CN117787830A - Unmanned forklift collaborative charging scheduling method - Google Patents
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Abstract
The invention discloses a cooperative charging scheduling method for an unmanned forklift, and belongs to the field of unmanned forklifts. The method comprises the steps of: s1, acquiring residual electric quantity and task chains of all unmanned forklifts; s2, grading the unmanned forklift by setting a threshold value of the residual electric quantity, and inserting a charging task into a task chain corresponding to the unmanned forklift according to the grading; s3, estimating the state of the unmanned forklift after the unmanned forklift is executed according to the task chain after the charging task is inserted, and adjusting the task chain according to the state and the task requirement; s4, executing the unmanned forklift according to the corresponding task chain. According to the invention, through hierarchical management and task chain planning of the unmanned forklift, the cooperative scheduling of the unmanned forklift operation tasks and the charging tasks is realized, and the charging efficiency and scheduling capability are improved, so that the efficiency and flexibility of the logistics and warehousing system are improved.
Description
Technical Field
The invention belongs to the field of unmanned forklifts, and particularly relates to a cooperative charging scheduling method for an unmanned forklift.
Background
Unmanned forklifts have become important material handling tools in modern logistics and warehousing systems. However, the charging requirements and scheduling problems of unmanned forklifts become key factors limiting their application. Conventional unmanned forklift charging scheduling methods are generally based on fixed charging stations and simple scheduling rules, which may not be able to accommodate complex logistical environments and dynamic charging requirements. Therefore, there is a need to develop a more intelligent and efficient unmanned forklift collaborative charging scheduling strategy.
In the prior art, a plurality of charging piles are arranged in an actual working condition environment, when an automatic guided vehicle needs to be charged, the automatic guided vehicle is directly controlled to a target charging pile (generally, the charging pile closest to the distance is taken as a target charging pile by taking the distance as a attention reference basis) for charging. Because the distance is used as the only reference for control, certain charging piles are seriously queued, and certain charging piles are seriously idle, so that the final automatic guided vehicle cannot be fully put into practical application, and the production efficiency is low.
Unmanned forklifts generally use lithium ion batteries as power sources, and their charging characteristics are such that as the amount of electricity increases, the charging speed gradually decreases. The charging speed of the unmanned forklift is generally higher than the power consumption speed, so that the number of charging piles in a system is generally less than that of the unmanned forklift in order to save equipment cost. If a plurality of unmanned forklifts need to be charged at the same time and charging resources are in shortage, the arriving unmanned forklifts must wait for the charging piles to be released, and the waiting time is too long, so that the dispatchable transportation resources in the system are reduced.
The comparison document (CN 117141263A) discloses an AGV trolley charging control method, an AGV trolley charging control system, an intelligent terminal and a storage medium, wherein the AGV trolley charging control method comprises the following steps: acquiring the battery residual capacity of the AGV; calculating the difference value between the residual electric quantity and the low electric quantity threshold value to determine an available electric quantity value; comparing the available electric quantity value with a low electric quantity approaching threshold range to judge whether the available electric quantity value is in the low electric quantity approaching threshold range or not; if so, sending a charging prompt, and acquiring a charging position of a preset charging pile; the AGV trolley is instructed to move to a charging position based on the charging prompt, a super capacitor preset on the AGV trolley is charged and stored with high current, charging is stopped until the super capacitor finishes storing energy, and a carrying prompt is sent out; AGV trolley is instructed to carry out carrying operation based on carrying prompt, and the storage battery of AGV trolley is charged with little electric current to instruct super capacitor.
The charging strategy of the comparison file is the same as the traditional charging strategy, namely a single charging threshold value is used, the unmanned forklift works normally when being higher than the threshold value, and the unmanned forklift automatically goes to the charging pile for charging after being lower than the threshold value until the unmanned forklift is fully charged. This strategy lacks flexibility: if the threshold value is too low, the electric quantity of the equipment is difficult to supplement in time when the system is idle; if the threshold is too high, the unmanned forklift charges early and it is difficult for the unmanned forklift system to provide sufficient transport capacity when the system is busy. In summary, the invention provides a collaborative charging scheduling method for an unmanned forklift.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a cooperative charging scheduling method of an unmanned forklift so as to achieve the purposes of improving charging efficiency and scheduling capacity and improving efficiency and flexibility of logistics and warehousing systems.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: the unmanned forklift collaborative charging scheduling method comprises the following steps of:
s1, acquiring residual electric quantity and task chains of all unmanned forklifts;
s2, grading the unmanned forklift by setting a threshold value of the residual electric quantity, and inserting a charging task into a task chain corresponding to the unmanned forklift according to the grading;
s3, estimating the state of the unmanned forklift after the unmanned forklift is executed according to the task chain after the charging task is inserted, and adjusting the task chain according to the state and the task requirement;
s4, executing the unmanned forklift according to the corresponding task chain.
Further, in the step S2, the threshold includes a safety threshold and an available threshold, and when the residual electric quantity of the unmanned forklift is greater than or equal to the available threshold, the level is 1, the operation is normally performed when the task chain has an operation task, and the operation-free task enters a schedulable state; when the residual electric quantity of the unmanned forklift is smaller than the available threshold and larger than the safety threshold, the level is 2, a transition state is represented, the operation tasks are normally executed when the task chain is provided with the operation tasks, and the charging tasks are inserted into the task chain when the task chain is not provided with the operation tasks; when the residual electric quantity of the unmanned forklift is smaller than or equal to the safety threshold, the unmanned forklift is in a level 3, a non-schedulable state is represented, and if the unmanned forklift has no operation task in a task chain or has an operation task but is not executed, a charging task is inserted in the task chain; if there is a job task in the task chain that is being executed, a charging task is inserted in the task chain after it completes the job task. Further, the setting of the safety threshold satisfies the condition: the residual electric quantity of the unmanned forklift meets the requirement that the unmanned forklift reaches the charging pile from any position.
Further, the threshold value further comprises a charging threshold value which is set for each unmanned forklift independently, the charging threshold value is set between the safety threshold value and the available threshold value, when the residual electric quantity of the unmanned forklift is smaller than the charging threshold value and an operation task exists in a task chain, if an idle charging pile exists, the unmanned forklift inserts the charging task into the task chain and executes the charging task, and if the idle charging pile does not exist, the unmanned forklift continues to execute the operation task; and when the residual electric quantity of the unmanned forklift is larger than or equal to the charging threshold value and the charging task is executed, if the operation task exists in the task chain or the unmanned forklift competing with the operation task for the lower electric quantity of the charging pile exists, ending the charging task, otherwise, continuing to execute the charging task.
Further, in the step S3, the power consumption model of the unmanned forklift and the charging model of the unmanned forklift are set up for prediction. The unmanned forklift electricity consumption model is as follows:
wherein,l is the residual electric quantity, L 0 For initial charge, w 1 And w 2 And the power consumption coefficient is respectively obtained from the statistics value of historical operation data of the unmanned forklift, and t is the operation time.
The unmanned forklift charging model is as follows:
L＝min(L 0 +k 1 tL 0 +L * +k 2 t)
wherein L is the residual electric quantity, L 0 For initial charge, k 1 And k 2 For the charging efficiency coefficient, t is the charging time, L * Is the inflection point of the charge efficiency curve.
Further, the charging threshold is set as follows: when no operation task exists in the task chain, the charging threshold values of all unmanned forklifts are taken as arithmetic average values of the inflection point electric quantity and the saturated electric quantity of the charging efficiency curve; when the task chain has operation tasks, all unmanned forklifts are arranged in sequence and grouped according to the residual electric quantity, the quantity of unmanned forklifts in each group is the number of charging piles, when the charging threshold value of the unmanned forklifts in the group with the lowest electric quantity is equal to the safety threshold value, for each two adjacent groups, the time for the unmanned forklifts in the group with the low electric quantity to be charged from the charging threshold value to the available threshold value is calculated according to the charging model, the electric quantity consumed by the unmanned forklifts in the group with the high electric quantity in the time is calculated according to the electric quantity consumption model, and if the residual electric quantity difference between the two adjacent groups is larger than the consumed electric quantity, the two groups of unmanned forklifts set the same charging threshold value; and if the residual electric quantity difference between two adjacent groups is smaller than or equal to the consumed electric quantity, the charging threshold value of the unmanned forklift with all the residual electric quantity lower than the low electric quantity group is increased by the same amount.
Further, in the step S3, the adjustment of the task chain includes assigning the task to other unmanned forklifts that meet the task demand after confirming that the remaining power of the unmanned forklifts to which the task belongs cannot meet the task demand through the status and the task demand.
Further, in the step S3, the adjustment of the task chain includes selecting a charging pile when the unmanned forklift performs the charging task, where the method for selecting the charging pile is as follows: the method comprises the steps that a nearest idle charging pile is preferentially selected, when the idle charging pile is not available, whether an unmanned forklift with the highest electric quantity being charged meets the condition of releasing the charging pile for a unmanned forklift with the low electric quantity is judged, when the condition is met, the unmanned forklift with the highest electric quantity finishes a charging task and releases the charging pile, and the unmanned forklift with the low electric quantity enters a charging state, wherein the condition is as follows: according to the unmanned forklift electric quantity consumption and the charging model calculation, when the high-electric quantity unmanned forklift residual electric quantity is consumed to a charging threshold value, the low-electric quantity unmanned forklift residual electric quantity is charged to be above an available threshold value.
The invention has the technical effects that: (1) According to the invention, the residual electric quantity of the unmanned forklift is divided into 3 levels by adopting a multi-level management mode, so that the charging efficiency of the unmanned forklift is improved; (2) The differential charging threshold is set, so that charging is finished and the charging pile is released under proper conditions, and the equipment utilization rate is improved; (3) The threshold value of the invention can be adjusted according to actual conditions, thus ensuring the flexibility of unmanned forklift charging scheduling; (4) According to the invention, the state of the unmanned forklift is estimated before the unmanned forklift executes the task, so that the safety of charging scheduling of the unmanned forklift is ensured; (5) The invention realizes the cooperative scheduling of the operation task and the charging task of the unmanned forklift and ensures the orderly and efficient scheduling of the unmanned forklift.
Drawings
Fig. 1 is a schematic diagram of a charge-discharge process and a threshold relationship of an unmanned forklift according to the unmanned forklift collaborative charge scheduling method.
Detailed Description
The following detailed description of the embodiments of the invention, given by way of example only, is presented in the accompanying drawings to aid those skilled in the art in a more complete, accurate and thorough understanding of the inventive concepts and aspects of the invention, and to facilitate their practice.
The unmanned forklift collaborative charging scheduling method takes the scheduling system comprising the upper computer as a carrier, can be widely applied to the fields of logistics, storage, manufacturing and the like, and is suitable for the scene of material handling, warehouse management and production line coordination which are required to be carried out by a large number of unmanned forklifts.
Firstly, an unmanned forklift scheduling system distributes operation tasks for all unmanned forklifts, wherein the operation tasks comprise transportation tasks and the like, and then the unmanned forklifts are scheduled by the unmanned forklift collaborative charging scheduling method, and the operation tasks are executed according to the following steps:
s1, acquiring residual electric quantity and task chains of all unmanned forklifts;
s2, grading the unmanned forklift by setting a threshold value of the residual electric quantity, and inserting a charging task into a task chain corresponding to the unmanned forklift according to the grading;
s3, estimating the state of the unmanned forklift after the unmanned forklift is executed according to the task chain after the charging task is inserted, and adjusting the task chain according to the state and the task requirement;
s4, executing the unmanned forklift according to the corresponding task chain.
Specifically, in the step S2, the invention adopts a multi-level management manner, the residual electric quantity of the unmanned forklift is divided into 3 levels by setting a threshold, the threshold comprises a safety threshold and an available threshold, the level is 1 when the residual electric quantity of the unmanned forklift is greater than or equal to the available threshold, the operation tasks are normally executed when the operation tasks are in a task chain, and the non-operation tasks enter a schedulable state; when the residual electric quantity of the unmanned forklift is smaller than the available threshold and larger than the safety threshold, the level is 2, a transition state is represented, the operation tasks are normally executed when the task chain is provided with the operation tasks, and the charging tasks are inserted into the task chain when the task chain is not provided with the operation tasks; when the residual electric quantity of the unmanned forklift is smaller than or equal to the safety threshold, the unmanned forklift is in a level 3, a non-schedulable state is represented, and if the unmanned forklift has no operation task in a task chain or has an operation task but is not executed, a charging task is inserted in the task chain; if there is a job task in the task chain that is being executed, a charging task is inserted in the task chain after it completes the job task. Wherein, the setting of the safety threshold value satisfies the condition: the residual electric quantity of the unmanned forklift meets the requirement that the unmanned forklift reaches the charging pile from any position. Therefore, the charging scheduling method based on multistage management preliminarily realizes the cooperative scheduling of the job tasks and the charging tasks, and improves the scheduling efficiency.
In order to improve the utilization rate of equipment, charging is finished and a charging pile is released in time under proper conditions, the threshold value further comprises a charging threshold value which is independently set for each unmanned forklift, and the charging threshold value with the differentiated characteristic has the significance of inducing part of unmanned forklifts to charge in advance and avoiding occupation conflict of the unmanned forklifts to limited charging resources. Specifically, the charging threshold is set between the safety threshold and the available threshold, when the residual electric quantity of the unmanned forklift is smaller than the charging threshold and a job task exists in a task chain, if an idle charging pile exists, the unmanned forklift inserts the charging task in the task chain and executes the charging task, and if the idle charging pile does not exist, the unmanned forklift continues to execute the job task; and when the residual electric quantity of the unmanned forklift is larger than or equal to the charging threshold value and the charging task is executed, if the operation task exists in the task chain or the unmanned forklift competing with the operation task for the lower electric quantity of the charging pile exists, ending the charging task, otherwise, continuing to execute the charging task. According to the charging scheduling method for multi-level management of the battery power, the charging tasks are reasonably inserted into the task chain of the unmanned forklift, so that the charging scheduling capability is improved, the competition rate of the charging piles is reduced, and the equipment utilization rate is improved.
As shown in fig. 1, the safety threshold L of the present invention safe Charge threshold L charge And an available threshold L over The scheduling method has the characteristic of dynamic, can be adjusted according to the busyness of a real-time scheduling system and the electric quantity relation between unmanned fork workshops, and improves the flexibility of the scheduling method.
The invention establishes an unmanned forklift electric quantity consumption model and an unmanned forklift charging model, wherein the unmanned forklift electric quantity consumption model is as follows:
wherein L is the residual electric quantity, L 0 For initial charge, w 1 And w 2 The power consumption coefficient of the unmanned forklift in the idle and full states is respectively, t is the working time, and the power consumption coefficient is traversed by the unmanned forkliftStatistics of the history job data are obtained.
The unmanned forklift charging model is as follows:
L＝min(L 0 +k 1 tL 0 +L * +k 2 t)
wherein L is the residual electric quantity, L 0 For initial charge, k 1 And k 2 For the charging efficiency coefficient, t is the charging time, L * For the inflection point of the charge efficiency curve, when the electric quantity is smaller than L according to the charge characteristics of the battery * During the time, unmanned forklift is high in charging speed, and the electric quantity is larger than L * After that, the charging speed becomes slow.
Based on the unmanned forklift electric quantity consumption model and the charging model, the charging threshold value is set as follows: when no operation task exists in the task chain, the charging threshold values of all unmanned forklifts are taken as arithmetic average values of the inflection point electric quantity and the saturated electric quantity of the charging efficiency curve; when an operation task exists in the task chain, all unmanned forklifts are arranged in sequence and grouped according to the residual electric quantity (the ascending order, descending order and other ordering modes can be selected according to actual conditions), the number of unmanned forklifts in each group is the number of charging piles, when the charging threshold value of the unmanned forklifts in the group with the lowest electric quantity is equal to the safety threshold value, for each two adjacent groups, the time for charging the unmanned forklifts in the group with the low electric quantity from the charging threshold value to the available threshold value is calculated according to a charging model, the electric quantity consumed by the unmanned forklifts in the group with the high electric quantity in the time is calculated according to an electric quantity consumption model, if the residual electric quantity difference between the two adjacent groups is larger than the consumed electric quantity, the situation that the occupied competition of the charging piles cannot occur between the two groups of unmanned forklifts is indicated, and the two groups of unmanned forklifts are provided with the same charging threshold value; if the difference of the residual electric power between two adjacent groups is smaller than or equal to the consumed electric power, that is, when the unmanned forklift of the low electric power group is not yet charged to the available threshold, the unmanned forklift of the high electric power group has generated charging demands, which may cause the occurrence of the situation of competing charging piles, the charging threshold of the unmanned forklift of the low electric power group should be improved, that is, the charging threshold of the unmanned forklift of all the residual electric power groups lower than the low electric power group is improved by equal amount.
In the scheduling decision, the more information and objects that the control system can acquire, the better the system optimization effect will generally be. In the unmanned forklift dispatching system, all transportation tasks generated by the warehouse management system WMS are operation tasks of the unmanned forklift, and tasks generated by the self-demand of equipment such as unmanned forklift charging belong to non-operation tasks and should be dispatched cooperatively. In order to reasonably insert a charging task into a task chain, when a transportation task scheduling scheme is analyzed, the electric quantity change of the unmanned forklift is estimated according to the operation state of the unmanned forklift, a charging process is required to be simulated after the charging task is inserted, and charging time and a result are fed back to the estimation of the unmanned forklift state. Before the unmanned forklift is executed according to the task chain, the state of the unmanned forklift after the unmanned forklift is executed is estimated to ensure the safe completion of the task, namely, the step S3: according to the task chain inserted with the charging task, the state of the unmanned forklift after the unmanned forklift is executed according to the task chain is estimated, and the task chain is adjusted according to the state and the task requirement.
Specifically, the path consumption in the process of the unmanned forklift can be calculated according to the electric quantity consumption model of the unmanned forklift, so that the energy utilization rate of the unmanned forklift when the unmanned forklift executes the task is obtained, the charging time of the unmanned forklift can be calculated according to the charging model of the unmanned forklift, so that the time utilization rate of the unmanned forklift when the unmanned forklift executes the task is obtained, the energy utilization rate and the time utilization rate of the unmanned forklift when the unmanned forklift executes the task are synthesized, namely, the task completion time is estimated on the basis of simultaneously considering the electric quantity consumption of the unmanned forklift when the unmanned forklift executes the task and the time consumption in the charging process, the task demand is combined, whether the corresponding unmanned forklift can complete the task is timely charged is judged, and then the task chain is adjusted based on the judgment, so that the task and the charging requirement are met, and the efficiency and the safety are ensured.
The task chain adjustment includes distributing the task to other unmanned forklifts meeting task requirements after confirming that the residual electric quantity of the unmanned forklifts to which the task belongs cannot meet the task requirements through the states and the task requirements. The adjustment is finished before the specific tasks of the unmanned forklift are executed, so that the safety and efficiency of scheduling of the unmanned forklift are guaranteed, the problem that the task being executed by the unmanned forklift cannot meet the requirements of task completion and timely charging of the unmanned forklift when the residual electric quantity of the unmanned forklift is lower than a safety threshold value is avoided, and the task handover phenomenon of other unmanned forklifts is needed.
Meanwhile, the task chain is adjusted by selecting the charging pile when the unmanned forklift executes the charging task, wherein the charging pile selecting method comprises the following steps: the method comprises the steps that a nearest idle charging pile is preferentially selected, when the idle charging pile is not available, whether an unmanned forklift with the highest electric quantity being charged meets the condition of releasing the charging pile for a unmanned forklift with the low electric quantity is judged, when the condition is met, the unmanned forklift with the highest electric quantity finishes a charging task and releases the charging pile, and the unmanned forklift with the low electric quantity enters a charging state, wherein the condition is as follows: according to the unmanned forklift electric quantity consumption and the charging model calculation, when the high-electric quantity unmanned forklift residual electric quantity is consumed to a charging threshold value, the low-electric quantity unmanned forklift residual electric quantity is charged to be above an available threshold value. Compared with the prior art, the improved charging method of the invention restricts the electric quantity difference between unmanned forklifts when competing with each other for charging piles, can avoid frequent alternate occupation of the charging piles, and simultaneously avoids shortage of charging resources.
And finally, executing the step S4, namely the unmanned forklift according to the corresponding task chain. After the unmanned forklift executes according to the adjusted task chain, on one hand, the timely and efficient completion of the operation tasks is guaranteed, and on the other hand, the unmanned forklift is guaranteed to be charged timely, so that the cooperative scheduling of the unmanned forklift operation tasks and the charging tasks is realized, the charging efficiency and scheduling capability are improved, and the efficiency and flexibility of logistics and warehousing systems are improved.
In order to further optimize the performance and effect of the charge scheduling system, the invention can also be adjusted in the following directions:
1. intelligent algorithm optimization: the scheduling algorithm may be further modified, for example, intelligent algorithms such as genetic algorithms, simulated annealing algorithms, etc., to optimize the charging schedule and scheduling strategy for better results.
2. Dynamic scheduling policy: real-time data and environment information can be introduced, so that a scheduling strategy can be dynamically adapted to the changed logistics demand and charging environment, and the flexibility and response capability of the system are improved.
3. Optimizing charging facilities: the layout and capacity planning of the charging facilities can be considered to be optimized to meet the charging requirements of different areas and time periods, and the congestion and waiting time of the charging station are reduced.
4. Energy management technology: the energy utilization and distribution can be optimized by combining energy management technology, such as using an energy storage system or intelligent charging piles, so that the charging efficiency and the sustainability of the system are improved.
These further optimized, improved methods may be further explored and practiced according to particular needs and research directions.
The invention is described above by way of example with reference to the accompanying drawings. It will be clear that the invention is not limited to the embodiments described above. As long as various insubstantial improvements are made using the method concepts and technical solutions of the present invention; or the invention is not improved, and the conception and the technical scheme are directly applied to other occasions and are all within the protection scope of the invention.
Claims (10)
1. The unmanned forklift collaborative charging scheduling method is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring residual electric quantity and task chains of all unmanned forklifts;
s2, grading the unmanned forklift by setting a threshold value of the residual electric quantity, and inserting a charging task into a task chain corresponding to the unmanned forklift according to the grading;
s3, estimating the state of the unmanned forklift after the unmanned forklift is executed according to the task chain after the charging task is inserted, and adjusting the task chain according to the state and the task requirement;
s4, executing the unmanned forklift according to the corresponding task chain.
2. The unmanned forklift collaborative charging scheduling method according to claim 1, wherein the method comprises the following steps: in the step S2, the threshold includes a safety threshold and an available threshold, and when the residual electric quantity of the unmanned forklift is greater than or equal to the available threshold, the level is 1, the operation is normally executed when the task chain has an operation task, and the operation-free task enters a schedulable state; when the residual electric quantity of the unmanned forklift is smaller than the available threshold and larger than the safety threshold, the level is 2, a transition state is represented, the operation tasks are normally executed when the task chain is provided with the operation tasks, and the charging tasks are inserted into the task chain when the task chain is not provided with the operation tasks; when the residual electric quantity of the unmanned forklift is smaller than or equal to the safety threshold, the unmanned forklift is in a level 3, a non-schedulable state is represented, and if the unmanned forklift has no operation task in a task chain or has an operation task but is not executed, a charging task is inserted in the task chain; if there is a job task in the task chain that is being executed, a charging task is inserted in the task chain after it completes the job task.
3. The unmanned forklift collaborative charging scheduling method according to claim 2, wherein: the setting of the safety threshold value meets the condition: the residual electric quantity of the unmanned forklift meets the requirement that the unmanned forklift reaches the charging pile from any position.
4. The unmanned forklift collaborative charging scheduling method according to claim 3, wherein: the threshold value further comprises a charging threshold value which is independently set for each unmanned forklift, the charging threshold value is set between the safety threshold value and the available threshold value, when the residual electric quantity of the unmanned forklift is smaller than the charging threshold value and an operation task exists in a task chain, if an idle charging pile exists, the unmanned forklift inserts the charging task into the task chain and executes the charging task, and if the idle charging pile does not exist, the unmanned forklift continues to execute the operation task; and when the residual electric quantity of the unmanned forklift is larger than or equal to the charging threshold value and the charging task is executed, if the operation task exists in the task chain or the unmanned forklift competing with the operation task for the lower electric quantity of the charging pile exists, ending the charging task, otherwise, continuing to execute the charging task.
5. The unmanned forklift collaborative charging scheduling method according to claim 1, wherein the method comprises the following steps: in the step S3, the power consumption model and the charging model of the unmanned forklift are estimated by establishing the power consumption model and the charging model of the unmanned forklift.
6. The unmanned forklift collaborative charging scheduling method according to claim 5, wherein the method comprises the following steps: the unmanned forklift electricity consumption model is as follows:
wherein L is the residual electric quantity, L 0 For initial charge, w 1 And w 2 And the power consumption coefficient is respectively obtained from the statistics value of historical operation data of the unmanned forklift, and t is the operation time.
7. The unmanned forklift collaborative charging scheduling method according to claim 5, wherein the method comprises the following steps: the unmanned forklift charging model is as follows:
L＝min(L 0 +k 1 tL 0 +L * +k 2 t)
wherein L is the residual electric quantity, L 0 For initial charge, k 1 And k 2 For the charging efficiency coefficient, t is the charging time, L * Is the inflection point of the charge efficiency curve.
8. The unmanned forklift collaborative charging scheduling method according to claims 4 and 5, wherein: the charging threshold is set as follows: when no operation task exists in the task chain, the charging threshold values of all unmanned forklifts are taken as arithmetic average values of the inflection point electric quantity and the saturated electric quantity of the charging efficiency curve; when the task chain has operation tasks, all unmanned forklifts are arranged in sequence and grouped according to the residual electric quantity, the quantity of unmanned forklifts in each group is the number of charging piles, when the charging threshold value of the unmanned forklifts in the group with the lowest electric quantity is equal to the safety threshold value, for each two adjacent groups, the time for the unmanned forklifts in the group with the low electric quantity to be charged from the charging threshold value to the available threshold value is calculated according to the charging model, the electric quantity consumed by the unmanned forklifts in the group with the high electric quantity in the time is calculated according to the electric quantity consumption model, and if the residual electric quantity difference between the two adjacent groups is larger than the consumed electric quantity, the two groups of unmanned forklifts set the same charging threshold value; and if the residual electric quantity difference between two adjacent groups is smaller than or equal to the consumed electric quantity, the charging threshold value of the unmanned forklift with all the residual electric quantity lower than the low electric quantity group is increased by the same amount.
9. The unmanned forklift collaborative charging scheduling method according to claim 5, wherein the method comprises the following steps: in the step S3, the adjustment of the task chain includes assigning the task to other unmanned forklifts that meet the task demand after confirming that the remaining power of the unmanned forklifts to which the task belongs cannot meet the task demand through the state and the task demand.
10. The unmanned forklift collaborative charging scheduling method according to claim 5, wherein the method comprises the following steps: in the step S3, the adjustment of the task chain includes selecting a charging pile when the unmanned forklift performs a charging task, where a method for selecting the charging pile is as follows: the method comprises the steps that a nearest idle charging pile is preferentially selected, when the idle charging pile is not available, whether an unmanned forklift with the highest electric quantity being charged meets the condition of releasing the charging pile for a unmanned forklift with the low electric quantity is judged, when the condition is met, the unmanned forklift with the highest electric quantity finishes a charging task and releases the charging pile, and the unmanned forklift with the low electric quantity enters a charging state, wherein the condition is as follows: according to the unmanned forklift electric quantity consumption and the charging model calculation, when the high-electric quantity unmanned forklift residual electric quantity is consumed to a charging threshold value, the low-electric quantity unmanned forklift residual electric quantity is charged to be above an available threshold value.
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