A Logistics Remote Control Method and System Based on Virtual Reality

Abstract

The present invention discloses a logistics remote control method and system based on virtual reality, which relates to the technical field of logistics transportation management. The invention reduces the possibility of human errors through automated packing plan generation and path planning. The system automatically calculates and generates a packing plan according to the characteristics of goods and loading constraints, avoiding the risk of manual operation errors. If the efficiency evaluation index is higher than 0.8, the system will confirm that the current plan is the optimal plan. The trajectory planning module combines the anchoring position of the target ship and the container yard position, generates multiple paths using the dynamic path planning method, and calculates the trajectory coefficient and transportation time. This path planning method based on real-time data can flexibly respond to sudden changes during transportation, reduce transportation time, and ensure timely completion of tasks. The dynamic path planning module can adjust the path and the position of the lifting point in real time, avoiding possible collisions or other safety hazards during the operation.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics transportation management, and specifically to a logistics remote control method and system based on virtual reality. Background Art

[0002] With the rapid development of the global logistics and transportation industry, especially in the fields of ports and container transportation, traditional logistics management and control methods are facing numerous challenges. These challenges are mainly reflected in aspects such as how to improve loading and unloading efficiency, reduce human errors, lower operating costs, and optimize resource allocation. Traditional packing scheme design, goods transportation route planning, and lifting equipment scheduling mostly rely on manual intervention, with low efficiency and being easily affected by human factors, resulting in problems such as unsmooth transportation processes, high rates of damaged goods, and long loading and unloading times, which greatly restrict the development of the logistics industry.

[0003] However, when applying existing virtual reality technologies in the logistics industry, some key technical problems still exist. For example, how to accurately simulate complex packing and transportation processes, how to combine the characteristics of goods with loading constraints and generate reasonable packing schemes based on this data, and how to effectively evaluate the efficiency of packing schemes. Existing technologies often neglect the comprehensive consideration of multiple variables such as goods characteristics, loading constraints, and transportation routes, and lack a systematic evaluation mechanism, resulting in unsatisfactory optimization effects for packing schemes and transportation routes. In addition, traditional path planning algorithms also have deficiencies when dealing with complex dynamic environments and changing logistics demands, and are unable to respond to emergencies in a timely manner, affecting the overall logistics efficiency. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a logistics remote control method and system based on virtual reality to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A logistics remote control system based on virtual reality, including a virtual construction module, a goods information collection module, a goods scheme generation module, a trajectory planning module, and an equipment control module;

[0006] The virtual construction module is used to construct a port scene model based on virtual reality technology, combine the basic information of goods and container loading rules, simulate the packing process of multiple groups of goods, and generate a visual logistics operation interface to display recommended packing schemes and transportation routes;

[0007] The goods information collection module is used to monitor the basic information of goods to be packed, including the size, weight, type, and storage location of the goods, and construct a goods feature set Pcs and a loading constraint set Czc;

[0008] The goods plan generation module is used to generate multiple container loading plans according to the goods feature set Pcs and the loading constraint set Czc, construct the container loading adaptation coefficient Cf, the space utilization coefficient Slx, and the loading balance coefficient Zphx, associate the container loading adaptation coefficient Cf, the space utilization coefficient Slx, and the loading balance coefficient Zphx to obtain the efficiency evaluation index Xl. If the efficiency evaluation index Xl > 0.8, the current container loading plan is confirmed.

[0009] After confirming the container loading plan, the trajectory planning module is used to collect the anchoring positions of several target ships in real time, determine the berth range corresponding to each ship, and generate the loading and unloading area; and calculate the recommended lifting points according to the storage position Ls(x, y) of the container in the yard and the anchor coordinates A(x, y) of the target ship. and based on the recommended lifting points use the dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and the total transportation time ;

[0010] The equipment control module is used to preset the time threshold X, and compare and evaluate the total transportation time with the time threshold X to screen the corresponding priority paths and execute the corresponding execution instructions.

[0011] Preferably, the goods information collection module includes a goods size collection unit, a goods weight collection unit, and a goods feature set output unit;

[0012] The goods size collection unit is used to use a laser measuring instrument and a goods scanner to collect the length L, width K, and height H of the goods to be containerized in real time;

[0013] The goods weight collection unit is used to use an electronic scale to obtain the net weight and gross weight of the goods to be containerized in real time, and associate the unique RFID number of the goods to be containerized;

[0014] The goods feature set output unit is used to obtain the classification category Ct of each piece of goods to be containerized. The classification category Ct includes fragile goods, dangerous goods, cold chain goods, and ordinary goods; and obtain the current storage position Pos of the goods to be containerized to construct the goods feature set Pcs as: ; n represents the total number of goods to be containerized.

[0015] Preferably, the goods information collection module further includes a loading construction unit;

[0016] The loading construction unit is used to define constraint conditions based on the goods feature set Pcs and the actual container loading requirements to construct the loading constraint set Czc:

[0017] The specific loading constraints include:

[0018] S11. First weight dimension constraint: The total weight of the goods loaded in each container shall not exceed the maximum load capacity, and the expression is as follows: , and the expression is as follows:

[0019] ;

[0020] Wherein, represents the gross weight of the i-th piece of goods, represents the maximum load capacity of the container; m is the total number of goods loaded in the container;

[0021] Second weight dimension constraint: The weight of the goods stacked in a single layer shall not exceed the maximum load capacity of the single stacked layer, and the expression is as follows: , and the expression is as follows:

[0022] ;

[0023] Wherein, represents the gross weight of the j-th piece of goods, represents the maximum load capacity of the single stacked layer; k is the number of goods in a single layer;

[0024] S12. Dimension constraint: The length , width and height of each piece of goods shall meet the dimension limitations of the internal space of the container, and the constraint condition expression is:

[0025] ;

[0026] Wherein, respectively represent the length, width and height inside the container;

[0027] S13. The classification categories Ct cannot be mixed and loaded, and corresponding containers shall be configured separately;

[0028] S14. Based on S11 - S13, the output loading constraint set Czc is: ; d represents the actual distance from the storage location of the goods to be packed into the container to the loading point.

[0029] Preferably, the goods scheme generation module includes a goods sorting initialization unit;

[0030] The goods sorting initialization unit is used to sort the goods to be packed in descending order according to the size of the goods to be packed in the container according to the goods feature set Pcs and the loading constraint set Czc, and load the goods to be packed into the container in sequence to create an initial packing scheme as the first arrangement scheme;

[0031] Take the first packing scheme as the parent generation, perform crossover and mutation operations, and generate the second packing scheme by swapping the positions of 30%-50% of the packed goods in the first packing scheme of the parent generation.

[0032] Preferably, the goods scheme generation module further includes an adaptation coefficient calculation unit and an assembly space calculation unit;

[0033] The adaptation coefficient calculation unit is used to calculate the space utilization of the second packing scheme according to the goods feature set Pcs and the loading constraint set Czc, and obtain the packing adaptation coefficient Cf through the following formula:

[0034] ;

[0035] ;

[0036] ;

[0037] In the formula, represents the space adaptation factor, represents the weight adaptation factor, and respectively represent the weights of the space adaptation factor and the weight adaptation factor ;

[0038] The assembly space calculation unit is used to collect the remaining space gap after the internal assembly of the container and the total weight of the packed goods in the single-layer area, and obtain the space utilization coefficient Slx and the loading balance coefficient Zphx through the following formula:

[0039] ;

[0040] ;

[0041] ;

[0042] In the formula, represents the total volume of the container, ; respectively represent the length, width, and height of the i-th piece of goods; m is the total number of goods loaded in the container; represents the gross weight of the j-th piece of goods, and k is the number of goods packed in a single layer; represents the total number of single-layer areas in the container, u represents the label of the single-layer area, represents the total weight of the packed goods in the u-th single-layer area, represents the average weight of all single-layer areas in the container.

[0043] Preferably, the goods plan generation module further includes an association unit and a first evaluation unit;

[0044] The association unit is used to extract the packing adaptation coefficient Cf, space utilization coefficient Slx, and loading balance coefficient Zphx of the second packing plan. After dimensionless processing, they are associated through the following formula to obtain the efficiency evaluation index Xl:

[0045] ;

[0046] In the formula, , and respectively represent the weights of the packing adaptation coefficient Cf, space utilization coefficient Slx, and loading balance coefficient Zphx;

[0047] The first evaluation unit is used to evaluate the efficiency evaluation index Xl to obtain the first evaluation result, including:

[0048] If the efficiency evaluation index Xl > 0.8, it means that the second packing plan is reasonable, and the adaptability, space utilization rate, and weight balance are all qualified. Use the current packing plan;

[0049] If the efficiency evaluation index Xl ≤ 0.8, it means that the second packing plan is unreasonable, and the adaptability, space utilization rate, and weight balance are all unqualified. Adjust the second packing plan, reallocate the goods, reorder the goods according to the goods size, adjust the priority loading order of the goods, and allocate the heavy goods to different single-layer areas so that the difference between the total weight of the goods in each single-layer area and the total weight of the goods in the adjacent single-layer area does not exceed 100 - 300 KG.

[0050] Preferably, the trajectory planning module includes an anchor position acquisition unit and a path trajectory calculation unit;

[0051] The anchor position acquisition unit is used to collect the anchor positions of several target ships in real time, obtain the anchor point coordinates A(x, y), map the anchor point coordinates to the reference coordinate system of the port scene model, and determine the berth range corresponding to each ship according to the anchor point coordinates A(x, y) to generate the loading and unloading area;

[0052] According to the storage position Ls(x, y) of the container in the yard and the anchor point coordinates A(x, y) of the target ship, the recommended lifting point is calculated through the following formula :

[0053] ;

[0054] ;

[0055] Among them, D represents the straight-line distance between the storage location Ls(x, y) of the container in the yard and the anchor point coordinates A(x, y) of the target ship. represents the priority weight of the container, represents the loading and unloading time required for the goods;

[0056] The path trajectory calculation unit is used to generate several paths according to the recommended lifting point using the dynamic path planning method, and calculate the trajectory coefficient of each path through the following formula and the total transportation time :

[0057] ;

[0058] ;

[0059] Among them, represents the number of turns in the current path, represents the total number of segments in the current path, represents the difference in lifting height of the current path, represents the maximum allowable lifting height; and represents the weight coefficient, which is used to balance the influence of turns and height changes on the trajectory coefficient;

[0060] represents the total transportation time, which measures the time required for the RTG to complete all tasks, represents the transportation distance between path node p and the (p + 1)-th path node, represents the average transportation speed of the RTG, represents the total amount of containers required at path node (p + 1), represents the total amount of containers transported by the RTG in a single trip, represents the total number of path nodes.

[0061] Preferably, the equipment control module includes a second evaluation unit and a control unit;

[0062] The second evaluation unit is used to preset a time threshold X, and compare the total transportation time with the time threshold X to obtain a second evaluation result, including:

[0063] If the total transportation time ≤ the time threshold X, it means that the current path and transportation strategy meet the time requirements and are classified into the qualified path group;

[0064] If the total transportation time > the time threshold X, it means that the current path and transportation strategy do not meet the time requirements.

[0065] Preferably, when receiving a qualified path group, the control unit preferentially screens the total transportation time ≤ time threshold X, and the total transportation time that is the smallest is used as the first-priority path. After generating a first path execution instruction, it is controlled and executed according to the transportation speed of the current rubber-tyred gantry crane;

[0066] When the total transportation time in several paths is all > time threshold X, select the total transportation time that is the smallest as the second-priority path, and generate a second strategy, including: increasing the average transportation speed of the current 10%-15% rubber-tyred gantry cranes, enabling 1-3 auxiliary equipment AGV vehicles to cooperate in handling 30% of the total container volume of path nodes, decomposing the goods exceeding the single rubber-tyred gantry crane's single transportation container total volume into multiple batches for transportation, adjusting the loading and unloading node order of the total volume of each batch of goods, and generating and executing a second path execution instruction according to the second-priority path.

[0067] A logistics remote control method based on virtual reality includes the following steps:

[0068] S1. Construct a port scene model based on virtual reality technology, combine the basic information of goods and the container loading rules, simulate the packing process of multiple groups of goods, and generate a visual logistics operation interface to display the recommended packing plan and transportation path;

[0069] S2. Monitor the basic information of the goods to be packed, including the size, weight, type and storage location of the goods, and construct a goods feature set Pcs and a loading constraint set Czc;

[0070] S3. According to the goods feature set Pcs and the loading constraint set Czc, generate multiple packing plans, and construct a packing adaptation coefficient Cf, a space utilization coefficient Slx and a loading balance coefficient Zphx. Associate the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx to obtain an efficiency evaluation index Xl. If the efficiency evaluation index Xl > 0.8, confirm the current packing plan;

[0071] S4. After confirming the packing plan, collect the anchoring positions of several target ships in real time, determine the berth range corresponding to each ship, and generate a loading and unloading area; and according to the storage position Ls(x, y) of the container in the yard and the anchor coordinates A(x, y) of the target ship, calculate the recommended lifting point , and based on the recommended lifting point use the dynamic path planning method to generate several paths, and calculate and obtain the trajectory coefficient and the total transportation time ;

[0072] S5. Preset a time threshold X and compare and evaluate the total transportation time with the time threshold X to screen the corresponding priority paths and execute the corresponding execution instructions.

[0073] The present invention provides a logistics remote control method and system based on virtual reality, having the following beneficial effects:

[0074] (1) The present invention constructs a set of goods characteristics and a set of loading constraints by using a goods information collection module to monitor the basic information of goods in real time, including size, weight, type, and storage location. This information provides reliable data support for optimizing the packing plan and transportation route, making the resource allocation more reasonable. The system can adjust the packing plan and route planning in real time to meet the dynamic logistics requirements and ensure the optimal allocation of resources. Traditional logistics management relies on manual decision-making, which is easily affected by human factors, resulting in errors and delays during transportation. By automatically generating the packing plan and route planning, the possibility of human errors is reduced, ensuring higher accuracy and reliability. The system automatically calculates and generates a packing plan according to the characteristics of goods and loading constraints, avoiding the risk of manual operation errors.

[0075] (2) By constructing a packing adaptation coefficient, a space utilization coefficient, and a loading balance coefficient and correlating them to generate an efficiency evaluation index, the present invention can accurately evaluate the effects of different packing plans. If the efficiency evaluation index is higher than 0.8, the system will confirm that the current plan is the optimal plan. This systematic evaluation mechanism improves the optimization effect of the packing plan and avoids the application of inefficient plans.

[0076] (3) The trajectory planning module combines the anchoring position of the target ship and the container yard position, uses the dynamic path planning method to generate multiple paths, and calculates the trajectory coefficient and transportation time. This path planning method based on real-time data can flexibly respond to sudden changes during transportation, reduce the transportation time, and ensure the timely completion of tasks. By using virtual reality technology to simulate the logistics process, potential operation risks and problems can be discovered in advance and corresponding preventive measures can be taken. The dynamic path planning module can adjust the path and the lifting point position in real time to avoid possible collisions or other safety hazards during operation, thus ensuring the safety of equipment and goods.

[0077] (4) In steps S1 - S5, a port scene model is constructed through virtual reality technology and the process of loading goods is simulated, which can display the recommended loading plan and transportation route in advance, effectively reducing errors and time waste in on - site operations, and improving the accuracy and efficiency of logistics operations. By combining the characteristics of goods and loading constraints, multiple loading plans are generated and evaluated based on the adaptation coefficient, space utilization coefficient, and loading balance coefficient to ensure the rationality of the loading plan. The confirmation mechanism with an efficiency evaluation index Xl≥0.8 guarantees the optimization and efficient execution of the loading plan, avoiding resource waste and unreasonable loading. The anchoring position of the target ship is collected in real - time, and the recommended lifting points are calculated based on the container storage positions. Further, multiple paths are generated through dynamic path planning, and the trajectory coefficient and total transportation time of the paths are calculated to ensure the optimization of the transportation route. By comparing the transportation time with the preset time threshold, the priority paths are screened to effectively avoid transportation delays and ensure the timely completion of operations. When the time threshold requirement is not met, the adaptability of logistics operations can be improved through priority path selection and dynamic adjustment, ensuring efficient operation in complex environments. This method provides an intelligent, flexible, and efficient operation management method for port logistics. The logistics remote control method based on virtual reality improves the intelligence and accuracy of port logistics operations, optimizes resource allocation, reduces waste of manpower and material resources, and effectively improves transportation efficiency and operation flexibility. Description of the Drawings

[0078] Figure 1 It is a schematic flow chart of a logistics remote control system based on virtual reality according to the present invention;

[0079] Figure 2 It is a schematic diagram of the steps of a logistics remote control method based on virtual reality according to the present invention. Detailed Embodiments

[0080] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0081] Embodiment 1

[0082] Please refer to Figure 1 , the present invention provides a logistics remote control system based on virtual reality, including a virtual construction module, a goods information collection module, a goods plan generation module, a trajectory planning module, and an equipment control module;

[0083] The virtual construction module is used to build a port scene model based on virtual reality technology, combine the basic information of goods and the container loading rules, simulate the loading process of multiple groups of goods, and generate a visual logistics operation interface to display the recommended loading plan and transportation route;

[0084] The goods information collection module is used to monitor the basic information of the goods to be loaded, including the size, weight, type and storage location of the goods, and construct a goods feature set Pcs and a loading constraint set Czc;

[0085] The goods plan generation module is used to generate multiple loading plans according to the goods feature set Pcs and the loading constraint set Czc, and construct a loading adaptation coefficient Cf, a space utilization coefficient Slx and a loading balance coefficient Zphx, associate the loading adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx to obtain an efficiency evaluation index Xl. If the efficiency evaluation index Xl > 0.8, the current loading plan is confirmed;

[0086] After confirming the loading plan, the trajectory planning module is used to collect the anchoring positions of several target ships in real time, determine the berth range corresponding to each ship, and generate a loading and unloading area; and according to the storage position Ls(x,y) of the container in the yard and the anchor point coordinates A(x,y) of the target ship, calculate the recommended lifting point and based on the recommended lifting point use the dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and the total transportation time ;

[0087] The equipment control module is used to preset a time threshold X, and compare and evaluate the total transportation time with the time threshold X to screen the corresponding priority paths and execute the corresponding execution instructions.

[0088] In this embodiment, through the virtual construction module, using virtual reality technology to simulate the port scene and the loading process, it is possible to plan and optimize the loading plan of goods in advance in a digital environment. This process not only improves the operation efficiency, but also avoids the errors or delays that may be caused by traditional manual planning, thus significantly reducing the loading and unloading time and improving the efficiency of the entire logistics process.

[0089] The present invention constructs a set of goods characteristics and a set of loading constraints by using a goods information collection module to monitor in real time the basic information of goods, including size, weight, type, and storage location. This information provides reliable data support for optimizing the packing plan and transportation route, making the allocation of resources more reasonable. The system can adjust the packing plan and route planning in real time to meet the dynamic logistics requirements and ensure the optimal allocation of resources. Traditional logistics management relies on manual decision-making, which is easily affected by human factors, resulting in errors and delays during transportation. Through automated packing plan generation and route planning, the possibility of human errors is reduced, ensuring higher accuracy and reliability. The system automatically calculates and generates a packing plan based on the characteristics of the goods and loading constraints, avoiding the risk of manual operation errors. By constructing a packing adaptation coefficient, a space utilization coefficient, and a loading balance coefficient and correlating them to generate an efficiency evaluation index, the present invention can accurately evaluate the effects of different packing plans. If the efficiency evaluation index is higher than 0.8, the system will confirm that the current plan is the optimal plan. This systematic evaluation mechanism improves the optimization effect of the packing plan and avoids the application of inefficient plans. The trajectory planning module combines the anchoring position of the target ship and the container yard position, uses dynamic path planning methods to generate multiple paths, and calculates the trajectory coefficient and transportation time. This path planning method based on real-time data can flexibly respond to sudden changes during transportation, reduce transportation time, and ensure the timely completion of tasks. By using virtual reality technology to simulate the logistics process, potential operation risks and problems can be discovered in advance, and corresponding preventive measures can be taken. The dynamic path planning module can adjust the path and the lifting point position in real time to avoid possible collisions or other safety hazards during operation, thus ensuring the safety of equipment and goods.

[0090] Embodiment 2

[0091] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the goods information collection module includes a goods size collection unit, a goods weight collection unit, and a goods characteristics set output unit;

[0092] The goods size collection unit is used to use a laser measuring instrument and a goods scanner to collect in real time the length L, width K, and height H of the goods to be packed; using a laser measuring instrument and a goods scanner to measure the goods to be packed in real time can obtain the length, width, and height of the goods with high precision, reducing the errors that may occur in traditional manual measurement. Accurate size data provides a reliable basis for subsequent packing plan generation and route planning, ensuring the accuracy of the packing plan.

[0093] The goods weight collection unit is used to use an electronic scale to obtain in real time the net weight and gross weight , and associate the unique RFID number of the goods to be packed; the system can track the weight change of each piece of goods in real time, avoiding the problem of unreasonable packing caused by inaccurate weight information of the goods.

[0094] The goods feature set output unit is used to obtain the classification category Ct of each piece of goods to be packed, and the classification category Ct includes fragile goods, dangerous goods, cold chain goods and ordinary goods; and obtain the current storage location Pos of the goods to be packed, so as to construct the goods feature set Pcs as: ; n represents the total number of goods to be packed.

[0095] In this embodiment, the accurate identification and processing of classification information enable the packing plan to be adjusted personalized according to the needs of different types of goods, avoiding the improper handling of fragile goods, dangerous goods or cold chain goods. The system can collect and update the basic information of the goods (such as size, weight, category and storage location) in real time, and dynamically manage the status of the goods. This enables flexible adjustment of logistics management in a dynamic environment to respond to emergencies in a timely manner, such as changes in the location of goods, weight adjustment or classification change. Through accurate goods characteristic data, the system can generate the optimal packing plan for each type of goods. During the path planning process, the system can automatically select the most suitable loading method and transportation route based on the size, weight, category and storage location of the goods, improving the loading and unloading efficiency and reducing the risks and losses during transportation.

[0096] Embodiment 3

[0097] This embodiment is an explanatory description based on Embodiment 1, please refer to Figure 1 , specifically, the goods information collection module further includes a loading construction unit;

[0098] The loading construction unit is used to define constraint conditions based on the goods feature set Pcs and the actual container loading requirements, so as to construct the loading constraint set Czc:

[0099] The specific loading constraint content includes:

[0100] S11. The first weight dimension constraint: the total weight of the goods loaded in each container cannot exceed the maximum bearing capacity , and the expression is as follows:

[0101] ;

[0102] Among them, represents the gross weight of the i-th piece of goods, represents the maximum bearing capacity of the container; m is the total number of goods loaded in the container;

[0103] The second weight dimension constraint: the weight of the goods stacked in a single layer shall not exceed the maximum bearing capacity of the single stacked layer , the expression is as follows:

[0104] ;

[0105] Among them, represents the gross weight of the j-th item, represents the maximum load-bearing capacity of a single stacked layer; k is the number of items in a single layer;

[0106] S12. Dimension constraint: The length , width and height of each item are restricted by the dimensions of the internal space of the load container. The constraint expression is:

[0107] ;

[0108] Among them, respectively represent the length, width and height inside the container;

[0109] S13. The classification category Ct cannot be mixed and loaded, and the corresponding container is configured separately;

[0110] S14. Based on S11 - S13, the output loading constraint set Czc is: ; d represents the actual distance from the storage location of the item to be loaded into the container to the loading point.

[0111] In this embodiment, by setting the first weight dimension constraint and the second weight dimension constraint, the system can effectively avoid overloading. The first weight dimension constraint ensures that the total loading weight of the container does not exceed the maximum load-bearing capacity, avoiding the safety risks caused by container overloading. The second weight dimension constraint further ensures that the weight of the single-layer stacked items does not exceed the maximum load-bearing capacity of a single stacked layer, ensuring that the items will not collapse or be damaged during the stacking process. These constraint measures enhance the safety and stability of container loading. The dimension constraint ensures the reasonable layout of the items inside the container, preventing space waste caused by the items being too large or too small. By restricting the length, width and height of the items, the system ensures that the items can effectively adapt to the internal space of the container, maximizing the space utilization rate. This enables the same container to load more items, thereby improving the logistics efficiency. According to the classification characteristics of the items (such as fragile items, dangerous goods, cold-chain items, etc.), the system ensures that different categories of items are not mixed through the classification category constraint. This can avoid the risk of fragile items and dangerous goods being mixed with ordinary items, ensuring the transportation safety of the items, meeting the relevant industry regulations, especially when dealing with dangerous goods and special items, ensuring that they will not cause damage to other items.

[0112] By comprehensively considering factors such as the weight, size, and classification of goods, the loading construction unit can generate a reasonable set of loading constraints Czc. According to these constraints, the system can plan the best loading scheme for each container, avoiding the inefficient or unreasonable loading problems that may occur in manual operations, reducing the loading time and labor costs, and improving the operation efficiency. Based on the distance between the actual storage location of the goods and the loading point, the system can dynamically adjust the loading scheme, making the loading and unloading process more flexible and efficient. By considering the actual distance from the storage location of the goods to be loaded into the container to the loading point, the system can optimize the loading sequence and path, improve the timeliness and accuracy of goods loading and unloading, and reduce unnecessary transportation and operation time.

[0113] Embodiment 4

[0114] This embodiment is an explanatory description carried out in Embodiment 1. Please refer to Figure 1 , specifically, the goods scheme generation module includes a goods sorting initialization unit;

[0115] The goods sorting initialization unit is used to sort the goods to be loaded into the container in descending order according to the size of the goods to be loaded into the container based on the goods feature set Pcs and the loading constraint set Czc, and sequentially load the goods to be loaded into the container to create an initial packing scheme as the first arrangement scheme;

[0116] Taking the first arrangement scheme as the parent generation, perform crossover and mutation operations, and generate a second packing scheme by exchanging the positions of 30%-50% of the packed goods in the first arrangement scheme of the parent generation.

[0117] In this embodiment, the goods sorting initialization unit sorts in descending order according to the size of the goods based on the goods feature set (Pcs) and the loading constraint set (Czc), ensuring that larger goods are loaded into the container first. This strategy helps to efficiently utilize the container space and prevent larger goods from occupying too much space due to improper position selection, resulting in other goods being unable to be loaded smoothly. By creating an initial packing scheme, the system can relatively quickly form a reasonable loading basis, providing a good starting point for subsequent optimization. By taking the first arrangement scheme as the parent generation and performing crossover operations, the system can effectively adjust the positions of the goods based on the packing scheme of the parent generation, exchanging the positions of 30%-50% of the packed goods. This crossover operation enables the packing scheme to further obtain an optimized loading result through the change of the positions of the goods while maintaining the initial good layout. The crossover and mutation operations simulate the mechanism of natural selection, thus continuously generating more reasonable packing schemes in multiple iterations.

[0118] Embodiment 5

[0119] This embodiment is an explanatory description carried out in Embodiment 1. Please refer to Figure 1, specifically, the goods plan generation module further includes an adaptation coefficient calculation unit and an assembly space calculation unit;

[0120] The adaptation coefficient calculation unit is used to calculate the space utilization of the second packing plan according to the goods feature set Pcs and the loading constraint set Czc, and obtain the packing adaptation coefficient Cf through the following formula:

[0121] ;

[0122] ;

[0123] ;

[0124] In the formula, represents the space adaptation factor, represents the weight adaptation factor, and respectively represent the weights of the space adaptation factor and the weight adaptation factor ;

[0125] The assembly space calculation unit is used to collect the remaining space gap after the internal assembly of the container and the total weight of the goods packed in the single-layer area, and obtain the space utilization coefficient Slx and the loading balance coefficient Zphx through the following formula:

[0126] ;

[0127] ;

[0128] ;

[0129] In the formula, represents the total volume of the container, ; respectively represent the length, width, and height of the i-th piece of goods; m is the total number of goods loaded in the container; represents the gross weight of the j-th piece of goods, and k is the number of goods packed in a single layer; represents the total number of single-layer areas in the container, u represents the label of the single-layer area, represents the total weight of the goods packed in the u-th single-layer area, represents the average weight of all single-layer areas in the container.

[0130] In this embodiment, the adaptation coefficient calculation unit calculates the space utilization of the second packing plan according to the set of goods characteristics (Pcs) and the set of loading constraints (Czc), so as to generate a packing adaptation coefficient (Cf). By calculating the space adaptation factor and the weight adaptation factor and combining the weights for comprehensive evaluation, the system can measure the space adaptability and weight balance of each packing plan. A higher adaptation coefficient (Cf) indicates that the goods are reasonably loaded in the container, the space is fully utilized, the weight distribution is balanced, reducing the problems of waste and uneven load, and improving the overall loading efficiency. The assembly space calculation unit calculates the space utilization coefficient (Slx) by collecting the remaining space gaps after the interior of the container is assembled. This coefficient reflects the utilization degree of the interior space of the container. A higher Slx value indicates that the space is effectively utilized and the loading density is high. This can help the system optimize the space allocation during the loading process, reduce voids, avoid space waste, improve the transportation efficiency of the container, and reduce the transportation cost. The assembly space calculation unit also calculates the loading balance coefficient (Zphx), which measures whether the weight distribution of the goods inside the container is uniform. By comprehensively considering the weights of the packed goods in each layer area, the system can evaluate the bearing capacity of each layer and dynamically adjust the loading strategy to ensure loading balance. The optimization of the loading balance coefficient can effectively avoid the tilting or damage of the container caused by uneven loading, and ensure the stability and safety of the transportation process.

[0131] Embodiment 6

[0132] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the goods plan generation module further includes an association unit and a first evaluation unit;

[0133] The association unit is used to extract the packing adaptation coefficient Cf, the space utilization coefficient Slx, and the loading balance coefficient Zphx of the second packing plan. After dimensionless processing, they are associated through the following formula to obtain the efficiency evaluation index Xl:

[0134] ;

[0135] In the formula, , and respectively represent the weights of the packing adaptation coefficient Cf, the space utilization coefficient Slx, and the loading balance coefficient Zphx;

[0136] The first evaluation unit is used to evaluate the efficiency evaluation index Xl to obtain a first evaluation result, including:

[0137] If the efficiency evaluation index Xl > 0.8, it means that the second packing plan is reasonable, and the adaptability, space utilization rate, and weight balance are all qualified. Use the current packing plan;

[0138] If the efficiency evaluation index Xl ≤ 0.8, it means that the second stowage plan is unreasonable, and its adaptability, space utilization rate, and weight balance are all unqualified. Adjust the second stowage plan, reallocate the goods, re - sort the goods according to their sizes, adjust the priority loading order of the goods, and distribute the heavy goods to different single - layer areas so that the difference in the total weight of the goods in each single - layer area and the total weight of the goods in the adjacent single - layer area does not exceed 100 - 300 KG.

[0139] In this embodiment, the first evaluation unit evaluates the second stowage plan based on the efficiency evaluation index (Xl). If the efficiency evaluation index (Xl) is greater than 0.8, it indicates that the adaptability, space utilization rate, and weight balance of the current stowage plan meet the requirements, and the current plan can be directly used, avoiding unnecessary recalculation and adjustment, and improving work efficiency. If the efficiency evaluation index (Xl) is less than or equal to 0.8, it means that the adaptability, space utilization rate, and weight balance of the current stowage plan are all unqualified. The system will automatically make adjustments. By re - allocating the goods and re - sorting the goods according to their sizes, it ensures that the distribution of the goods in the container is more reasonable and meets the loading constraint conditions. By optimizing the loading order and adjusting the distribution of heavy goods to different single - layer areas, the system ensures that the weight difference in each area does not exceed 100 - 300 KG, thereby optimizing the loading balance. 0.8 means that the adaptability, space utilization rate, and weight balance of the plan reach 80% of the ideal value, which means that the remaining 20% of the space or configuration can tolerate a certain degree of non - ideal state. In the process of logistics loading, it is almost impossible to achieve complete optimization and a perfect loading plan. Therefore, leaving a certain tolerance (20%) helps to accommodate certain errors or deficiencies in actual operations.

[0140] Embodiment 7

[0141] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the trajectory planning module includes an anchor - dropping position acquisition unit and a path - trajectory calculation unit;

[0142] The anchor - dropping position acquisition unit is used to collect the anchor - dropping positions of several target ships in real - time, obtain the anchor - point coordinates A(x, y), map the anchor - point coordinates to the reference coordinate system of the port scene model, determine the berth range corresponding to each ship according to the anchor - point coordinates A(x, y), and generate the loading and unloading area;

[0143] According to the storage position Ls(x, y) of the container in the yard and the anchor - point coordinates A(x, y) of the target ship, the recommended lifting point is calculated through the following formula :

[0144] ;

[0145] ;

[0146] Among them, D represents the straight-line distance between the storage location Ls(x, y) of the container in the yard and the anchor point coordinates A(x, y) of the target ship. represents the container priority weight. represents the loading and unloading time required for the goods.

[0147] The path trajectory calculation unit is used to generate several paths according to the recommended lifting point using the dynamic path planning method, and calculate the trajectory coefficient of each path through the following formula and the total transportation time :

[0148] ;

[0149] ;

[0150] Among them, represents the number of turns in the current path. represents the total number of segments in the current path. represents the difference in lifting height of the current path. represents the maximum allowable lifting height. and represent the weight coefficients, which are used to balance the influence of turns and height changes on the trajectory coefficient.

[0151] represents the total transportation time, which measures the time required for the RTG to complete all tasks. represents the transportation distance between the path node p and the p + 1 path node. represents the average transportation speed of the RTG. represents the total amount of containers required at the path node p + 1. represents the total amount of containers transported by the RTG in a single trip. represents the total number of path nodes.

[0152] In this embodiment, by accurately calculating the distance between the storage position of the container and the ship's anchor point, the optimal lifting point can be recommended, which can reduce unnecessary path movement, improve the efficiency of the lifting operation, and thus reduce the loading and unloading time. The trajectory planning module calculates the path using the dynamic path planning method, which can handle path changes in actual operations. Considering factors such as the number of turns and height changes, it further optimizes the route and time of the loading and unloading operation. Especially in complex environments, dynamic programming can adjust the path according to real-time situations, reducing unnecessary delays. The calculation of the path trajectory not only considers the distance of the path but also introduces factors such as the number of turns and the difference in lifting height, which can effectively avoid time delays or equipment wear caused by improper path selection. The setting of the weight coefficient helps to balance the influence of turns and height changes on trajectory optimization in path planning, making the path more in line with the actual operation requirements. Reasonable path planning can reduce unnecessary driving distance and turns during transportation, reduce the equipment burden, and save energy. Especially in a high-frequency operation environment such as a port, reducing the ineffective part of the path can effectively improve the equipment usage efficiency and extend its service life. By introducing trajectory planning and dynamic path adjustment, the overall logistics management is further optimized, reducing the uncertainty during the loading and unloading process, improving the overall transportation capacity of the port and the accuracy of container loading and unloading operations. The system can respond to changes in real time, improving the intelligent level of logistics operations. By calculating the total transportation time of the path, the system can more accurately estimate the time required to complete all tasks, thereby better scheduling and managing the tasks, reducing waiting time, ensuring that the containers can be loaded and unloaded on time, and improving the completion efficiency of the overall tasks.

[0153] Embodiment 8

[0154] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the equipment control module includes a second evaluation unit and a control unit;

[0155] The second evaluation unit is used to preset a time threshold X and compare the total transportation time with the time threshold X to obtain a second evaluation result, including:

[0156] If the total transportation time ≤ the time threshold X, it means that the current path and transportation strategy meet the time requirements and are classified into the qualified path group;

[0157] If the total transportation time > the time threshold X, it means that the current path and transportation strategy do not meet the time requirements.

[0158] The control unit is used to, when receiving the qualified path group, preferentially screen the total transportation time ≤ the time threshold X, and the total transportation time The shortest one is taken as the first-priority path. After generating the first path execution instruction, it is controlled and executed according to the transportation speed of the current rubber-tyred gantry crane.

[0159] When the total transportation time among several paths is all > the time threshold X, select the one with the shortest total transportation time as the second-priority path and generate the second strategy, including: increasing the average transportation speed of the current rubber-tyred gantry crane by 10%-15%, enabling 1-3 auxiliary equipment AGV vehicles to cooperate in handling 30% of the total container volume of the path nodes, decomposing the goods exceeding the single rubber-tyred gantry crane's single transportation container volume into multiple batches for transportation, adjusting the loading and unloading node order of the total packing volume of each batch, and generating and executing the second path execution instruction according to the second-priority path.

[0160] In this embodiment, by comparing the total transportation time with the preset time threshold, the effectiveness of the current path can be evaluated in a timely manner. When the total transportation time meets the preset time requirement, the system will automatically screen out the optimal path, thereby improving the operation efficiency and avoiding unnecessary time waste. The equipment control module can not only select the most suitable path according to the evaluation result, but also take effective remedial measures when the time threshold is not met. For example, means such as increasing the transportation speed, enabling auxiliary equipment, and transporting in batches are used to ensure that the task can be completed on time even in an emergency.

[0161] Embodiment 9

[0162] A virtual reality-based logistics remote control method. Please refer to Figure 2 , including the following steps:

[0163] S1. Build a port scene model based on virtual reality technology, combine the basic information of the goods and the container loading rules, simulate the packing process of multiple groups of goods, and generate a visual logistics operation interface to display the recommended packing plan and transportation path;

[0164] S2. Monitor the basic information of the goods to be packed, including the size, weight, type and storage location of the goods, and build a goods feature set Pcs and a loading constraint set Czc;

[0165] S3. Generate multiple packing plans according to the goods feature set Pcs and the loading constraint set Czc, and build a packing adaptation coefficient Cf, a space utilization coefficient Slx and a loading balance coefficient Zphx. Associate the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx to obtain an efficiency evaluation index Xl. If the efficiency evaluation index Xl > 0.8, confirm the current packing plan;

[0166] S4. After confirming the container loading plan, collect the anchoring positions of several target ships in real time, determine the berth range corresponding to each ship, and generate the loading and unloading area; and calculate the recommended lifting points according to the storage position Ls(x,y) of the container in the yard and the anchor coordinates A(x,y) of the target ship. , and based on the recommended lifting points Use the dynamic path planning method to generate several paths, and calculate and obtain the trajectory coefficient of each path and the total transportation time ;

[0167] S5. Preset a time threshold X, and compare and evaluate the total transportation time with the time threshold X to screen the corresponding priority paths and execute the corresponding execution instructions.

[0168] In this embodiment, in steps S1 - S5, by constructing a port scene model through virtual reality technology and simulating the container loading process, it is possible to display the recommended container loading plan and transportation path in advance, effectively reducing errors and time waste in on-site operations, and improving the accuracy and efficiency of logistics operations. By combining the characteristics of the goods and loading constraints, multiple sets of container loading plans are generated, and evaluated based on the adaptation coefficient, space utilization coefficient, and loading balance coefficient to ensure the rationality of the container loading plan. The confirmation mechanism with an efficiency evaluation index Xl≥0.8 guarantees the optimization and efficient execution of the container loading plan, avoiding resource waste and unreasonable loading. The anchoring positions of the target ships are collected in real time, and the recommended lifting points are calculated based on the container storage position. Further, multiple paths are generated through dynamic path planning, and the trajectory coefficient and total transportation time of the paths are calculated to ensure the optimization of the transportation path. By comparing the transportation time with the preset time threshold, the priority paths are screened to effectively avoid transportation delays and ensure the timely completion of operations. When the time threshold requirement is not met, it is possible to improve the adaptability of logistics operations through priority path selection and dynamic adjustment, ensuring high - efficiency operation in a complex environment. This method provides an intelligent, flexible, and efficient operation management method for port logistics. The logistics remote control method based on virtual reality improves the intelligence and accuracy of port logistics operations, optimizes resource allocation, reduces waste of manpower and material resources, and effectively improves transportation efficiency and operation flexibility.

[0169] The setting of the size of the threshold is for the convenience of comparison. Regarding the size of the threshold, it depends on the amount of sample data and the base quantity set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0170] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. As mentioned above, the above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A logistics remote control system based on virtual reality, characterized in that: It includes virtual construction module, product information collection module, product plan generation module, trajectory planning module and equipment control module; The virtual construction module is used to construct a port scene model based on virtual reality technology, combine basic information of goods and container loading rules, simulate the packing process of multiple groups of goods, and generate a visual logistics operation interface to display recommended packing solutions and transportation routes; The goods information collection module is used to monitor the basic information of the goods to be packed, including the size, weight, type and storage location of the goods, and to construct a goods feature set Pcs and a loading constraint set Czc; The product solution generation module is used to generate multiple packing solutions according to the product feature set Pcs and the loading constraint set Czc, and construct the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx, and after dimensionless processing, the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx are associated through the following formula to obtain the efficiency evaluation index Xl: Xl=Cf×α+Slx×β+Zphx×γ; In the formula, α, β and γ represent the weights of the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx respectively; If the efficiency evaluation index Xl>0.8, the current packing plan is confirmed; After confirming the loading plan, the trajectory planning module is used to collect the anchoring positions of several target ships in real time, determine the berth range corresponding to each ship, and generate the loading and unloading area; and calculate the recommended lifting point P according to the storage position Ls (x, y) of the container in the yard and the anchor point coordinates A (x, y) of the target ship. lift (x,y), and according to the recommended lifting point P lift (x,y) Use the dynamic path planning method to generate several paths and calculate the trajectory coefficient K of each path traj and the total transport time T total ; The equipment control module is used to preset a time threshold X and calculate the total transportation time T total The time threshold X is compared and evaluated to select the corresponding priority path and execute the corresponding execution instruction.

2. A virtual reality-based logistics remote control system according to claim 1, characterized in that: The product information collection module includes a product size collection unit, a product weight collection unit and a product feature set output unit; The cargo size collection unit is used to collect the length L, width K and height H of the cargo to be packed in real time using a laser measuring instrument and a cargo scanner; The cargo weight collection unit is used to use an electronic scale to obtain the net weight W of the cargo to be packed in real time. n and gross weight W g , and associate the unique RFID number of the goods to be packed; The product feature set output unit is used to obtain the classification category Ct of each product to be packed, and the classification category Ct includes fragile goods, dangerous goods, cold chain goods and ordinary goods; and obtain the current storage location Pos of the product to be packed to construct the product feature set Pcs as follows: Pcs = {(L, K, H, W n ,W g ,Ct,Pos) i |i=1,2,...,n|}; n represents the total number of goods to be packed.

3. A virtual reality-based logistics remote control system according to claim 2, characterized in that: The product information collection module also includes a loading construction unit; The loading construction unit is used to define constraints based on the product feature set Pcs and the actual container loading requirements to construct a loading constraint set Czc: Specific loading constraints include: S11. First weight dimension constraint: The total weight of the goods loaded in each container cannot exceed the maximum load capacity W g,max , the expression is as follows: Among them, W g,i represents the gross weight of the i-th item, W g,max It represents the maximum carrying capacity of the container; m is the total number of goods loaded in the container; Second weight dimension constraint: The weight of a single-layer stack of goods must not exceed the maximum load-bearing capacity W of the stacked single layer. s,max , the expression is as follows: Among them, W g,j represents the gross weight of the jth item, W s,max represents the maximum load-bearing capacity of a single stacked layer; k is the number of goods in a single layer; S12. Size constraint: the length L of each item i , Width K i and height H i , the size limit of the internal space of the load container, the constraint expression is: (L i ,K i ,H i )≤(CL,CK,CH); Among them, CL, CK, and CH represent the length, width, and height of the container interior, respectively; S13. Classification categories Ct cannot be mixed, and corresponding containers must be configured separately; S14. Based on S11-S13, the output loading constraint set Czc is: Czc = {(W g,max ,W s,max ,CL,CK,CH,Ct,d)}; d represents the actual distance from the storage location of the goods to be packed to the loading point.

4. A virtual reality-based logistics remote control system according to claim 3, characterized in that: The product solution generation module includes a product sorting initialization unit; The product sorting initialization unit is used to sort the products to be packed from large to small according to the product feature set Pcs and the loading constraint set Czc, and sequentially load the products to be packed into the container to create an initial packing plan as the first arrangement plan; The first arrangement scheme is used as the parent, and crossover and mutation operations are performed to generate a second packing scheme by exchanging the positions of 30%-50% of the packed goods in the first arrangement scheme of the parent.

5. A virtual reality-based logistics remote control system according to claim 4, characterized in that: The product solution generation module also includes an adaptation coefficient calculation unit and an assembly space calculation unit; The adaptation coefficient calculation unit is used to calculate the space utilization of the second packing solution according to the product feature set Pcs and the loading constraint set Czc, and obtain the packing adaptation coefficient Cf by the following formula: Cf=Cf space ×w1+Cf weight ×w2; In the formula, Cf space represents the spatial adaptation factor, Cf weight represents the weight adaptation factor, w1 and w2 represent the spatial adaptation factor Cf space and weight adaptation factor Cf weight The weight of The assembly space calculation unit is used to collect the remaining space gap after assembly inside the container and the total weight of the packed goods in the single-layer area, and calculate the space utilization coefficient Slx and the loading balance coefficient Zphx through the following formula: Where V container Indicates the total volume of the container, V container =CL×CK×CH; L i , K i , H i Respectively represent the length, width, and height of the i-th item; m is the total number of items loaded in the container; W g,j represents the gross weight of the jth item, k is the number of items packed in a single layer; G represents the total number of single-layer areas in the container, u represents the number of single-layer areas, and W u represents the total weight of the packed goods in the u-th single-layer area, Indicates the average weight of all single-layer areas within a container.

6. A virtual reality-based logistics remote control system according to claim 5, characterized in that: The product solution generation module also includes an association unit and a first evaluation unit; The associating unit is used to extract the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx of the second packing solution and associate them to obtain the efficiency evaluation index Xl: The first evaluation unit is used to evaluate the efficiency evaluation index X1 to obtain a first evaluation result, including: If the efficiency evaluation index Xl>0.8, it means that the second packing scheme is reasonable, and the adaptability, space utilization and weight balance are all qualified, and the current packing scheme is used; If the efficiency evaluation index Xl≤0.8, it means that the second packing plan is unreasonable, and its adaptability, space utilization and weight balance are all unqualified. The second packing plan should be adjusted, the goods should be redistributed, the goods should be re-sorted by size, the goods priority loading order should be adjusted, and the heavy goods should be allocated to different single-layer areas so that the difference between the total weight of the goods in each single-layer area and the total weight of the goods in the adjacent single-layer area does not exceed 100-300KG.

7. A virtual reality-based logistics remote control system according to claim 1, characterized in that: The trajectory planning module includes an anchor position acquisition unit and a path trajectory calculation unit; The anchor position acquisition unit is used to acquire the anchor positions of several target ships in real time, obtain the anchor point coordinates A(x, y), map the anchor point coordinates to the reference coordinate system of the port scene model, determine the berth range corresponding to each ship according to the anchor point coordinates A(x, y), and generate the loading and unloading area; According to the storage position Ls (x, y) of the container in the yard and the anchor coordinates A (x, y) of the target ship, the recommended lifting point P is calculated by the following formula: lift (x,y): D=Ls(x,y)-A(x,y) P lift (x,y)=arg min[D+W p ×R s ]; Where D is the straight-line distance between the container storage location Ls(x,y) in the yard and the target ship's anchor coordinate A(x,y), and W p represents the container priority weight, R s Indicates the loading and unloading time required for the goods; The path trajectory calculation unit is used to calculate the recommended lifting point P lift (x, y) Use the dynamic path planning method to generate several paths, and calculate the trajectory coefficient K of each path using the following formula: traj and the total transport time T total : Among them, N turns Indicates the number of turns in the current path, N segments Indicates the total number of segments of the current path, ΔH path Indicates the lifting height difference of the current path, H max Indicates the maximum allowable lifting height; θ and ω represent weight coefficients, which are used to balance the effects of turning and height changes on the trajectory coefficient; T total Indicates the total transport time, which measures the time required for the tire crane to complete all tasks. p,p+1 represents the transportation distance between path node p and p+1 path nodes, V t Indicates the average transport speed of the tire crane, L p+1 Indicates the total amount of containers required at path node p+1, Ca t represents the total amount of containers transported by the tire crane in a single trip, and Y represents the total number of path nodes.

8. A virtual reality-based logistics remote control system according to claim 7, characterized in that: The device control module includes a second evaluation unit and a control unit; The second evaluation unit is used to preset a time threshold X and calculate the total transportation time T total Compare with the time threshold X to obtain a second evaluation result, including: If the total transportation time is T total ≤ time threshold X, indicating that the current path and transportation strategy meet the time requirements and are classified as qualified path groups; If the total transportation time is T total >Time threshold X, indicating that the current path and transportation strategy do not meet the time requirements.

9. A virtual reality-based logistics remote control system according to claim 8, characterized in that: The control unit is used to preferentially screen the total transportation time T after receiving the qualified path group. total ≤ time threshold X, and total transportation time T total The smallest one is taken as the first priority path, and after the first path execution instruction is generated, it is controlled and executed according to the current transport speed of the tire crane; When the total transportation time T among several routes total Average> time threshold X, select total transportation time T total The smallest one is taken as the second priority path, and the second strategy is generated, including: increasing the average transportation speed of the current 10%-15% of the tire cranes, enabling 1-3 auxiliary equipment AGV vehicles to jointly handle the total amount of containers at 30% of the path nodes, which will exceed the total amount of containers transported by a single tire crane in a single time Ca t The goods are decomposed into multiple batches for transportation, the order of loading and unloading nodes of the total amount of each batch of packaging is adjusted, and the second path execution instruction is generated and executed according to the second priority path.

10. A virtual reality-based logistics remote control method, applied to a virtual reality-based logistics remote control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Build a port scene model based on virtual reality technology, combine basic information of goods and container loading rules, simulate the packing process of multiple groups of goods, and generate a visual logistics operation interface to display recommended packing solutions and transportation routes; S2. Monitor the basic information of the goods to be packed, including the size, weight, type and storage location of the goods, and construct the goods feature set Pcs and the loading constraint set Czc; S3. Generate multiple packing schemes according to the product feature set Pcs and the loading constraint set Czc, and construct the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx. Associate the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx to obtain the efficiency evaluation index Xl. If the efficiency evaluation index Xl>0.8, confirm the current packing scheme; S4. After confirming the loading plan, collect the anchoring positions of several target ships in real time, determine the berth range corresponding to each ship, and generate the loading and unloading area; and calculate the recommended lifting point P according to the storage position Ls (x, y) of the container in the yard and the anchor point coordinates A (x, y) of the target ship. lift (x,y), and according to the recommended lifting point P lift (x,y) Use the dynamic path planning method to generate several paths and calculate the trajectory coefficient K of each path traj and the total transport time T total ; S5, used to preset the time threshold X and the total transportation time T total The time threshold X is compared and evaluated to select the corresponding priority path and execute the corresponding execution instruction.

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