An unmanned forklift charging strategy generation method, system, device and storage medium

Abstract

The present application relates to unmanned forklift charging technical field, especially in a kind of unmanned forklift charging strategy generation method, system, equipment and storage medium;The unmanned forklift charging strategy generation method includes steps: when system scheduling state is for starting new job task state, then obtain warehouse storage occupancy rate;Warehouse storage occupancy rate is analyzed, to obtain forklift real-time state;When forklift real-time state is forklift idle state, then according to forklift idle state, system scheduling state is updated to start new job task state, in the update system scheduling state, according to the preset charging trigger condition, forklift group is divided;Charging forklift group and waiting forklift group are analyzed, to obtain forklift group charging strategy;With system scheduling state analysis as starting point, when starting new job, warehouse storage occupancy rate is used to deduce forklift load, forklift is idle, then update scheduling state, divide forklift group and generate strategy in combination with charging station state, improve job continuity and resource utilization.

Description

Technical Field

[0001] This invention relates to the field of unmanned forklift charging technology, and in particular to a method, system, device and storage medium for generating unmanned forklift charging strategies. Background Technology

[0002] In fully automated warehousing and logistics scenarios, unmanned forklifts, as core equipment for material handling, need to achieve efficient and continuous operation. However, under current technology, there are significant shortcomings in the formulation of charging strategies for unmanned forklifts. On the one hand, there is a lack of comprehensive consideration of system scheduling status and warehouse storage space occupancy rate, making it difficult to accurately judge the real-time status of forklifts. This results in the inability to start new work tasks in a timely manner when forklifts are idle, causing resource idleness. On the other hand, when it is necessary to arrange charging for a group of forklifts, it is impossible to divide the forklift group according to the working status of the charging station, resulting in unreasonable allocation of charging resources. This leads to either charging congestion or resource waste, which seriously affects the operating efficiency of unmanned forklifts and the overall scheduling efficiency of the system, and fails to meet the needs of fully automated workshops for efficient and intelligent logistics handling. Summary of the Invention

[0003] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide a method, system, device and storage medium for generating charging strategies for unmanned forklifts.

[0004] The first aspect of this invention provides a method for generating charging strategies for unmanned forklifts, applied to an unmanned forklift charging strategy generation system. The unmanned forklift charging strategy generation system includes a forklift group and warehouse storage locations. The method includes the following steps: acquiring the system scheduling status and analyzing it; when the system scheduling status is a state of waiting to start a new task, acquiring the warehouse storage location occupancy rate; analyzing the warehouse storage location occupancy rate to obtain the real-time status of the forklifts; analyzing the real-time status of the forklifts; when the real-time status of the forklifts is a forklift idle state, updating the system scheduling status to a state of starting a new task based on the forklift idle state to obtain an updated system scheduling status; in the updated system scheduling status, dividing the forklift group according to preset charging trigger conditions to obtain a charging forklift group and a waiting forklift group; acquiring the working status of the charging station group, and analyzing the charging forklift group and the waiting forklift group based on the working status to obtain a charging strategy for the forklift group.

[0005] Furthermore, the unmanned forklift charging strategy generation system also includes a charging station group. The step of dividing the forklift group according to preset charging trigger conditions to obtain a charging forklift group and a waiting forklift group includes: acquiring the forklift group's range characteristic parameters; obtaining the workload from the updated system scheduling status; analyzing the workload based on the range characteristic parameters to obtain the total power required for the operation; acquiring the forklift group's power data to obtain first power data; analyzing the first power data and the total power required for the operation according to the charging trigger conditions to obtain a first analysis result; determining whether the first analysis result meets the charging trigger conditions; if the first analysis result meets the charging trigger conditions, then performing data statistics on the charging station group to obtain the number of charging stations; and dividing the forklift group according to the first power data and the number of charging stations to obtain a charging forklift group and a waiting forklift group.

[0006] Furthermore, the step of dividing the forklift group based on the first power data and the number of charging stations to obtain a charging forklift group and a waiting forklift group includes: obtaining the number of material feeding points from the updated system scheduling status; generating a shift cycle based on the range characteristic parameters, workload, and number of material feeding points; analyzing the first power data and the number of charging stations to obtain the forklift charging demand; analyzing the forklift charging demand and the shift cycle to obtain a priority sequence; and dividing the forklift group according to the priority sequence to obtain a charging forklift group and a waiting forklift group.

[0007] Furthermore, the step of analyzing the charging forklift group and the waiting forklift group based on their working status to obtain a forklift group charging strategy includes: analyzing the charging forklift group based on its working status and a preset iteration stop condition to obtain a first charging strategy; acquiring the power data of the waiting forklift group to obtain second power data; obtaining charging reservation requirements from the forklift charging requirements; determining a second charging strategy based on the charging reservation requirements, the working status, and the second power data; and generating a forklift group charging strategy based on the first charging strategy and the second charging strategy.

[0008] Further, the step of analyzing the charging forklift group based on the working state and preset iteration stopping conditions to obtain the first charging strategy includes: analyzing the charging forklift group based on the working state to obtain the charging path and the first forklift group; performing slope detection on the charging path based on preset slope detection conditions to obtain the slope detection result; when the slope detection result indicates that the charging path meets the slope detection condition, then analyzing the first forklift group based on the iteration stopping conditions and preset charging conditions to obtain a charging mode set sequence; and generating the first charging strategy based on the charging mode set sequence and the charging path.

[0009] Furthermore, the unmanned forklift charging strategy generation system also includes a group of material feeding points. The step of analyzing the charging forklift group based on the working status to obtain the charging path and the first forklift group includes: obtaining the upper limit of charging quantity from the working status; filtering the charging forklift group based on the upper limit of charging quantity to obtain the first forklift group; obtaining the coordinate data of the first forklift group to obtain the first coordinate data; obtaining the coordinate data of the material feeding point group and the coordinate data of the charging station group to obtain the second coordinate data and the third coordinate data; and analyzing the first coordinate data, the second coordinate data, and the third coordinate data to obtain the charging path.

[0010] Further, the step of analyzing the first forklift group according to the iteration stopping condition and the preset charging condition to obtain a charging mode set sequence includes: analyzing the first forklift group according to the charging condition to obtain a charging mode set; performing quantity statistics on the first forklift group to obtain quantity statistics; analyzing the data statistics according to the iteration stopping condition to obtain a second analysis result; if the second analysis result is that the data statistics do not meet the iteration stopping condition, then returning to the working state of obtaining the charging station group according to the preset system running time until the data statistics meet the iteration stopping condition; if the second analysis result is that the data statistics meet the iteration stopping condition, then obtaining all charging mode sets to obtain a charging mode set sequence.

[0011] Furthermore, an unmanned forklift charging strategy generation system includes: a control device and a forklift group and warehouse storage locations electrically connected to the control device; the control module is used to execute an unmanned forklift charging strategy generation method as described in any one of the above.

[0012] Furthermore, an unmanned forklift charging strategy generation device includes: a memory and at least one processor, wherein the memory stores instructions; at least one processor invokes the instructions in the memory to cause the computer device to execute the various steps of the unmanned forklift charging strategy generation method as described in any one of the above.

[0013] Furthermore, a computer-readable storage medium stores instructions that, when executed by a processor, implement the steps of a method for generating a charging strategy for an unmanned forklift as described in any of the preceding claims.

[0014] In the technical solution of this invention, starting with system scheduling status analysis, warehouse storage space occupancy rate analysis is triggered when a new operation is about to start, and forklift load is inferred (e.g., low occupancy rate corresponds to idle forklifts), providing a basis for starting a new operation; when it is confirmed that the forklifts are idle, the system scheduling status is updated, and then the forklift group is divided according to the charging triggering conditions, which not only selects the forklifts that need to be charged first to prevent insufficient power from affecting the operation, but also identifies the waiting forklift group that can perform tasks first, achieving an initial match between operation and charging demand; finally, a charging strategy is generated in combination with the working status of the charging station group to adapt resource allocation (e.g., prioritizing the use of idle stations), avoiding charging congestion or resource idleness, ensuring efficient charging of forklifts and orderly connection of waiting forklifts, and comprehensively improving the continuity of forklift group operations and the utilization rate of charging and storage resources. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0016] Figure 1 This is a first flowchart of a method for generating a charging strategy for an unmanned forklift, provided by an embodiment of the present invention.

[0017] Figure 2 This is a second flowchart of a method for generating a charging strategy for an unmanned forklift, provided in an embodiment of the present invention.

[0018] Figure 3 This is a third flowchart of a method for generating charging strategies for unmanned forklifts provided in an embodiment of the present invention;

[0019] Figure 4 This is a fourth flowchart of a method for generating charging strategies for unmanned forklifts provided in an embodiment of the present invention;

[0020] Figure 5 The fifth flowchart of a method for generating charging strategies for unmanned forklifts provided in an embodiment of the present invention;

[0021] Figure 6 The sixth flowchart of a method for generating charging strategies for unmanned forklifts provided in an embodiment of the present invention;

[0022] Figure 7 The seventh flowchart of a method for generating charging strategies for unmanned forklifts provided in an embodiment of the present invention;

[0023] Figure 8 This is a schematic diagram of the structure of an unmanned forklift charging strategy generation system provided in an embodiment of the present invention;

[0024] Figure 9 This is a schematic diagram of the structure of an unmanned forklift charging strategy generation device provided in an embodiment of the present invention.

[0025] In the attached diagram, 1-forklift group; 2-warehouse storage location; 3-charging station group; 4-feeding point group. Detailed Implementation

[0026] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] For ease of understanding, the specific process of an embodiment of the present invention is described below. A method for generating an unmanned forklift charging strategy is applied to an unmanned forklift charging strategy generation system. The unmanned forklift charging strategy generation system includes a forklift group 1 and warehouse storage locations 2. Please refer to [link / reference]. Figure 1 One embodiment of the unmanned forklift charging strategy generation method of the present invention includes:

[0028] 101. Obtain the system scheduling status and analyze it;

[0029] 102. When the system scheduling status is "awaiting the start of a new job task", obtain the occupancy rate of warehouse storage location 2;

[0030] In this embodiment, when the system is in a state of waiting to start a new job, it triggers subsequent analysis actions on warehouse storage location 2 and forklift status, which is the starting condition of the process; when the system meets the new job start state, it obtains the warehouse storage location occupancy rate. The warehouse storage location occupancy rate is a key indicator reflecting the storage and turnover of warehouse materials. By analyzing this indicator, the operating load of the forklift can be indirectly inferred; for example, a low storage location occupancy rate means that the demand for material turnover is small and the forklift is in an idle state; a high storage location occupancy rate means that the forklift is likely in a busy operating state.

[0031] 103. Analyze the warehouse storage space occupancy rate to obtain the real-time status of forklifts;

[0032] 104. Analyze the real-time status of the forklift;

[0033] 105. When the forklift's real-time status is "forklift idle", the system scheduling status is updated to "start new job task" based on the forklift's idle status, so as to obtain the updated system scheduling status.

[0034] In this embodiment, based on the analysis results of warehouse storage space occupancy rate, it is determined whether the forklift is in an idle or busy state. Only when the forklift has no task being performed and the external storage space conditions support it will it be determined to be in an idle state.

[0035] 106. In the updated system scheduling state, the forklift group 1 is divided according to the preset charging trigger conditions to obtain the charging forklift group and the waiting forklift group.

[0036] In this embodiment, under the updated system scheduling state, forklift group 1 is divided into two categories, charging and waiting, according to the charging triggering conditions. This can accurately select forklifts that need to be charged first, avoiding insufficient power from affecting subsequent operations. At the same time, waiting forklift groups are clearly defined, and tasks can be executed first and then charging can be arranged, so as to achieve a reasonable match between operation and charging needs, and improve the operation continuity and resource utilization of forklift group 1.

[0037] 107. Obtain the working status of charging station group 3, and analyze the charging forklift group and waiting forklift group based on the working status to obtain the charging strategy for the forklift group.

[0038] In this embodiment, by acquiring the working status of charging station group 3 and combining it with the charging forklift group and waiting forklift group status, a charging strategy is generated. This strategy can adapt to charging station resources (such as prioritizing the allocation of idle stations) to avoid charging congestion or resource idleness. At the same time, it meets the needs of different forklift groups 1, ensuring efficient charging forklifts and orderly connection of waiting forklifts, thereby improving the utilization rate of charging resources and the operational continuity of forklift group 1. The scheduling system automatically issues charging tasks to unmanned forklifts according to the charging strategy of the forklift group. The unmanned forklifts automatically go to the charging area. When the battery is fully charged or the battery meets the basic power consumption requirements for handling operations (handling tasks are issued during the charging process), the scheduling system controls the unmanned forklifts to switch to task mode.

[0039] In this embodiment, starting with system scheduling status analysis, warehouse storage space occupancy analysis is triggered when a new operation is about to start to infer forklift load (e.g., low occupancy rate corresponds to idle forklifts), providing a basis for starting a new operation. When it is confirmed that the forklifts are idle, the system scheduling status is updated, and then forklift group 1 is divided according to charging trigger conditions. This not only filters out forklifts that need to be charged first to prevent insufficient power from affecting the operation, but also identifies the waiting forklift group that can perform tasks first, achieving an initial match between operation and charging demand. Finally, a charging strategy is generated based on the working status of charging station group 3 to adapt resource allocation (e.g., prioritizing the use of idle stations), avoiding charging congestion or resource idleness, ensuring efficient charging of forklifts and orderly connection of waiting forklifts, and comprehensively improving the operational continuity of forklift group 1 and the utilization rate of charging and storage resources.

[0040] Please see Figure 2 A second embodiment of the unmanned forklift charging strategy generation method in this invention includes:

[0041] 201. Obtain the range characteristics parameters of forklift group 1;

[0042] In this embodiment, the range characteristics parameters include the forklift's full-load power consumption (e.g., 280W / hour), the forklift's no-load power consumption (e.g., 150W / hour), and the battery capacity (e.g., 50Ah). These parameters directly determine the power consumption rate of the forklift when completing different tasks and are the core basis for calculating the power required for the operation. For example, a forklift consumes 0.28kWh of power in 1 hour of full-load operation.

[0043] 202. Obtain the job workload from the updated system scheduling status;

[0044] In this embodiment, the total material delivery volume, average distance per task, and task type ratio (e.g., 70% full-load operation and 30% empty-load operation) are used to quantify the total workload that the forklift needs to complete, so as to avoid inaccurate power analysis due to deviations in task volume estimation.

[0045] 203. Analyze the workload based on the endurance characteristic parameters to obtain the total amount of electricity required for the operation;

[0046] In this embodiment, the logic for generating the total power required for the operation is as follows: First, the task is divided according to the proportion of full load and no load of the task. Then, the corresponding range characteristic parameters of the forklift (full load / no load power consumption) are matched to calculate the power consumption of each task segment. Then, the power consumption of each segment is summarized, while reserving 10% of the power as a redundancy to deal with emergencies. Finally, the total power required to support the completion of all operations is obtained.

[0047] 204. Obtain the battery power data of forklift group 1 to obtain the first battery power data;

[0048] 205. Analyze the first power data and the total power required for the operation based on the charging trigger conditions to obtain the first analysis result;

[0049] 206. Determine whether the first analysis result meets the charging trigger condition;

[0050] 207. If the first analysis result is that the charging triggering condition is met, then perform data statistics on charging station group 3 to obtain the number of charging stations.

[0051] In this embodiment, the charging trigger condition is the core standard for determining whether the forklift needs to be charged. The charging trigger condition is that the actual available power of the forklift is less than 80% of the total power required for the operation (with a 20% buffer). First, the real-time power of the forklift in the first power data is converted into the actual available power, and then compared with the total power required for the operation. If the condition is met, it means that the power of the forklift is insufficient to support the operation, and the subsequent charging resource statistics and allocation process needs to be started.

[0052] 208. Divide forklift group 1 according to the first power data and the number of charging stations to obtain charging forklift group and waiting forklift group;

[0053] In this embodiment, the battery life parameters and workload are first obtained, and the power consumption is split and matched according to the task type. The total power required for the operation is calculated by summarizing the power consumption and reserving redundancy, so as to ensure that the power calculation is close to reality. Then, the first power data is obtained, and the charging trigger conditions are combined to determine whether charging is required. If the conditions are met, the number of effective charging stations is counted. Finally, the forklift group is divided into groups 1 according to the first power data and the number of charging stations to ensure that high-demand forklifts are charged first, avoid resource waste and operation interruption, and help the warehouse logistics operate efficiently and orderly.

[0054] Please see Figure 3 A third embodiment of the unmanned forklift charging strategy generation method in this invention includes:

[0055] 301. Obtain the number of feeding points from the updated system scheduling status;

[0056] In this embodiment, the number of material feeding points is extracted from the updated system scheduling status (i.e., the system status after starting a new task). Its core function is to quantify the spatial complexity of the work scenario. The number of material feeding points directly affects the forklift's travel path planning (e.g., frequent back-and-forth trips are required for multiple material feeding points) and the operation time (e.g., the delivery path for 3 material feeding points is longer than 1). It is a key spatial parameter for generating shift cycles. For example, in a new task in the dewatering workshop, the number of material feeding points is 3 (corresponding to 3 production machines with different cycle times). Therefore, the workload of the forklift among multiple material feeding points needs to be calculated based on this number.

[0057] 302. Generate shift cycles based on endurance parameters, workload, and number of material feeding points;

[0058] In this embodiment, the range characteristic parameter clearly defines the upper limit of power support, providing a basic range boundary for cycle setting; the workload quantifies the total workload of the task and calculates the basic time required to complete the task; the number of feeding points reflects the complexity of the work space, and multiple feeding points require an increase in the round-trip path coefficient to correct the time consumption of a single task (e.g., 3 feeding points require a path coefficient of 1.5 times compared to 1 point). When calculating, the total operation time is calculated first, and then 20% redundant power is reserved based on the full charge range to finally determine the maximum continuous operation time of the forklift in a single operation, forming a shift cycle that ensures that the power is not exhausted and fits the production feeding rhythm;

[0059] 303. Analyze the initial power data and the number of charging stations to obtain the forklift charging demand;

[0060] In this embodiment, the current battery level and battery health of the forklift are first extracted from the first battery data. The difference between this and the battery level required to complete one shift cycle is calculated to determine if there is a battery shortage. Then, the number of charging stations (the scale of effective charging resources) is combined to analyze the urgency of the shortage and the resource matching degree. Both factors are used to determine the forklift charging demand.

[0061] 304. Analyze forklift charging demand and shift cycles to obtain a priority sequence;

[0062] In this embodiment, the urgency of forklift charging needs is taken as the primary dimension, and the forklifts are sorted according to the size of the power shortage (e.g., shortage > 10% > 5%-10% > 0-5%). Then, the correlation of shift cycle is combined to prioritize forklifts that must be used during the shift before forklifts that can be used between shifts. The two are combined to form a priority sequence to ensure that forklifts with high urgent needs and critical operations are given priority.

[0063] 305. Divide forklift group 1 according to priority sequence to obtain charging forklift group and waiting forklift group;

[0064] In this embodiment, the effective charging resource limit corresponding to the number of charging stations is first determined (e.g., 2 charging stations correspond to a limit of 2). Then, forklifts are selected from high to low according to the priority sequence: the top N (N=resource limit) are included in the charging forklift group and can be allocated charging resources immediately; the remaining forklifts are assigned to the waiting forklift group and will be scheduled when resources are available, so as to achieve precise matching of resources and demand.

[0065] In this embodiment, the number of material feeding points is first obtained from the updated system scheduling status to quantify the complexity of the workspace and provide key parameters for the generation of subsequent shift cycles. Then, the shift cycle is generated by combining the battery life characteristics, workload, and number of material feeding points, ensuring that the forklift battery is not depleted while also aligning with the production rhythm. The charging demand of forklifts is determined by analyzing the initial battery data and the number of charging stations to avoid ineffective resource allocation. A priority sequence is formed based on the urgency of charging demand and the correlation with the shift cycle to ensure that high-value operations are prioritized. Finally, charging forklift groups and waiting forklift groups are divided according to priority and resource limits to achieve precise matching of resources and demand, thereby improving the efficiency and continuity of warehouse operations.

[0066] Please see Figure 4 The fourth embodiment of the unmanned forklift charging strategy generation method in this invention includes:

[0067] 401. Analyze the charging forklift group based on the working status and preset iteration stopping conditions to obtain the first charging strategy;

[0068] In this embodiment, the first charging strategy is adapted through an iterative mechanism, which can ensure that high-priority forklifts are charged in a timely and efficient manner, laying the foundation for subsequent operations and improving charging efficiency and operational continuity.

[0069] 402. Obtain the battery data of the waiting forklift group to obtain the second battery data;

[0070] In this embodiment, the second power data includes collecting the real-time power of the waiting forklift group (e.g., forklift D: 35%, forklift E: 42%), the power decline rate (e.g., 2% per hour when idle), and calculating the latest charging start time for the waiting forklift group (e.g., forklift D with 35% power, the low power threshold of 20% corresponds to charging in 7.5 hours at the latest).

[0071] 403. Obtain charging reservation requirements from forklift charging needs;

[0072] In this embodiment, the charging reservation requirements include the target charging capacity (e.g., forklift E needs to be charged to 60%), the task time window (e.g., forklift D needs to perform the task 8 hours later), and the charging station type preference (e.g., only compatible with fast charging stations).

[0073] 404. Determine the second charging strategy based on charging reservation requirements, working status, and second battery level data;

[0074] 405. Generate a forklift group charging strategy based on the first charging strategy and the second charging strategy;

[0075] In this embodiment, a first charging strategy is generated by combining the charging station's operating status and iteration stopping conditions. An iterative mechanism ensures strategy adaptation, prioritizing timely and efficient charging for high-priority forklifts to improve charging efficiency and operational continuity, laying the foundation for subsequent processes. Next, second-level power data of the waiting forklift group is acquired to accurately grasp its real-time power, power consumption rate, and latest charging time. At the same time, charging reservation requirements are extracted to clarify the energy replenishment target and time window. A second charging strategy is formulated based on the charging station's operating status to avoid charging delays caused by waiting forklifts. Finally, the two strategies are integrated to form a forklift group charging strategy, achieving full coverage of the charging needs of the entire forklift group. This approach avoids resource waste and demand omissions while dynamically balancing resource load, contributing to the stable and efficient operation of warehousing and logistics.

[0076] Please see Figure 5 The fifth embodiment of the unmanned forklift charging strategy generation method in this invention includes:

[0077] 501. Analyze the charging forklift group based on its working status to obtain the charging path and the first forklift group.

[0078] In this embodiment, the first forklift group and charging path are determined by analyzing the working status of the charging station. Priority charging targets are selected based on the maximum number of charging vehicles and forklift priority to avoid resource waste and overloading. The coordinate planning path avoids obstacles and ensures driving efficiency. This can improve the utilization rate of charging resources and the driving safety of forklifts, lay the foundation for the implementation of subsequent charging strategies, and help the efficient operation of warehousing and logistics.

[0079] 502. Perform slope detection on the charging path according to the preset slope detection conditions to obtain the slope detection results;

[0080] In this embodiment, the slope detection logic is as follows: The charging path is divided into segments at 5-meter intervals. The slope value of each segment is detected one by one using the slope sensor preset in the workshop or the slope data on the map, so as to avoid sudden power consumption or insufficient power of the forklift when climbing due to local steep slopes. If the detection result meets the conditions (such as referring to the ground requirements of the national standard "GB / T20721-2006": the maximum allowable value of the undulation within 1m² should be ≤3mm, the road slope: the road slope (H / L) is defined as the maximum value of the ratio of the road surface horizontal height difference to the route length within a length range of more than 100mm; the maximum allowable value of the road slope must be less than ≤0.05, and for parking points that require precise positioning of unmanned forklifts or AMRs (automated mobile robots), it must be ≤0.01); Step height: Step height is defined as the maximum difference in road surface level within a length range of 100mm. Steps are not allowed at the parking locations of unmanned forklifts or AMRs. The maximum allowable step height in other areas must be ≤5mm. Ditch width: Ditches are not allowed at the parking locations of unmanned forklifts or AMRs. The maximum allowable ditch width in other areas must be ≤8mm. If the ditch width exceeds the maximum allowable value, the step height requirement applies. If the requirements are not met (e.g., road slope ≥0.01, steps at the unmanned forklift location, step height exceeding the maximum allowable value in other areas, ditch at the unmanned forklift location, ditch width exceeding the maximum allowable value in other areas), the path must be replanned until it complies with the requirements.

[0081] 503. When the slope detection result shows that the charging path meets the slope detection condition, the first forklift group is analyzed according to the iteration stop condition and the preset charging condition to obtain the charging mode set sequence.

[0082] In this embodiment, after the charging path meets the slope requirements, the first forklift group generates a set sequence of charging modes by combining the iterative stopping conditions and charging conditions. This ensures that the path is safe and there is no risk of climbing, and also ensures that all forklifts are adapted to the mode that matches the power and task requirements through iteration, avoiding adaptation omissions and mismatches, providing accurate mode basis for subsequent charging strategies, and helping unmanned forklifts to charge efficiently and orderly.

[0083] 504. Generate the first charging strategy based on the charging mode set sequence and the charging path;

[0084] In this embodiment, the first forklift group is selected based on the charging station's operating status, the maximum charging capacity, and forklift priority to avoid resource waste and overloading. Simultaneously, obstacle avoidance paths are planned to improve charging resource utilization and forklift driving safety, laying the foundation for subsequent processes. Next, the slope of the charging path is checked in segments, and any irregularities are replanned to avoid sudden power consumption or insufficient power due to steep slopes, ensuring driving safety. Once the path meets the standards, the first forklift group is analyzed using iteration and charging conditions to generate a charging mode set sequence. This ensures that all forklifts in the charging group are matched to a mode that meets their own needs, avoiding omissions and mismatches. Finally, the mode sequence and charging path are integrated to generate the first charging strategy, helping unmanned forklifts charge efficiently and orderly, ensuring continuous and stable operation of warehousing and logistics.

[0085] Please see Figure 6 The sixth embodiment of a method for generating charging strategies for unmanned forklifts according to the present invention includes:

[0086] 601. Obtain the maximum charging limit from the working status;

[0087] In this embodiment, the maximum number of charging stations refers to the maximum number of forklifts that can be charged simultaneously by all charging stations in the current state (no faults, full load operation); for example, the total maximum number of charging stations (each supporting 1 forklift to charge at the same time) is "2", which directly determines the upper limit of the scale of the first forklift group.

[0088] 602. Filter the charging forklift groups according to the maximum charging capacity to obtain the first forklift group;

[0089] In this embodiment, the “priority charging subset” selected from the charging forklift group (all forklifts that need charging) is selected based on the maximum number of charging units and the forklift priority (such as prioritizing forklifts with high urgent needs). It is the core object of the charging resources that can be allocated at the current stage.

[0090] 603. Obtain the coordinate data of the first forklift group to obtain the first coordinate data;

[0091] In this embodiment, the first coordinate data is the real-time position coordinates of each forklift in the first forklift group (based on the workshop coordinate system, such as (X1,Y1)), which is the starting point data for path planning.

[0092] 604. Obtain the coordinate data of feeding point group 4 and charging station group 3 to obtain the second coordinate data and the third coordinate data;

[0093] In this embodiment, the second coordinate data, the location coordinates of the feeding point group 4 (e.g., (X2, Y2)...), is used to avoid busy operating areas (e.g., material loading and unloading areas around the feeding points); the third coordinate data, the location coordinates of the charging station group 3 and the precise coordinates of the charging positions (e.g., (X3, Y3)...), is the endpoint data for path planning;

[0094] 605. Analyze the first coordinate data, the second coordinate data, and the third coordinate data to obtain the charging path;

[0095] In this embodiment, the planning logic of the charging path is as follows: First, the first coordinate data (real-time position of the first forklift group), the second coordinate data (position of material feeding point group 4), and the third coordinate data (position of charging station group) are mapped to the same workshop coordinate system to clarify the spatial distribution of the forklift starting point, material feeding point avoidance area, and charging station endpoint; then, based on the "straight-line distance from the forklift starting point to the charging station endpoint", the working radius of the material feeding point marked by the second coordinate data (such as a 5-meter radius around the perimeter) and ground obstacle areas are avoided; at the same time, the path turning points are adjusted in combination with the minimum turning radius and maximum climbing ability of the forklift (such as a slope ≤ 3%) to ensure that the forklift travels without jamming; finally, a continuous path with a starting point, passing through obstacle-free turning points and an endpoint is determined to ensure that the path is executable and without safety risks;

[0096] In this embodiment, from the perspective of resource matching, based on the total maximum charging capacity of all charging stations operating at full capacity without faults, and combined with forklift priority selection, the first forklift group is selected. This avoids overloading of charging stations and ensures that forklifts with high urgent needs can obtain charging resources first, improving the utilization rate of charging resources and the continuity of operations, and reducing the risk of material delivery interruption for high-priority tasks. From the perspective of path planning, three types of coordinate data are collected and mapped to the same workshop coordinate system. Based on the shortest straight-line distance, the system avoids material feeding points and obstacles, and combines forklift performance adjustment inflection points to plan a safe and executable charging path. This reduces the accident rate of forklift driving, reduces power consumption and docking adjustment time during the journey, improves the charging docking success rate, provides a clear basis for subsequent charging execution, reduces the overall system complexity, and ensures the efficient advancement of unmanned forklift charging and operations.

[0097] Please see Figure 7 The seventh embodiment of a method for generating charging strategies for unmanned forklifts according to the present invention includes:

[0098] 701. Analyze the first forklift group based on the charging conditions to obtain a set of charging modes;

[0099] In this embodiment, based on charging conditions (such as forklift battery threshold, charging efficiency requirements, and task time window), an individual suitability analysis is performed on the first forklift group (the selected subset of forklifts to be charged). For example, a fast charging mode set is assigned to forklifts with battery levels below 30%, and a balanced charging mode set is assigned to forklifts with battery levels between 30% and 60% and no urgent tasks, thereby generating a charging mode set.

[0100] 702. Count the number of forklifts in the first group to obtain the count value;

[0101] 703. Analyze the statistical values ​​of the data based on the iteration stopping condition to obtain the second analysis result;

[0102] In this embodiment, the iteration stopping condition is that the data statistics value is equal to the number statistics value of the forklifts in the charging forklift group. For example, if the first forklift group contains 3 forklifts, the number statistics value is "3", which only reflects the size of the first forklift group.

[0103] 704. If the second analysis result is that the data statistics value does not meet the iteration stop condition, then return to the execution of obtaining the working status of charging station group 3 according to the preset system running time until the data statistics value meets the iteration stop condition.

[0104] In this embodiment, if the iteration stop condition is not met, the system running time (e.g., 15 minutes) is used to reacquire the working status of the charging station group 3;

[0105] 705. If the second analysis result shows that the data statistics meet the iteration stopping condition, then obtain all charging mode sets to obtain the charging mode set sequence;

[0106] In this embodiment, this step ensures that all forklifts in the charging forklift group complete the charging mode adaptation and integrate them into an orderly set of charging modes. On the one hand, it avoids omissions in forklift adaptation and matches each forklift with a dedicated charging mode that matches its power status and task requirements. On the other hand, it provides a complete and clear execution basis for subsequent batch charging operations, effectively improving the comprehensiveness and systematicness of the charging process and helping the unmanned forklift group to complete charging efficiently and orderly.

[0107] In this embodiment, based on charging conditions, the charging mode is precisely adapted for the first forklift group, ensuring that each forklift has a charging solution that suits its own needs. Through a quantity statistics and iteration mechanism, the iteration stops when the data statistics value equals the quantity statistics value of the charging forklift group. If the iteration stops when the condition is not met, the working status of the charging station group 3 is reacquired after a preset time of 15 minutes, ensuring that all forklifts that need to be charged are adapted. The resulting ordered charging mode set sequence provides a complete and clear execution basis for subsequent batch charging, and improves the comprehensiveness and systematicness of the charging process. This effectively helps the unmanned forklift group to complete charging efficiently and orderly, ensuring the continuity and stability of warehousing and logistics operations.

[0108] The above describes a method for generating an unmanned forklift charging strategy according to an embodiment of the present invention. The following describes a system for generating an unmanned forklift charging strategy according to an embodiment of the present invention. Please refer to [link / reference]. Figure 8 One embodiment of the unmanned forklift charging strategy generation system of the present invention includes:

[0109] An unmanned forklift charging strategy generation system includes: a control device and a forklift group 1 and a warehouse storage location 2 electrically connected to the control device; the control module is used to execute an unmanned forklift charging strategy generation method as described above.

[0110] Figure 9 This is a schematic diagram of the structure of an unmanned forklift charging strategy generation device 900 provided in an embodiment of the present invention. This unmanned forklift charging strategy generation device 900 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 910 (e.g., one or more processors) and a memory 920, and one or more storage media 930 (e.g., one or more mass storage devices) storing application programs 933 or data 932. The memory 920 and storage media 930 can be temporary or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the unmanned forklift charging strategy generation device 900. Furthermore, the processor 910 may be configured to communicate with the storage media 930 and execute a series of instruction operations in the storage media 930 on the unmanned forklift charging strategy generation device 900 to implement the steps of the unmanned forklift charging strategy generation method provided in the above-described method embodiments.

[0111] An unmanned forklift charging strategy generation device 900 may further include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating systems 931, such as Windows Server, MacOSX, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 9 The illustrated structure of an unmanned forklift charging strategy generation device does not constitute a limitation on an unmanned forklift charging strategy generation device. It may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0112] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the steps of a method for generating a charging strategy for an unmanned forklift.

[0113] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system, device, or unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0114] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present 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.

[0115] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for generating a charging strategy for an unmanned forklift, characterized in that, An unmanned forklift charging strategy generation system is applied, the system comprising a forklift fleet and warehouse storage locations, and also including a charging station fleet. The unmanned forklift charging strategy generation method includes the following steps: Obtain the system scheduling status and analyze it; When the system scheduling status is "awaiting the start of a new job task", the warehouse storage space occupancy rate is obtained. Warehouse space occupancy rate is a key indicator reflecting the storage and turnover of warehouse materials. A low space occupancy rate means that forklifts are idle; a high space occupancy rate means that forklifts are in operation. Analyze warehouse storage space occupancy rates to obtain real-time forklift status; Analyze the real-time status of the forklift; When the forklift's real-time status is "forklift idle", the system scheduling status is updated to "start new job task" based on the forklift's idle status, thus obtaining the updated system scheduling status. In the updated system scheduling state, the forklift group is divided according to the preset charging trigger conditions to obtain the charging forklift group and the waiting forklift group. The step of dividing the forklift group according to preset charging trigger conditions to obtain a charging forklift group and a waiting forklift group includes: Obtain the range characteristics parameters of the forklift fleet; The job workload is obtained from the updated system scheduling status; The workload is analyzed based on the battery life characteristics to obtain the total amount of electricity required for the operation; Obtain the battery data of the forklift group to obtain the initial battery data; The first power data and the total power required for the operation are analyzed based on the charging triggering conditions to obtain the first analysis result; Determine whether the first analysis result meets the charging trigger condition; If the first analysis result shows that the charging triggering condition is met, then the charging station group is statistically analyzed to obtain the number of charging stations. The forklift groups are divided based on the initial power data and the number of charging stations to obtain charging forklift groups and waiting forklift groups. The process of dividing the forklift group based on the first power data and the number of charging stations to obtain a charging forklift group and a waiting forklift group includes: The number of feeding points is obtained from the updated system scheduling status; Shift cycles are generated based on endurance characteristics, workload, and the number of material feeding points. Specifically: endurance characteristics define the upper limit of battery capacity, providing a basic endurance boundary for cycle setting; workload quantifies the total task load and calculates the basic time required to complete the task; the number of material feeding points reflects the complexity of the workspace, and multiple feeding points require an additional round-trip path coefficient to adjust the time consumption per task. The initial power consumption data and the number of charging stations were analyzed to determine the forklift charging demand. Analyze forklift charging demand and shift cycles to obtain a priority sequence; The forklift group is divided according to a priority sequence to obtain a charging forklift group and a waiting forklift group. The system acquires the working status of the charging station group and analyzes the charging forklift group and the waiting forklift group based on the working status to obtain the charging strategy for the forklift group.

2. The method for generating a charging strategy for an unmanned forklift as described in claim 1, characterized in that, The analysis of the charging forklift group and the waiting forklift group based on their working status to obtain a forklift group charging strategy includes: The charging forklift group is analyzed based on its working status and preset iteration stopping conditions to obtain the first charging strategy. Obtain the battery data of the waiting forklift group to obtain the second battery data; The charging reservation demand is obtained from the charging demand of forklifts; The second charging strategy is determined based on charging reservation demand, working status, and second power data. A forklift group charging strategy is generated based on the first charging strategy and the second charging strategy.

3. The method for generating a charging strategy for an unmanned forklift as described in claim 2, characterized in that, The analysis of the charging forklift group based on its working status and preset iteration stopping conditions to obtain a first charging strategy includes: The charging forklift group is analyzed based on its working status to obtain the charging path and the first forklift group. The slope of the charging path is detected according to the preset slope detection conditions to obtain the slope detection results. When the slope detection result indicates that the charging path meets the slope detection condition, the first forklift group is analyzed according to the iteration stop condition and the preset charging condition to obtain the charging mode set sequence. The first charging strategy is generated based on the sequence of charging modes and the charging path.

4. The method for generating a charging strategy for an unmanned forklift as described in claim 3, characterized in that, The unmanned forklift charging strategy generation system also includes a group of material feeding points. The analysis of the charging forklift group based on its working status to obtain the charging path and the first forklift group includes: The maximum number of charges can be obtained from the working status; The charging forklift group is filtered according to the maximum charging capacity to obtain the first forklift group; Obtain the coordinate data of the first forklift group to obtain the first coordinate data; Obtain the coordinate data of the material feeding point group and the charging station group to obtain the second coordinate data and the third coordinate data; The first, second, and third coordinate data are analyzed to obtain the charging path.

5. The method for generating a charging strategy for an unmanned forklift as described in claim 3, characterized in that, The analysis of the first forklift group based on the iteration stopping condition and the preset charging condition to obtain a set sequence of charging modes includes: The first group of forklifts is analyzed based on the charging conditions to obtain a set of charging modes; The number of forklifts in the first group is counted to obtain the statistical value; The statistical values ​​of the data are analyzed based on the iteration stopping conditions to obtain the second analysis result; If the second analysis result is that the data statistics value does not meet the iteration stop condition, then the system will return to the working status of obtaining the charging station group according to the preset system running time until the data statistics value meets the iteration stop condition. If the second analysis result shows that the data statistics meet the iteration stopping condition, then all charging mode sets are obtained to get the charging mode set sequence.

6. A charging strategy generation system for unmanned forklifts, characterized in that, include: A control device and a group of forklifts and warehouse storage locations electrically connected to the control device; the control device is used to execute a method for generating an unmanned forklift charging strategy as described in any one of claims 1-5.

7. A charging strategy generation device for unmanned forklifts, characterized in that, include: A memory and at least one processor, wherein the memory stores instructions; At least one of the processors invokes the instructions in the memory to cause the computer device to perform the steps of the unmanned forklift charging strategy generation method as described in any one of claims 1-5.

8. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the various steps of the unmanned forklift charging strategy generation method as described in any one of claims 1-5.

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