the timestamp 0.06s. This association operation is performed on all points, generating a structured time-series data sequence. Each item in the sequence contains a timestamp, the 3D coordinates at that moment, and the local curvature value at that location. For example, at time 0.04s, the spreader is located at coordinates (10.25, 25.32, 14.78), and the local curvature of its trajectory is 0.7010. In this way, the spreader trajectory curvature sequence is generated.

[0071] Please see Figure 3 The specific steps of S2 are as follows:

[0072] S201: Based on the curvature sequence of the spreader trajectory, perform time sequence detection on the continuous curvature values ​​in the sequence, determine whether the curvature is in an upward state and mark it, and generate a curvature rise marker sequence;

[0073] Based on the generated curvature sequence of the lifting device trajectory, the continuous curvature values ​​in the sequence are detected in time sequence. This process does not rely on any preset threshold, but rather judges the trend of change by comparing two adjacent curvature values ​​in time. Based on the local curvature sequence [0.5264, 0.7010, 0.7637] generated in the previous embodiment, and supplemented with the local curvature value K5=0.7500 of the subsequent acquisition point P5 at t5=0.08s, a longer sequence [0.5264, 0.7010, 0.7637, 0.7500] is obtained, corresponding to points P2, P3, P4, and P5 respectively. The detection process starts from the second element of the sequence and compares it with the previous element one by one. For point P2 (corresponding to curvature K2=0.5264), since it is the starting point of the comparison in the sequence, there is no curvature value from the previous moment to compare, so the state is not judged as "rising". The curvature rise marker is recorded as "0". For point P3 (corresponding to curvature K3=0.7010), the curvature value is compared with the curvature value K2=0.5264 of the previous time point P2. A numerical comparison is performed: 0.7010>0.5264, the inequality holds. Therefore, the curvature at point P3 is determined to be increasing. The curvature increase marker is recorded as "1". For point P4 (corresponding to curvature K4=0.7637), the curvature value is compared with the curvature value K3=0.7010 of the previous time point P3. A numerical comparison is performed: 0.7637>0.7010, the inequality holds. Therefore, the curvature at point P4 is determined to be increasing. The curvature increase marker is recorded as "1". For point P5 (corresponding to curvature K5=0.7500), the curvature value is compared with the curvature value K4=0.7637 of the previous time point P4. A numerical comparison is performed: 0.7500 > 0.7637, the inequality is not true. Therefore, it is determined that the curvature at point P5 is not in an increasing state. The curvature increase indicator is marked as "0". The indicator values ​​obtained after the above point determination are arranged in the same time order as the original points. In this way, a binary sequence corresponding one-to-one with the curvature sequence of the spreader trajectory is generated, namely the curvature increase indicator sequence. Among them, the first "0" corresponds to point P2, the second "1" corresponds to point P3, the third "1" corresponds to point P4, and the fourth "0" corresponds to point P5.

[0074] S202: Call the curvature rise identifier sequence and obtain the spatial distance data from the leading edge of the spreader to the container stacking boundary. Compare each spatial distance value with the preset spatial distance threshold, filter the distance points that do not exceed the spatial distance threshold, and obtain the set of distances that do not reach the threshold.

[0075] The generated curvature rise identifier sequence is invoked, and the spatial distance data from the leading edge of the spreader to the container stacking boundary at corresponding points P2, P3, P4, and P5 is obtained. The distance data is obtained in real-time by a LiDAR sensor installed at the front of the spreader. In this embodiment, the spatial distance data (unit: meters) at the corresponding points are: D2 = 5.5 meters, D3 = 4.2 meters, D4 = 3.1 meters, and D5 = 2.4 meters. Then, each spatial distance value is compared with a preset spatial distance threshold. The spatial distance threshold is set based on the total distance required to ensure safe braking of the spreader at its maximum operating speed. This threshold is set by calculating the physical parameters of the braking process. Specific parameters include: the maximum operating speed of the spreader, system reaction time, and braking deceleration. The maximum operating speed of the spreader is set to 2.5 meters per second, referring to the technical specifications of automated terminal bridge cranes. The system reaction time, i.e., the time from when the sensor detects a hazard to when the braking system begins to execute the braking action, including the delay in data transmission, processing, and mechanical response, is set to 0.5 seconds. The braking deceleration is set to a constant value of 1.0 m / s², based on the safety regulations for lifting equipment and relevant experimental data. The calculation process for the spatial distance threshold is as follows: Reaction distance = Maximum operating speed of the lifting device × System reaction time = 2.5 m / s × 0.5 s = 1.25 m. Braking distance = (Maximum operating speed of the lifting device)² / (2 × Braking deceleration) = (2.5 m / s)² / (2 × 1.0 m / s²) = 6.25 / 2 = 3.125 m. Total safe braking distance = Reaction distance + Braking distance = 1.25 m + 3.125 m = 4.375 m. To allow for a certain safety margin, the spatial distance threshold is set to 4.5 m. Threshold setting experimental verification process: To verify the rationality of this threshold, a braking experiment was conducted at the test site. The lifting device was controlled to approach a simulated obstacle at different speeds, and a braking command was triggered at a specific distance point, recording the braking stop position.

[0076] Table 1: Experimental data on braking distance at different speeds

[0077]

[0078] Subsequently, the spatial distance values ​​of each point are compared with a threshold of 4.5 meters, filtering out points whose distances do not exceed (i.e., are less than or equal to) the threshold. For example, comparing D2 = 5.5 meters: 5.5 > 4.5, this point is not filtered. Comparing D3 = 4.2 meters: 4.2 < 4.5, this point is filtered. Comparing D4 = 3.1 meters: 3.1 < 4.5, this point is filtered. Comparing D5 = 2.4 meters: 2.4 < 4.5, this point is filtered. All filtered points are combined into a set, resulting in the set of points whose distances do not reach the threshold. This set contains points {P3, P4, P5}.

[0079] S203: Based on the correspondence between the curvature rise identifier sequence and the distance set that does not reach the threshold, retrieve the points that simultaneously satisfy the curvature rise state and the spatial distance not reaching the threshold, and aggregate and mark the points to generate potential risk points;

[0080] Based on the generated curvature rise indicator sequence and the generated unreached threshold distance set {P3, P4, P5}, points that simultaneously meet two conditions are identified by searching the correspondence between the two. The first condition is that the curvature rise indicator of the point is "1", and the second condition is that the point belongs to the unreached threshold distance set. This process performs a logical AND judgment once for each point. The specific search process is as follows: For point P2: its curvature rise indicator is "0", which does not meet the first condition. Therefore, P2 is not selected as a potential risk point. For point P3: its curvature rise indicator is "1", which meets the first condition. It is checked whether it exists in the unreached threshold distance set {P3, P4, P5}, and it exists. Therefore, P3 meets both conditions and is marked. For point P4: its curvature rise indicator is "1", which meets the first condition. It is checked whether it exists in the unreached threshold distance set {P3, P4, P5}, and it exists. Therefore, P4 meets both conditions and is marked. For point P5: its curvature rise indicator is "0", which does not meet the first condition. Therefore, P5 was not selected as a potential risk point. All marked points P3 and P4 were aggregated to form a set of potential risk points. The data for each point was fully recorded, including timestamps, 3D coordinates, local curvature values, curvature increase indicators, and spatial distance values. Potential risk point P3: Timestamp t3 = 0.04s, coordinates (10.25, 25.32, 14.78), local curvature 0.7010, curvature increase indicator "1", spatial distance 4.2 meters. Potential risk point P4: Timestamp t4 = 0.06s, coordinates (10.42, 25.45, 14.65), local curvature 0.7637, curvature increase indicator "1", spatial distance 3.1 meters. The results show that within the time interval of 0.04 seconds to 0.06 seconds, the spreader's trajectory not only rapidly changed direction (curvature continuously increasing) but also approached the container stacking area (spatial distance less than the safety threshold), constituting a continuous potential collision risk. The generated set of potential risk points is {P3, P4}.

[0081] Please see Figure 4 The specific steps of S3 are as follows:

[0082] S301: Collect tangential velocity data of the spreader in the corresponding direction based on potential risk points, and record the index position of each risk point and the instantaneous tangential velocity value to obtain a tangential velocity sequence;

[0083] Based on the generated set of potential risk points {P3, P4}, the tangential velocity data of the lifting equipment in the corresponding directions of these two points are collected. The tangential velocity is calculated by calling the displacement vector magnitude and time interval calculated in S101. The instantaneous tangential velocity at any point is obtained by dividing the displacement vector magnitude between that point and the previous point by the sampling time interval (0.02 seconds). The index positions P3 and P4 of each risk point are recorded in correspondence with the calculated instantaneous tangential velocity values. For the potential risk point P3, the calculation of its instantaneous tangential velocity calls the magnitude L2 of the vector V2 pointing from P2 to P3. In the embodiment of S101, the calculated value of L2 is 0.2565 meters. Therefore, the calculation process of the tangential velocity V-t3 at point P3 is: V-t3 = L2 / sampling time interval = 0.2565 meters / 0.02 seconds = 12.825 meters / second. For potential risk point P4, the calculation of its instantaneous tangential velocity uses the magnitude L3 of the vector V3 pointing from P3 to P4. In the embodiment of S101, the calculated value of L3 is 0.2504 meters. Therefore, the calculation process of the tangential velocity V-t4 at point P4 is: V-t4 = L3 / sampling time interval = 0.2504 meters / 0.02 seconds = 12.520 meters / second. The calculated tangential velocity values ​​are associated with the corresponding potential risk point index positions. The velocity at point P3 is 12.825 meters / second, and the velocity at point P4 is 12.520 meters / second. These velocity values ​​are arranged according to the time order of the risk points to obtain the tangential velocity sequence: [12.825, 12.520].

[0084] S302: Call the tangential velocity sequence, and calculate the ratio between each spatial distance and the corresponding tangential velocity based on the spatial distance value corresponding to the potential risk point. Use the ratio result as the collision time advance and summarize it to generate the collision time advance sequence.

[0085] The generated tangential velocity sequence [12.825, 12.520] is called, and a ratio is calculated between each spatial distance and the corresponding tangential velocity based on the spatial distance values ​​at the corresponding times of potential risk points P3 and P4. This calculation is used to calculate the time required for the spreader to reach the container stack boundary at the current speed. In the embodiment of S202, the spatial distance D3 of point P3 is 4.2 meters, and the spatial distance D4 of point P4 is 3.1 meters. The ratio calculation for potential risk point P3 is performed: the spatial distance D3 of point P3 is divided by the tangential velocity V-t3 to obtain the collision time advance TTC-3. TTC-3 = D3 / V-t3 = 4.2 meters / 12.825 meters / second ≈ 0.3275 seconds. The ratio calculation for potential risk point P4 is performed: the spatial distance D4 of point P4 is divided by the tangential velocity V-t4 to obtain the collision time advance TTC-4 for that point. TTC-4 = D4 / V-t4 = 3.1 m / 12.520 m / s ≈ 0.2476 s. The advantage of this method is that by combining two different dimensions of physical quantities—spatial distance and instantaneous velocity—the complex spatial situation is transformed into a single, intuitive time-dimensional indicator. All calculated ratios are summarized according to the time sequence of potential risk points to generate a collision time advance sequence. The collision time advance sequence is [0.3275, 0.2476]. Each value in this sequence represents the remaining time before a collision occurs at times P3 and P4, if the spreader maintains its current constant speed.

[0086] S303: Based on the numerical distribution of the collision time lead sequence, perform time sequence prediction processing, and correlate the predicted time sequence results with the locations of potential risk points to obtain the risk time prediction value;

[0087] Based on the generated collision time advance sequence [0.3275, 0.2476], the numerical distribution is subjected to time-series prediction processing. This processing predicts the future time point when the value reaches zero (i.e., a collision occurs) by analyzing the rate of change of the collision time advance at consecutive time points. The prediction processing here uses linear extrapolation and does not rely on a preset model. First, the rate of change of the collision time advance sequence is calculated. The timestamps of P3 and P4 (t3=0.04s, t4=0.06s) and the corresponding collision time advances (TTC-3=0.3275s, TTC-4=0.2476s) are used. The time change Δt=t4-t3=0.06 seconds-0.04 seconds=0.02 seconds. The collision time advance change ΔTTC=TTC-4-TTC-3=0.2476 seconds-0.3275 seconds=-0.0799 seconds. The rate of change gradient = ΔTTC / Δt = -0.0799 seconds / 0.02 seconds = -3.995. The gradient value indicates that the collision time advance is decreasing at a rate of approximately 3.995 seconds per second. Then, based on the state of the last measurement (point P4), a prediction is made to calculate the time required for the collision time advance TTC-4 to decrease to 0 from time t4. The predicted remaining time = TTC-4 / (-gradient) = 0.2476 / 3.995 ≈ 0.0620 seconds. The predicted absolute collision time = t4 + predicted remaining time = 0.06 seconds + 0.0620 seconds = 0.1220 seconds. This predicted time difference of 0.0620 seconds is used as the predicted risk time associated with the potential risk point location {P3, P4}. This predicted risk time is compared to the "emergency braking response threshold". This threshold is based on the shortest time from receiving a command to actuating the emergency brake in a safety control system (such as a safety PLC). This shortest time does not include human judgment and represents only the system's extreme response time. According to the technical specifications of the industrial safety controller, this threshold is set to 0.1 seconds. Comparison results: The predicted risk time of 0.0620 seconds is less than the emergency braking response threshold of 0.1 seconds. This result indicates that from the moment of the last potential risk point P4, the remaining system reaction time (0.0620 seconds) is insufficient to complete braking through the conventional safety procedures.

[0088] Please see Figure 5 The specific steps of S4 are as follows:

[0089] S401: Collect the three-dimensional pose parameters of the spreader for the corresponding time period based on the risk time prediction value, aggregate the three-dimensional pose data of multiple time periods according to the time index, and call the spatial geometry construction operation to form the three-dimensional outer envelope surface of the spreader in the three-dimensional coordinate system.

[0090] Based on the calculated risk time prediction value of 0.0620 seconds, a critical time period was determined. This time period begins at the timestamp t4 = 0.06 seconds of the last potential risk point P4 and ends at the predicted absolute collision time point of 0.1220 seconds. Within this time period [0.06 seconds, 0.1220 seconds], the three-dimensional pose parameters of the spreader were acquired. These parameters were provided by the inertial measurement unit (IMU) integrated on the spreader at a sampling frequency of 50 Hz, i.e., a set of data was acquired every 0.02 seconds. The three-dimensional pose data acquired at multiple moments within this time period were aggregated according to the time index. The acquisition time points were t4 = 0.06 seconds, t5 = 0.08 seconds, t6 = 0.10 seconds, and t7 = 0.12 seconds. The specific pose data acquired are listed in the table below.

[0091] Table 2: Three-dimensional pose parameters of the lifting device during key time periods

[0092]

[0093] Table 2 shows the aggregated complete pose data for four time points. Next, a spatial geometry construction operation is invoked to form the 3D outer envelope of the spreader within this time period in a 3D coordinate system. This operation first requires a precise geometric model of the spreader; a standard 40-foot spreader is defined as 12.2 meters long, 2.4 meters wide, and 0.5 meters high. In its local coordinate system, the coordinates of eight vertices are predefined, with the origin (0, 0, 0) at the spreader's geometric center. For example, the local coordinates of the "front-left-up" vertex are (-6.1, 1.2, 0.25). Then, for each time point in Table 2, the coordinates of the eight vertices of the spreader are transformed from the local coordinate system to the global 3D coordinate system. Taking the "front-left-up" vertex at time t6=0.10 seconds as an example, its global coordinates are obtained by first transforming the local coordinates (-6.1, 1.2, 0.25) into three dimensions based on the attitude angles (roll -0.2 degrees, pitch 0.2 degrees, yaw 8.0 degrees) at that time, and then superimposing the rotated coordinates with the center point coordinates (10.78, 25.63, 14.36) at that time by translation. This coordinate transformation is performed on all 8 vertices (32 points in total) at all 4 time points. Finally, a three-dimensional convex hull operation is performed on these 32 global coordinate points. This operation constructs a minimal convex polyhedron that can contain all 32 points. The surface of this convex polyhedron is the three-dimensional outer envelope surface swept by the movement of the spreader during this time period.

[0094] S402: Call the three-dimensional outer envelope surface of the spreader, and calculate the difference between the spatial distances from multiple points on the outer envelope surface to the boundary coordinate points based on the coordinate system position of the container stacking boundary, to obtain the spatial distance error sequence;

[0095] The generated 3D outer envelope surface is invoked, and spatial distance difference calculations are performed based on the known coordinate system position of the container stacking boundary. In this embodiment, the container stacking boundary is defined as a fixed plane perpendicular to the X-axis, and its position is calibrated by on-site sensors before operation, set to X = 10.80 meters. The calculation process involves subtracting the X-coordinate values ​​of the 32 vertices on the outer envelope surface in S401 (i.e., the transformed global coordinates of the 8 vertices of the spreader at 4 time points) from the X-coordinate value of the boundary plane, 10.80 meters, point by point. A positive difference indicates that the vertex is outside the boundary (safe side), and a negative difference indicates that the vertex has intruded into the boundary (dangerous side). The same difference calculation is performed on all 32 vertices on the 3D outer envelope surface. All calculated differences are arranged in the original index order of the vertices. The complete calculation results for the 32 spatial distance error values ​​are as follows, grouped by time point: 8 vertex errors (meters) at time t=0.06: [0.081, 0.081, -0.459, -0.459, 0.085, 0.085, -0.455, -0.455]; 8 vertex errors (meters) at time t=0.08: [0.076, 0.076, -0.504] [0.504, 0.080, 0.080, -0.500, -0.500], the errors of the 8 vertices (meters) at time t=0.10 seconds are: [0.045, 0.045, -0.555, -0.555, 0.049, 0.049, -0.551, -0.551], and the errors of the 8 vertices (meters) at time t=0.12 seconds are: [-0.012, -0.012], -0.504, -0.080, -0.080, -0.500, -0.500]. The 32 values ​​[0.081, 0.081, -0.459, -0.459, 0.085, 0.085, -0.455, -0.455, 0.076, 0.076, -0.504] are combined sequentially to obtain the complete spatial distance error sequence: [0.081, 0.081, -0.459, -0.459, 0.085, -0.455, -0.455, 0.076, 0.076, -0.504] , -0.504, 0.080, 0.080, -0.500, -0.500, 0.045, 0.045, -0.555, -0.555, 0.049, 0.049, -0.551, -0.551, -0.012, -0.012, -0.612, -0.612, -0.008, -0.008, -0.608, -0.608).

[0096] S403: Based on the spatial distance error sequence, all error points are aggregated and calibrated, and their distribution in three-dimensional space is mapped and encoded to generate the spatial error distribution of the lifting device;

[0097] Based on the generated spatial distance error sequence containing 32 values, all points with negative error values ​​are aggregated and calibrated. In this embodiment, the sequence is checked, and all points corresponding to values ​​less than zero are identified as error points. There are 16 error points in this sequence. These error points, along with their corresponding 3D coordinates and error values, are aggregated into an error point set. Subsequently, the distribution of these error points in 3D space is mapped and encoded. The mapping and encoding process first divides the front surface of the spreader (the side with a positive X-coordinate in the spreader's local coordinate system) along its length (Y-axis direction) into three preset regions. The spreader width is 2.4 meters, and the Y-axis range is [-1.2 meters, 1.2 meters]. The region division is as follows: Left region: Y-coordinate range [-1.2 meters, -0.4 meters]; Middle region: Y-coordinate range (-0.4 meters, 0.4 meters); Right region: Y-coordinate range [0.4 meters, 1.2 meters]. These three regions together constitute a one-dimensional encoding space. Next, it is necessary to determine which region each error point belongs to. This process requires transforming the global coordinates of the error point back to the local coordinate system of the spreader at the time of occurrence. Taking an error point at time t=0.12 seconds with an error value of -0.012 meters as an example, its global X-coordinate is 10.80 - 0.012 = 10.788 meters. Its complete global coordinates are set to (10.788, 25.10, 14.25). By performing an inverse transformation (first translation, then reverse rotation) on the spreader's pose at t=0.12 seconds, the center point (10.82, 25.70, 14.23) and attitude angle (-0.3°, 0.3°, 9.5°) are calculated to determine the coordinates of this point in the spreader's local coordinate system. The calculated local coordinates are set to (6.1, -0.8, 0.25). The local Y-coordinate is -0.8 meters, falling within the left zone [-1.2 meters, -0.4 meters]. Taking an error point at time t=0.12 seconds with an error value of -0.008 meters as an example, the global coordinates are set to (10.792, 26.25, 14.24). Through the same inverse transformation process, the calculated local coordinates are set to (6.1, 0.7, 0.25). The local Y coordinate is 0.7 meters, falling within the right zone [0.4 meters, 1.2 meters]. The same region judgment is performed on all 16 error points. After judgment, it is found that in this embodiment, the error point appears in both the left and right zones, while there is no error point in the middle zone. Finally, a 3-bit binary code is generated based on the distribution of error points in each zone. Each bit of the code corresponds to the left zone, middle zone, and right zone from left to right. If there is at least one error point in a certain zone, the bit is recorded as "1", otherwise it is recorded as "0". Since the error point appears in the left and right zones, but not in the middle zone, the generated mapping code is "101". This code is the spatial error distribution of the spreader.

[0098] Please see Figure 6 The specific steps of S5 are as follows:

[0099] S501: Based on the spatial error distribution of the spreader, a projection is established in the vertical direction corresponding to the container stacking boundary surface, and the displacement data of multiple coordinate points in the spatial error distribution is converted to the vertical plane to generate the error projection area;

[0100] Based on the generated spatial error distribution of the spreader, i.e., the set of error points represented by the "101" code, a projection is established in the vertical direction corresponding to the container stacking boundary surface. The container stacking boundary surface is defined in the code as a vertical plane with X = 10.80 meters. The projection operation extracts the Y and Z coordinates from the three-dimensional global coordinates of all aggregated and calibrated error points (i.e., points with negative spatial distance errors) to form a two-dimensional point set. These two-dimensional points collectively define a region on the vertical boundary plane. Specifically, from the generated spatial distance error sequence, the vertices corresponding to all negative errors are extracted. In the embodiment of S403, these error points mainly appear at t = 0.12 seconds. To form a region, the four outermost vertices that can enclose all intrusion points are selected. Set the global coordinates of these four vertices as follows: Point A: (10.788, 25.10, 14.25), with an error of -0.012 meters; Point B: (10.788, 25.10, 13.75), with an error of -0.012 meters; Point C: (10.792, 26.25, 14.24), with an error of -0.008 meters; Point D: (10.792, 26.25, 13.74), with an error of -0.008 meters. Transform the displacement data of these four points, i.e., their Y and Z coordinates, to a vertical plane with an X = 10.80 meter coordinate. This transformation process directly takes the Y and Z values ​​to obtain four two-dimensional projection points: Projection point A: (25.10, 14.25), Projection point B: (25.10, 13.75), Projection point C: (26.25, 14.24), and Projection point D: (26.25, 13.74). These four two-dimensional projection points form a quadrilateral on the YZ plane. Geometrically constructing the region enclosed by these four points yields a convex polygon. This polygon is the error projection region.

[0101] S502: Call the error projection area and calculate the ratio between the projection area and the total area of ​​the boundary surface based on the current planar area parameters of the container stacking boundary surface to obtain the projection coverage ratio;

[0102] The generated error projection region is invoked, and proportional calculations are performed based on the pre-acquired current planar area parameters of the container stacking boundary. First, the area of ​​the error projection region is calculated. This region is defined by four vertices: A(25.10, 14.25), B(25.10, 13.75), C(26.25, 14.24), and D(26.25, 13.74). To simplify the calculation, this quadrilateral is approximated as a rectangle, with its width determined by the maximum and minimum values ​​of the Y-coordinate and its height by the maximum and minimum values ​​of the Z-coordinate. Region width = 26.25 m - 25.10 m = 1.15 m. Region height = 14.25 m - 13.74 m = 0.51 m. Error projection region area = region width × region height = 1.15 m × 0.51 m = 0.5865 square meters. Next, the total area of ​​the container stacking boundary is obtained. The parameter describes the area of ​​the end face of the target container where the spreader will be operated. This data was retrieved from the Terminal Operating System (TOS) operational instructions. The target of this operation was a standard 9-foot-6-inch high (commonly known as a high cube container) container, with an end face width of 8 feet and a height of 9 feet 6 inches. Converted to international standard units, this translates to a width of 2.4384 meters and a height of 2.8956 meters. The total area of ​​the boundary surface = 2.4384 meters × 2.8956 meters = 7.0603 square meters. Finally, the ratio of the error projection area to the total boundary surface area was calculated. Projection coverage ratio = Error projection area / Total boundary surface area = 0.5865 square meters / 7.0603 square meters ≈ 0.08307. These ratios were then aggregated to generate the projection coverage ratio.

[0103] S503: Based on the projection coverage ratio and the risk time prediction value, compare the coverage ratio with the coverage ratio threshold, and at the same time judge the time prediction value with the time threshold. If both conditions are met, mark the area on the stack boundary surface to generate the hoisting positioning risk area.

[0104] The coverage ratio threshold is set based on the coverage of the error projection area and the stack boundary surface during the operation of the spreader;

[0105] Based on the calculated projection coverage ratio of 0.08307 and the generated risk time prediction value of 0.0620 seconds, a dual-condition judgment is performed. The first condition is to compare the projection coverage ratio with a coverage ratio threshold. The threshold is set to distinguish between minor scratches and severe misalignment of the spreader. This threshold was determined through a series of simulation experiments: in a simulation environment, the spreader approaches the container with varying degrees of misalignment, the projection coverage ratio of each collision is recorded, and technicians determine its risk level. Experimental data shows that collisions with a coverage ratio below 2% typically only cause paint scratches; ratios between 2% and 5% may cause minor damage to corner fittings or locks; and ratios above 5% have a very high probability of causing structural damage to the container or failure of the lifting operation. Based on this experimental data, to identify misalignment situations with substantial danger, the coverage ratio threshold is set to 5% (i.e., 0.05). Comparison process: The calculated projection coverage ratio of 0.08307 is compared with the threshold of 0.05. 0.08307 > 0.05, this inequality holds. The first condition is satisfied. The second conditional judgment involves comparing the predicted risk time with a time threshold. This time threshold is the "emergency braking response threshold" defined in S303, with a value of 0.1 seconds, representing the minimum time required for the control system to execute emergency braking. The comparison process is as follows: the predicted risk time of 0.0620 seconds is compared with the threshold of 0.1 seconds. 0.0620 seconds < 0.1 seconds, so the inequality holds. The second condition is satisfied. The advantage of this approach is that by combining the spatial severity of the risk (reflected by the coverage ratio) with the urgency of the time (reflected by the predicted time), a precise assessment of the risk level can be achieved. Since both conditions are met, the system determines the current situation as a high-risk event. Subsequently, the error projection area generated by S501 is marked on the container stacking boundary surface (the plane with X = 10.80 meters) in the 3D visualization interface. The marked area is uniquely determined by the coordinates of four vertices A (25.10, 14.25), B (25.10, 13.75), C (26.25, 14.24), and D (26.25, 13.74), and is highlighted in red. This marked area is the generated hoisting positioning risk area. The results show that a collision zone with an area of ​​0.5865 square meters is about to form, and the remaining reaction time is insufficient to avoid it by conventional means, requiring immediate execution of the highest priority avoidance actions.

[0106] Please see Figure 7 Remote control system for lifting and positioning of container cranes in ports, including:

[0107] The trajectory generation module collects the spatial position coordinate sequence of the spreader during operation, analyzes the vector angle and displacement length of adjacent points of the spreader and calculates the local curvature value, generates the spreader trajectory curvature sequence and transmits it to the risk identification module;

[0108] The risk identification module compares the spreader trajectory curvature sequence with the collected spatial distance from the leading edge of the spreader to the container stacking boundary. When the spreader trajectory curvature sequence is rising and the spatial distance does not reach the spatial distance threshold, potential risk points are generated and transmitted to the collision prediction module.

[0109] The collision prediction module collects the tangential velocity of the spreader in the corresponding direction based on the potential risk points, calculates the ratio of spatial distance to tangential velocity as the collision time lead, performs time-series prediction, generates risk time prediction values, and transmits them to the error analysis module.

[0110] The error analysis module, based on the risk time prediction value, collects the three-dimensional pose parameters of the spreader for the corresponding time period and constructs a three-dimensional outer envelope surface, calculates the error of the spatial distance with the container stacking boundary coordinates, generates the spreader spatial error distribution and transmits it to the risk assessment module.

[0111] The risk assessment module projects the spatial error distribution of the spreader onto the vertical plane of the container stacking boundary and calculates the coverage ratio of the projected area. When the coverage ratio exceeds the coverage ratio threshold and the risk time prediction value is earlier than the time threshold, a lifting positioning risk area is generated.

[0112] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A remote control method for lifting and positioning of a port container crane, characterized in that, Includes the following steps: S1: Collect the spatial position coordinate sequence of the spreader during operation, analyze the vector angle and displacement length of adjacent points of the spreader and calculate the local curvature value to generate the spreader trajectory curvature sequence; S2: Based on the comparison between the curvature sequence of the spreader trajectory and the collected spatial distance from the leading edge of the spreader to the container stacking boundary, when the curvature sequence of the spreader trajectory is rising and the spatial distance does not reach the spatial distance threshold, potential risk points are generated; S3: Based on the potential risk points, collect the tangential velocity of the lifting device in the corresponding direction, calculate the ratio of spatial distance to tangential velocity as the collision time advance, perform time-series prediction, and generate risk time prediction values; S4: Based on the predicted risk time value, collect the three-dimensional pose parameters of the spreader for the corresponding time period and construct a three-dimensional outer envelope surface, calculate the error of the spatial distance with the container stacking boundary coordinates, and generate the spreader spatial error distribution; S5: Based on the spatial error distribution of the spreader, project it onto the vertical plane of the container stacking boundary and calculate the coverage ratio of the projected area. When the coverage ratio exceeds the coverage ratio threshold and the risk time prediction value is earlier than the time threshold, generate the lifting positioning risk area.

2. The remote control method for lifting and positioning of a port container crane according to claim 1, characterized in that, The lifting device trajectory curvature sequence specifically includes a local curvature sequence, an adjacent point angle sequence, and a trajectory point index. The potential risk points include position coordinates, trajectory curvature state, and spatial distance state. The risk time prediction value specifically refers to the collision time advance, time-series prediction result, and risk point index. The lifting device spatial error distribution includes pose parameters, outer envelope boundary, and spatial error distance value. The lifting positioning risk area specifically includes a risk area index, coverage ratio parameters, and time threshold state.

3. The remote control method for lifting and positioning of a port container crane according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collect the spatial position coordinate sequence of the lifting device during operation, and vectorize the three-dimensional coordinate difference between adjacent points. Then, calculate the angle between the direction difference of adjacent vectors based on the magnitude of each vector to generate the angle sequence between adjacent points. S102: Based on the adjacent point angle sequence, call the modulus data of the adjacent point vectors, perform joint operation on the angle value and modulus value, calculate the local curvature value of each point, and arrange them according to the point order to obtain the local curvature sequence. S103: Based on the local curvature sequence, concatenate all curvature values ​​in the sequence according to the time index and maintain the correspondence with the position points to generate the curvature sequence of the lifting device trajectory.

4. The remote control method for lifting and positioning of a port container crane according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the curvature sequence of the lifting device trajectory, perform time sequence detection on the continuous curvature values ​​in the sequence, determine whether the curvature is in an upward state and mark it, and generate a curvature rising identifier sequence. S202: Call the curvature rise identifier sequence and obtain the spatial distance data from the front edge of the spreader to the container stacking boundary. Compare each spatial distance value with a preset spatial distance threshold, filter the distance points that do not exceed the spatial distance threshold, and obtain the set of distances that do not reach the threshold. S203: Based on the correspondence between the curvature rise identifier sequence and the distance set that does not reach the threshold, retrieve the points that simultaneously satisfy the curvature rise state and the spatial distance not reaching the threshold, and aggregate and mark the points to generate potential risk points.

5. The remote control method for lifting and positioning of a port container crane according to claim 4, characterized in that, The spatial distance threshold is determined by statistically analyzing the spatial distance data from the leading edge of the spreader to the container stacking boundary, extracting the minimum safe operating interval value, and then weighting and correcting it in conjunction with the dynamic offset margin generated during equipment operation.

6. The remote control method for lifting and positioning of a port container crane according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Collect tangential velocity data of the spreader in the corresponding direction based on the potential risk points, and record the index position of each risk point and the instantaneous tangential velocity value to obtain a tangential velocity sequence; S302: Call the tangential velocity sequence, and according to the spatial distance value corresponding to the potential risk point, perform a ratio calculation on each spatial distance and the corresponding tangential velocity, use the ratio result as the collision time advance and summarize it to generate a collision time advance sequence; S303: Based on the numerical distribution of the collision time advance sequence, perform time sequence prediction processing, and correlate the predicted time sequence results with the locations of potential risk points to obtain the risk time prediction value.

7. The remote control method for lifting and positioning of a port container crane according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the predicted risk time value, collect the three-dimensional pose parameters of the lifting device for the corresponding time period, aggregate the three-dimensional pose data of multiple time periods according to the time index, and call the spatial geometric construction operation to form the three-dimensional outer envelope surface of the lifting device in the three-dimensional coordinate system. S402: Call the three-dimensional outer envelope surface of the spreader, and according to the coordinate system position of the container stacking boundary, perform difference calculation on the spatial distance from multiple points on the outer envelope surface to the boundary coordinate point to obtain a spatial distance error sequence; S403: Based on the spatial distance error sequence, aggregate and calibrate all error points, and map and encode their distribution in three-dimensional space to generate the spatial error distribution of the lifting device.

8. The remote control method for lifting and positioning of a port container crane according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the spatial error distribution of the spreader, a projection is established in the vertical direction corresponding to the container stacking boundary surface, and the displacement data of multiple coordinate points in the spatial error distribution is converted to the vertical plane to generate the error projection area; S502: Call the error projection area, and calculate the ratio of the projection area to the total area of ​​the boundary surface based on the current planar area parameters of the container stacking boundary surface to obtain the projection coverage ratio; S503: Based on the value of the projection coverage ratio and the risk time prediction value, compare the coverage ratio with the coverage ratio threshold, and at the same time judge the time prediction value with the time threshold. If both conditions are met, mark the area on the stack boundary surface to generate the hoisting positioning risk area. The coverage ratio threshold is set based on the coverage of the error projection area and the stacking boundary surface during the operation of the lifting device.

9. The remote control method for lifting and positioning of a port container crane according to claim 8, characterized in that, The time threshold is set by statistically analyzing the time distribution of risk events and normal operation periods during the operation of the spreader, using the shortest warning time before the risk event as a reference benchmark, and then combining it with the allowable safety buffer time in the operation process.

10. A remote control system for lifting and positioning of container cranes in ports, characterized in that, The system is used to implement the remote control method for lifting and positioning of a port container crane as described in any one of claims 1-9, the system comprising: The trajectory generation module collects the spatial position coordinate sequence of the spreader during operation, analyzes the vector angle and displacement length of adjacent points of the spreader and calculates the local curvature value, generates the spreader trajectory curvature sequence and transmits it to the risk identification module; The risk identification module compares the curvature sequence of the spreader trajectory with the collected spatial distance from the leading edge of the spreader to the container stacking boundary. When the curvature sequence of the spreader trajectory is rising and the spatial distance does not reach the spatial distance threshold, a potential risk point is generated and transmitted to the collision prediction module. The collision prediction module collects the tangential velocity of the lifting device in the corresponding direction based on the potential risk points, calculates the ratio of spatial distance to tangential velocity as the collision time advance and performs time-series prediction, generates risk time prediction values ​​and transmits them to the error analysis module. The error analysis module, based on the risk time prediction value, collects the three-dimensional pose parameters of the spreader for the corresponding time period and constructs a three-dimensional outer envelope surface, calculates the error of the spatial distance with the container stacking boundary coordinates, generates the spreader spatial error distribution and transmits it to the risk assessment module. The risk assessment module calculates the coverage ratio of the projection area based on the spatial error distribution of the spreader and its projection onto the vertical plane of the container stacking boundary. When the coverage ratio exceeds the coverage ratio threshold and the risk time prediction value is earlier than the time threshold, a lifting positioning risk area is generated.

Images