Path planning method for AGV cluster energy consumption optimization

Abstract

The invention provides a path planning method for AGV cluster energy consumption optimization, and belongs to the technical field of AGV path planning. The AGV cluster energy consumption optimization path planning method comprises the steps of obtaining current cluster path planning data, performing energy-saving optimization analysis based on load limitation, and forming load limitation energy-saving optimization data; according to the load limit energy-saving optimization data, carrying out material transportation time limit-based energy-saving adjustment analysis to form time limit energy-saving adjustment data; and performing real-time energy consumption optimization monitoring analysis according to the time limit energy-saving adjustment data to form cluster real-time energy consumption optimization data. According to the method, cluster path planning operation which is more energy-saving and meets production requirements is realized based on consideration of energy consumption optimization.

Description

Technical Field

[0001] This invention relates to the field of AGV path planning technology, and in particular to a path planning method for optimizing the energy consumption of AGV clusters. Background Technology

[0002] AGV cluster path planning is an important research direction in the field of intelligent logistics and automation, aiming to plan efficient and conflict-free paths for multiple AGVs (Automated Guided Vehicles) in complex environments.

[0003] Currently, there are various technical solutions for how to perform fast and effective path planning for AGVs. However, most of them focus on work efficiency and operational coordination, and rarely consider energy consumption to fully achieve the optimal energy-consuming path planning results.

[0004] Therefore, designing a path planning method for AGV cluster energy consumption optimization, and achieving more energy-efficient cluster path planning operations that meet production requirements based on energy consumption optimization considerations, is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a path planning method for optimizing the energy consumption of AGV clusters.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a path planning method for optimizing the energy consumption of an AGV cluster is provided. The method includes: acquiring current cluster path planning data; performing energy-saving optimization analysis based on load constraints to generate load-constrained energy-saving optimization data; performing energy-saving adjustment analysis based on material handling time limits based on the load-constrained energy-saving optimization data to generate time-limited energy-saving adjustment data; and performing real-time energy consumption optimization monitoring and analysis based on the time-limited energy-saving adjustment data to generate real-time cluster energy consumption optimization data.

[0007] Therefore, the above method fully considers the optimization of the AGV path in the cluster from three aspects: material load limit, time limit, and real-time monitoring. It achieves more comprehensive and effective energy consumption optimization by considering energy consumption from different perspectives. Compared with a single energy consumption optimization method, optimizing the path by considering energy consumption from multiple aspects is more comprehensive and effective. It can effectively ensure the timeliness and efficiency of material transportation while achieving a real energy reduction effect.

[0008] Optionally, the current cluster path planning data is obtained, and energy-saving optimization analysis based on load constraints is performed to form load-constrained energy-saving optimization data, including: determining the target load of different target AGVs based on the current cluster path planning data; performing loading capacity analysis based on the target load of different target AGVs to identify all target loading AGVs; and performing combined transport energy-saving optimization analysis on different target loading AGVs to form load-constrained energy-saving optimization data.

[0009] Therefore, the analysis of energy consumption optimization for AGV clusters first considers that current AGV cluster path planning is mostly efficiency-oriented, resulting in high energy consumption. However, improving efficiency does not necessarily increase the overall production efficiency of the system, since different processes have objective production time consumption. Therefore, optimizing the planned path from the perspective of energy consumption optimization is necessary. Energy consumption optimization is nothing more than three major aspects: path selection, load optimization, and operation control. This application considers implementing energy consumption optimization based on the current path planning. Therefore, the analysis follows the order from load to path selection to operation control, since these three factors are progressive in terms of the conditional elements of path planning. That is, to complete the adjustment of operation control, the path must be determined first, and the path selection is implemented after the load is determined, since the load determines the destination or destination combination of materials. The consideration of load-based energy consumption optimization mainly focuses on whether energy consumption can be reduced by reducing the total number of trips or the idle part of the AGVs. Of course, this requires comparing and confirming the energy consumption of the original path with the energy consumption of the load optimization. Load optimization is not unlimited; the carrying capacity of AGVs must also be considered. Therefore, it is necessary to determine the remaining carrying capacity of AGVs based on the current planning data, and then determine whether different AGVs have the capacity to increase their load, so as to achieve reasonable load optimization to reduce energy consumption.

[0010] Optionally, based on the target load of different target AGVs, a loading capacity analysis is performed to identify all target loading AGVs, including: determining the target maximum load of different target AGVs based on the current cluster path planning data; determining the corresponding target capacity load for different target AGVs based on the corresponding target maximum load and target load; and performing a loading capacity analysis for different target AGVs based on the corresponding target capacity load in the following manner: if for any other target AGV that does not contain itself, there exists a corresponding target load that does not exceed the target capacity load, then the target AGV is identified as a target loading AGV, and the set of other AGVs whose target loads do not exceed the target capacity load corresponding to the target loading AGV is formed into a combined transport target set.

[0011] Therefore, the purpose of load capacity analysis is to identify AGVs with remaining load capacity that can accommodate the loads of other AGVs, providing basic reference data for subsequent combined transport. The analysis method here is to determine the load situation of the AGVs under the current plan, and then obtain the limit value for further load increases. Considering the merging, one AGV will inevitably transfer the current load of another AGV to carry on its existing load. Therefore, it is necessary to determine whether the remaining load capacity of the AGVs under the current plan can still accommodate the current loads of other AGVs. After all, material transportation cannot arbitrarily divide the load on the AGVs; the transported object has a relatively complete structure. Therefore, analyzing from the perspective of accommodating the entire current load of the AGVs is reasonable and objective. If this situation exists, the prerequisite for combined transport is met.

[0012] Optionally, for different target loading AGVs, a combined transport energy-saving optimization analysis is performed to generate load-limited energy-saving optimization data. This includes: sequentially extracting different target AGVs from the combined transport target set corresponding to the target loading AGVs; determining the loading distance between the two loading points, the unloading distance between the two unloading points, the current location of the two AGVs, and the latest allowed arrival time to the two unloading points based on the current planned paths of the two AGVs; performing path planning based on the current location, loading distance, unloading distance, and latest allowed arrival time to determine a combined transport plan for a single AGV that satisfies the latest allowed arrival time of the two unloading points. The process involves: obtaining the total energy consumption of the combined transport path and the total independent energy consumption of each AGV when transporting materials individually; determining the energy consumption difference of the combined transport; and performing the following analysis and judgment based on the energy consumption difference of all AGVs in the combined transport target set corresponding to the target loading AGV: if there is a positive energy consumption difference, the combined transport planning path corresponding to the largest energy consumption difference is determined as the combined transport planning data corresponding to the target loading AGV; if there is no positive energy consumption difference, the target loading AGV will not be processed for combined transport; and performing material transport interference analysis on the combined transport planning data corresponding to different target loading AGVs to form load-limited energy-saving optimization data.

[0013] Therefore, energy-saving analysis of combined transport includes two aspects. Firstly, it requires obtaining the necessary data for combined transport planning for different AGVs from the target set of combined transport objectives. Secondly, it involves rationally selecting the most energy-efficient combined transport combinations based on energy consumption differences from the combined transport combinations within the set. For combined transport planning analysis, to achieve combined transport planning, it is necessary to obtain information on loading points, unloading points, AGV locations, and material transport time limits. Only then can the shortest path selection between two AGVs be completed during combined transport planning, with the material transport time limit as the objective. Here, the latest allowed arrival time can be the actual bottom-line time limit of the unloading point, or a time limit with tolerance considering unexpected events in the path operation. Combined transport planning is essentially a path planning analysis for a single AGV transporting materials at multiple points. This path planning analysis is a mature path planning technology; combining IoT data can quickly achieve path planning. Of course, path planning needs to fully consider the spatiotemporal interference between the planned path and other current paths to form an implementable planned path. For each target loading AGV, multiple transport combinations can be selected based on the corresponding transport target set. It should be noted that this application only considers pairwise combinations of different AGVs. More combinations are ignored due to the complexity of the analysis and the low probability of their existence under transport limitations. Furthermore, for pairwise combinations, to improve the stability of the load after transport, the volume of the transported goods can be considered during the transport analysis. If volume is not considered, methods such as binding need to be considered when the volume is large. The purpose of transport is to reduce energy consumption, so it is reasonable and necessary to select the combination with the lowest energy consumption among pairwise combinations. Of course, the premise is that the energy consumption of the transport must be less than the total energy consumption of individual material transport. Therefore, the energy consumption difference of the transport is obtained, which is the difference between the total energy consumption of individual material transport and the total energy consumption of the transport. The larger the difference, the lower the relative energy consumption of the transport. After selecting the transport combination for each target loading AGV, it is also necessary to consider whether there is overlap of AGV objects in these pairwise combinations, as this will affect the actual control during implementation. In addition, the combined transport planning data should also include the planning for the return of the remaining AGVs after the selected AGVs for combined transport. Since all AGVs will return to their positions after completing their tasks, the energy consumption of the returning AGVs does not need to be considered in the optimization analysis.

[0014] Optionally, for the combined transport planning data corresponding to different target loading AGVs, material transport interference analysis is performed to form load-limited energy-saving optimization data. This includes: for different target loading AGVs, determining the corresponding two AGV objects based on the corresponding combined transport planning data; performing a duplicate comparison of the AGV objects involved in different combined transport planning data: if no AGV duplicates exist between any two combined transport planning data, then all combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form load-limited energy-saving optimization data; if there are duplicate AGVs between two combined transport planning data, then the total combined transport energy consumption of the duplicated combined transport planning data is compared, the combined transport planning data with the lower total combined transport energy consumption is retained, and a new combined transport analysis is performed on the target loading AGVs corresponding to the combined transport planning data that are not retained to form a new combined transport data. The planning data is repeatedly compared with the combined transport planning data for new trips until it is confirmed that the new combined transport planning data will no longer be duplicated. Then, all combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form load-limited energy-saving optimization data. If there are duplicate AGVs in two combined transport planning data, the total combined transport energy consumption of the duplicated combined transport planning data is compared. The combined transport planning data with the lower total combined transport energy consumption is retained, and a new combined transport analysis is performed on the target loading AGVs corresponding to the excluded combined transport planning data to form new combined transport planning data. The combined transport planning data for new trips is repeatedly compared until no new combined transport planning data is formed. Then, all combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form load-limited energy-saving optimization data.

[0015] Therefore, the main purpose of material transport interference analysis is to confirm whether there are duplicate AGV objects involved in the generated combined transport planning data. For duplicate combined transport planning data, the one with the lowest total combined transport energy consumption is selected and retained from the perspective of energy consumption, while the other duplicate combined transport planning data is excluded. After exclusion, there will be situations where the corresponding target loading AGV loses its combined transport. However, the combined transport target set corresponding to the target loading AGV may have more than one target AGV, so it is advisable to continue to obtain combined transport planning data. The basis for obtaining the data is still to use the combination with the largest positive energy consumption difference among the remaining target AGVs as the target for planning. Of course, the re-planned combined transport planning data still needs to continue to undergo repeated comparative analysis until no duplicates occur or there are no objects that can be combined transported for analysis.

[0016] Optionally, based on the load-limited energy-saving optimization data, an energy-saving adjustment analysis based on the material transportation time limit is performed to form time-limited energy-saving adjustment data, including: based on the load-limited energy-saving optimization data, an overall energy-saving adjustment analysis based on the material transportation time limit is performed to form overall time-limited adjustment energy-saving data; based on the overall time-limited adjustment energy-saving data, an independent time-limited energy-saving adjustment analysis for the target object is performed to form time-limited energy-saving adjustment data.

[0017] Therefore, energy-saving adjustment analysis based on material transportation time limits mainly focuses on adjusting the time spent on material transportation. The goal of this adjustment is to use the latest time limit required at the unloading point as a benchmark, thus effectively reducing energy consumption while ensuring the material transportation time limit requirements are met. Of course, this implementation-based energy-saving adjustment can be considered from both a holistic and individual perspective. After all, planning data, considering coordination, often prioritizes high efficiency while neglecting energy conservation; therefore, unified speed adjustments can be made at the overall level. Secondly, the situation of individual AGVs can be considered. Since different AGVs have different arrival times and time limits due to cluster planning, this can be reasonably considered and analyzed.

[0018] Optionally, based on the load-limited energy-saving optimization data, an overall energy-saving adjustment analysis based on material handling time limits is performed to form overall time limit adjustment energy-saving data, including: determining the target planned arrival time of each target AGV to the final unloading point and the corresponding target planned allowable time limit for the final unloading point based on the load-limited energy-saving optimization data; determining the corresponding target time tolerance for different target AGVs based on the corresponding target planned arrival time and target planned allowable time limit; obtaining the minimum target time tolerance for different target AGVs and marking it as the time limit allowable adjustment amount; and adjusting the speed of each target AGV along the entire planned path with the time limit allowable adjustment amount as the target to form overall time limit adjustment energy-saving data.

[0019] Therefore, the goal of the overall energy-saving adjustment analysis is to uniformly extend the material transport time of the AGV cluster, which is achieved by uniformly reducing speed. The basis for achieving this goal is that all AGVs in the current path planning of the AGV cluster arrive at their destination ahead of schedule and within the stipulated time limit. Therefore, the difference between the arrival time of each AGV based on the planned data and the latest allowed arrival time is used for judgment. If a difference exists, it means that the planned arrival time of the AGV cluster closest to the latest arrival time is earlier than the latest arrival time. Therefore, this time difference of the AGV closest to the latest arrival time can be used as the target value for overall adjustment, and the arrival time of all AGVs is uniformly delayed by this time difference. This approach achieves energy saving relatively efficiently and quickly, as it does not need to consider the spatiotemporal interference between different AGVs after speed reduction.

[0020] Optionally, with the allowable adjustment amount for the time limit as the target, the speed of each target AGV is adjusted along the entire planned path to form overall time limit adjustment energy-saving data. This includes: determining the speed change information along the entire route for different target AGVs based on the corresponding planned path; uniformly reducing the speed of different target AGVs based on the speed change information until the time difference between the time the adjusted target AGV arrives at the final unloading point and the time before the adjustment is equal to the allowable adjustment amount for the time limit; and aggregating the adjusted path planning data of all target AGVs to form overall time limit adjustment energy-saving data.

[0021] Therefore, the overall adjustment specifically uses speed reduction as the adjustment method. To ensure that the speed reduction adjustment of each AGV does not cause spatiotemporal interference, the speed value of each AGV is uniformly reduced along the path. The goal of the adjustment is that the difference between the arrival time at the destination after speed reduction and the original arrival time is exactly equal to the allowable adjustment amount. This allows for efficient and rapid energy-saving adjustments at the overall level. While overall speed reduction adjustments can usually be implemented quickly, special cases may arise where overall adjustment is not feasible. In such cases, the adjustment result may result in path interference, but this can be resolved through subsequent optimization based on individual adjustments.

[0022] Optionally, based on the overall time-limited energy-saving adjustment data, an independent time-limited energy-saving adjustment analysis is performed for the target objects to form time-limited energy-saving adjustment data. This includes: determining the planning path information of different target AGVs based on the overall time-limited energy-saving adjustment data; adjusting the speed of different target AGVs with the target planned allowable time limit as the target to form corresponding independent speed reduction planning paths; performing interference adjustment processing on the path intersections of the independent speed reduction planning paths corresponding to all target AGVs in the following manner: adjusting the speed increase of any one of the target AGVs within the speed adjustment range centered on the path intersection point, so that the two target AGVs always have a distance of not less than the interference interval limit within the speed adjustment range; obtaining the planning paths of all target AGVs after speed adjustment to form time-limited energy-saving adjustment data.

[0023] Therefore, independent energy-saving adjustments for the target AGV mainly involve slowing down to make the arrival time equal to or close to the allowable latest arrival time. This application adopts the following approach: first, directly use the allowable latest arrival time as a benchmark to slow down and form a preliminary path plan; then, consider the intersection points where interference occurs with other AGVs along the path. If spatiotemporal interference exists at the intersection points, speed adjustment is performed, since further slowing down would exceed the required latest arrival time limit. Here, the speed increase considers a reasonable range of AGV speed variations within a given time limit. The speed adjustment range can be determined based on actual conditions. The goal of the speed increase is to ensure that the distance between two interfering AGVs within the speed adjustment range is always no less than the interference interval limit. The interference interval limit can be determined based on actual conditions. This approach fully guarantees the minimum optimal and most efficient speed increase adjustment, avoiding a significant reduction in energy-saving effects. The speed increase adjustment target for the two interfering AGVs can be randomly selected or selected based on set rules.

[0024] Optionally, based on the time-limited energy-saving adjustment data, real-time energy consumption optimization monitoring and analysis are performed to form real-time energy consumption optimization data for the cluster. This includes: acquiring real-time material handling data and determining the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point; if the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point is less than the interference interval limit, then: if the speed of the increasing target AGV is reduced, and the speed reduction adjustment makes the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point not less than the interference interval limit, then the speed reduction adjustment information is determined as the real-time optimization planning data for the corresponding target AGV; if the speed of the increasing target AGV is reduced, and the speed reduction adjustment makes the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point still less than the interference interval limit, then the speed of the increasing target AGV will be adjusted again until the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point is always not less than the interference interval limit, and the speed increase adjustment information is determined as the real-time optimization planning data for the corresponding target AGV; combining the real-time optimization planning data and the planning data of the remaining target AGVs, a cluster real-time energy consumption optimization data is formed.

[0025] Therefore, the purpose of real-time energy consumption optimization monitoring and analysis is to avoid interference events caused by unexpected situations during actual material transportation, and to take early action to prevent them. From an energy consumption perspective, the first approach is to slow down the AGVs that are already accelerating. After all, of the two objects involved in the interference, the one that wasn't previously accelerated must have its acceleration adjusted. However, from an energy consumption perspective, reducing energy consumption is the primary consideration, so slowing down the already accelerated AGVs is the first choice. If this first choice cannot be achieved, then acceleration should continue. If accelerating the previously non-accelerated AGVs is less effective in preventing interference than continuing to accelerate the already accelerated AGVs, since their acceleration intervals may need to be further reduced, the acceleration limit should be set so that the minimum interval within the range exactly equals the interval limit. Acceleration adjustments can be based on acceleration or speed changes, determined according to the actual situation to achieve the best adjustment effect. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the architecture of an AGV cluster energy consumption optimization path planning system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a path planning method for optimizing energy consumption in an AGV cluster, provided in an embodiment of the present invention; Figure 3 A flowchart illustrating the energy-saving optimization adjustment process of a path planning method for optimizing energy consumption in an AGV cluster, as provided in an embodiment of the present invention. Figure 4 This is a schematic diagram of the structure of an AGV cluster energy consumption optimization path planning system provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0028] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0029] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0030] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0031] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or processing device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or processing device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0032] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0033] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0034] To facilitate understanding of the embodiments of the present invention, firstly, using Figure 1 The path planning method for AGV cluster energy consumption optimization, as shown in the figure, is described in detail using the path planning system for AGV cluster energy consumption optimization applicable to embodiments of the present invention.

[0035] For example, Figure 1 This is a schematic diagram of the architecture of an AGV cluster energy consumption optimization path planning system provided in an embodiment of the present invention. Figure 1 As shown, the system may include: a path planning system and AGV equipment.

[0036] Figure 2 This is a flowchart illustrating a path planning method for optimizing energy consumption in an AGV cluster, as provided in an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram illustrating the energy-saving optimization adjustment process of a path planning method for optimizing energy consumption in an AGV cluster, provided in an embodiment of the present invention. This path planning method for optimizing energy consumption in an AGV cluster is applicable to the aforementioned system, and the specific process is as follows: S1: Obtain the current cluster path planning data, perform energy-saving optimization analysis based on load constraints, and generate load-constrained energy-saving optimization data.

[0038] Obtain current cluster path planning data, perform energy-saving optimization analysis based on load constraints, and generate load-constrained energy-saving optimization data, including: determining the target load of different target AGVs based on the current cluster path planning data; performing loading capacity analysis based on the target load of different target AGVs to identify all target loading AGVs; and performing combined transport energy-saving optimization analysis on different target loading AGVs to generate load-constrained energy-saving optimization data.

[0039] The analysis of energy consumption optimization for AGV clusters first considers that current AGV cluster path planning is mostly efficiency-oriented, resulting in high energy consumption. However, improving efficiency does not necessarily increase the overall production efficiency of the system, as different processes have objective time consumption. Therefore, optimizing the planned paths from the perspective of energy consumption optimization is necessary. Energy consumption optimization is nothing more than three major aspects: path selection, load optimization, and operation control. This application considers implementing energy consumption optimization based on the current path planning. Therefore, the analysis follows the order from load to path selection to operation control, since these three factors are progressive in terms of the conditions for path planning. That is, to complete the adjustment of operation control, the path must first be determined. Path selection is implemented after the load is determined, since the load determines the destination or combination of destinations for material transportation. The consideration of load-based energy consumption optimization mainly focuses on whether energy consumption can be reduced by reducing the total number of trips or the idle part of the AGVs. Of course, this requires comparing and confirming the energy consumption of the original path with the energy consumption of the load optimization. Load optimization is not unlimited; the carrying capacity of AGVs must also be considered. Therefore, it is necessary to determine the remaining carrying capacity of AGVs based on the current planning data, and then determine whether different AGVs have the capacity to increase their load, so as to achieve reasonable load optimization to reduce energy consumption.

[0040] Based on the target load of different target AGVs, a loading capacity analysis is performed to identify all target loading AGVs. This includes: determining the target maximum load of different target AGVs based on the current cluster path planning data; determining the corresponding target capacity load for different target AGVs based on their corresponding target maximum load and target load; and performing a loading capacity analysis for different target AGVs based on their corresponding target capacity load in the following manner: if for any other target AGV that does not contain itself, there exists a corresponding target load that does not exceed the target capacity load, then the target AGV is identified as a target loading AGV, and the set of other AGVs whose target loads do not exceed the target capacity load corresponding to the target loading AGV is formed into a combined transport target set.

[0041] The purpose of load capacity analysis is to identify AGVs with remaining load capacity that can accommodate the loads of other AGVs, providing basic reference data for subsequent combined transport. The analysis method involves determining the load status of AGVs under the current plan, and then obtaining the limit value for further load increases. Considering merging, one AGV will inevitably transfer the current load of another AGV to carry its existing load. Therefore, it is necessary to determine whether the remaining load capacity of the AGVs under the current plan can still accommodate the current loads of other AGVs. After all, material transportation cannot arbitrarily divide the load on AGVs; the transported object has a relatively complete structure. Therefore, analyzing from the perspective of accommodating the entire current load of the AGVs is reasonable and objective. If this situation exists, the prerequisite for combined transport is met.

[0042] For AGVs with different target loading conditions, a combined transport energy-saving optimization analysis is performed to generate load-limited energy-saving optimization data. This includes: sequentially extracting different target AGVs from the combined transport target set corresponding to the target loading AGVs; determining the loading distance between the two loading points, the unloading distance between the two unloading points, the current location of the two AGVs, and the latest allowed arrival time to the two unloading points based on the current planned paths of the two AGVs; and performing path planning based on the current location, loading distance, unloading distance, and latest allowed arrival time to determine a combined transport planning path for a single AGV that satisfies the latest allowed arrival time of the two unloading points. The process involves: obtaining the total energy consumption of the combined transport path and the total energy consumption of the independent transport when two AGVs transport materials separately, and determining the energy consumption difference of the combined transport; based on the energy consumption difference of all AGVs in the combined transport target set corresponding to the target loading AGV, the following analysis and judgment are performed: if there is a positive energy consumption difference, the combined transport planning path corresponding to the largest energy consumption difference is determined as the combined transport planning data corresponding to the target loading AGV; if there is no positive energy consumption difference, the target loading AGV will not be processed for combined transport; and performing transport interference analysis on the combined transport planning data corresponding to different target loading AGVs to form load-limited energy-saving optimization data.

[0043] Energy-saving analysis for combined transport includes two aspects. First, it requires obtaining the necessary data for combined transport planning for different AGVs from the target set of combined transport objectives. Second, it involves rationally selecting the most energy-efficient combined transport combinations from the combined transport set based on energy consumption differences. For combined transport planning analysis, it is necessary to obtain information on loading points, unloading points, AGV locations, and material transport time limits. This allows for the selection of the shortest path between two AGVs based on the material transport time limit. Here, the latest allowed arrival time can be the actual minimum time limit for the unloading point or a time limit with tolerance considering unexpected events during path operation. Essentially, combined transport planning is also a path planning analysis for multi-point material transport by a single AGV. This path planning analysis is a mature path planning technology; integrating IoT data can quickly achieve path planning. Of course, path planning needs to fully consider the spatiotemporal interference between the planned path and other current paths to form an implementable planned path. For each target loading AGV, multiple transport combinations can be selected based on the corresponding transport target set. It should be noted that this application only considers pairwise combinations of different AGVs. More combinations are ignored due to the complexity of the analysis and the low probability of their existence under transport limitations. Furthermore, for pairwise combinations, to improve the stability of the load after transport, the volume of the transported goods can be considered during the transport analysis. If volume is not considered, methods such as binding need to be considered when the volume is large. The purpose of transport is to reduce energy consumption, so it is reasonable and necessary to select the combination with the lowest energy consumption among pairwise combinations. Of course, the premise is that the energy consumption of the transport must be less than the total energy consumption of individual material transport. Therefore, the energy consumption difference of the transport is obtained, which is the difference between the total energy consumption of individual material transport and the total energy consumption of the transport. The larger the difference, the lower the relative energy consumption of the transport. After selecting the transport combination for each target loading AGV, it is also necessary to consider whether there is overlap of AGV objects in these pairwise combinations, as this will affect the actual control during implementation. In addition, the combined transport planning data should also include the planning for the return of the remaining AGVs after the selected AGVs for combined transport. Since all AGVs will return to their positions after completing their tasks, the energy consumption of the returning AGVs does not need to be considered in the optimization analysis.

[0044] For AGVs with different target loading, material transport interference analysis is performed on the combined transport planning data to generate load-limited energy-saving optimization data. This includes: for different target loading AGVs, determining the two corresponding AGV objects based on the corresponding combined transport planning data; comparing the AGV objects involved in different combined transport planning data for redundancy: if no AGVs are duplicated between any two combined transport planning data, then all combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form load-limited energy-saving optimization data; if AGVs are duplicated between two combined transport planning data, then the total combined transport energy consumption of the duplicated combined transport planning data is compared, the combined transport planning data with the lower total combined transport energy consumption is retained, and a new combined transport analysis is performed on the target loading AGVs corresponding to the excluded combined transport planning data to form a new combined transport plan. The data is repeatedly compared with the combined transport planning data for new trips until it is confirmed that the new combined transport planning data will no longer be duplicated. Then, all combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form load-limited energy-saving optimization data. If there are duplicate AGVs in two combined transport planning data, the total combined transport energy consumption of the duplicated combined transport planning data is compared. The combined transport planning data with the lower total combined transport energy consumption is retained, and a new combined transport analysis is performed on the target loading AGVs corresponding to the excluded combined transport planning data to form new combined transport planning data. The combined transport planning data for new trips is repeatedly compared until no new combined transport planning data is formed. Then, all combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form load-limited energy-saving optimization data.

[0045] Interference analysis in material transport primarily confirms whether there are duplicate AGV objects involved in the generated combined transport planning data. For duplicate combined transport planning data, the one with the lowest total combined transport energy consumption is selected and retained from an energy consumption perspective, while the other duplicate combined transport planning data is excluded. After exclusion, there may be situations where the corresponding target loading AGV is no longer involved in the combined transport. However, the target set corresponding to the target loading AGV may contain more than one target AGV, so it is advisable to continue acquiring combined transport planning data. The acquisition basis is still to use the combination with the largest positive energy consumption difference among the remaining target AGVs as the target for planning. Of course, the re-planned combined transport planning data still needs to undergo repeated comparative analysis until no duplicates occur or there are no objects that can be combined transported for analysis.

[0046] S2: Based on the load-limited energy-saving optimization data, perform energy-saving adjustment analysis based on material transportation time limits to generate time-limited energy-saving adjustment data.

[0047] Based on the load-limited energy-saving optimization data, energy-saving adjustment analysis based on material transportation time limits is performed to form time-limited energy-saving adjustment data. This includes: based on the load-limited energy-saving optimization data, overall energy-saving adjustment analysis based on material transportation time limits is performed to form overall time-limited adjustment energy-saving data; based on the overall time-limited adjustment energy-saving data, independent time-limited energy-saving adjustment analysis for target objects is performed to form time-limited energy-saving adjustment data.

[0048] Energy-saving adjustment analysis based on material handling time limits mainly focuses on adjusting the time spent on material handling. The goal is to achieve energy savings by using the latest time limit required at the unloading point as a benchmark. This allows for effective energy reduction while ensuring the material handling time limit is met. Of course, this implementation-based energy-saving adjustment can be considered from both a holistic and individual perspective. After all, planned data, considering coordination, often prioritizes high efficiency while neglecting energy conservation; therefore, unified speed adjustments can be made at the overall level. Secondly, the situation of individual AGVs can be considered. Since different AGVs have different arrival times and time limits due to cluster planning, this can be reasonably analyzed.

[0049] Based on load-limited energy-saving optimization data, an overall energy-saving adjustment analysis based on material handling time limits is conducted to form overall time limit adjustment energy-saving data. This includes: determining the target planned arrival time of each target AGV to the final unloading point and the corresponding target planned allowable time limit for the final unloading point based on the load-limited energy-saving optimization data; determining the corresponding target time tolerance for different target AGVs based on the corresponding target planned arrival time and target planned allowable time limit; obtaining the minimum target time tolerance for different target AGVs and marking it as the allowable time limit adjustment amount; and adjusting the speed of each target AGV along the entire planned path with the allowable time limit adjustment amount as the target to form overall time limit adjustment energy-saving data.

[0050] The goal of the overall energy-saving adjustment analysis is to uniformly extend the material transport time of the AGV cluster, achieved by uniformly reducing its speed. This is based on the premise that all AGVs in the current path planning of the AGV cluster arrive at their destination ahead of schedule and within the stipulated time limit. Therefore, the difference between the planned arrival time of each AGV based on the planned data and the latest allowed arrival time is used for judgment. If a difference exists, it means that the planned arrival time of the AGV closest to the latest arrival time is earlier than the latest arrival time. Therefore, this time difference can be used as the target value for overall adjustment, uniformly delaying the arrival time of all AGVs by this time difference. This approach achieves energy savings relatively efficiently and quickly, as it eliminates the need to consider the spatiotemporal interference between different AGVs after speed reduction.

[0051] Using the allowable adjustment amount for time limits as the target, the speed of each target AGV is adjusted along the entire planned path to form overall time limit adjustment energy-saving data. This includes: determining the speed change information along the entire route for different target AGVs based on the corresponding planned path; uniformly reducing the speed of different target AGVs based on the speed change information until the time difference between the time the adjusted target AGV arrives at the final unloading point and the time before the adjustment is equal to the allowable adjustment amount for time limits; and collecting the adjusted path planning data of all target AGVs to form overall time limit adjustment energy-saving data.

[0052] The overall adjustment specifically uses speed reduction as the adjustment method. To ensure that the speed reduction adjustment of each AGV does not cause spatiotemporal interference, the speed value of each AGV is uniformly reduced along the path. The goal of the adjustment is that the difference between the arrival time at the destination after speed reduction and the original arrival time is exactly equal to the allowable adjustment amount. This allows for efficient and rapid energy-saving adjustments at the overall level. While overall speed reduction adjustments can usually be implemented quickly, special cases may arise where overall adjustment is not feasible. In such cases, the adjustment result may lead to path interference, but this can be resolved through subsequent optimization based on individual adjustments.

[0053] Based on the overall time-limit adjustment energy-saving data, conduct independent time-limit energy-saving adjustment analysis for target objects to form time-limit energy-saving adjustment data, including: determining the planning path information of different target AGVs based on the overall time-limit adjustment energy-saving data; for different target AGVs, perform speed reduction adjustment with the target planned allowable time limit as the target to form corresponding independent speed reduction planning paths; perform interference adjustment processing on the path intersections of the independent speed reduction planning paths corresponding to all target AGVs in the following way: for any path intersection, adjust the speed of any one of the target AGVs within the speed adjustment range centered on the path intersection point, so that the two target AGVs always have a distance of not less than the interference interval limit within the speed adjustment range; obtain the planning paths of all target AGVs after speed adjustment to form time-limit energy-saving adjustment data.

[0054] Independent energy-saving adjustments are made for the target AGV, primarily by reducing its speed to make its arrival time equal to or close to the allowable latest arrival time. This application employs a method that first directly reduces speed based on the allowable latest arrival time to form a preliminary path plan. Then, it considers intersections along the path where interference occurs with other AGVs. If spatiotemporal interference exists at these intersections, speed adjustments are made, as further speed reduction would exceed the required latest arrival time limit. Here, the speed increase considers a reasonable range of AGV speed variations within a given time frame. The speed adjustment range can be determined based on actual conditions. The goal of the speed increase is to ensure that the distance between two interfering AGVs within the speed adjustment range is never less than the interference interval limit. The interference interval limit can also be determined based on actual conditions. This approach fully guarantees the minimum optimal and most efficient speed increase adjustment, avoiding significant reduction in energy-saving effects. The speed increase adjustment target for the two interfering AGVs can be randomly selected or selected based on predefined rules.

[0055] S3: Based on the time-limited energy-saving adjustment data, perform real-time energy consumption optimization monitoring and analysis to generate real-time energy consumption optimization data for the cluster.

[0056] Based on the time-limited energy-saving adjustment data, real-time energy consumption optimization monitoring and analysis are performed to form real-time energy consumption optimization data for the cluster. This includes: acquiring real-time material handling data and determining the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point; if the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point is less than the interference interval limit, then: if the speed-increasing target AGV is decelerated, and the deceleration adjustment makes the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point not less than the interference interval limit, then the deceleration adjustment information is determined as the real-time optimization planning data for the corresponding target AGV; if the speed-increasing target AGV is decelerated, and the deceleration adjustment makes the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point still less than the interference interval limit, then the speed-increasing target AGV will be adjusted again to increase speed until the distance between two target AGVs within the speed adjustment range corresponding to the path intersection point is always not less than the interference interval limit, and the speed-increasing adjustment information is determined as the real-time optimization planning data for the corresponding target AGV; combining the real-time optimization planning data and the planning data of the remaining target AGVs, real-time energy consumption optimization data for the cluster is formed.

[0057] The purpose of real-time energy consumption optimization monitoring and analysis is to avoid interference events caused by unexpected situations during actual material transportation and to take early action to prevent them. From an energy consumption perspective, the first approach is to slow down AGVs that are already accelerating. After all, of the two objects involved in the interference, only the one that wasn't previously accelerated can have its acceleration adjusted. However, from an energy consumption perspective, reducing energy consumption is the primary consideration, so slowing down AGVs that are already accelerating is the first choice. If this first choice cannot be achieved, then acceleration can continue. If accelerating AGVs that weren't previously accelerating is less effective in preventing interference than continuing to accelerate those that are already accelerating, since their acceleration intervals may need to be further reduced, the acceleration limit should be set so that the minimum interval within the range exactly equals the interval limit. Acceleration adjustments can be based on acceleration or speed changes, determined according to the actual situation to achieve the best adjustment effect.

[0058] Figure 4 This is a schematic diagram of the structure of an AGV cluster energy-optimized path planning system provided in an embodiment of the present invention. Exemplarily, this system can be a network device, or a chip (system) or other component or assembly that can be configured within the network device. Figure 4 As shown, the system includes a data acquisition unit for acquiring current cluster path planning data and real-time material handling data; an energy-saving analysis unit for performing load-limit-based energy-saving optimization analysis on the current cluster path planning data acquired by the data acquisition unit to form load-limit-based energy-saving optimization data, and performing time-limit-based energy-saving adjustment analysis based on the material handling time limit based on the load-limit-based energy-saving optimization data to form time-limit-based energy-saving adjustment data; and a real-time monitoring unit for performing real-time energy consumption optimization monitoring and analysis based on the real-time material handling data acquired by the data acquisition unit and the time-limit-based energy-saving adjustment data formed by the energy-saving analysis unit to form real-time cluster energy consumption optimization data.

[0059] This system forms an energy consumption optimization system for AGV clusters through different functional units. The data acquisition unit completes effective data acquisition, the energy-saving analysis unit forms the optimal low-energy-consumption adjustment planning path, and the real-time monitoring unit performs controllable predictive adjustment processing. This achieves orderly and compliant cluster control while effectively reducing energy consumption, which is an important material basis for realizing energy consumption optimization of AGV clusters.

[0060] It should be understood that, in the embodiments of the present invention, the unit with data processing function can be a central processing unit (CPU). This processor can also 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 can be a microprocessor or any conventional processor.

[0061] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0062] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0063] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0064] 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, or a combination of computer software and electronic hardware. 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.

[0065] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0066] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0067] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0068] In addition, the functional units in the various embodiments of the present 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.

[0069] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a 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, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0070] 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 changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A path planning method for optimizing energy consumption in an AGV cluster, characterized in that, The method includes: Obtain current cluster path planning data, perform energy-saving optimization analysis based on load constraints, and generate load-constrained energy-saving optimization data; Based on the load-limited energy-saving optimization data, an energy-saving adjustment analysis based on material transportation time limits is performed to generate time-limited energy-saving adjustment data; Based on the time-limited energy-saving adjustment data, real-time energy consumption optimization monitoring and analysis are performed to form real-time energy consumption optimization data for the cluster.

2. The method according to claim 1, characterized in that, The process of acquiring current cluster path planning data, performing energy-saving optimization analysis based on load constraints, and generating load-constrained energy-saving optimization data includes: Based on the current cluster path planning data, determine the target load of different target AGVs; Based on the target load of different target AGVs, load capacity analysis is performed to identify all target load AGVs; For different target-loaded AGVs, a combined transport energy-saving optimization analysis is performed to generate the load-limited energy-saving optimization data.

3. The method according to claim 2, characterized in that, The process involves analyzing the loading capacity based on the target load of different target AGVs to identify all target loading AGVs, including: Based on the current cluster path planning data, the target maximum load of different target AGVs is determined; For different target AGVs, the corresponding target load capacity is determined based on the corresponding target maximum load and the target load. For different target AGVs, the loading capacity is analyzed according to the corresponding target load capacity in the following manner: If for any other target AGV that does not contain the target AGV itself, there exists a corresponding target load that does not exceed the target capacity load, then the target AGV is designated as the target loading AGV, and the set of other AGVs whose target load does not exceed the target capacity load corresponding to the target loading AGV is formed into a combined transport target set.

4. The method according to claim 3, characterized in that, The process of performing combined transport energy-saving optimization analysis on different target AGVs to generate load-limited energy-saving optimization data includes: Extract the different target AGVs in the combined transport target set corresponding to the target loading AGV in sequence, and determine the loading distance between the two loading points, the unloading distance between the two unloading points, the current location of the two AGVs, and the latest allowed arrival time to the two unloading points based on the current planned paths of the two AGVs. Based on the current location, loading interval, unloading interval, and latest allowed arrival time, a route planning is performed to determine a combined transport route for a single AGV that satisfies the latest allowed arrival time of the two unloading points. Obtain the total energy consumption of the combined transport route and the total energy consumption of the independent transport when the two AGVs transport materials separately, and determine the energy consumption difference of the combined transport. Based on the energy consumption difference of all AGVs in the target transport set corresponding to the target loaded AGV, the following analysis and judgment are performed: If the combined transport energy consumption difference is positive, then the combined transport planning path corresponding to the largest combined transport energy consumption difference is determined as the combined transport planning data corresponding to the target loaded AGV; If the combined transport energy consumption is not positive, the target loading AGV will not perform combined transport processing; For different target AGVs, the combined transport planning data is used to perform material transport interference analysis to form the load limit energy-saving optimization data.

5. The method according to claim 4, characterized in that, The process of performing material transport interference analysis on the combined transport planning data corresponding to different target AGVs to generate load-limited energy-saving optimization data includes: For different targets, load AGVs and determine the corresponding two AGV objects based on the corresponding combined transport planning data; A repetitive comparison was performed on the AGV objects involved in the different combined transport planning data: If no two of the combined transport planning data have duplicate AGVs, then the load-limited energy-saving optimization data is formed by combining all the combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing. If two AGVs in the combined transport planning data are duplicated, the total energy consumption of the combined transport planning data that are duplicated is compared. The combined transport planning data with the lower total energy consumption is retained. For the target loading AGVs corresponding to the combined transport planning data that are not retained, a new combined transport analysis is performed to form new combined transport planning data. The combined transport planning data of the new trip is repeatedly compared until it is confirmed that the new combined transport planning data is no longer duplicated. Then, all the combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form the load limit energy saving optimization data. If two AGVs in the combined transport planning data are duplicated, the total energy consumption of the duplicated combined transport planning data is compared. The combined transport planning data with the lower total energy consumption is retained. For the target loading AGVs corresponding to the combined transport planning data that are not retained, a new combined transport analysis is performed to form new combined transport planning data. The combined transport planning data of the new trip is repeatedly compared until no new combined transport planning data is formed. Then, all the combined transport planning data and the current planning data corresponding to the remaining target AGVs that have not undergone combined transport processing are combined to form the load-limited energy-saving optimization data.

6. The method according to claim 5, characterized in that, The step of performing energy-saving adjustment analysis based on material handling time limits according to the load-limited energy-saving optimization data to form time-limited energy-saving adjustment data includes: Based on the load limit energy-saving optimization data, an overall energy-saving adjustment analysis based on material transportation time limit is performed to form overall time limit adjustment energy-saving data. Based on the overall time-limited energy-saving data, an independent time-limited energy-saving adjustment analysis is performed for the target object to generate the time-limited energy-saving adjustment data.

7. The method according to claim 6, characterized in that, The step of performing an overall energy-saving adjustment analysis based on the load-limit energy-saving optimization data and the material transportation time limit to form overall time limit adjustment energy-saving data includes: Based on the load-limited energy-saving optimization data, the target planned arrival time of each target AGV to the final unloading point and the corresponding target planned allowable time limit for the final unloading point are determined; For different target AGVs, the corresponding target time tolerance is determined based on the corresponding target planned arrival time and the target planned allowable time limit; Obtain the minimum target time tolerance value corresponding to different target AGVs, and define it as the allowable adjustment amount of the time limit; Using the time limit adjustment amount as the target, the speed reduction of each target AGV is adjusted along the entire planned path to form the overall time limit adjustment energy-saving data.

8. The method according to claim 7, characterized in that, The step of adjusting the speed of each target AGV along the entire planned path, with the time limit adjustment amount as the target, to form the overall time limit adjustment energy-saving data, includes: For different target AGVs, the speed change information along the entire route is determined based on the corresponding planned path; For different target AGVs, the speed is uniformly reduced according to the speed change information until the time difference between the time the target AGV arrives at the final unloading point after adjustment and the time before adjustment is equal to the time limit allowable adjustment amount. The adjusted path planning data of all the target AGVs are collected to form the overall time limit adjustment energy saving data.

9. The method according to claim 8, characterized in that, The step of adjusting energy-saving data based on the overall time limit, and performing independent time-limit energy-saving adjustment analysis for the target object to form the time-limit energy-saving adjustment data includes: Based on the overall time limit adjustment energy-saving data, the planning path information for different target AGVs is determined; For different target AGVs, the speed reduction is adjusted based on the target plan's allowable time limit to form corresponding independent speed reduction planning paths; For the path intersections of the independent deceleration planning paths corresponding to all the target AGVs, the following interference adjustment process is applied: For any path intersection point, within a speed adjustment range centered on the center of the path intersection point, the speed increase of either of the target AGVs is adjusted, such that within the speed adjustment range, the two target AGVs always maintain a distance not less than the interference interval limit. Obtain the planned paths of all target AGVs after speed adjustment to form the time-limited energy-saving adjustment data.

10. The method according to claim 9, characterized in that, The step of performing real-time energy consumption optimization monitoring and analysis based on the time-limited energy-saving adjustment data to form cluster real-time energy consumption optimization data includes: Acquire real-time material handling data to determine the distance between the two target AGVs within the speed adjustment range corresponding to the path intersection point; If the distance between the two target AGVs within the speed adjustment range corresponding to the path intersection point is less than the interference interval limit, then: If the target AGV with the increased speed is decelerated, and the deceleration adjustment is such that the distance between the two target AGVs within the speed adjustment range corresponding to the path intersection is not less than the interference interval limit, then the deceleration adjustment information is determined as the real-time optimization planning data for the corresponding target AGV. If the target AGV with increased speed is decelerated, and the deceleration adjustment makes the distance between the two target AGVs within the speed adjustment range corresponding to the path intersection point still less than the interference interval limit, then the target AGV with increased speed will be adjusted to increase speed again until the distance between the two target AGVs within the speed adjustment range corresponding to the path intersection point is always not less than the interference interval limit. The speed adjustment information is then determined as the real-time optimization planning data for the corresponding target AGV. The real-time optimization planning data and the planning data of the other target AGVs are combined to form the real-time energy consumption optimization data of the cluster.

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