A port crane operation control method and device

Abstract

The present application provides a method and device for controlling the operation of a port crane, which relates to the field of crane operation technology, including: activating environmental acquisition sensors, establishing a real-time environmental data set, building a digital twin model, and performing dynamic risk identification of items, obtaining operation tasks, establishing a benchmark operation path, parsing the operation tasks to obtain the data of the hoisted objects, reading the pre-control data of the port crane, performing response fluctuation fitting, updating the digital twin model according to the response fluctuation fitting results and the benchmark operation path, performing proximity warning based on the dynamic risk identification of items, generating feedback control data, and optimizing the control of the port crane. The present application can solve the technical problems in the prior art that the operation safety is insufficient and the efficiency is low due to inaccurate risk prediction and control in traditional crane operations. By integrating environmental sensors, establishing and updating digital twin models, the safety and efficiency of port crane operations are improved.

Description

Technical Field

[0001] The present application relates to the technical field of crane operation, and in particular to a port crane operation control method and device. Background Art

[0002] With the growth of global trade, ports are key nodes for cargo transit, and their efficiency and safety have become increasingly important considerations. In port operations, cranes are the core equipment for completing cargo loading and unloading. However, traditional crane operation control methods have some significant shortcomings. Traditional crane operation control methods often rely on the operator's personal experience and intuition, resulting in inaccurate risk prediction and control during the operation process. Due to the lack of accurate data support and systematic risk analysis, cranes are prone to safety problems such as collisions and overloading during operation, which not only endangers the safety of equipment and cargo, but may also cause personal injury. Due to the lack of real-time data and advanced control strategies, cranes often cannot achieve the optimal operation path and operation mode during operation, resulting in low operation efficiency and prolonged cargo loading and unloading time. In addition, the existing crane operation control methods are also insufficient in response speed and accuracy when dealing with emergencies. In the complex and changing port environment, responding quickly and accurately to emergencies is crucial to ensuring the safety and continuity of operations.

[0003] In summary, the prior art has technical problems such as insufficient operation safety and low efficiency due to inaccurate risk prediction and control in traditional crane operations. Summary of the invention

[0004] The purpose of this application is to provide a port crane operation control method and device to solve the technical problems in the prior art that the operation is insufficiently safe and inefficient due to inaccurate risk prediction and control in traditional crane operations.

[0005] In view of the above problems, the present application provides a port crane operation control method and device.

[0006] In a first aspect, the present application provides a port crane operation control method, which is implemented by a port crane operation control device, wherein the port crane operation control method includes: activating the port's environmental acquisition sensors, collecting port data based on the environmental acquisition sensors, and establishing a real-time environmental data set within a predetermined area; building a digital twin model of the port with the real-time environmental data set, and performing dynamic risk identification of items in the real-time environmental data set based on the port's historical data; obtaining the port crane's operating tasks, and establishing a benchmark operating path based on the operating tasks; parsing the operating tasks, and obtaining the hoisted object data, the hoisted object data including mass distribution data and volume data of the hoisted object; reading the port crane pre-control data mapped to the operating tasks, performing response fluctuation fitting based on the pre-control data, mass distribution data, and volume data, and updating the digital twin model according to the response fluctuation fitting results and the benchmark operating path; performing proximity warning based on the dynamic risk identification of items through the updated digital twin model, and generating feedback control data according to the proximity warning results; and optimizing the control of the port crane based on the feedback control data.

[0007] In the second aspect, the present application also provides a port crane operation control device for executing a port crane operation control method as described in the first aspect, wherein the port crane operation control device includes: a data acquisition module, the data acquisition module is used to activate the port's environmental acquisition sensors, perform port data acquisition based on the environmental acquisition sensors, and establish a real-time environmental data set within a predetermined area; a digital twin model building module, the digital twin model building module is used to build a digital twin model of the port with the real-time environmental data set, and perform dynamic risk identification of items in the real-time environmental data set based on the port's historical data; a task acquisition module, the task acquisition module is used to acquire the port crane's operating tasks, and establish a benchmark operating path based on the operating tasks ; Task analysis module, the task analysis module is used to analyze the operation task and obtain the data of the hoisted object, the hoisted object data includes the mass distribution data and volume data of the hoisted object; Response fluctuation fitting module, the response fluctuation fitting module is used to read the port crane pre-control data mapped with the operation task, and perform response fluctuation fitting based on the pre-control data, mass distribution data and volume data, and update the digital twin model according to the response fluctuation fitting result and the benchmark operation path; Early warning feedback module, the early warning feedback module is used to perform approach warning based on the dynamic risk identification of objects through the updated digital twin model, and generate feedback control data according to the approach warning result; Control optimization module, the control optimization module is used to optimize the control of the port crane based on the feedback control data.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By activating the port's environmental collection sensors, port data collection is performed based on the environmental collection sensors to establish a real-time environmental data set within a predetermined area; the digital twin model of the port is built with the real-time environmental data set, and the dynamic risk identification of items in the real-time environmental data set is performed based on the port's historical data; the port crane's operating tasks are obtained, and a benchmark operating path is established based on the operating tasks; the operating tasks are analyzed to obtain the hoisted object data, and the hoisted object data includes the mass distribution data and volume data of the hoisted object; the port crane pre-control data mapped to the operating tasks is read, and response fluctuation fitting is performed based on the pre-control data, mass distribution data, and volume data, and the digital twin model is updated according to the response fluctuation fitting results and the benchmark operating path; the updated digital twin model is used to perform proximity warning based on the dynamic risk identification of items, and feedback control data is generated according to the proximity warning results; and the control optimization of the port crane is performed based on the feedback control data. In other words, by integrating environmental sensors, establishing and updating the digital twin model, the safety and efficiency of port crane operations are improved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A schematic diagram of a process flow of a port crane operation control method for this application;

[0013] Figure 2 This is a structural schematic diagram of a port crane operation control device of the present application.

[0014] Explanation of the accompanying drawings: data acquisition module 11, digital twin model building module 12, task acquisition module 13, task analysis module 14, response fluctuation fitting module 15, early warning feedback module 16, control optimization module 17. DETAILED DESCRIPTION

[0015] This application provides a port crane operation control method and device to solve the technical problems in the prior art that the operation safety is insufficient and the efficiency is low due to inaccurate risk prediction and control in traditional crane operations. By integrating environmental sensors, establishing and updating digital twin models, the safety and efficiency of port crane operations are improved.

[0016] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0017] For example, please refer to the attached Figure 1 The present application provides a port crane operation control method, wherein the port crane operation control method is applied to a port crane operation control device, and the port crane operation control method specifically includes the following steps:

[0018] Step 1: Activate the port's environmental collection sensors, collect port data based on the environmental collection sensors, and establish a real-time environmental data set in a predetermined area.

[0019] Specifically, various sensors are installed in the port, including but not limited to wind speed sensors, temperature and humidity sensors, cameras, infrared sensors, noise monitors, etc., to monitor and collect environmental information. Activating the sensor and collecting data means that the sensor starts working to collect environmental parameters such as object location, temperature, humidity, wind speed, etc. These data may be real-time or periodically collected, depending on the type and configuration of the sensor. The collected data is integrated into a data set that contains the real-time status of the environment in the predetermined area. The predetermined area can be a specific loading and unloading area, a storage area, or the entire port area. By establishing a real-time environmental data set in the predetermined area, abnormal conditions can be monitored and preventive measures can be taken in a timely manner.

[0020] Step 2: Build a digital twin model of the port using the real-time environmental data set, and perform dynamic risk identification of items in the real-time environmental data set based on the port's historical data.

[0021] Specifically, the real-time environmental data set is used to build a digital twin model of the port. The digital twin model is a virtual, digital replica that simulates the physical environment and operation process of the actual port, reflecting the actual situation of the port in real time, including cargo location, equipment status, environmental conditions, etc. The historical data of the port is used to dynamically identify the risks of items in the real-time environmental data set, and analyze the patterns and trends in the historical data. Considering historical accident patterns, risk factors under specific conditions, etc., the risks of items in the current data set are predicted and identified, such as unstable cargo stacking and flammable items near heat sources. A digital twin model that can reflect the port status in real time is created, and through historical data analysis, the items in the real-time data set are dynamically identified for risk, which greatly improves the safety and efficiency of port operations. By predicting and identifying potential risks, ports can take preventive measures in a timely manner to avoid accidents, while optimizing resource allocation and operation processes.

[0022] Step 3: Obtain the operation tasks of the port cranes, and establish a reference operation path based on the operation tasks.

[0023] Specifically, the operating tasks of the port cranes are obtained, including loading and unloading, stacking, and transportation of cargo. Each task has its specific requirements and goals, such as the type of cargo to be moved, weight, destination, etc. After obtaining these task information, a benchmark operating path is established based on these tasks. The standard moving routes and operating steps predetermined by the crane when performing the task, including the various stages of lifting, moving, and placing cargo, are designed to ensure that the crane can complete the task efficiently and safely. The benchmark operating path refers to the theoretical path for the crane to perform the task, which is an ideal path planned based on the operating task and port layout. By presetting the benchmark operating path, the uncertainty and delays in the operation process can be reduced, and the operating efficiency of the crane can be improved.

[0024] Step 4: Analyze the operation task and obtain the data of the hoisted object, wherein the data of the hoisted object includes the mass distribution data and volume data of the hoisted object.

[0025] Specifically, the acquired work tasks are analyzed in detail to understand the specific requirements of the tasks, including the type, quantity, destination and other information of the cargo to be hoisted. According to the analyzed work tasks, the specific data of the hoisted objects are obtained from the port's database or related systems, including the mass distribution and volume of the hoisted objects. Mass distribution data refers to the distribution of the weight of the cargo in its various parts or points, and volume data refers to the three-dimensional dimensions of the cargo, usually including length, width and height. Obtaining detailed data on the hoisted objects, including mass distribution and volume, helps ensure that the crane can operate safely and stably when performing tasks.

[0026] Step 5: Read the pre-control data of the port crane mapped with the operation task, perform response fluctuation fitting based on the pre-control data, mass distribution data, and volume data, and update the digital twin model according to the response fluctuation fitting results and the benchmark operation path.

[0027] Specifically, the pre-control data is read. The pre-control data refers to the control parameters determined before the crane is operated, such as speed, acceleration, lifting height, etc. It is the preset control parameters when operating the crane to perform a specific task, mapped with the operation task, and provides guidance for the crane operation. Response fluctuation fitting is performed based on the pre-control data, mass distribution data, and volume data. Response fluctuation fitting is a technology used to predict the dynamic response of a system when it is subjected to external disturbances. In this process, the pre-control data, the mass distribution data of the hoisted object, and the volume data are input into the steady-state analysis network to simulate the dynamic behavior of the crane when performing operations. Through the steady-state analysis network, the response of the crane under external disturbances such as wind speed changes and changes in the tension of the hoisting point is predicted, including swaying, tilting, or rotation. The digital twin model is updated according to the response fluctuation fitting results and the benchmark operation path. The digital twin model is a virtual, digital copy that simulates the physical environment and operation process of the actual port. By updating the digital twin model to more accurately reflect the current operation situation and potential risks, it helps operators make more informed decisions and improve the safety and efficiency of operations. Combined with the response fluctuation fitting results, the benchmark operation path is optimized to improve operation efficiency and safety.

[0028] Step 6: Use the updated digital twin model to conduct proximity warning based on the dynamic risk identification of objects, and generate feedback control data based on the proximity warning results.

[0029] Specifically, after the digital twin model is updated, it can simulate the actual dynamic response of the crane when performing operations. By analyzing the dynamic risk identification of items in the model, the potential risks that the crane may encounter during the operation are predicted. The first penalty coefficient is established based on the evaluation results of the basic importance of the items, the second penalty coefficient is established based on the evaluation results of the importance of the hoisted objects, and the third penalty coefficient is established based on the dynamic risk identification results of the items. A distance threshold is established based on the first penalty coefficient, the second penalty coefficient, and the third penalty coefficient to determine whether the distance between the hoisted object and the surrounding objects or environment is less than the safety threshold. The digital twin model is used to predict and identify possible risks, such as the risk of collision between the crane and the cargo, and the possible swaying of the cargo. Based on the results of the proximity warning identification, a warning or indication is generated to alert the operator to potential risks. The proximity warning system established by comprehensively considering multiple factors can more accurately predict and identify potential safety risks, thereby improving the safety of operations.

[0030] Step 7: Optimize the control of the port crane based on the feedback control data.

[0031] Specifically, based on the warning results, feedback control data is generated to adjust the crane's operating strategy to reduce risks. The generated control data is applied to the actual operation of the crane to achieve more efficient and safer operations, including adjusting the crane's operating strategy, optimizing its speed and steering, and improving its interaction with the surrounding environment. For example, suppose that when the crane is performing a lifting task, the proximity warning system predicts that the crane's swing amplitude exceeds the safety threshold at a specific moment. Based on this warning result, feedback control data is generated to adjust the crane's speed and acceleration to reduce the swing amplitude. Through the feedback control data generated based on the proximity warning results, possible problems in the crane's operation can be discovered and solved in a timely manner, the risk of accidents can be reduced, the operating strategy can be adjusted, the crane's operating efficiency can be improved, and the operation time can be reduced.

[0032] Further, step five of this application includes:

[0033] Establish wind data mapped with the reference operation path, wherein the wind data includes wind direction data and wind magnitude data; establish the windward area of ​​the hoisted object according to the wind direction data and volume data; obtain the hoisting point position vector of the hoisted object, and calculate the centroid of the hoisted object according to the hoisting point position vector, mass distribution data and volume data; use the pre-control data, mass distribution data, volume data, wind data, windward area, hoisting point position vector and centroid of the hoisted object as input data, input them into the steady-state analysis network to perform response fluctuation fitting, and establish the response fluctuation fitting result.

[0034] Specifically, wind data that the crane may encounter when executing the benchmark operation path is collected and mapped, including wind direction and wind force. Wind direction data refers to the direction of the wind, usually expressed as an angle or direction name (such as north, east, south, west); wind force data refers to the strength of the wind, usually expressed as wind speed (such as meters per second) or wind force level (such as Beaufort wind scale). The known volume data and the wind direction data collected in real time are used to calculate the windward area of ​​the hoisted object in the wind. The windward area refers to the area projected by the object in the direction of the wind, which is used to evaluate the impact of the wind on the object. Suppose a port crane hoists a rectangular cargo with a volume data of 10 meters long, 2 meters wide, and 3 meters high. The current wind direction data shows that the dominant wind direction is southwest wind. Based on these data, the windward area of ​​the cargo is calculated. If the wind blows from the long side, the windward area will be length × height, that is, 10 meters × 3 meters = 30 square meters. If the wind blows from the wide side, the windward area will be width × height, that is, 2 meters × 3 meters = 6 square meters. According to the actual wind direction, select the corresponding windward area for calculation.

[0035] Determine the position where the crane hook contacts the cargo, i.e., the lifting point, which is usually represented by a three-dimensional vector, including its coordinates on the x, y, and z axes. The lifting point position vector is crucial to ensure the balance of the cargo during the lifting process. Calculate the center of mass of the hoisted material based on the lifting point position vector, mass distribution data, and volume data. The center of mass is the geometric center when the mass of an object is evenly distributed, and is used to evaluate the balance and stability of the object. By calculating the center of mass, ensure that the crane's lifting point is correctly selected to avoid tilting or rotation of the cargo during the lifting process. The pre-control data, mass distribution data, volume data, wind data, windward area, lifting point position vector, and the center of mass of the hoisted material are used as input data and input into the linear dynamic evaluation subnetwork and the angular dynamic evaluation subnetwork. Use the output results of the steady-state analysis network to perform response fluctuation fitting. According to the calculated total external force and total torque acting on the hoisted object, use the output results of the steady-state analysis network to simulate the dynamic response of the crane when performing the lifting task, including cargo sway, crane stability, etc. The fitting results will show the expected response of the crane under different conditions, helping the operator or system to predict and prepare for possible fluctuations or instabilities, so that appropriate measures can be taken to ensure the safety and efficiency of operations.

[0036] Furthermore, the present application also includes the following steps:

[0037] The steady-state analysis network includes a linear power evaluation subnetwork and an angular power evaluation subnetwork, as follows:

[0038] ;

[0039] ;

[0040] in, Represents the sum of all external forces acting on the hoisted object. is the mass of the hoisted object, is the acceleration due to gravity, is the air density, Windward area, is the air resistance coefficient, which is determined by the shape and surface condition of the hoisted object. is the relative wind speed, For wind data, is the speed data of the hoisted object obtained from the pre-control data, is the relative wind speed, is the direction unit vector of the relative wind speed, Indicates the total number of lifting points. Represent any lifting point, Characterization The tension of each lifting point, For the The tension direction unit vector of the lifting point, Represents the sum of all moments acting on the hoisted object. is the centroid position vector, For the The position vector of the lifting point, is the angular velocity of the hoisted object, is the moment of inertia tensor of the object; response fluctuation fitting is performed according to the output results of the linear dynamic evaluation subnetwork and the angular dynamic evaluation subnetwork.

[0041] Specifically, the steady-state analysis network includes a linear dynamic evaluation subnetwork and an angular dynamic evaluation subnetwork, which predict and analyze the various forces (including gravity, wind force, tension at the lifting point, etc.) and moments (due to the rotation of the object and the tension at the lifting point) to which the crane is subjected when performing operations. The linear dynamic evaluation subnetwork predicts the linear motion law of the object in a steady state by analyzing the resultant force and moment of the object, as well as the mass, inertia, shape and other factors of the object. ; It represents the sum of all external forces acting on the hoisted object, affecting the linear motion of the object, that is, the acceleration and velocity of the object. is the weight of the hoisted object, where is the mass of the hoisted object, is the acceleration due to gravity. represents the force due to air resistance, where is the air density, is the windward area, is the air resistance coefficient, is the relative wind speed, is the direction unit vector of the relative wind speed. is the wind speed The speed of the hoisted object difference. is the sum of the tensions at all lifting points, where is the total number of lifting points, Any lifting point, Characterization The tension of each lifting point, For the The unit vector of the tension direction at each lifting point.

[0042] The angular dynamics evaluation subnetwork predicts the angular motion law of an object in a steady state by analyzing factors such as the torque acting on the object, the object's moment of inertia, and the object's shape. ; Characterizes the sum of all torques acting on the hoisted object, affecting the object's angular motion, that is, the object's angular velocity and angular acceleration. is the torque due to gravity and air resistance, where is the centroid position vector. is the sum of the moments generated by the tension at all lifting points, where For the The position vector of the lifting point. is the angular velocity of the hoisted object, is the moment of inertia tensor of the object.

[0043] The pre-control data, mass distribution data, volume data, wind force data, windward area, hoisting point position vector, and the centroid of the hoisted material are used as input data and input into the linear dynamic evaluation subnetwork and the angular dynamic evaluation subnetwork. According to the output results of these networks, the dynamic fluctuations that the crane may encounter in actual operation, such as swaying, tilting, or rotation, are predicted, and the operation strategy is adjusted. The adjustment strategy includes adjusting the position of the hoisting point, adjusting the speed and acceleration of the crane, and taking other measures to prevent the impact of dynamic fluctuations on the safety of the operation. Response fluctuation fitting is a technique used to predict the dynamic response of the system when it is subjected to external disturbances. In port crane operations, the purpose of response fluctuation fitting is to predict the dynamic fluctuations encountered by the crane when performing operations, and adjust the operation strategy accordingly to ensure the safety and efficiency of the operation. By performing response fluctuation fitting, the dynamic response of the crane under wind speed changes is predicted, so that corresponding measures can be taken to ensure the safety and efficiency of the operation.

[0044] Furthermore, the present application also includes the following steps:

[0045] The wind force impact of the hoisted object in each direction is fitted according to the mass distribution data and volume data, and a directional wind force warning threshold is established; a trigger analysis of the directional wind force warning threshold is performed through the wind force data, and an abnormal warning is reported based on the trigger analysis result.

[0046] Specifically, according to the mass distribution data and volume data of the hoisted object, combined with the output results of the steady-state analysis network, the wind force impact in each direction is fitted, and these data are used to simulate and predict the impact of wind forces in different directions on the hoisted object. Through this fitting, the magnitude and direction of the wind force are evaluated, so as to predict the push, pull or swaying degree that the cargo may be subjected to. According to the fitting results, the wind force impact in each direction is determined, and the corresponding wind warning threshold is established. These warning thresholds can be determined based on historical data, industry standards and expert experience to ensure that the crane can issue a warning in time when encountering abnormal wind. The real-time collected wind data is compared with the previously set directional wind warning threshold. If the real-time wind data exceeds the set threshold, it indicates that there is a potential risk or abnormal situation, such as strong wind causing cargo swaying or unstable crane operation. Based on the results of the trigger analysis, the system will issue an abnormal warning and notify the operator through visual or auditory signals (such as alarm sounds, flashing lights). The purpose of the abnormal warning is to remind the operator to pay attention to potential risks and take corresponding measures, such as suspending operations, adjusting operating strategies or strengthening safety measures. By establishing directional wind warning thresholds, the impact of wind speed on crane operations can be accurately assessed, and warnings can be issued in a timely manner to reduce the risk of accidents.

[0047] Furthermore, step six of this application includes:

[0048] Perform basic importance evaluation of items in the real-time environmental data set to establish a first penalty coefficient; obtain the importance evaluation result of the hoisted object and establish a second penalty coefficient; establish a third penalty coefficient based on the dynamic risk identification of the item; establish a distance threshold based on the first penalty coefficient, the second penalty coefficient, and the third penalty coefficient; perform proximity warning identification of the updated digital twin model through the distance threshold to establish a proximity warning result.

[0049] Specifically, the real-time environmental data set contains various environmental factors that affect safety in port operations, such as wind speed, visibility, tide, etc. The basic importance evaluation of items is a quantitative assessment of the impact of these environmental factors on the safety of the hoisted objects. Considering multiple factors, such as the value of the goods, fragility, impact on the operation process, etc., evaluate and determine the relative importance of the hoisted objects under the current environmental conditions, and identify which items are critical to operational safety or efficiency and which may require additional attention or protection. Establishing the first penalty coefficient is a mathematical process that determines a value based on the results of the basic importance evaluation of items. The first penalty coefficient is often used to weigh different risks and benefits to ensure that the most important items and potential risks are prioritized when performing tasks.

[0050] A comprehensive analysis of multiple factors of the hoisted object, such as the value of the goods, vulnerability, and impact on the operation process, determines the importance of the goods in the hoisting process, so as to give corresponding attention and protection when performing the task. Establishing the second penalty coefficient is a mathematical process that determines a value based on the evaluation results of the importance of the hoisted object. The second penalty coefficient is usually used to further weigh risks and benefits to ensure that the operation strategy can be more finely adjusted when performing tasks to meet the special needs of different items. The dynamic risk identification of items refers to the assessment of the dynamic risks faced by the hoisted objects based on real-time data and predictive analysis, including factors such as wind force, stability of the hoisting point, and swaying of the goods during the hoisting process. The third penalty coefficient is a value determined based on the dynamic risk identification results of the items, which is used to consider the dynamic risks of the items in the subsequent decision-making process. By establishing the third penalty coefficient, the operation strategy is adjusted according to the real-time risk situation of the items to improve the safety and efficiency of the operation. For example, if the forecast shows that the goods sway a lot during the hoisting process, the crane's operation strategy is adjusted according to this coefficient, such as reducing the speed or adjusting the position of the hoisting point to ensure the safety and stability of the goods during the hoisting process.

[0051] A distance threshold is established based on the first penalty coefficient, the second penalty coefficient, and the third penalty coefficient. The distance threshold is a specific distance range used to determine whether the distance between the hoisted object and the surrounding objects or environment is less than the safety threshold, combining the importance and risk of the object to more accurately assess and respond to potential risks. The digital twin model is a virtual, digital replica that simulates the physical environment and operation process of the actual port. Through the updated digital twin model, possible risks are predicted and identified, such as the risk of collision between the crane and the cargo, possible swaying of the cargo, etc. Approach warning identification refers to the use of the digital twin model to predict and identify possible risks, and the approach warning result is a warning signal issued by the system to remind the operator to pay attention to possible safety risks and take corresponding measures. By comprehensively considering the importance and risk of the object, as well as the dynamic risk identification results, a distance threshold based on multiple risk factors is established, which helps to more accurately assess and respond to potential risks when performing tasks. By combining risk assessments of different factors, the operation strategy can be adjusted more finely, thereby improving the safety and efficiency of operations.

[0052] Furthermore, the present application also includes the following steps:

[0053] Based on the control optimization results, risk check points are established; attention is identified for the risk check points and real-time attention data is established; early warning verification of the control optimization results is performed through the real-time attention data, and shutdown feedback is generated based on the early warning verification results to perform shutdown control of the port crane.

[0054] Furthermore, the present application also includes the following steps:

[0055] Determine whether there is a control behavior that continuously breaks through the early warning verification result; if so, generate a forced shutdown instruction, and control the port crane to shut down based on the forced shutdown instruction.

[0056] Specifically, according to the feedback control data, the crane operation parameters and strategies are adjusted to obtain the control optimization results. Through control optimization, some key parameters or operation points are identified, which are points that need special attention and inspection during the crane operation process, namely risk check points. These points are links in the operation process that may have risks or require special attention. According to the identification results of the risk check points, the behavior and status of the crane at these key points are monitored and recorded in real time, including parameters such as the speed, position, posture, and load condition of the crane. By paying attention to the data in real time, potential problems can be discovered and handled in a timely manner, thereby improving the safety and efficiency of the operation. The previous control optimization results are verified using real-time data, and the real-time data is compared with the optimized control parameters and strategies to check whether the risks are effectively reduced and the safety of the operation is improved. When serious safety hazards or abnormal operations are found, signals or instructions are generated to instruct the operator to stop the operation of the crane.

[0057] By monitoring the real-time attention data, it is determined whether there is a control behavior that continuously breaks the warning verification result. The continuous breakthrough warning verification result means that the crane exceeds the safety range for multiple times in a row during operation. If so, a forced stop instruction is generated. The forced stop instruction is the highest level of safety instruction, instructing the operator to stop the operation of the crane immediately to avoid potential serious accidents. Based on the forced stop instruction, the port crane is controlled to stop and ensure the safety of the crane and the operator. By establishing risk checkpoints, real-time monitoring and verification, the control optimization results during the crane operation are ensured, effectively reducing risks and improving the safety of operations. The generation and execution of forced stop instructions are key measures to ensure the safety of port crane operations, and help prevent accidents caused by operating errors or equipment failures.

[0058] In summary, the port crane operation control method provided by the present application has the following technical effects:

[0059] By activating the port's environmental collection sensors, port data collection is performed based on the environmental collection sensors to establish a real-time environmental data set within a predetermined area; the digital twin model of the port is built with the real-time environmental data set, and the dynamic risk identification of items in the real-time environmental data set is performed based on the port's historical data; the port crane's operating tasks are obtained, and a benchmark operating path is established based on the operating tasks; the operating tasks are analyzed to obtain the hoisted object data, and the hoisted object data includes the mass distribution data and volume data of the hoisted object; the port crane pre-control data mapped to the operating tasks is read, and response fluctuation fitting is performed based on the pre-control data, mass distribution data, and volume data, and the digital twin model is updated according to the response fluctuation fitting results and the benchmark operating path; the updated digital twin model is used to perform proximity warning based on the dynamic risk identification of items, and feedback control data is generated according to the proximity warning results; and the control optimization of the port crane is performed based on the feedback control data. In other words, by integrating environmental sensors, establishing and updating the digital twin model, the safety and efficiency of port crane operations are improved.

[0060] Embodiment 2: Based on the same inventive concept as the port crane operation control method in the above embodiment, the present application also provides a port crane operation control device, please refer to the attached Figure 2 , the port crane operation control device comprises:

[0061] The data acquisition module 11 is used to activate the port's environment acquisition sensors, collect port data based on the environment acquisition sensors, and establish a real-time environment data set in a predetermined area.

[0062] The digital twin model building module 12 is used to build a digital twin model of the port with the real-time environmental data set, and to perform dynamic risk identification of items in the real-time environmental data set based on the historical data of the port.

[0063] The task acquisition module 13 is used to acquire the operation task of the port crane and establish a reference operation path based on the operation task.

[0064] The task analysis module 14 is used to analyze the operation task and obtain the data of the hoisted object, wherein the data of the hoisted object includes the mass distribution data and volume data of the hoisted object.

[0065] The response fluctuation fitting module 15 is used to read the port crane pre-control data mapped with the operation task, perform response fluctuation fitting based on the pre-control data, mass distribution data and volume data, and update the digital twin model according to the response fluctuation fitting result and the benchmark operation path.

[0066] The early warning feedback module 16 is used to perform proximity warning based on the dynamic risk identification of the object through the updated digital twin model, and generate feedback control data according to the proximity warning result.

[0067] The control optimization module 17 is used to optimize the control of the port crane based on the feedback control data.

[0068] Furthermore, the response fluctuation fitting module 15 in the port crane operation control device is also used for:

[0069] Establish wind data mapped with the reference operation path, wherein the wind data includes wind direction data and wind magnitude data; establish the windward area of ​​the hoisted object according to the wind direction data and volume data; obtain the hoisting point position vector of the hoisted object, and calculate the centroid of the hoisted object according to the hoisting point position vector, mass distribution data and volume data; use the pre-control data, mass distribution data, volume data, wind data, windward area, hoisting point position vector and centroid of the hoisted object as input data, input them into the steady-state analysis network to perform response fluctuation fitting, and establish the response fluctuation fitting result.

[0070] Furthermore, the response fluctuation fitting module 15 in the port crane operation control device is also used for:

[0071] The steady-state analysis network includes a linear power evaluation subnetwork and an angular power evaluation subnetwork, as follows:

[0072] ;

[0073] ;

[0074] in, Represents the sum of all external forces acting on the hoisted object. is the mass of the hoisted object, is the acceleration due to gravity, is the air density, Windward area, is the air resistance coefficient, which is determined by the shape and surface condition of the hoisted object. is the relative wind speed, For wind data, is the speed data of the hoisted object obtained from the pre-control data, is the relative wind speed, is the direction unit vector of the relative wind speed, Indicates the total number of lifting points. Represent any lifting point, Characterization The tension of each lifting point, For the The tension direction unit vector of the lifting point, Represents the sum of all moments acting on the hoisted object. is the centroid position vector, For the The position vector of the lifting point, is the angular velocity of the hoisted object, is the moment of inertia tensor of the object; response fluctuation fitting is performed according to the output results of the linear dynamic evaluation subnetwork and the angular dynamic evaluation subnetwork.

[0075] Furthermore, the response fluctuation fitting module 15 in the port crane operation control device is also used for:

[0076] The wind force impact of the hoisted object in each direction is fitted according to the mass distribution data and volume data, and a directional wind force warning threshold is established; a trigger analysis of the directional wind force warning threshold is performed through the wind force data, and an abnormal warning is reported based on the trigger analysis result.

[0077] Furthermore, the early warning feedback module 16 in the port crane operation control device is also used for:

[0078] Perform basic importance evaluation of items in the real-time environmental data set to establish a first penalty coefficient; obtain the importance evaluation result of the hoisted object and establish a second penalty coefficient; establish a third penalty coefficient based on the dynamic risk identification of the item; establish a distance threshold based on the first penalty coefficient, the second penalty coefficient, and the third penalty coefficient; perform proximity warning identification of the updated digital twin model through the distance threshold to establish a proximity warning result.

[0079] Furthermore, the control optimization module 17 in the port crane operation control device is also used for:

[0080] Based on the control optimization results, risk check points are established; attention is identified for the risk check points and real-time attention data is established; early warning verification of the control optimization results is performed through the real-time attention data, and shutdown feedback is generated based on the early warning verification results to perform shutdown control of the port crane.

[0081] Furthermore, the control optimization module 17 in the port crane operation control device is also used for:

[0082] Determine whether there is a control behavior that continuously breaks through the early warning verification result; if so, generate a forced shutdown instruction, and control the port crane to shut down based on the forced shutdown instruction.

[0083] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The port crane operation control method and specific examples in the first embodiment are also applicable to the port crane operation control device in the present embodiment. Through the above detailed description of the port crane operation control method, those skilled in the art can clearly know the port crane operation control device in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0084] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be 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 present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0085] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A port crane operation control method, characterized in that: include: Activate the environment collection sensor of the port, collect port data based on the environment collection sensor, and establish a real-time environment data set in a predetermined area; Building a digital twin model of the port using the real-time environmental data set, and dynamically identifying the risks of items in the real-time environmental data set based on the historical data of the port; Acquire the operation tasks of the port crane, and establish a reference operation path based on the operation tasks; Analyze the operation task and obtain the data of the hoisted object, wherein the data of the hoisted object includes the mass distribution data and volume data of the hoisted object; Read the pre-control data of the port crane mapped with the operation task, perform response fluctuation fitting based on the pre-control data, mass distribution data, and volume data, and update the digital twin model according to the response fluctuation fitting result and the benchmark operation path; The updated digital twin model is used to provide proximity warnings based on the dynamic risk identification of items, and feedback control data is generated based on the proximity warning results; Control optimization of port cranes based on feedback control data; The step of performing response fluctuation fitting based on the pre-control data, mass distribution data, and volume data, and updating the digital twin model according to the response fluctuation fitting result and the benchmark operation path, further includes: Establishing wind data mapped with a reference operation path, wherein the wind data includes wind direction data and wind magnitude data; Establishing the windward area of ​​the hoisted object according to the wind direction data and the volume data; Obtaining a hoisting point position vector of the hoisted object, and calculating the center of the hoisted object according to the hoisting point position vector, mass distribution data, and volume data; The pre-control data, mass distribution data, volume data, wind force data, windward area, hoisting point position vector, and the centroid of the hoisted material are input as input data to a steady-state analysis network to perform response fluctuation fitting and establish a response fluctuation fitting result; The pre-control data, mass distribution data, volume data, wind force data, windward area, hoisting point position vector, and the centroid of the hoisted material are input as input data to the steady-state analysis network to perform response fluctuation fitting, and further include: The steady-state analysis network includes a linear power evaluation subnetwork and an angular power evaluation subnetwork, as follows: ； ； in, Represents the sum of all external forces acting on the hoisted object. is the mass of the hoisted object, is the acceleration due to gravity, is the air density, is the windward area, is the air resistance coefficient, which is determined by the shape and surface condition of the hoisted object. is the relative wind speed, For wind data, is the speed data of the hoisted object obtained from the pre-control data, is the relative wind speed, is the direction unit vector of the relative wind speed, Indicates the total number of lifting points. Represent any lifting point, Characterization The tension of each lifting point, For the The tension direction unit vector of the lifting point, Represents the sum of all moments acting on the hoisted object. is the centroid position vector, For the The position vector of the lifting point, is the angular velocity of the hoisted object, is the moment of inertia tensor of the object; The response fluctuation fitting is performed based on the output results of the linear dynamic evaluation subnetwork and the angular dynamic evaluation subnetwork.

2. A port crane operation control method as claimed in claim 1, characterized in that: Also includes: Fit the wind force impact in all directions of the hoisted object based on mass distribution data and volume data, and establish directional wind force warning thresholds; The wind data is used to perform a trigger analysis of a directional wind warning threshold, and an abnormal warning is reported based on the trigger analysis result.

3. A port crane operation control method as claimed in claim 1, characterized in that: The method of performing proximity warning based on the dynamic risk identification of the object through the updated digital twin model and generating feedback control data according to the proximity warning result also includes: Performing a basic importance evaluation of items on the real-time environment data set and establishing a first penalty coefficient; Obtain the importance evaluation result of the hoisted object and establish the second penalty coefficient; Establishing a third penalty coefficient according to the dynamic risk identification of the item; Establishing a distance threshold based on the first penalty coefficient, the second penalty coefficient, and the third penalty coefficient; The updated digital twin model proximity warning recognition is performed through the distance threshold to establish a proximity warning result.

4. A port crane operation control method as claimed in claim 1, characterized in that: Also includes: Establish risk checkpoints based on control optimization results; Identify the risk checkpoints and establish real-time attention data; The real-time attention data is used to perform early warning verification of the control optimization result, and shutdown feedback is generated based on the early warning verification result to perform shutdown control of the port crane.

5. A port crane operation control method as claimed in claim 4, characterized in that: The generating of shutdown feedback based on the early warning verification result also includes: Determine whether there are control behaviors that continuously break through the early warning verification results; If yes, a forced shutdown instruction is generated, and the port crane is controlled to shut down based on the forced shutdown instruction.

6. A port crane operation control device, characterized in that: Steps for implementing a port crane operation control method as described in any one of claims 1 to 5, the port crane operation control device comprising: A data collection module, the data collection module is used to activate the port's environmental collection sensors, collect port data based on the environmental collection sensors, and establish a real-time environmental data set in a predetermined area; A digital twin model building module, wherein the digital twin model building module is used to build a digital twin model of the port with the real-time environment data set, and to perform dynamic risk identification of items in the real-time environment data set based on the historical data of the port; A task acquisition module, the task acquisition module is used to acquire the operation task of the port crane and establish a reference operation path based on the operation task; A task analysis module, the task analysis module is used to analyze the operation task and obtain the data of the hoisted object, the data of the hoisted object includes the mass distribution data and volume data of the hoisted object; A response fluctuation fitting module, which is used to read the pre-control data of the port crane mapped with the operation task, perform response fluctuation fitting based on the pre-control data, mass distribution data, and volume data, and update the digital twin model according to the response fluctuation fitting result and the benchmark operation path; An early warning feedback module, which is used to perform proximity warning based on the dynamic risk identification of items through the updated digital twin model, and generate feedback control data according to the proximity warning results; A control optimization module is used to optimize the control of the port crane based on feedback control data.

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