Logistics remote control method and system based on virtual reality

Abstract

The invention discloses a logistics remote control method and system based on virtual reality, relates to the technical field of logistics transportation management, and reduces the possibility of human errors through automatic encasement scheme generation and path planning. The system automatically calculates and generates a boxing scheme according to goods characteristics and loading constraints, and the risk of manual operation errors is avoided. And if the efficiency evaluation index is higher than 0.8, the system confirms that the current scheme is the optimal scheme. The trajectory planning module combines the anchoring position of the target ship and the container yard position, generates a plurality of paths by using a dynamic path planning method, and calculates a trajectory coefficient and transportation time. The path planning method based on real-time data can flexibly cope with sudden changes in the transportation process, the transportation time is shortened, and it is ensured that tasks are completed in time. The dynamic path planning module can adjust the path and the position of a hoisting point in real time, and collision or other potential safety hazards possibly occurring in the operation process are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics and transportation management, and in particular 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 field of ports and container transportation, traditional logistics management and control methods are facing many challenges. These challenges are mainly reflected in how to improve loading and unloading efficiency, reduce human errors, reduce operating costs, and optimize resource allocation. Traditional packaging scheme design, cargo transportation route planning, and lifting equipment scheduling mostly rely on manual intervention, which is inefficient and easily affected by human factors, resulting in problems such as unsmooth transportation process, high cargo damage rate, and long loading and unloading time, which greatly restricts the development of the logistics industry.

[0003] However, the application of existing virtual reality technology in the logistics industry still faces some key technical challenges. For example, how to accurately simulate the complex packing and transportation process, how to combine the characteristics of the goods with the loading constraints, and generate a reasonable packing plan based on these data, how to effectively evaluate the efficiency of the packing plan, etc. Existing technologies often ignore the comprehensive consideration of multiple variables such as product characteristics, loading constraints, and transportation routes, and lack a systematic evaluation mechanism, resulting in unsatisfactory optimization effects of packing plans and transportation routes. In addition, traditional path planning algorithms also have shortcomings when dealing with complex dynamic environments and changing logistics needs, and are unable to respond to emergencies in a timely manner, affecting overall logistics efficiency. Summary of the invention

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

[0005] To achieve the above objectives, the present invention is implemented 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 plan generation module, a trajectory planning module and an 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 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, the current packing solution 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 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 , and according to the recommended lifting points Use dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and total shipping time ; The equipment control module is used to preset a time threshold X and calculate the total transportation time. The time threshold X is compared and evaluated to select the corresponding priority path and execute the corresponding execution instruction.

[0006] Preferably, 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 of the cargo to be packed in real time. and gross weight , 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 position Pos of the product to be packed to construct the product feature set Pcs as: ; n represents the total number of goods to be packed.

[0007] Preferably, the product information collection module further 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 carrying capacity , the expression is as follows: ; in, represents the gross weight of the i-th item, 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 of the stacked single layer , the expression is as follows: ; in, represents the gross weight of the jth item, 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 of each product ,width and height , the size limit of the internal space of the load container, the constraint expression is: ; in, Respectively represent the length, width and height of the container; 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: ; d represents the actual distance from the storage location of the goods to be packed to the loading point.

[0008] Preferably, 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 the second packing scheme by exchanging the positions of 30%-50% of the packed items in the first arrangement scheme of the parent.

[0009] Preferably, the product solution generation module further 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: ; ; ; In the formula, represents the spatial adaptation factor, represents the weight adaptation factor, and Represent the spatial adaptation factor and weight adaptation factor 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: ; ; ; In the formula, Indicates the total volume of the container. ; Respectively represent the length, width and height of the i-th item; m is the total number of items loaded in the container; represents the gross weight of the jth item, k is the number of items packed in a single layer; It indicates the total number of single-layer areas in the container, and u indicates the number of the single-layer area. 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.

[0010] Preferably, the product solution generation module further 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 then associate them through the following formula to obtain the efficiency evaluation index Xl after dimensionless processing: ; In the formula, , and They represent the weights of the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx respectively; 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.

[0011] Preferably, 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 point coordinates A (x, y) of the target ship, the recommended lifting point is calculated by the following formula: : ; ; Where D represents the straight-line distance between the container storage location Ls(x,y) in the yard and the target ship's anchor point coordinates A(x,y). represents the container priority weight, Indicates the loading and unloading time required for the goods; The path trajectory calculation unit is used to calculate the recommended lifting point Use the dynamic path planning method to generate several paths, and calculate the trajectory coefficient of each path using the following formula: and total shipping time : ; ; in, Indicates the number of turns in the current path, Indicates the total number of segments of the current path. Indicates the lifting height difference of the current path. Indicates the maximum permissible lifting height; and Represents the weight coefficient, which is used to balance the impact of turning and altitude change on the trajectory coefficient; Indicates the total transport time, which measures the time required for the RTG to complete all tasks. represents the transportation distance between path node p and p+1 path nodes, Indicates the average transport speed of the tire crane, Indicates the total amount of containers required at path node p+1, Indicates the total volume of containers transported by the tire crane in a single trip. Indicates the total number of path nodes.

[0012] Preferably, the device control module comprises 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 Compare with the time threshold X to obtain a second evaluation result, including: If the total transportation time ≤ 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 >Time threshold X, indicating that the current path and transportation strategy do not meet the time requirements.

[0013] Preferably, the control unit is used to prioritize the total transportation time after receiving the qualified path group. ≤ time threshold X, and total transportation time 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 in several routes Average> time threshold X, select total transportation time The smallest path 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 volume of containers at 30% of the path nodes, which will exceed the total volume of containers transported by a single tire crane in a single trip 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.

[0014] A logistics remote control method based on virtual reality comprises the following steps: 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 based on the storage position Ls (x, y) of the container in the yard and the anchor point coordinates A (x, y) of the target ship , and according to the recommended lifting points Use dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and total shipping time ; S5, used to preset the time threshold X and the total transportation time The time threshold X is compared and evaluated to select the corresponding priority path and execute the corresponding execution instruction.

[0015] The present invention provides a logistics remote control method and system based on virtual reality, which has the following beneficial effects: (1) The present invention uses a product information collection module to monitor the basic information of the product in real time, including size, weight, type and storage location, so as to construct a product feature set and a loading constraint set. This information provides reliable data support for optimizing packing plans and transportation routes, making resource allocation more reasonable. The system can adjust the packing plan and route planning in real time to cope with dynamically changing logistics needs and ensure the optimal allocation of resources. Traditional logistics management relies on manual decision-making and is easily affected by human factors, resulting in errors and delays in the transportation process. Through automated packing plan generation and route planning, the possibility of human error is reduced, ensuring higher accuracy and reliability. The system automatically calculates and generates packing plans based on product characteristics and loading constraints, avoiding the risk of manual operation errors.

[0016] (2) By constructing the packing adaptation coefficient, space utilization coefficient and loading balance coefficient and associating them to generate an efficiency evaluation index, the present invention can accurately evaluate the effects of different packing solutions. If the efficiency evaluation index is higher than 0.8, the system will confirm that the current solution is the optimal solution. This systematic evaluation mechanism improves the optimization effect of the packing solution and avoids the application of inefficient solutions.

[0017] (3) The trajectory planning module combines the anchoring position of the target ship and the location of the container yard, uses a 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 in the transportation process, reduce transportation time, and ensure timely completion of tasks. By simulating the logistics process using virtual reality technology, potential operational 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 lifting point position in real time to avoid collisions or other safety hazards that may occur during operation, thereby ensuring the safety of equipment and goods.

[0018] (4) In steps S1-S5, the port scene model is constructed through virtual reality technology and the packing process of goods is simulated. The recommended packing scheme and transportation route can be displayed in advance, which effectively reduces errors and time waste in on-site operations and improves the accuracy and efficiency of logistics operations. By combining the characteristics of the goods and loading constraints, multiple packing schemes are generated, and the packing schemes are evaluated based on the adaptation coefficient, space utilization coefficient and loading balance coefficient to ensure the rationality of the packing scheme. The confirmation mechanism of the efficiency evaluation index Xl≥0.8 ensures the optimization and efficient execution of the packing scheme, avoiding resource waste and unreasonable loading. The anchor position of the target ship is collected in real time, and the recommended lifting point is calculated based on the container storage position. Multiple paths are further generated through dynamic path planning, and the trajectory coefficient and total transportation time of the path are calculated to ensure the optimization of the transportation path. By comparing the transportation time with the preset time threshold, the priority path is selected to effectively avoid transportation delays and ensure that the operation is completed on time. When the time threshold requirement is not met, the priority path selection and dynamic adjustment can be used to improve the resilience of logistics operations and ensure that efficient operation can be maintained 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 manpower and material waste, and effectively improves transportation efficiency and operational flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a logistics remote control system based on virtual reality according to the present invention; Figure 2 The present invention is a schematic diagram of the steps of a logistics remote control method based on virtual reality. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 See also 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; 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 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, the current packing solution 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 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 , and according to the recommended lifting points Use dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and total shipping time ; The equipment control module is used to preset a time threshold X and calculate the total transportation time. The time threshold X is compared and evaluated to select the corresponding priority path and execute the corresponding execution instruction.

[0022] In this embodiment, by using virtual construction modules and virtual reality technology to simulate port scenes and packing processes, the loading plan of goods can be planned and optimized in advance in a digital environment. This process not only improves operational efficiency, but also avoids errors or delays that may be caused by traditional manual planning, thereby significantly reducing loading and unloading time and improving the efficiency of the entire logistics process.

[0023] The present invention monitors the basic information of goods in real time through the goods information collection module, including size, weight, type and storage location, so as to construct a goods feature set and a loading constraint set. This information provides reliable data support for optimizing the packing scheme and transportation path, making the allocation of resources more reasonable. The system can adjust the packing scheme and path planning in real time to cope with dynamically changing logistics needs and ensure the optimal allocation of resources. Traditional logistics management relies on manual decision-making and is easily affected by human factors, resulting in errors and delays in the transportation process. Through automated packing scheme generation and path planning, the possibility of human error is reduced, ensuring higher accuracy and reliability. The system automatically calculates and generates packing schemes according to the characteristics of the goods and loading constraints, avoiding the risk of manual operation errors. By constructing the packing adaptation coefficient, space utilization coefficient and loading balance coefficient, and associating them to generate an efficiency evaluation index, the present invention can accurately evaluate the effects of different packing schemes. If the efficiency evaluation index is higher than 0.8, the system will confirm that the current scheme is the optimal scheme. This systematic evaluation mechanism improves the optimization effect of the packing scheme and avoids the application of inefficient schemes. The trajectory planning module combines the anchoring position of the target ship and the location of the container yard, uses a 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 in the transportation process, reduce transportation time, and ensure timely completion of tasks. By simulating the logistics process using virtual reality technology, potential operational 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 lifting point position in real time to avoid collisions or other safety hazards that may occur during operation, thereby ensuring the safety of equipment and goods.

[0024] Example 2 This example is an explanation of Example 1. Figure 1 Specifically, the product information collection module includes a product size collection unit, a product weight collection unit and a product feature set output unit;

[0025] The cargo size acquisition unit is used to use a laser measuring instrument and a cargo scanner to collect the length L, width K and height H of the cargo to be packed in real time; the laser measuring instrument and the cargo scanner are used to measure the cargo to be packed in real time, and the length, width and height of the cargo can be obtained with high precision, reducing the errors that may occur in traditional manual measurement. Accurate size data provides a reliable basis for the subsequent packing plan generation and path planning, ensuring the accuracy of the packing plan.

[0026] The cargo weight collection unit is used to obtain the net weight of the cargo to be packed in real time using an electronic scale. and gross weight , and associate the unique RFID number of the goods to be packed; the system can track the weight changes of each item in real time, avoiding unreasonable packing problems due to inaccurate weight information.

[0027] 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 position Pos of the product to be packed to construct the product feature set Pcs as: ; n represents the total number of goods to be packed.

[0028] In this embodiment, accurate identification and processing of classification information enables the packing plan to be personalized according to the needs of different types of goods, avoiding 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 logistics management to be flexibly adjusted in a dynamic environment and respond to emergencies in a timely manner, such as changes in the location of goods, weight adjustments or classification changes. 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 appropriate loading method and transportation route based on the size, weight, category and storage location of the goods, improve loading and unloading efficiency, and reduce risks and losses during transportation.

[0029] Example 3 This example is an explanation of Example 1. Figure 1 ,Specifically, the product information collection module also includes a loading construction unit;

[0030] 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 carrying capacity , the expression is as follows: ; in, represents the gross weight of the i-th item, 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 of the stacked single layer , the expression is as follows: ; in, represents the gross weight of the jth item, 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 of each product ,width and height , the size limit of the internal space of the load container, the constraint expression is: ; in, Respectively represent the length, width and height of the container; 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: ; d represents the actual distance from the storage location of the goods to be packed to the loading point.

[0031] In this embodiment, by setting the first weight dimension constraint and the second weight dimension constraint, the system can effectively avoid the occurrence of overloading. The first weight dimension constraint ensures that the total loading weight of the container will not exceed the maximum load capacity, avoiding the safety risk caused by container overloading. The second weight dimension constraint further ensures that the weight of the single-layer stacked goods will not exceed the maximum load capacity of the stacked single layer, ensuring that the goods will not collapse or be damaged during the stacking process. These constraints enhance the safety and stability of container loading. The size constraint ensures the reasonable layout of the goods inside the container and prevents the waste of space caused by the excessive or too small size of the goods. By limiting the length, width and height of the goods, the system ensures that the goods can effectively adapt to the internal space of the container and maximize the space utilization. This allows the same container to load more goods, thereby improving logistics efficiency. According to the classification characteristics of the goods (such as fragile goods, dangerous goods, cold chain goods, etc.), the system ensures that goods of different categories are not mixed through classification category constraints. This can avoid the risk of mixing fragile and dangerous goods with ordinary goods, ensure the safe transportation of goods, and comply with relevant industry regulations, especially when it comes to dangerous goods and special items, to ensure that they will not cause damage to other goods.

[0032] By comprehensively considering factors such as the weight, size, and classification of the goods, the loading construction unit can generate a reasonable set of loading constraints Czc. Based on these constraints, the system can plan the best loading plan for each container, avoiding inefficient or unreasonable loading problems that may occur in manual operations, reducing loading time and labor costs, and improving operational efficiency. Based on the distance between the actual storage location of the goods and the loading point, the system can dynamically adjust the loading plan to make the loading and unloading process more flexible and efficient. By considering the actual distance from the storage location of the goods to be packed to the loading point, the system can optimize the loading order and path, improve the timeliness and accuracy of cargo loading and unloading, and reduce unnecessary transportation and operation time.

[0033] Example 4 This example is an explanation of Example 1. Figure 1 ,Specifically, the product plan generation module includes a product sorting initialization unit;

[0034] 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 the second packing scheme by exchanging the positions of 30%-50% of the packed items in the first arrangement scheme of the parent.

[0035] In this embodiment, the goods sorting initialization unit sorts the goods from large to small 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 prevents larger goods from occupying too much space due to improper location selection, resulting in other goods being unable to be loaded smoothly. By creating an initial packing plan, the system can quickly form a reasonable loading basis, thereby providing a good starting point for subsequent optimization. By taking the first arrangement plan as the parent and performing a crossover operation, the system can effectively adjust the position of the goods based on the parent packing plan, exchanging 30%-50% of the positions of the packed goods. This crossover operation enables the packing plan to obtain a more optimized loading result by changing the position of the goods while maintaining the initial good layout. The crossover and mutation operations simulate the mechanism of natural selection, thereby continuously generating more reasonable packing plans in multiple iterations.

[0036] Example 5 This example is an explanation of Example 1. Figure 1 ,Specifically, the product solution generation module also includes an adaptation coefficient calculation unit and an assembly space calculation unit;

[0037] 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: ; ; ; In the formula, represents the spatial adaptation factor, represents the weight adaptation factor, and Represent the spatial adaptation factor and weight adaptation factor 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: ; ; ; In the formula, Indicates the total volume of the container. ; Respectively represent the length, width and height of the i-th item; m is the total number of items loaded in the container; represents the gross weight of the jth item, k is the number of items packed in a single layer; It indicates the total number of single-layer areas in the container, and u indicates the number of the single-layer area. 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.

[0038] In this embodiment, the adaptation coefficient calculation unit calculates the space utilization of the second packing scheme according to the product feature set (Pcs) and the loading constraint set (Czc), thereby generating 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 scheme. A higher adaptation coefficient (Cf) indicates that the loading position of the goods in the container is reasonable, the space is fully utilized, the weight distribution is balanced, the waste and uneven load problems are reduced, and the overall loading efficiency is improved. The assembly space calculation unit calculates the space utilization coefficient (Slx) by collecting the remaining space gap after assembly inside the container. This coefficient reflects the utilization degree of the internal 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 space allocation during loading, reduce gaps, avoid space waste, improve the transportation efficiency of the container, and reduce transportation costs. 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 weight of the packed goods in each layer area, the system can evaluate the carrying capacity of each layer and dynamically adjust the loading strategy to ensure balanced loading. 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.

[0039] Example 6 This example is an explanation of Example 1. Figure 1 ,Specifically, the product solution generation module also includes an association unit and a first evaluation unit;

[0040] 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 then associate them through the following formula to obtain the efficiency evaluation index Xl after dimensionless processing: ; In the formula, , and They represent the weights of the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx respectively; 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.

[0041] In this embodiment, the first evaluation unit evaluates the second packing scheme 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 and weight balance of the current packing scheme meet the requirements, and the current scheme can be used directly, 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 and weight balance of the current packing scheme are all unqualified. The system will automatically adjust to ensure that the distribution of goods in the container is more reasonable and meets the loading constraints by reallocating the goods and reordering the goods according to size. 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 100-300KG, thereby optimizing the balance of loading. 0.8 means that the adaptability, space utilization and weight balance of the scheme have reached 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, complete optimization and perfect loading plan are almost impossible to achieve, so leaving a certain tolerance (20%) will help accommodate certain errors or deficiencies in actual operations.

[0042] Example 7 This example is an explanation of Example 1. Figure 1 ,Specifically, 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 point coordinates A (x, y) of the target ship, the recommended lifting point is calculated by the following formula: : ; ; Where D represents the straight-line distance between the container storage location Ls(x,y) in the yard and the target ship's anchor point coordinates A(x,y). represents the container priority weight, Indicates the loading and unloading time required for the goods; The path trajectory calculation unit is used to calculate the recommended lifting point Use the dynamic path planning method to generate several paths, and calculate the trajectory coefficient of each path using the following formula: and total shipping time : ; ; in, Indicates the number of turns in the current path, Indicates the total number of segments of the current path. Indicates the lifting height difference of the current path. Indicates the maximum permissible lifting height; and Represents the weight coefficient, which is used to balance the impact of turning and altitude change on the trajectory coefficient; Indicates the total transport time, which measures the time required for the RTG to complete all tasks. represents the transportation distance between path node p and p+1 path nodes, Indicates the average transport speed of the tire crane, Indicates the total amount of containers required at path node p+1, Indicates the total volume of containers transported by the tire crane in a single trip. Indicates the total number of path nodes.

[0043] In this embodiment, by accurately calculating the distance between the storage location of the container and the anchor point of the ship, 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 uses a dynamic path planning method to calculate the path, which can cope with path changes in actual operations, and further optimize the route and time of loading and unloading operations by taking into account factors such as the number of turns and height changes. Especially in complex environments, dynamic planning can adjust the path according to real-time conditions to reduce unnecessary delays. The calculation of the path trajectory not only takes into account 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 losses caused by improper path selection. The setting of the weight coefficient helps to balance the impact of turns and height changes on trajectory optimization in path planning, so that the path is more in line with actual operation requirements. Reasonable path planning can reduce unnecessary driving distances and turns during transportation, reduce equipment burden, and save energy. Especially in high-frequency operation environments such as ports, reducing the invalid parts in the path can effectively improve the efficiency of equipment use and extend its service life. By introducing trajectory planning and dynamic path adjustment, the overall logistics management has been further optimized, reducing the uncertainty in 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 and improve the intelligence 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 tasks, reducing waiting time, ensuring that containers can be loaded and unloaded on time, and improving the overall efficiency of task completion.

[0044] Example 8 This example is an explanation of Example 1. Figure 1 ,Specifically, 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 Compare with the time threshold X to obtain a second evaluation result, including: If the total transportation time ≤ 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 >Time threshold X, indicating that the current path and transportation strategy do not meet the time requirements.

[0045] The control unit is used to preferentially screen the total transportation time after receiving the qualified path group. ≤ time threshold X, and total transportation time 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 in several routes Average> time threshold X, select total transportation time The smallest path 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 volume of containers at 30% of the path nodes, which will exceed the total volume of containers transported by a single tire crane in a single trip 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.

[0046] 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 select the optimal path, thereby improving work efficiency and avoiding unnecessary waste of time. The equipment control module can not only select the most appropriate path based on the evaluation results, but also take effective remedial measures when the time threshold is not met. For example, measures such as increasing the transportation speed, enabling auxiliary equipment, and transporting in batches can ensure that the task can be completed on time even in an emergency.

[0047] Example 9 A logistics remote control method based on virtual reality, see Figure 2 , including the following steps: 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 based on the storage position Ls (x, y) of the container in the yard and the anchor point coordinates A (x, y) of the target ship , and according to the recommended lifting points Use dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and total shipping time ; S5, used to preset the time threshold X and the total transportation time The time threshold X is compared and evaluated to select the corresponding priority path and execute the corresponding execution instruction.

[0048] In this embodiment, steps S1-S5, by constructing a port scene model through virtual reality technology and simulating the packing process of goods, can display the recommended packing scheme and transportation path in advance, effectively reduce errors and time waste in on-site operations, and improve the accuracy and efficiency of logistics operations. By combining the characteristics of the goods and loading constraints, multiple packing schemes are generated, and the rationality of the packing scheme is ensured based on the adaptation coefficient, space utilization coefficient and loading balance coefficient. The confirmation mechanism of the efficiency evaluation index Xl≥0.8 ensures the optimization and efficient execution of the packing scheme, avoiding resource waste and unreasonable loading. The anchor position of the target ship is collected in real time, and the recommended lifting point is calculated based on the container storage position. Multiple paths are further generated through dynamic path planning, and the trajectory coefficient and total transportation time of the path are calculated to ensure the optimization of the transportation path. By comparing the transportation time with the preset time threshold, the priority path is screened, transportation delays are effectively avoided, and the operation is completed on time. When the time threshold requirements are not met, the resilience of logistics operations can be improved through priority path selection and dynamic adjustment, ensuring that efficient operation can be maintained 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 manpower and material waste, and effectively improves transportation efficiency and operational flexibility.

[0049] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0050] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within 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 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, the current packing solution 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 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 , and according to the recommended lifting points Use dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and total shipping time ; The equipment control module is used to preset a time threshold X and calculate the total transportation time. 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 of the cargo to be packed in real time. and gross weight , 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 position Pos of the product to be packed to construct the product feature set Pcs as: ; 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 carrying capacity , the expression is as follows: ； in, represents the gross weight of the i-th item, 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 of the stacked single layer , the expression is as follows: ； in, represents the gross weight of the jth item, 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 of each product ,width and height , the size limit of the internal space of the load container, the constraint expression is: ； in, Respectively represent the length, width and height of the container; 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: ; 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 the second packing scheme by exchanging the positions of 30%-50% of the packed items 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: ； ； ； In the formula, represents the spatial adaptation factor, represents the weight adaptation factor, and Represent the spatial adaptation factor and weight adaptation factor 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: ； ； ； In the formula, Indicates the total volume of the container. ; Respectively represent the length, width and height of the i-th item; m is the total number of items loaded in the container; represents the gross weight of the jth item, k is the number of items packed in a single layer; It indicates the total number of single-layer areas in the container, and u indicates the number of the single-layer area. 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 then associate them through the following formula to obtain the efficiency evaluation index Xl after dimensionless processing: ； In the formula, , and They represent the weights of the packing adaptation coefficient Cf, the space utilization coefficient Slx and the loading balance coefficient Zphx respectively; 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 is calculated by the following formula: : ； ； Where D represents the straight-line distance between the container storage location Ls(x,y) in the yard and the target ship's anchor point coordinates A(x,y). represents the container priority weight, Indicates the loading and unloading time required for the goods; The path trajectory calculation unit is used to calculate the recommended lifting point Use the dynamic path planning method to generate several paths, and calculate the trajectory coefficient of each path using the following formula: and total shipping time : ； ； in, Indicates the number of turns in the current path, Indicates the total number of segments of the current path. Indicates the lifting height difference of the current path. Indicates the maximum permissible lifting height; and Represents the weight coefficient, which is used to balance the impact of turning and altitude change on the trajectory coefficient; Indicates the total transport time, which measures the time required for the RTG to complete all tasks. represents the transportation distance between path node p and p+1 path nodes, Indicates the average transport speed of the tire crane, Indicates the total amount of containers required at path node p+1, Indicates the total volume of containers transported by the tire crane in a single trip. Indicates 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 Compare with the time threshold X to obtain a second evaluation result, including: If the total transportation time ≤ 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 >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 after receiving the qualified path group. ≤ time threshold X, and total transportation time 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 in several routes Average> time threshold X, select total transportation time The smallest path 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 volume of containers at 30% of the path nodes, which will exceed the total volume of containers transported by a single tire crane in a single trip 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 based on the storage position Ls (x, y) of the container in the yard and the anchor point coordinates A (x, y) of the target ship , and according to the recommended lifting points Use dynamic path planning method to generate several paths and calculate the trajectory coefficient of each path and total shipping time ; S5, used to preset the time threshold X and the total transportation time The time threshold X is compared and evaluated to select the corresponding priority path and execute the corresponding execution instruction.

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