Ring-shaped shuttle task scheduling method and device, computer equipment and storage medium

Abstract

The application discloses a ring-shaped shuttle task scheduling method and device, computer equipment and a storage medium. The method comprises the following steps: collecting real-time running data of a ring-shaped shuttle line to generate a system state snapshot; performing multi-round random pairing on an idle trolley and a to-be-transported cargo to generate n groups of initial matching sequences; randomly selecting two groups to perform fragment interception and mixed recombination to form new sequences until all the initial sequences are updated into new matching sequences; calculating the total running time of each group of new matching sequences; sorting the matching sequences according to the total running time, selecting the first n groups and randomly extracting part of them for mixed recombination again; determining the optimal n groups of matching sequences; and selecting a group with the shortest total running time to issue to a corresponding ring-shaped shuttle vehicle to execute a scheduling task. The method can improve the running efficiency and logistics processing capacity of the ring-shaped shuttle system, and provides an advanced technical solution for a logistics system in an intelligent manufacturing scene.

Description

Technical Field

[0001] This invention relates to the field of circular shuttle mission scheduling technology, and more specifically to circular shuttle mission scheduling methods, apparatus, computer equipment and storage media. Background Technology

[0002] With the rapid development of intelligent manufacturing, logistics efficiency plays a crucial role in the production process. Circular shuttles, as highly efficient logistics handling equipment, are widely used in intelligent manufacturing scenarios. By circulating along a logistics line on a circular track, they effectively improve logistics efficiency. The circular operation mode of the shuttle allows it to tightly connect various logistics equipment such as conveyor lines, AGVs (Automated Guided Vehicles), stacker cranes, and logistics endpoints such as platforms and shelves, achieving seamless integration of upstream and downstream cargo flow, making it an indispensable key link in intelligent manufacturing factories.

[0003] However, traditional circular shuttle systems suffer from numerous problems in task scheduling. Their scheduling strategies are relatively simplistic, primarily relying on immediate response upon arrival at the nearest location. While this strategy can achieve timely response and rapid operation to some extent, it lacks a holistic perspective, leading to a series of inefficiencies.

[0004] First, in terms of task allocation, traditional scheduling strategies are prone to uneven task distribution. For example, in scenario 1, idle vehicles may be concentrated in a certain area, allowing tasks in that area to be scheduled in a timely manner, while other areas lack vehicles, causing tasks to remain unexecuted for a long time, which in turn leads to congestion on other lines.

[0005] Secondly, in terms of multi-task processing, traditional scheduling strategies suffer from the problem of task serial waiting. As described in Scenario 2, when two adjacent stations generate tasks simultaneously, traditional scheduling will prioritize processing the nearest task. Only after the nearest goods are completed can the next vehicle handle the task from the other station. This undoubtedly increases the waiting time for tasks and reduces overall logistics efficiency.

[0006] Furthermore, traditional scheduling strategies have shortcomings in considering the operational characteristics of circular shuttles. Circular shuttles operate at lower speeds in curved areas compared to straight tracks. In scenario 3, if a task is located ahead of a curve and the nearest shuttle immediately goes to handle it, it will cause severe congestion for the shuttles behind. Traditional scheduling does not consider the number of subsequent shuttles or the potential congestion.

[0007] In addition, when a task is issued, if there is no vehicle nearby, a vehicle needs to be dispatched from a distance to pick up the goods. This will result in untimely and uneven task processing and low overall efficiency, as described in Scenario 4.

[0008] Finally, traditional scheduling strategies cannot take into account global scheduling scenarios, only considering tasks related to loop connections. Once a station on the loop becomes congested, it will inevitably lead to congestion at the downstream stations it connects to, thus causing congestion throughout the entire warehouse, as described in Scenario 5.

[0009] In summary, traditional circular shuttle task scheduling strategies have numerous shortcomings, severely impacting logistics efficiency and the overall operational efficiency of the intelligent manufacturing system. Therefore, a new task scheduling method is urgently needed that can take a holistic perspective, rationally arrange task execution, optimize logistics task scheduling and path planning, address the problems existing in current technologies, and improve the overall performance and logistics efficiency of the circular shuttle system. Summary of the Invention

[0010] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, apparatus, computer equipment and storage medium for scheduling circular shuttle missions.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] The circular shuttle task scheduling method includes:

[0013] Step S110: Collect real-time operating data of the circular shuttle line, including the position, speed, load status of idle trolleys, and the start, end, and priority of the goods to be transported, and generate a system status snapshot;

[0014] Step S120: Based on the principle of nearest random matching, the idle cars in the system status snapshot are randomly paired with the goods to be transported in multiple rounds to generate n sets of initial matching sequences;

[0015] Step S130: Randomly select two groups from the n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences;

[0016] Step S140: Calculate the total runtime for each new matching sequence, taking into account the currently executing task.

[0017] Step S150: Sort the matching sequences according to the total running time, select the first n groups and randomly select a portion of them for remixing and recombination, keeping the total number of groups n.

[0018] Step S160: Repeat steps S130 to S150 until the preset iteration time or number of iterations is reached, and determine the n sets of matching sequences with the best runtime efficiency.

[0019] Step S170: Calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task.

[0020] The present invention also provides a circular shuttle vehicle task scheduling device, comprising:

[0021] The data acquisition and generation unit is used to collect real-time operating data of the circular shuttle line, including the position, speed, and load of idle trolleys, as well as the start point, end point, and priority of the goods to be transported, and to generate a system status snapshot.

[0022] The pairing unit is used to randomly pair the idle vehicles in the system state snapshot with the goods to be transported in multiple rounds according to the principle of nearest random matching, and generate n sets of initial matching sequences;

[0023] The recombination and update unit is used to randomly select two groups from n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences;

[0024] Combined with the computing unit, it is used to calculate the total runtime for each new set of matching sequences, taking into account the currently executing tasks;

[0025] The sorting and recombination unit is used to sort the matching sequence according to the total runtime, select the first n groups and randomly select a portion of them for re-mixing and recombination, keeping the total number of groups n.

[0026] The execution unit is used to repeatedly perform the following steps: "Randomly select two groups from n initial matching sequences, extract segments, mix and recombine them to form new sequences, and repeat this process until all initial sequences are updated to new matching sequences; calculate the total runtime of each new matching sequence based on the currently executing task; sort the matching sequences according to the total runtime, select the top n groups and randomly extract a portion from them for further mixing and recombination, keeping the total number of groups at n", until the preset iteration time or number of iterations is reached, and determine the n matching sequences with the best runtime efficiency.

[0027] The computational execution unit is used to calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task.

[0028] The present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the above-described method.

[0029] The present invention also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0030] The advantages of this invention compared to existing technologies are as follows: By collecting real-time operational data of the circular shuttle line and generating a system state snapshot, comprehensive data support is provided for global scheduling. Based on this data, task allocation and path planning can be optimized from a global perspective, avoiding task backlog or congestion in local areas and ensuring the efficient operation of the entire system. Furthermore, by employing the principle of nearest random matching and a multi-round random pairing strategy, an initial matching sequence is generated. This sequence is continuously optimized through operations such as fragment truncation and hybrid recombination. This dynamic random optimization method not only quickly adapts to changes in system state but also effectively avoids global suboptimal problems caused by local optima. Simultaneously, the introduction of randomness enhances the system's anti-interference capability, allowing for rapid adjustment of scheduling strategies and maintaining stable system operation even in the face of unexpected situations (such as equipment failure or temporary task insertion). Moreover, through multi-round iterative optimization, the optimal matching sequence for runtime efficiency can be determined, and the set with the shortest total runtime is selected and assigned to the circular shuttle for task execution, effectively reducing task waiting time and empty travel distance, and improving the timeliness and uniformity of task processing.

[0031] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram illustrating an application scenario of the circular shuttle task scheduling method provided in an embodiment of the present invention;

[0034] Figure 2 This is a flowchart illustrating the circular shuttle task scheduling method provided in an embodiment of the present invention.

[0035] Figure 3 This is a schematic block diagram of a circular shuttle vehicle task scheduling device provided in an embodiment of the present invention;

[0036] Figure 4 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0039] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0040] Please see Figure 1 and Figure 2 , Figure 1 This is a schematic diagram illustrating an application scenario of the circular shuttle task scheduling method provided in an embodiment of the present invention. Figure 2 This is a schematic flowchart illustrating the circular shuttle task scheduling method provided in an embodiment of the present invention. The circular shuttle task scheduling method is applied to a server, which interacts with terminals. Through innovative technologies such as global optimization, dynamic random optimization, and efficient task processing, it significantly improves the operating efficiency and logistics processing capacity of the circular shuttle system, effectively solving many problems existing in traditional scheduling strategies and providing an advanced technical solution for logistics systems in intelligent manufacturing scenarios.

[0041] Figure 2 This is a flowchart illustrating the circular shuttle task scheduling method provided in an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S170.

[0042] Step S110: Collect real-time operating data of the circular shuttle line, including the position, speed, load status of idle trolleys, and the start, end, and priority of the goods to be transported, and generate a system status snapshot;

[0043] Specifically, during implementation, the system first acquires real-time operational data from the circular shuttle line through a sensor network and data acquisition module. This data includes the location, speed, and load status of idle trolleys, as well as information such as the start and end points and priorities of goods to be transported. This data is integrated to generate a system status snapshot for subsequent scheduling decisions. For example, position and speed sensors are installed on the circular track to monitor the trolley's operational status in real time; simultaneously, cargo information is synchronized to the scheduling system through the warehouse management system (WMS).

[0044] In other words, through real-time data acquisition, the system can comprehensively perceive the operating status and task requirements of the circular shuttle, providing a data foundation for subsequent global optimization and scheduling. Simultaneously, system status snapshots can be updated in real time, ensuring that scheduling decisions are based on the latest operational data, thereby improving the accuracy and adaptability of scheduling.

[0045] In one embodiment, the real-time operation data of the circular shuttle line is collected, including the position, speed, and load of idle trolleys, as well as the start, end, and priority of the goods to be transported, and a system status snapshot is generated, including:

[0046] Real-time data collection of the operation status of the circular shuttle line is performed. The operation status data includes at least the real-time location, speed, and load of the idle trolleys, as well as the starting platform, ending platform, and task priority information of all goods to be transported on the circular shuttle line. A system status snapshot is generated based on the collected data.

[0047] Specifically, a comprehensive sensor network is deployed in the circular shuttle system to collect operational status data in real time. These sensors include:

[0048] Position sensors: Installed on the circular track, these sensors monitor the precise position of each vehicle in real time. These sensors can be based on RFID (Radio Frequency Identification), laser positioning, or visual recognition technology, enabling them to acquire vehicle position information with high accuracy and high frequency (e.g., multiple times per second).

[0049] Speed ​​sensor: Used to measure the real-time speed of the vehicle. The speed sensor can be integrated into the vehicle's drive system or used for non-contact measurement via an external radar sensor.

[0050] Load sensors: Installed on the trolley, these sensors are used to monitor the trolley's load status in real time. These sensors can be pressure sensors, capable of accurately measuring the current load weight of the trolley.

[0051] Task information acquisition module: Integrates with warehouse management system (WMS) or task scheduling system to obtain real-time information on the origin platform, destination platform, and task priority of goods to be transported. This information is transmitted to the central scheduling system via network.

[0052] The system includes a position sensor that monitors the vehicle's location in real time and sends the data to the central dispatch system. For example, the vehicle's location information is updated every second. A speed sensor measures the vehicle's speed and synchronizes the speed data to the central dispatch system. A load sensor monitors the vehicle's load weight in real time and transmits the data to the central dispatch system. The task information acquisition module obtains the origin and destination stations and task priority information of the goods to be transported from the WMS (Warehouse Management System) and associates this information with the vehicle's operational data.

[0053] After receiving real-time data from the sensor network, the central dispatch system integrates this data and generates a snapshot of the system state at the current moment. The snapshot includes the following:

[0054] Idle vehicle information: Real-time location, speed, and load status of each idle vehicle;

[0055] Information on goods to be transported: the origin platform, destination platform, and task priority for each task to be transported;

[0056] System operating status: including the overall operation of the circular shuttle line, such as the distribution of vehicles and the backlog of tasks.

[0057] The central dispatch system synchronizes data from different sensors, ensuring consistent timestamps across all data. It then associates the vehicle's operational data with the task information of the goods to be transported, forming a complete system state description. The integrated data is then encapsulated into a system state snapshot for subsequent dispatch decisions. This snapshot can be stored in memory as a data structure for rapid retrieval and processing.

[0058] In other words, by collecting real-time operational status data of the circular shuttle line, the system can comprehensively perceive the current operational status. Real-time monitoring of position, speed, and load data allows the scheduling system to accurately grasp the status of each vehicle; real-time acquisition of task information ensures that scheduling decisions are based on the latest task requirements. This comprehensive perception capability provides a solid data foundation for subsequent global optimization scheduling, avoiding scheduling errors caused by incomplete information. Furthermore, real-time data acquisition ensures that the system status snapshot reflects the current operational status, exhibiting high dynamic adaptability. Compared to traditional static data acquisition methods, real-time data acquisition can quickly respond to changes in system status, such as sudden vehicle malfunctions or urgent task insertions. This real-time nature allows the scheduling system to adjust scheduling strategies promptly, ensuring efficient task execution. Moreover, the system status snapshot, as the basis for global optimization scheduling, provides comprehensive and accurate input data for subsequent scheduling algorithms. By integrating vehicle operation data and task information, the scheduling system can optimize task allocation and path planning from a global perspective. This global optimization capability significantly improves the operational efficiency of the circular shuttle system, reduces task waiting time and vehicle empty running distance, and enhances overall logistics processing capacity.

[0059] Step S120: Based on the principle of nearest random matching, the idle cars in the system status snapshot are randomly paired with the goods to be transported in multiple rounds to generate n sets of initial matching sequences;

[0060] Specifically, based on system state snapshots, the scheduling system adopts a proximity-based random matching principle, randomly pairing idle vehicles with goods to be transported in multiple rounds. For example, the system prioritizes vehicles closer to the origin of the goods and randomly generates multiple initial matching sequences (assuming n sets). This randomness avoids local optima caused by fixed matching patterns, providing diverse initial solutions for subsequent optimization.

[0061] In other words, the random matching principle can generate diverse initial solutions, avoiding local optima caused by fixed matching patterns and providing a wider space for global optimization. Simultaneously, through the proximity matching principle, the system can quickly generate initial matching sequences, improving scheduling response speed.

[0062] In one embodiment, the step of randomly pairing idle vehicles in the system state snapshot with goods to be transported in multiple rounds according to the principle of proximity random matching, generating n sets of initial matching sequences, includes:

[0063] Based on the principle of proximity and random matching, multiple rounds of random pairing are performed on the idle vehicles and goods to be transported in the system state snapshot to generate n initial matching sequences. Each initial matching sequence corresponds to an idle vehicle and one or more goods transportation tasks assigned to it.

[0064] Specifically, the system extracts the position, speed, and load status of all idle trolleys, as well as the origin, destination, and priority information of all goods awaiting transport from the system state snapshot. For each idle trolley, the distance between it and the origin stations of all goods awaiting transport is calculated. This distance can be a straight-line distance or the actual travel distance based on the track. For example, the straight-line distance between the trolley's current position and the origin station of the goods can be calculated using the Euclidean distance formula:

[0065] ;

[0066] Among them, (x 小车 y 小车 (x) is the coordinate of the car. 货物起点 y 货物起点 () represents the coordinates of the origin of the goods.

[0067] Based on the calculated distance matrix, the nearest cargo task is selected for each idle vehicle. If multiple vehicles simultaneously select the same task, it is randomly assigned to one of the vehicles, ensuring that each task is assigned only once. Randomness is introduced on top of proximity matching. For example, for each vehicle, several of its nearest tasks (e.g., its three nearest tasks) are randomly selected, and then one task is randomly chosen from these for assignment. This randomness avoids local optima caused by consistently selecting the nearest task, increasing the diversity of the solutions.

[0068] Repeat the above process multiple times (e.g., 10 times), generating a set of initial matching sequences each time. Each set of initial matching sequences contains an idle vehicle and one or more cargo transportation tasks assigned to it. For example, the first round generates the first set of initial matching sequences, the second round generates the second set of initial matching sequences, and so on, until n sets of initial matching sequences are generated.

[0069] Through multiple rounds of random pairing, n initial matching sequences are generated. Each round of pairing is based on real-time data from system state snapshots, ensuring that each generated matching sequence has a certain degree of randomness and diversity. The initial matching sequences generated in each round are then recorded, forming a set containing n matching sequences. Each matching sequence corresponds to an idle vehicle and its assigned task; there can be one or more tasks, depending on the vehicle's capacity and the urgency of the task. The generated initial matching sequences are then stored in memory as a data structure for subsequent optimization. For example, a list or array can be used to store each matching sequence, where each sequence contains vehicle information and assigned task information.

[0070] In other words, by introducing randomness, the local optimum problem caused by the fixed selection of the nearest task in traditional scheduling methods is avoided. Random pairing generates diverse initial matching sequences, providing a wider space for subsequent optimization processes and thus increasing the possibility of global optimization. Furthermore, based on the principle of proximity matching, the system can quickly assign tasks to each idle vehicle, generating initial matching sequences. This rapid response capability ensures the efficiency of the scheduling system, generating multiple feasible scheduling schemes in a short time, providing a foundation for subsequent optimization. In addition, multiple rounds of random pairing generate n different initial matching sequences, each with a certain degree of randomness and diversity. This diversity provides rich initial schemes for subsequent optimization algorithms, increasing the probability of finding the global optimum and improving the overall performance of the scheduling system.

[0071] Step S130: Randomly select two groups from the n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences;

[0072] Specifically, two sets are randomly selected from the generated n initial matching sequences, and these two sets of sequences are then truncated and recombinated. For example, a portion of the task fragment is truncated from each set of sequences, and these fragments are randomly combined into a new sequence. This process is repeated until all initial sequences are updated to entirely new matching sequences. This operation is similar to the crossover operation in genetic algorithms, generating new solutions through the recombination of gene fragments.

[0073] In other words, fragment truncation and recombination can generate new matching sequences, increasing the diversity of schemes and further optimizing scheduling results. At the same time, by simulating the evolutionary process, the system can gradually eliminate suboptimal matching schemes and retain better schemes, improving the global optimization capability of scheduling.

[0074] In one embodiment, the step of randomly selecting two groups from n initial matching sequences for fragment truncation, mixing, and recombination to form a new sequence, and repeating this process until all initial sequences are updated to entirely new matching sequences, includes:

[0075] Two target sequences are randomly selected from n initial matching sequences. Random segment extraction is performed on each target sequence to obtain two sub-sequences. The two sub-sequences are mixed and split into two new segments. The two new segments are then backfilled into the corresponding original two target sequences to form two new matching sequences. The above process is repeated until n new matching sequences are generated.

[0076] Specifically, number the n groups of initial matching sequences (such as from 1 to n). Use a random number generator to randomly select two different numbers from these n numbers, and the corresponding two groups of matching sequences are the target sequences. Extract the two selected groups of target sequences from the set of initial matching sequences for subsequent fragment truncation and hybrid recombination operations.

[0077] For each group of target sequences, randomly determine the truncation positions. For example, for a sequence of length L, randomly generate two integers i and j (satisfying 1 ≤ i < j ≤ L), representing the start and end positions of the truncation. According to the determined truncation positions, truncate a subsequence from each group of target sequences. For example, truncate the subsequence from position i to position j from the first group of target sequences, denoted as subsequence A; truncate the subsequence from position k to position m from the second group of target sequences, denoted as subsequence B.

[0078] Perform a hybrid processing on the two truncated subsequences (subsequence A and subsequence B). For example, subsequence A and subsequence B can be spliced together to form a longer hybrid sequence. Then, re - split the hybrid sequence into two new fragments. For example, randomly select a splitting point and split the hybrid sequence into two new fragments with approximately equal lengths. Assume the length of the hybrid sequence is L′, randomly select an integer p (satisfying 1 < p < L′), and split the hybrid sequence at position p into two new fragments: new fragment 1 and new fragment 2.

[0079] Backfill the two new fragments into the corresponding original two groups of target sequences respectively. For example, backfill new fragment 1 to the truncation position of the first group of target sequences (replacing the original subsequence A), and backfill new fragment 2 to the truncation position of the second group of target sequences (replacing the original subsequence B). After the backfilling is completed, two new groups of matching sequences are formed. These two new groups of matching sequences replace the original two groups of target sequences and are put back into the set of initial matching sequences.

[0080] Repeat the above operations of random selection, fragment truncation, hybrid recombination, and backfilling. Each operation will update the two groups of matching sequences. Continue this process until all the initial matching sequences are updated to brand - new matching sequences. After several rounds of operations, finally generate n groups of brand - new matching sequences. Each group of sequences has undergone multiple fragment truncations and hybrid recombinations, with high diversity and optimization potential.

[0081] In other words, by randomly selecting target sequences and performing fragment truncation and recombination, diverse matching sequences can be generated. This diversity provides rich initial schemes for subsequent optimization algorithms, increases the probability of finding the global optimum, and significantly improves the optimization capability of the scheduling system. Furthermore, the fragment truncation and recombination process is similar to the crossover operation in genetic algorithms. By simulating an evolutionary process, the system can gradually eliminate suboptimal matching schemes and retain better ones. This evolutionary mechanism allows the scheduling system to dynamically adapt to complex operating environments, improving the system's robustness and adaptability. In addition, random fragment truncation and recombination introduce randomness, avoiding the local optimum problem caused by fixed matching patterns. This randomness allows the system to escape local optima traps and explore a wider solution space, thereby increasing the possibility of global optimization.

[0082] Step S140: Calculate the total runtime for each new matching sequence, taking into account the currently executing task.

[0083] Specifically, based on the currently executing task, the system calculates the total runtime for each new matching sequence. This includes the time it takes for the vehicle to travel from its current location to the cargo starting point, the time it takes to transport the cargo to the destination, and possible waiting times. Through simulation, the system can accurately calculate the total runtime for each matching sequence, providing a basis for subsequent sorting and selection.

[0084] In other words, by calculating the total runtime, the system can accurately evaluate the performance of each matching sequence, providing a scientific basis for selecting the optimal solution. Simultaneously, by considering the currently executing task, the system can dynamically adjust the scheduling scheme, ensuring the real-time nature and accuracy of scheduling decisions.

[0085] In one embodiment, calculating the total runtime for each new set of matching sequences, in conjunction with the currently executing task, includes:

[0086] Based on the transportation tasks currently being performed on the circular shuttle line, the runtime efficiency is calculated for each of the n new matching sequences to obtain the total runtime for each matching sequence.

[0087] Specifically, the system first obtains information on currently executing transportation tasks from the scheduling system. This information includes the current location, destination location, remaining travel time, task priority, and whether it is in a waiting state for each vehicle. Simultaneously, the current tasks are divided into two categories: assigned and currently executing tasks, and waiting tasks that have been generated but not yet assigned to a vehicle.

[0088] For each task in each new matching sequence, calculate its total runtime separately, as follows:

[0089] Calculate the travel time of an empty vehicle to the pickup station: Determine the distance between the current position of the vehicle and the pickup station, and calculate the time required for the vehicle to travel from its current position to the pickup station based on the actual operating speed of the circular shuttle line (considering the speed difference between straight lines and curves);

[0090] Calculate the waiting time caused by the preceding trolley placing or picking up goods: Check if there are other trolleys performing tasks at the picking and placing stations. If there are trolleys performing tasks, calculate the waiting time for the current task. The waiting time depends on the completion time of the preceding task. If the preceding task has not been completed when the trolley arrives, it must wait for the preceding task to be completed before starting execution.

[0091] Calculate the operation time required to complete the pickup after the cart arrives: Based on the task type and cargo attributes (such as cargo size, weight, etc.), determine the operation time required to complete the pickup after the cart arrives. The pickup operation time can be a fixed value or dynamically adjusted according to the complexity of the cargo;

[0092] Calculate the operation time required for the trolley to travel from the pickup station to the delivery station and complete the delivery: Determine the distance between the pickup and delivery stations, and calculate the time required for the trolley to travel from the pickup station to the delivery station based on the actual operating speed of the circular shuttle line. Simultaneously, determine the operation time required for the trolley to complete the delivery after arriving at its destination. The delivery time can also be a fixed value or dynamically adjusted according to the complexity of the goods.

[0093] The total runtime of each task is obtained by summing the times of the four elements mentioned above. For all tasks in each matching sequence, the total runtime of each task is summed to obtain the total runtime of the matching sequence.

[0094] In other words, by comprehensively considering empty vehicle travel time, waiting time, pickup operation time, and delivery operation time, the system can accurately assess the total runtime of each matching sequence. This accurate assessment ensures that scheduling decisions are based on actual operating conditions, avoiding scheduling errors caused by neglecting certain key factors, and improving the feasibility and reliability of the scheduling scheme. Furthermore, real-time consideration of currently executing tasks and vehicle status allows the scheduling system to dynamically adapt to the operating status of the circular shuttle line. For example, when there is congestion or task backlog on a certain path, the system can calculate the waiting time and rationally adjust the allocation of subsequent tasks, reducing the impact of congestion on overall efficiency and improving the system's dynamic adaptability. In addition, accurate runtime calculation allows the system to rationally arrange the execution order of tasks, reducing task waiting time and empty vehicle travel distance. For example, by optimizing task allocation, the system can ensure that vehicles can quickly pick up the next task after completing the current one, reducing vehicle idle time and improving resource utilization.

[0095] Step S150: Sort the matching sequences according to the total running time, select the first n groups and randomly select a portion of them for remixing and recombination, keeping the total number of groups n.

[0096] Specifically, all new matching sequences are sorted according to the total runtime, and the top n sequences with the shortest runtime are selected. Then, a portion of these n sequences is randomly selected, and fragmentation and recombination are performed again, keeping the total number of sequences at n. This process further optimizes the matching sequences and improves the quality of the scheduling scheme.

[0097] In other words, by sorting and re-mixing, the system can progressively optimize the matching sequence, improving the quality and efficiency of the scheduling scheme. Simultaneously, the system dynamically adjusts the matching sequence in each iteration, ensuring that the scheduling scheme adapts to changes in the system state and achieves global optimality.

[0098] In one embodiment, the step of sorting the matching sequences according to the total runtime, selecting the top n groups, and randomly selecting a portion from them for further mixing and recombination to maintain a total of n groups includes:

[0099] Sort the n matching sequences in ascending order of total runtime, and select the top n matching sequences. Randomly select a portion of the target sequences from the selected n matching sequences according to a preset probability (e.g., 10%). Perform random segmentation, mixing, splitting, and recombination operations on the selected target sequences to obtain the recombined matching sequences. Replace the original selected target sequences with the recombined matching sequences to ensure that the total number of matching sequences remains n.

[0100] Specifically, the total runtime of each matching sequence is obtained from the previous runtime efficiency calculation. All matching sequences are sorted in ascending order of total runtime. Sequences with shorter runtimes are ranked first, indicating that these sequences are more efficient under the current scheduling scheme. The top n matching sequences are selected from the sorted sequences, and these sequences will serve as the basis for subsequent optimizations.

[0101] Set a preset probability, such as 10%, to determine how many sets to select from the n matching sequences for further optimization. Based on the preset probability, randomly select several target sequences from the n matching sequences. For example, if n is 100 sets, randomly select 10 sets as target sequences with a 10% probability.

[0102] For each selected target sequence, a random cutoff point is chosen to divide the sequence into two subsequences. The subsequences from different target sequences are then mixed and re-split into two new segments. For example, after concatenating the subsequences of two target sequences, a random split point is selected to divide it into two new segments. These new segments are then recombined into a complete matching sequence, replacing the original selected target sequence.

[0103] The original selected target sequence is replaced with the new sequence after mixing and recombination. It is ensured that the total number of matching sequences remains n after the replacement, thus guaranteeing the continuity and stability of the optimization process.

[0104] In other words, by sorting the matching sequences according to their total runtime and selecting the top-ranked sequences, the system can focus on optimizing the more efficient scheduling schemes. This selection mechanism ensures the efficiency of the optimization process, avoids ineffective optimization of inefficient schemes, and thus rapidly improves overall scheduling efficiency. Furthermore, randomly selecting some sequences for mixing and recombination introduces randomness, further enhancing the diversity of scheduling schemes. This diversity helps explore a wider solution space, preventing the optimization process from getting trapped in local optima, thereby increasing the likelihood of finding the global optimum. Moreover, keeping the total number of matching sequences constant during the optimization process ensures the stability and continuity of the optimization process. This stability allows the system to quickly move to the next round of optimization after each round, avoiding optimization interruptions or efficiency drops due to changes in the number of sequences.

[0105] Step S160: Repeat steps S130 to S150 until the preset iteration time or number of iterations is reached, and determine the n sets of matching sequences with the best runtime efficiency.

[0106] Specifically, the system repeatedly executes steps S130 to S150 until the preset iteration time or number of iterations is reached. In each iteration, the system continuously updates and optimizes the matching sequence, ultimately determining the n sets of matching sequences with optimal runtime efficiency. For example, when the calculation time of a single iteration is ≤500ms, the preset number of iterations is 100; when the calculation time of a single iteration is >500ms and ≤1s, the preset number of iterations is 50; when the calculation time of a single iteration is >1s, the preset iteration time is 30s, ensuring that the iterative optimization achieves the best effect within the system's response capability range.

[0107] In other words, through multiple iterative optimizations, the system can find the optimal matching sequence from a large number of possible scheduling schemes, achieving global optimization. Simultaneously, the iterative optimization process ensures the efficiency and accuracy of scheduling decisions, significantly improving the operational efficiency of the circular shuttle system.

[0108] Step S170: Calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task.

[0109] Specifically, after completing all iterative optimizations, the system calculates the total runtime of the n sets of matching sequences with optimal runtime efficiency, and selects the set with the shortest total runtime to send to the corresponding circular shuttle car to execute the scheduling task. For example, the system sends the task instructions from the optimal matching sequence to the car, and the car executes the cargo transportation task according to the instructions.

[0110] In other words, by selecting the matching sequence with the shortest total runtime for execution, the system can ensure efficient task execution, reduce task waiting time, and minimize empty trip distance of the vehicle. Simultaneously, the execution of the optimal scheduling scheme significantly improves the overall performance and logistics processing capacity of the system, achieving efficient operation of the circular shuttle system.

[0111] In one embodiment, the step of calculating the total runtime of the n sets of matching sequences with optimal runtime efficiency and selecting the set with the shortest total runtime to send to the corresponding circular shuttle for scheduling tasks includes:

[0112] The total runtime of the n matching sequences with the best runtime efficiency is calculated again. The matching result with the shortest total runtime is selected and sent to the corresponding circular shuttle for execution to complete the task scheduling.

[0113] Specifically, after multiple rounds of optimization, the system has generated n sets of matching sequences with optimal runtime efficiency. To further ensure that the selected scheduling scheme is optimal, the system recalculates the total runtime of these n sets of matching sequences. The specific steps are as follows:

[0114] Each task in the matching sequence is analyzed one by one to determine the starting platform, ending platform, cargo attributes, and task priority of each task.

[0115] Calculate the runtime of each task:

[0116] Calculate the time it takes for an empty vehicle to travel from its current location to the pickup station.

[0117] Consider the waiting time caused by the vehicle ahead dropping or picking up goods.

[0118] Calculate the operation time required to complete the pickup after the trolley arrives at its destination.

[0119] Calculate the time required for the trolley to travel from the pickup station to the delivery station and complete the delivery.

[0120] Total runtime: Add up the runtime of each task to get the total runtime of each matching sequence.

[0121] The total runtime of the n matching sequences is compared, and the matching result with the shortest total runtime is found. Then, the matching result with the shortest total runtime is recorded as the optimal solution for this task scheduling.

[0122] Based on the selected optimal matching results, the system generates specific task instructions, including information such as task allocation for each vehicle, pickup and drop-off platforms, and task priorities. These task instructions are then sent to the corresponding circular shuttle vehicles, which perform the pickup, transportation, and drop-off operations according to the instructions, ensuring smooth task execution.

[0123] In other words, by recalculating the total runtime of the n matching sequences with optimal runtime efficiency, the system can further filter out the scheduling scheme with the shortest total runtime. This dual verification mechanism ensures that the final selected scheduling scheme is the optimal solution among all optimized schemes, thereby improving the efficiency and reliability of task scheduling. Furthermore, selecting the matching result with the shortest total runtime for execution can significantly reduce task waiting time and the vehicle's empty driving distance, allowing the vehicle to complete the task in the most efficient way, improving resource utilization and overall logistics efficiency. Moreover, by accurately calculating and filtering the optimal matching results, the system can automatically select the best scheduling scheme, reducing the need for manual intervention. This improvement in intelligence not only enhances the system's operating efficiency but also strengthens its stability and reliability.

[0124] The aforementioned circular shuttle task scheduling method generates a system state snapshot by collecting real-time operational data of the circular shuttle line (including the position, speed, and load of idle shuttles, as well as the start, end, and priority of the goods to be transported). This provides comprehensive data support for global scheduling. Based on this data, task allocation and path planning can be optimized from a global perspective, avoiding task backlog or congestion in local areas and ensuring the efficient operation of the entire system. Furthermore, by employing the principle of nearest-neighbor random matching and a multi-round random pairing strategy, an initial matching sequence is generated. This sequence is continuously optimized through operations such as fragment truncation and hybrid recombination. This dynamic random optimization method not only quickly adapts to changes in system state but also effectively avoids global suboptimal problems caused by local optima. Simultaneously, the introduction of randomness enhances the system's anti-interference capability, allowing for rapid adjustment of the scheduling strategy and maintaining stable system operation even in the face of unexpected situations (such as equipment failure or temporary task insertion). Moreover, through multi-round iterative optimization, the optimal matching sequence for runtime efficiency can be determined, and the set with the shortest total runtime is selected and assigned to the circular shuttle for task execution. This effectively reduces task waiting time and empty shuttle distance, improving the timeliness and uniformity of task processing.

[0125] Figure 3 This is a schematic block diagram of a circular shuttle vehicle task scheduling device 300 provided in an embodiment of the present invention. Figure 3As shown, corresponding to the above-described circular shuttle task scheduling method, the present invention also provides a circular shuttle task scheduling device 300. This circular shuttle task scheduling device 300 includes a unit for executing the above-described circular shuttle task scheduling method, and the device can be configured in a server. Specifically, please refer to... Figure 3 The circular shuttle mission scheduling device 300 includes:

[0126] The data acquisition and generation unit 301 is used to collect real-time operating data of the circular shuttle line, including the position, speed, load status of idle trolleys, and the start, end and priority of the goods to be transported, and to generate a system status snapshot.

[0127] The pairing unit 302 is used to randomly pair the idle trolleys in the system state snapshot with the goods to be transported in multiple rounds according to the principle of nearest random matching, and generate n sets of initial matching sequences;

[0128] The recombination and update unit 303 is used to randomly select two groups from n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences;

[0129] The calculation unit 304 is used to calculate the total runtime of each new matching sequence in conjunction with the currently executing task.

[0130] The sorting and recombination unit 305 is used to sort the matching sequence according to the total running time, select the first n groups and randomly select a portion of them for re-mixing and recombination, keeping the total number of groups n.

[0131] The execution determination unit 306 is used to repeatedly execute the process of "randomly selecting two groups from n initial matching sequences, extracting segments, mixing and recombinating them to form new sequences, and repeating this process until all initial sequences are updated to new matching sequences; calculating the total runtime of each new matching sequence based on the currently executing task; sorting the matching sequences according to the total runtime, selecting the top n groups and randomly extracting a portion from them for further mixing and recombination, keeping the total number of groups at n", until the preset iteration time or number of iterations is reached, and determining the n matching sequences with the best runtime efficiency.

[0132] The computational execution unit 307 is used to calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task.

[0133] In one embodiment, the acquisition and generation unit 301 includes:

[0134] Real-time data collection of the operation status of the circular shuttle line is performed. The operation status data includes at least the real-time location, speed, and load of the idle trolleys, as well as the starting platform, ending platform, and task priority information of all goods to be transported on the circular shuttle line. A system status snapshot is generated based on the collected data.

[0135] In one embodiment, the pairing unit 302 includes:

[0136] Based on the principle of proximity and random matching, multiple rounds of random pairing are performed on the idle vehicles and goods to be transported in the system state snapshot to generate n initial matching sequences. Each initial matching sequence corresponds to an idle vehicle and one or more goods transportation tasks assigned to it.

[0137] In one embodiment, the reorganization and update unit 303 includes:

[0138] Two target sequences are randomly selected from n initial matching sequences. Random segment extraction is performed on each target sequence to obtain two sub-sequences. The two sub-sequences are mixed and split into two new segments. The two new segments are then backfilled into the corresponding original two target sequences to form two new matching sequences. The above process is repeated until n new matching sequences are generated.

[0139] In one embodiment, the combined computing unit 304 includes:

[0140] Based on the transportation tasks currently being performed on the circular shuttle line, the runtime efficiency is calculated for each of the n new matching sequences to obtain the total runtime for each matching sequence.

[0141] In one embodiment, the sorting and reorganization unit 305 includes:

[0142] Sort the n matching sequences in ascending order of total runtime, and select the top n matching sequences. Randomly select a portion of the target sequences from the selected n matching sequences according to a preset probability. Perform random segmentation, mixing, splitting, and recombination operations on the selected target sequences to obtain the recombined matching sequences. Replace the original selected target sequences with the recombined matching sequences to ensure that the total number of matching sequences remains n.

[0143] In one embodiment, the computation execution unit 307 includes:

[0144] The total runtime of the n matching sequences with the best runtime efficiency is calculated again. The matching result with the shortest total runtime is selected and sent to the corresponding circular shuttle for execution to complete the task scheduling.

[0145] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned circular shuttle task scheduling device 300 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0146] The aforementioned circular shuttle task scheduling device 300 can be implemented as a computer program, which can, for example... Figure 4 It runs on the computer device shown.

[0147] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0148] See Figure 4 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0149] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a circular shuttle task scheduling method.

[0150] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0151] The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a circular shuttle task scheduling method.

[0152] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0153] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps:

[0154] Step S110: Collect real-time operating data of the circular shuttle line, including the position, speed, load status of idle trolleys, and the start, end, and priority of the goods to be transported, and generate a system status snapshot;

[0155] Step S120: Based on the principle of nearest random matching, the idle cars in the system status snapshot are randomly paired with the goods to be transported in multiple rounds to generate n sets of initial matching sequences;

[0156] Step S130: Randomly select two groups from the n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences;

[0157] Step S140: Calculate the total runtime for each new matching sequence, taking into account the currently executing task.

[0158] Step S150: Sort the matching sequences according to the total running time, select the first n groups and randomly select a portion of them for remixing and recombination, keeping the total number of groups n.

[0159] Step S160: Repeat steps S130 to S150 until the preset iteration time or number of iterations is reached, and determine the n sets of matching sequences with the best runtime efficiency.

[0160] Step S170: Calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task.

[0161] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0162] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0163] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein when executed by a processor, the computer program causes the processor to perform the following steps:

[0164] Step S110: Collect real-time operating data of the circular shuttle line, including the position, speed, load status of idle trolleys, and the start, end, and priority of the goods to be transported, and generate a system status snapshot;

[0165] Step S120: Based on the principle of nearest random matching, the idle cars in the system status snapshot are randomly paired with the goods to be transported in multiple rounds to generate n sets of initial matching sequences;

[0166] Step S130: Randomly select two groups from the n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences;

[0167] Step S140: Calculate the total runtime for each new matching sequence, taking into account the currently executing task.

[0168] Step S150: Sort the matching sequences according to the total running time, select the first n groups and randomly select a portion of them for remixing and recombination, keeping the total number of groups n.

[0169] Step S160: Repeat steps S130 to S150 until the preset iteration time or number of iterations is reached, and determine the n sets of matching sequences with the best runtime efficiency.

[0170] Step S170: Calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task.

[0171] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0172] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0173] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0174] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0175] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0176] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for scheduling tasks for a loop shuttle vehicle, the method comprising: include: Step S110: Collect real-time operating data of the circular shuttle line, including the position, speed, load status of idle trolleys, and the start, end, and priority of the goods to be transported, and generate a system status snapshot; Step S120: Based on the principle of nearest random matching, the idle cars in the system status snapshot are randomly paired with the goods to be transported in multiple rounds to generate n sets of initial matching sequences; Step S130: Randomly select two groups from the n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences; Step S140: Calculate the total runtime for each new matching sequence, taking into account the currently executing task. Step S150: Sort the matching sequences according to the total running time, select the first n groups and randomly select a portion of them for remixing and recombination, keeping the total number of groups n. Step S160: Repeat steps S130 to S150 until the preset iteration time or number of iterations is reached, and determine the n sets of matching sequences with the best runtime efficiency. Step S170: Calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task. The system collects real-time operational data of the circular shuttle line, including the position, speed, and load of idle trolleys, as well as the start, end, and priority of goods to be transported, and generates a system status snapshot, including: Real-time data collection of the operation status of the circular shuttle line. The operation status data includes at least the real-time location, speed, and load of the idle trolleys, as well as the starting platform, ending platform, and task priority information of all goods to be transported on the circular shuttle line. A system status snapshot is generated based on the collected data at the current moment. The process of randomly selecting two groups from n initial matching sequences, truncating and recombining them to form new sequences, and repeating this process until all initial sequences are updated to entirely new matching sequences includes: Two target sequences are randomly selected from n initial matching sequences. A random segment truncation operation is performed on each target sequence to obtain two sub-sequences. The two sub-sequences are then mixed and split into two new segments. The two new segments are then backfilled into the corresponding original two target sequences to form two new matching sequences. The process of random selection, truncation, mixing, and backfilling is repeated until n new matching sequences are generated. Specifically, number the n groups of initial matching sequences from 1 to n. Use a random number generator to randomly select two different numbers from the n numbers. The corresponding two groups of matching sequences are the target sequences. Then extract the two selected groups of target sequences from the set of initial matching sequences for subsequent fragment truncation and hybrid recombination operations. For each group of target sequences, randomly determine the truncation position. For a sequence with length L, randomly generate two integers i and j, where 1 ≤ i < j ≤ L, representing the start and end positions of the truncation. According to the determined truncation positions, truncate a subsequence from each group of target sequences, that is, truncate the subsequence from position i to position j from the first group of target sequences, denoted as subsequence A; truncate the subsequence from position k to position m from the second group of target sequences, denoted as subsequence B. Then perform a hybrid processing on the truncated subsequences A and B, that is, splice subsequence A and subsequence B together to form a longer hybrid sequence, and then randomly select a splitting point to split the hybrid sequence into two new fragments of roughly equal length. Then backfill the two new fragments into the corresponding original two groups of target sequences respectively. After the backfill, two new groups of matching sequences are formed. These two new groups of matching sequences replace the original two groups of target sequences and are put back into the set of initial matching sequences. Repeat the above operations of random selection, fragment truncation, hybrid recombination, and backfill. Each operation will update the two groups of matching sequences. Continue this process until all the initial matching sequences are updated to brand-new matching sequences. After several rounds of operations, finally generate n groups of brand-new matching sequences, and each group of sequences has undergone multiple fragment truncations and hybrid recombinations.

2. The method of claim 1, wherein, According to the principle of nearest random matching, perform multiple rounds of random pairing on the idle trolleys and the goods to be transported in the system state snapshot, generating n groups of initial matching sequences, including: Based on the principle of nearest random matching, perform multiple rounds of random pairing processing on the idle trolleys and the goods to be transported in the system state snapshot, generating n groups of initial matching sequences. Each group of initial matching sequences corresponds to an idle trolley and one or more goods transportation tasks assigned to it.

3. The method of claim 1, wherein, Combined with the tasks currently being executed, calculate the total running duration of each group of brand-new matching sequences, including: Combined with the transportation tasks being executed on the circular shuttle line, calculate the running efficiency of each of the n groups of brand-new matching sequences respectively to obtain the total running duration corresponding to each group of matching sequences.

4. The method of claim 1, wherein, Sort the matching sequences according to the total running duration, select the first n groups and randomly extract some of them for further hybrid recombination, keeping the total number as n groups, including: Sort the n groups of matching sequences in ascending order of the total running duration, select the n groups of matching sequences with the top rankings; randomly extract some target sequences from the selected n groups of matching sequences according to a preset probability, and perform random fragment truncation, hybrid splitting, and recombination operations on the selected target sequences again to obtain the recombined matching sequences, and replace the original selected target sequences with the recombined matching sequences to ensure that the total number of matching sequences remains n groups.

5. The method of claim 1, wherein, The calculation of the total runtime of the n sets of matching sequences with optimal runtime efficiency, and the selection of the set with the shortest total runtime to be assigned to the corresponding circular shuttle for scheduling tasks, includes: The total runtime of the n matching sequences with the best runtime efficiency is calculated again. The matching result with the shortest total runtime is selected and sent to the corresponding circular shuttle for execution to complete the task scheduling.

6. Ring shuttle task scheduling device, characterized in that, include: The data acquisition and generation unit is used to collect real-time operating data of the circular shuttle line, including the position, speed, and load of idle trolleys, as well as the start point, end point, and priority of the goods to be transported, and to generate a system status snapshot. The pairing unit is used to randomly pair the idle vehicles in the system state snapshot with the goods to be transported in multiple rounds according to the principle of nearest random matching, and generate n sets of initial matching sequences; The recombination and update unit is used to randomly select two groups from n initial matching sequences, perform segment extraction, mix and recombine to form a new sequence, and repeat this process until all initial sequences are updated to new matching sequences; Combined with the computing unit, it is used to calculate the total runtime for each new set of matching sequences, taking into account the currently executing tasks; The sorting and recombination unit is used to sort the matching sequence according to the total runtime, select the first n groups and randomly select a portion of them for re-mixing and recombination, keeping the total number of groups n. The execution unit is used to repeatedly execute the process of "randomly selecting two groups from n initial matching sequences, extracting and recombining segments to form new sequences, and repeating this process until all initial sequences are updated to new matching sequences; calculating the total runtime of each new matching sequence based on the currently executing task; sorting the matching sequences according to the total runtime, selecting the top n groups and randomly extracting a portion from them for further mixing and recombination, keeping the total number of groups at n", until the preset iteration time or number of iterations is reached, and determining the n matching sequences with the best runtime efficiency. The computational execution unit is used to calculate the total runtime of the n sets of matching sequences with the best runtime efficiency, and select the set with the shortest total runtime to send to the corresponding circular shuttle to perform the scheduling task. The acquisition and generation unit includes: Real-time data collection of the operation status of the circular shuttle line. The operation status data includes at least the real-time location, speed, and load of the idle trolleys, as well as the starting platform, ending platform, and task priority information of all goods to be transported on the circular shuttle line. A system status snapshot is generated based on the collected data at the current moment. The reorganization and update unit includes: Two target sequences are randomly selected from n initial matching sequences. A random segment truncation operation is performed on each target sequence to obtain two sub-sequences. The two sub-sequences are then mixed and split into two new segments. The two new segments are then backfilled into the corresponding original two target sequences to form two new matching sequences. The process of random selection, truncation, mixing, and backfilling is repeated until n new matching sequences are generated. Specifically, number the n groups of initial matching sequences from 1 to n. Use a random number generator to randomly select two different numbers from the n numbers. The corresponding two groups of matching sequences are the target sequences. Then extract the two selected groups of target sequences from the initial matching sequence set for subsequent fragment truncation and hybrid recombination operations. For each group of target sequences, randomly determine the truncation position. For a sequence with length L, randomly generate two integers i and j, where 1 ≤ i < j ≤ L, representing the start and end positions of the truncation. According to the determined truncation position, truncate a subsequence from each group of target sequences, that is, truncate the subsequence from position i to position j from the first group of target sequences, denoted as subsequence A; truncate the subsequence from position k to position m from the second group of target sequences, denoted as subsequence B. Then perform a hybrid processing on the truncated subsequences A and B, that is, splice subsequence A and subsequence B together to form a longer hybrid sequence, and then re - split the hybrid sequence into two new fragments, that is, randomly select a splitting point to split the hybrid sequence into two new fragments with approximately equal lengths. Then backfill the two new fragments into the corresponding original two groups of target sequences respectively. After the backfill, two new groups of matching sequences are formed. These two new groups of matching sequences replace the original two groups of target sequences and are put back into the initial matching sequence set. Repeat the above operations of random selection, fragment truncation, hybrid recombination, and backfill. Each operation will update the two groups of matching sequences. Continue this process until all the initial matching sequences are updated to brand - new matching sequences. After several rounds of operations, finally generate n groups of brand - new matching sequences, and each group of sequences has undergone multiple fragment truncations and hybrid recombinations.

7. A computer device, comprising: The computer device includes a memory and a processor. A computer program is stored on the memory. When the processor executes the computer program, the method described in any one of claims 1 to 5 is implemented.

8. A storage medium, characterized by The storage medium stores a computer program. When the computer program is executed by a processor, the method described in any one of claims 1 to 5 is implemented.

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