A hoist multi-task execution sequence feasibility screening and cooperative control method

Abstract

This application provides a method for feasibility screening and collaborative control of the execution sequence of multiple tasks on a hoist, relating to the field of automated logistics warehousing technology. The method includes receiving a task queue, using an energy matching feasibility function to verify the feasibility of potential energy compensation between different tasks, generating a set of priority candidate sequences, performing a connection synchronization feasibility verification on each candidate sequence, and retaining the verified sequences in the feasible set; establishing a comprehensive objective function L(π)=α×W(π)+β×T(π), where W(π) includes unit lifting energy consumption, idle movement energy consumption, and energy compensation benefits, and dynamically adjusting the weighting coefficients α and β according to the system's accumulation and filling rate, solving for the optimal sequence in the feasible set; dynamically adjusting the speed curve and controlling connection coordination according to the weight of the material bin during execution, and triggering rescheduling after each task is completed. This application has the effects of high operating efficiency and energy saving.

Description

Technical Field

[0001] This invention relates to the field of automated logistics warehousing technology, and in particular to a method for feasibility screening and collaborative control of the multi-task execution sequence of a hoist. Background Technology

[0002] In automated storage and retrieval systems (AS / RS), the hoist is the core vertical conveying equipment connecting different floors. The operating efficiency of the hoist system directly affects the throughput capacity of the entire AS / RS, while its energy consumption significantly impacts warehousing operating costs. Current research on energy-saving control of hoists mainly focuses on three aspects: First, reducing single-task energy consumption by optimizing the speed curve parameters of a single hoist stroke. For example, patent CN202110039862.5 discloses a stroke control method for energy-saving operation of a mine hoist, controlling a single stroke by solving for the optimal parameters of the S-shaped speed curve. Second, in stacker crane systems, utilizing the reuse of empty storage spaces to generate task sequence constraints, establishing a scheduling model with the goal of minimizing total energy consumption, and reducing total system energy consumption by optimizing the task execution order. Third, using energy recovery devices to convert the gravitational potential energy of the stacker crane or hoist during its descent into electrical energy and store or recycle it.

[0003] However, the above solutions all have the following shortcomings: First, existing scheduling optimization methods only consider the impact of task sequence on energy consumption, completely ignoring the risk of connection conflicts that may be caused by task sequence at the execution level. When the hoist switches tasks between multiple floors, the conveying speed of the upstream conveyor line, the arrival time of the hoist, and the occupancy time of the shuttle car at the docking port need to be precisely matched. An inappropriate scheduling sequence, even with extremely low energy consumption, can lead to docking port congestion and equipment waiting for each other, which will significantly reduce the actual throughput of the system. Second, existing energy recovery solutions only discuss how to recover and reuse potential energy, without considering the constraints of energy reuse operations on the time window of the equipment. Using the energy recovered from the descent of the previous task to drive the ascent of the next task necessarily requires the two tasks to be closely connected in time, but such a close connection sequence may be infeasible due to connection conflicts. Third, in existing solutions, task optimization at the scheduling layer and connection coordination at the execution layer are isolated from each other, and no overall solution has been formed that can simultaneously handle scheduling energy consumption optimization and connection synchronization feasibility.

[0004] To address the aforementioned issues, there is an urgent need for a joint control method that can optimize the execution sequence of multiple tasks on the hoist under the constraint of the connection synchronization feasibility window, thereby maximizing system throughput and minimizing operating energy consumption. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a method for feasibility screening and collaborative control of the multi-task execution sequence of a hoist.

[0006] The feasibility screening and collaborative control method for multi-task execution sequence of a hoist provided in this application adopts the following technical solution: A method for feasibility screening and collaborative control of multi-task execution sequence in a hoist includes the following steps: S1. Receive the task queue, where each task in the task queue contains the weight m of the hopper. i Starting floor F i start Target floor F i target Task priority p i And the accumulation buffer capacity C of the upstream conveyor connection. i ; S2. Use the energy matching feasibility function to perform energy matching checks on any two tasks in the task queue to determine whether one task and another task have the feasibility of direct potential energy compensation. S3. Based on the energy matching feasibility verification results, generate a set of priority candidate execution orders. Each candidate order is a permutation of the task queue π={π(1),π(2),…,π(n)}. S4. Perform a connection synchronization feasibility verification on each candidate order in the priority candidate execution order set to determine whether the order will cause the upstream conveyor line connection port to be blocked, and retain the candidate order that passes the connection synchronization feasibility verification to the feasible order set. S5. Establish a comprehensive objective function L(π)=α×W(π)+β×T(π) with the feasible order set as the domain, where W(π) is the estimated total energy consumption corresponding to the candidate order π, T(π) is the estimated total time corresponding to the candidate order π, and α and β are the energy consumption weight coefficient and time weight coefficient, respectively. S6. Based on the real-time operating status of the current system, dynamically adjust the values ​​of the energy consumption weight coefficient α and the time weight coefficient β according to the smooth curve. S7. Calculate the optimal execution order π*=argmin L(π) in the feasible order set based on the comprehensive objective function L(π); S8. Issue execution instructions according to the optimal execution order. For each unit task, dynamically adjust the lifting speed curve of the hoist based on the measured weight of the material box, and control the upstream conveyor line to transport the material box to the dock in advance to wait, so that the time when the material box arrives at the dock matches the time when the hoist arrives at the target floor.

[0007] Optionally, the energy matching feasibility function is defined as Binary(task i, task j) = 1 if and only if the target floor of task i is higher than the starting floor of task j and the weight of the bin of task i is greater than the weight of the bin of task j, while satisfying the floor difference H. ij=|F i target -F j start | Less than the preset maximum energy transfer distance H max When Binary(task i, task j) = 1, it means that when task i is executed after task j, the gravitational potential energy released by task j during descent can be directly transferred to task i for ascent, forming real-time energy matching.

[0008] Optionally, the connection synchronization feasibility verification includes the following sub-steps: obtaining the current accumulation filling amount q of the upstream conveyor line. curr And the shuttle's estimated arrival time t shuttle expected For the k-th task π(k) in the candidate sequence π, calculate the estimated arrival time (EAT) of the elevator to the docking point of task π(k) based on the current position and time of the elevator after completing the previous k-1 tasks. k If the estimated arrival time of the hoist is EAT k If the candidate sequence π is later than the latest allowed time for the upstream conveyor line to deliver the hopper to the docking point and later than the expected release time for the shuttle to occupy the docking point, and the accumulated filling amount exceeds the buffer capacity threshold, then the candidate sequence π is determined to be inconsistent with the docking synchronization feasibility conditions.

[0009] Optionally, the total energy consumption estimate W(π) consists of three parts: the sum of the lift-up energy consumption of each unit task, the sum of the idle movement energy consumption between tasks, and the sum of the energy compensation benefits, expressed as follows: , Where δ k As an indicator function, δ is defined when Binary(π(k+1),π(k))=1. k =1, otherwise δ k =0, the calculation formulas for each energy consumption item are as follows: E task =(m·g·H) / η motor Where m is the weight of the hopper (positive for rising, negative for falling), g = 9.8 m / s², H is the vertical lifting distance, and η motor =0.85 is the motor efficiency; E idle =(M empty ·g·D) / η motor M empty To measure the unloaded mass of the lifting machine, D represents the vertical distance of unloaded movement; E comp =η transfer ·(m i -m j )·g·H ij , where η transfer =0.85 is the energy transfer efficiency, m i For the weight of the next heavy-load mission, mj For the weight of the previous light-load mission, H ij Take min(H) i H j ).

[0010] Optionally, the method for dynamically adjusting the energy consumption weighting coefficient α and the time weighting coefficient β includes: real-time monitoring of the accumulated filling amount q of the upstream conveyor line. curr Total accumulation buffer q max The ratio r=q curr / q max When the ratio r exceeds the first blockage threshold r1, the priority of the energy consumption weight coefficient α is gradually reduced while the priority of the time weight coefficient β is gradually increased over a period of time. When the ratio r is lower than the second unblocking threshold r2, the priority of the energy consumption weight coefficient α is gradually increased while the priority of the time weight coefficient β is gradually reduced over a period of time. The gradual adjustment of the weights is achieved through iterative updates with a fixed step size to avoid continuous oscillations in the execution order caused by abrupt changes.

[0011] Optionally, during the sequential issuance of execution instructions, a re-evaluation procedure is automatically triggered after each unit task is completed. Starting from step one, the remaining task queue is reread, and the optimal execution order is recalculated using the latest accumulation fill amount and shuttle position status to achieve dynamic rescheduling.

[0012] Optionally, the energy compensation includes a real-time delivery mode and an intermediate storage mode. The real-time delivery mode is suitable for situations where two consecutive tasks meet the energy matching feasibility conditions, and the electrical energy recovered during the descent of task j is directly supplied to the ascent drive of task i via the DC bus. The intermediate storage mode is suitable for situations where there is a time interval between two tasks or the energy matching feasibility function is 0, and the electrical energy recovered during the descent of task j is temporarily stored in the energy storage device and released when needed for the ascent of subsequent tasks.

[0013] Optionally, the specific rules for dynamically adjusting the lifting speed curve of the elevator based on the measured weight of the material bin are as follows: set a reference speed v base When the measured weight m of the hopper exceeds the overload threshold m heavy At that time, the maximum speed will be limited to the heavy load speed v. heavy =γ×v base γ is the heavy-load speed coefficient less than 1; when the measured weight m of the hopper is lower than the no-load threshold m light At that time, adjust the maximum speed to the no-load speed v. light =δ×v base δ is the no-load speed coefficient greater than 1; when the measured weight of the hopper is between these two values, the speed is determined according to the linear interpolation rule at v. heavy to v lightThe values ​​are taken between these ranges, and the speed changes follow a preset S-shaped curve to reduce mechanical impact.

[0014] In summary, this application includes at least one of the following beneficial technical effects: First, by introducing a connection synchronization feasibility verification step, the existing technology can only optimize energy consumption but cannot guarantee the physical feasibility of the sequence in terms of scheduling optimization, thus ensuring the executability of the optimization results in actual operation. Second, by establishing an energy matching feasibility function and organically combining it with connection feasibility verification, the core contradiction of "the order of energy saving is not feasible" in the existing energy recovery scheme is solved, and an execution feasibility filtering layer is added to the scheduling optimization framework guided by minimizing energy consumption. Third, by adaptively adjusting the dynamic weight coefficients, the system can smoothly switch between two modes: smooth flow priority and energy saving priority, which avoids congestion during peak hours and maximizes energy saving during low load. Fourth, through the dynamic coupling of task order optimization at the scheduling layer, energy compensation mechanism, and connection and coordination at the execution layer, the system throughput and operating energy consumption are optimized globally, forming a complete technology chain. Attached Figure Description

[0015] Figure 1 This is an overall control flowchart of the method of the present invention; Figure 2 This is a schematic diagram illustrating the energy matching feasibility test and compensation mode selection of the present invention; Figure 3 This is a logical diagram illustrating the feasibility verification of the connection synchronization of the present invention; Figure 4 This is a schematic diagram illustrating the principle of dynamic weight coefficient adjustment in this invention. Detailed Implementation

[0016] The terminology used in the following embodiments is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to also include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one, two, or more than two. The term “and / or” is used to describe the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can indicate: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character “ / ” generally indicates that the preceding and following related objects are in an “or” relationship.

[0017] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0018] The following is in conjunction with the appendix Figure 1-4 The present invention will be described in further detail below.

[0019] This application discloses a feasibility screening and collaborative control method for the multi-task execution sequence of a hoist, referring to... Figure 1 This includes the following steps: S1. Task queue reception and feature extraction: The controller receives a task queue from the warehouse management system. Each task contains the weight m of the material bin. i Starting floor F i start Target floor F i target Task priority p i And the accumulation buffer capacity C of the upstream conveyor connection. i The accumulation buffer capacity C i This indicates the maximum number of bins that can be temporarily stored at the docking point, and this parameter determines the docking point's time tolerance for scheduling order.

[0020] S2. Energy matching feasibility verification, refer to Figure 2 The controller uses an energy matching feasibility function to perform an energy matching check on any two tasks in the task queue, determining whether one task and another have the feasibility of direct potential energy compensation. The energy matching feasibility function is defined as Binary(task i, task j) = 1 if and only if two conditions are met simultaneously: First, the target floor of task i is higher than the starting floor of task j, i.e., task i needs to go up while task j needs to go down, and the weight of the bin of task i is greater than the weight of the bin of task j, i.e., the weight of the uploading heavy-load task is greater than the weight of the downloading light-load task; Second, the floor difference H ij =|F i target -F j start | Less than the preset maximum energy transfer distance H maxWhen Binary(task i, task j) = 1, it means that when task i is executed after task j, the gravitational potential energy released by task j during descent can be directly transferred to task i for ascent drive, forming real-time energy matching. This test provides a quantitative basis for the energy compensation potential of subsequent task sequence generation. Energy compensation includes real-time transfer mode and intermediate storage mode. Real-time transfer mode is suitable for situations where two consecutive tasks meet the energy matching feasibility conditions. The electrical energy recovered during the descent of task j is directly supplied to the ascent drive of task i via the DC bus. This mode has the highest energy transfer efficiency and the simplest system to implement. Intermediate storage mode is suitable for situations where there is a time interval between two tasks or the energy matching feasibility function is 0. The electrical energy recovered during the descent of task j is temporarily stored in the energy storage device and released when the subsequent task needs to ascend. This mode can perform energy scheduling across multiple tasks, but the energy transfer efficiency is relatively low.

[0021] S3. Generation of the priority candidate order set: Based on the energy matching feasibility verification result and the task priority information, the controller generates a priority candidate execution order set. Each candidate order is a permutation of the task queue π={π(1),π(2),…,π(n)}. This generation process prioritizes arranging adjacent task pairs with energy matching feasibility to maximize energy compensation benefits.

[0022] S4. Feasibility verification of connection synchronization, refer to Figure 3 The controller performs a connection synchronization feasibility verification for each candidate order in the priority candidate execution order set, determining whether the order will cause blockage at the upstream conveyor connection port. Specifically, the controller obtains the current accumulation filling amount q of the upstream conveyor line. curr And the shuttle's estimated arrival time t shuttle expected For the k-th task π(k) in the candidate sequence π, the controller calculates the estimated arrival time (EAT) of the elevator to the docking point of task π(k) based on the current position and time of the elevator after completing the previous k-1 tasks. k If the estimated arrival time of the hoist is EAT k If a candidate sequence π is found to be out of compliance with connection synchronization feasibility conditions, and the material bin is delivered to the docking point later than the latest allowed time by the upstream conveyor line and later than the expected release time of the shuttle car occupying the docking point, and the accumulated filling amount exceeds the buffer capacity threshold, then the candidate sequence π is deemed to be out of compliance with connection synchronization feasibility conditions. Candidate sequences that pass this verification are retained in the set of feasible sequences. This step filters out physically executable candidate sequences and eliminates infeasible sequences that, although low in energy consumption, would cause connection conflicts.

[0023] S5. Establishment of the comprehensive objective function: The controller establishes a comprehensive objective function L(π) = α × W(π) + β × T(π) with the feasible sequence set as its domain. Here, W(π) is the estimated total energy consumption corresponding to candidate sequence π, T(π) is the estimated total time corresponding to candidate sequence π, α and β are the energy consumption weighting coefficient and time weighting coefficient, respectively. The estimated total energy consumption W(π) consists of three parts: the sum of the energy consumption for each unit task's ascent and descent, the sum of the energy consumption for idle movement between tasks, and the sum of the energy compensation benefits, expressed as follows: , Where δ k As an indicator function, δ is defined when Binary(π(k+1),π(k))=1. k =1, otherwise δ k =0, the calculation formulas for each energy consumption item are as follows: E task =(m·g·H) / η motor Where m is the weight of the hopper (positive for rising, negative for falling), g = 9.8 m / s², H is the vertical lifting distance, and η motor =0.85 is the motor efficiency; E idle =(M empty ·g·D) / η motor M empty To measure the unloaded mass of the lifting machine, D represents the vertical distance of unloaded movement; E comp =η transfer ·(m i -m j )·g·H ij , where η transfer =0.85 is the energy transfer efficiency, m i For the weight of the next heavy-load mission, m j For the weight of the previous light-load mission, H ij Take min(H) i H j ).

[0024] S6. Real-time adjustment of dynamic weighting coefficients, refer to Figure 4 The controller dynamically adjusts the values ​​of the energy consumption weight coefficient α and the time weight coefficient β according to the real-time operating status of the system and follows a smooth curve. The controller also monitors the accumulated filling amount q of the upstream conveyor line in real time. curr Total accumulation buffer q max The ratio r=q curr / q maxWhen the ratio r exceeds the first blockage threshold r1, such as 0.8, it indicates that there is a risk of blockage in the upstream conveyor line. The controller gradually reduces the priority of the energy consumption weight coefficient α while gradually increasing the priority of the time weight coefficient β over a period of time, that is, prioritizing the smooth operation of the system and ensuring throughput. When the ratio r is lower than the second unblocking threshold r2, such as 0.3, the controller gradually increases the priority of the energy consumption weight coefficient α while gradually reducing the priority of the time weight coefficient β over a period of time, that is, prioritizing energy-saving operation. The gradual adjustment of weights is achieved through iterative updates with a fixed step size to avoid continuous oscillation of the execution order caused by abrupt changes.

[0025] S7. Calculation and selection of the optimal execution order: The controller calculates the optimal execution order π*=argmin L(π) in the feasible order set based on the comprehensive objective function L(π). This calculation can be performed by traversing each candidate order in the feasible order set or by using a heuristic algorithm to approximate the optimal solution.

[0026] S8. Coordinated control during the execution phase: The controller issues execution instructions according to the optimal execution sequence. For each unit task, the controller dynamically adjusts the lifting speed curve of the elevator based on the measured weight of the material box, and controls the upstream conveyor line to transport the material box to the dock in advance to wait, so that the time when the material box arrives at the dock matches the time when the elevator arrives at the target floor. The specific rule for the controller to dynamically adjust the lifting speed curve of the elevator based on the measured weight of the material box is: set a base speed v base When the measured weight m of the hopper exceeds the overload threshold m heavy At that time, the maximum speed will be limited to the heavy load speed v. heavy =γ×v base γ is a heavy-load speed coefficient less than 1, preferably 0.8; when the measured weight m of the hopper is lower than the no-load threshold m light At that time, adjust the maximum speed to the no-load speed v. light =δ×v base δ is a no-load speed coefficient greater than 1, preferably 1.2; when the measured weight of the hopper is between the two values, the speed is determined according to a linear interpolation rule at v. heavy to v light The values ​​are taken between these ranges, and the speed changes follow a preset S-shaped curve to reduce mechanical impact.

[0027] As a preferred technical solution, during the process of issuing execution instructions in sequence, the controller automatically triggers a re-evaluation program after each unit task is completed. It rereads the remaining task queue from the beginning of the step and recalculates the optimal execution order using the latest accumulation and filling amount and shuttle position status to achieve dynamic rescheduling and adapt to dynamic changes in warehouse operations.

[0028] The following example demonstrates all aspects of this invention in a real-world scenario implemented in a 5-story automated warehouse. The hoist is driven by a servo motor and equipped with an energy recovery device and energy storage capacitor. The WMS issues a task queue containing 8 tasks with the following parameters: Task A, 500kg bin weight, from floor 1 to floor 5, high priority; Task B, 300kg bin weight, from floor 5 to floor 1, medium priority; Task C, 600kg bin weight, from floor 2 to floor 4, high priority; Task D, 200kg bin weight, from floor 4 to floor 2, low priority; Task E, 450kg bin weight, from floor 3 to floor 5, medium priority; Task F, 150kg bin weight, from floor 5 to floor 3, low priority; Task G, 550kg bin weight, from floor 1 to floor 4, high priority; Task H, 250kg bin weight, from floor 4 to floor 1, medium priority. The controller executes the first step after receiving the data; The second step is the energy matching test: Examine A (1-5, 500 kg) against B (5-1, 300 kg), and F. A target =5>F B start =5,m A =500>300, floor difference 0≤H max Binary(A,B)=1, and similarly, C and D, E and F, and G and H all satisfy the condition; The third step is to generate a candidate order: prioritize matching adjacent energy pairs to obtain a candidate order π1={A,B,C,D,E,F,G,H}, etc. Step 4: Connection and Synchronization Verification: Taking π1 as an example, calculate the EAT of each task. k For example, Task B: After completing A, the hoist is on the 5th floor. It takes 3 seconds to move unloaded to the 1st floor. EAT B =5 seconds to rise + 3 seconds no load = 8 seconds. Upstream conveyor feeding time: 7 seconds; shuttle release time: 6.5 seconds; EAT B Later than the shuttle release but earlier than the congestion threshold, the buffer is not full, verification passes, all tasks pass, π1 is reserved; Step 5: Establish the objective function: Initially α=0.7, β=0.3, calculate W(π1) as follows: Unit task energy consumption E task (Floor height 4 meters, g=9.8, η=0.85): E taskA =(500×9.8×16) / 0.85≈92235J≈25.62Wh, E taskB =(300×9.8×16) / 0.85 (negative value, decrease) ≈ -13.82Wh, E taskC =(600×9.8×8) / 0.85≈15.38Wh, E taskD=(200×9.8×8) / 0.85 (negative) ≈ -2.56Wh, E taskE =(450×9.8×8) / 0.85≈11.54Wh, E taskF =(150×9.8×8) / 0.85 (negative)≈-1.92Wh, E taskG =(550×9.8×12) / 0.85≈19.06Wh, E taskH =(250×9.8×12) / 0.85 (negative) ≈ -4.81Wh, ΣE task =25.62-13.82+15.38-2.56+11.54-1.92+19.06-4.81=48.49Wh; Unloaded mobile energy consumption E idle (M) empty =800kg): A completes on the 5th floor, B starts on the 5th floor and moves 0 meters; B completes on the 1st floor, C starts on the 2nd floor and moves 4 meters on the 1st floor, E idle =(800×9.8×4) / 0.85≈10.25Wh; C is completed on the 4th floor, D starts on the 4th floor and moves 0; D is completed on the 2nd floor, E starts on the 3rd floor and moves 1 floor 4 meters, E idle ≈10.25Wh; E is completed on the 5th floor, F starts on the 5th floor and moves 0; F is completed on the 3rd floor, G starts on the 1st floor and moves 2 floors and 8 meters, E idle ≈20.50Wh; G completes at level 4, H starts at level 4, moves 0; H completes at level 1, ends; ΣE idle ≈10.25 + 10.25 + 20.5 = 41Wh; Energy compensation benefit: AB:E comp =0.85×(500-300)×9.8×16≈26656J≈7.40Wh; CD: 0.85×(600-200)×9.8×8≈26656J≈7.40Wh; EF :0.85×(450-150)×9.8×8≈20000J≈5.56Wh; GH: 0.85×(550-250)×9.8×12≈30000J≈8.33Wh; ΣE comp =28.69Wh, W(π1)=48.49+41-28.69=60.8Wh, T(π1) is approximately 240 seconds based on the lifting and lowering time and the no-load time, L=0.7×60.8+0.3×240=42.56+72=114.56; Step 6: Dynamic adjustment: During the peak period, r rises to 0.85, exceeding 0.8. Adjust α=0.4, β=0.6, and recalculate L=0.4×60.8+0.6×240=24.32+144=168.32. Still choose π1. The optimal order for the seventh step is determined to be π1; Step 8: Task A is a heavy load of 500kg, with a speed of v=0.8×2.0=1.6m / s, rising and falling in an S-shaped curve; Task B descends to generate electricity and transfers energy to A in real time; Task C ascends to utilize the electricity from B, etc. Each task is re-evaluated after completion.

[0029] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or variations made by those skilled in the art based on the content disclosed in the present invention should be included within the scope of protection set forth in the claims.

Claims

1. A method for feasibility screening and collaborative control of the multi-task execution sequence of a hoist, characterized in that, Includes the following steps: S1. Receive the task queue, where each task in the task queue contains the weight m of the hopper. i Starting floor F i start Target floor F i target Task priority p i And the accumulation buffer capacity C of the upstream conveyor connection. i ; S2. Use the energy matching feasibility function to perform energy matching checks on any two tasks in the task queue to determine whether one task and another task have the feasibility of direct potential energy compensation. S3. Based on the energy matching feasibility verification results, generate a set of priority candidate execution orders. Each candidate order is a permutation of the task queue π={π(1),π(2),…,π(n)}. S4. Perform a connection synchronization feasibility verification on each candidate order in the priority candidate execution order set to determine whether the order will cause the upstream conveyor line connection port to be blocked, and retain the candidate order that passes the connection synchronization feasibility verification to the feasible order set. S5. Establish a comprehensive objective function L(π)=α×W(π)+β×T(π) with the feasible order set as the domain, where W(π) is the estimated total energy consumption corresponding to the candidate order π, T(π) is the estimated total time corresponding to the candidate order π, and α and β are the energy consumption weight coefficient and time weight coefficient, respectively. S6. Based on the real-time operating status of the current system, dynamically adjust the values ​​of the energy consumption weight coefficient α and the time weight coefficient β according to the smooth curve. S7. Calculate the optimal execution order π*=argminL(π) in the feasible order set based on the comprehensive objective function L(π); S8. Issue execution instructions according to the optimal execution order. For each unit task, dynamically adjust the lifting speed curve of the hoist based on the measured weight of the material box, and control the upstream conveyor line to transport the material box to the dock in advance to wait, so that the time when the material box arrives at the dock matches the time when the hoist arrives at the target floor.

2. The feasibility screening and collaborative control method for the multi-task execution sequence of a hoist according to claim 1, characterized in that: The energy matching feasibility function is defined as Binary(task i, task j) = 1 if and only if the target floor of task i is higher than the starting floor of task j and the weight of the bin in task i is greater than the weight of the bin in task j, while also satisfying the floor difference H. ij =|F i target -F j start | Less than the preset maximum energy transfer distance H max When Binary(task i, task j) = 1, it means that when task i is executed after task j, the gravitational potential energy released by task j during descent can be directly transferred to task i for ascent, forming real-time energy matching.

3. The feasibility screening and collaborative control method for the multi-task execution sequence of a hoist according to claim 1, characterized in that: The feasibility verification of the connection synchronization includes the following sub-steps: obtaining the current accumulation and filling amount q of the upstream conveyor line. curr And the shuttle's estimated arrival time t shuttle expected For the k-th task π(k) in the candidate sequence π, calculate the estimated arrival time (EAT) of the elevator to the docking point of task π(k) based on the current position and time of the elevator after completing the previous k-1 tasks. k If the estimated arrival time of the hoist is EAT k If the candidate sequence π is later than the latest allowed time for the upstream conveyor line to deliver the hopper to the docking point and later than the expected release time for the shuttle to occupy the docking point, and the accumulated filling amount exceeds the buffer capacity threshold, then the candidate sequence π is determined to be inconsistent with the docking synchronization feasibility conditions.

4. The feasibility screening and collaborative control method for the multi-task execution sequence of a hoist according to claim 1, characterized in that: The total energy consumption estimate W(π) consists of three parts: the sum of the energy consumption for take-off and landing of each unit task, the sum of the energy consumption for idle movement between tasks, and the sum of the energy compensation benefits, expressed as follows: ， Where δ k As an indicator function, δ is defined when Binary(π(k+1),π(k))=1. k =1, otherwise δ k =0, the calculation formulas for each energy consumption item are as follows: E task =(m·g·H) / η motor Where m is the weight of the hopper (positive for rising, negative for falling), g = 9.8 m / s², H is the vertical lifting distance, and η motor =0.85 is the motor efficiency; E idle =(M empty ·g·D) / η motor M empty To measure the unloaded mass of the lifting machine, D represents the vertical distance of unloaded movement; E comp =η transfer ·(m i -m j )·g·H ij , where η transfer =0.85 is the energy transfer efficiency, m i For the weight of the next heavy-load mission, m j For the weight of the previous light-load mission, H ij Take min(H) i H j ).

5. The feasibility screening and collaborative control method for the multi-task execution sequence of a hoist according to claim 1, characterized in that: The method for dynamically adjusting the energy consumption weighting coefficient α and the time weighting coefficient β includes: real-time monitoring of the accumulation and filling amount q of the upstream conveyor line. curr Total accumulation buffer q max The ratio r=q curr / q max When the ratio r exceeds the first blockage threshold r1, the priority of the energy consumption weight coefficient α is gradually reduced while the priority of the time weight coefficient β is gradually increased over a period of time. When the ratio r is lower than the second unblocking threshold r2, the priority of the energy consumption weight coefficient α is gradually increased while the priority of the time weight coefficient β is gradually reduced over a period of time. The gradual adjustment of the weights is achieved through iterative updates with a fixed step size to avoid continuous oscillations in the execution order caused by abrupt changes.

6. The feasibility screening and collaborative control method for the multi-task execution sequence of a hoist according to claim 1, characterized in that: During the sequential issuance of execution instructions, a re-evaluation procedure is automatically triggered after each unit task is completed. Starting from step one, the remaining task queue is reread, and the optimal execution order is recalculated using the latest accumulation fill amount and shuttle position status to achieve dynamic rescheduling.

7. The feasibility screening and collaborative control method for the multi-task execution sequence of a hoist according to claim 1, characterized in that: The energy compensation includes a real-time delivery mode and an intermediate storage mode. The real-time delivery mode is suitable for situations where two consecutive tasks meet the energy matching feasibility conditions. During the descent of task j, the electrical energy recovered is directly supplied to the ascent drive of task i via the DC bus. The intermediate storage mode is suitable for situations where there is a time interval between two tasks or the energy matching feasibility function is 0. The electrical energy recovered during the descent of task j is temporarily stored in the energy storage device and released when needed for the ascent of subsequent tasks.

8. The feasibility screening and collaborative control method for the multi-task execution sequence of a hoist according to claim 1, characterized in that: The specific rules for dynamically adjusting the lifting speed curve of the elevator based on the measured weight of the hopper are as follows: Set a baseline speed v base When the measured weight m of the hopper exceeds the overload threshold m heavy At that time, the maximum speed will be limited to the heavy load speed v. heavy =γ×v base γ is the heavy-load speed coefficient less than 1; when the measured weight m of the hopper is lower than the no-load threshold m light At that time, adjust the maximum speed to the no-load speed v. light =δ×v base δ is the no-load speed coefficient greater than 1; when the measured weight of the hopper is between these two values, the speed is determined according to the linear interpolation rule at v. heavy to v light The values ​​are taken between these ranges, and the speed changes follow a preset S-shaped curve to reduce mechanical impact.

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