Intelligent scheduling optimization method, device and equipment for double-deck planning of crown block and medium

Abstract

The embodiment of the present disclosure provides a kind of overhead crane double-layer planning intelligent scheduling optimization method, device, equipment and medium, belong to control technical field, specifically include: step 1, the task schedule data contained in the production scheduling plan is acquired;Step 2, with the minimum maximum completion time as the goal, with the space-time characteristics of overhead crane as constraint condition, constructs overhead crane scheduling model;Step 3, in upper layer planning, based on task schedule data, overhead crane scheduling model is solved using improved whale optimization algorithm, and overhead crane task sequence is generated;Step 4, in lower layer planning, based on priority dynamic adjustment strategy and dynamic time interval strategy, overhead crane task sequence is path planned, and overhead crane running track is acquired;Step 5, repeat step 2 to step 4, until reach termination condition, obtain the optimal scheduling track and task allocation scheme of multiple overhead cranes.By the scheme of the present disclosure, the scheduling efficiency, accuracy and cooperativity are improved.

Description

Technical Field

[0001] This disclosure relates to the field of control technology, and in particular to a method, apparatus, equipment and medium for intelligent scheduling optimization of overhead cranes with two-layer planning. Background Technology

[0002] Overhead cranes, as large-scale lifting and handling equipment, are widely used in industrial production workshops such as steel mills and copper smelting plants. Rapid and efficient multi-head crane coordinated scheduling is crucial for maintaining a stable production rhythm and improving efficiency in modern industrial workshops. However, in actual production, overhead crane scheduling heavily relies on manual labor and lacks information such as the current status of the cranes and task priorities. This leads to problems such as delayed issuance of scheduling instructions, ambiguous task priorities, and random avoidance between cranes, resulting in poor coordination among multiple cranes. The proposals of strategies such as high-quality development and Made in China 2035 are driving the transformation and upgrading of traditional manufacturing to intelligent manufacturing. Efficient multi-head crane scheduling methods are an important research direction for improving industrial production efficiency and ensuring production safety.

[0003] Currently, methods for solving overhead crane scheduling optimization problems are mainly divided into two categories: exact optimization algorithms and approximate optimization algorithms. Exact optimization algorithms achieve accurate solutions by accurately modeling the actual problem and using operations research algorithms (such as the simplex method, branch and bound algorithm, and dynamic programming algorithm) or specialized solvers (such as Gurobi and CPlex). However, the complexity, nonlinearity, and uncertainty of industrial scheduling processes make the construction of exact models extremely difficult. Furthermore, computational costs increase significantly with the expansion of the problem size. Approximate optimization algorithms are typically intelligent optimization algorithms, aiming to find a satisfactory solution between the optimal solution and the performance requirements. They are characterized by fast solution and adaptability to different types of complex problems, thus becoming the current mainstream method. However, most current research still has the following shortcomings: 1) The solution accuracy needs further improvement; 2) The use of passive transport avoidance strategies leads to invalid and repeated movements of the overhead cranes, thus reducing transport efficiency; 3) The transport process lacks a dynamic adjustment mechanism for overall priority, usually only considering task priority, which may affect the system's coordination and efficiency.

[0004] It is evident that there is an urgent need for a method that can achieve accurate and rapid task allocation while effectively planning the crane path, so as to better improve the algorithm's operating efficiency and the coordination between cranes and cranes. This is a two-layer planning intelligent scheduling optimization method for cranes. Summary of the Invention

[0005] In view of this, the present disclosure provides a method, apparatus, equipment and medium for intelligent scheduling optimization of overhead crane two-layer planning, which at least partially solves the problems of poor scheduling efficiency, accuracy and coordination in the prior art.

[0006] In a first aspect, embodiments of this disclosure provide a method for intelligent scheduling optimization of overhead cranes with two-layer planning, including:

[0007] Step 1: Obtain the task schedule data contained in the production scheduling plan, wherein the task schedule data includes the start position, target position and start time of each task;

[0008] Step 2: With the goal of minimizing and maximizing the completion time, and with the spatiotemporal characteristics of the overhead crane as constraints, construct an overhead crane scheduling model;

[0009] Step 3: In the upper-level planning, based on the task plan table data, the improved whale optimization algorithm is used to solve the crane scheduling model and generate the crane task sequence.

[0010] Step 4: In the lower-level planning, based on the priority dynamic adjustment strategy and the dynamic time interval strategy, the path planning of the crane task sequence is performed to obtain the crane running trajectory.

[0011] Step 5: Repeat steps 2 to 4 until the termination condition is met, and obtain the optimal dispatch trajectory and task allocation scheme for multiple overhead cranes.

[0012] According to a specific implementation of an embodiment of this disclosure, step 3 specifically includes:

[0013] Step 3.1: Use the task schedule data as the data input for the overhead crane scheduling model;

[0014] Step 3.2: Solve the crane scheduling model using the improved whale optimization algorithm to obtain the continuous value sequence X(t) of the crane tasks;

[0015] Step 3.3: Discretize X(t) to obtain the discrete sequence value Y(t) of the crane task. The discretization formula is as follows:

[0016]

[0017] The formula represents X(t) i ∈[lb,ub] maps to the real number range [1,l] i Within [ ], and make the mapped real number convert to the nearest integer value and assign it to Y(t). i , where Y(t) i Let l represent the discrete value of the i-th dimension of Y(t). i Let X(t) represent the number of available overhead cranes in the i-th dimension. i Let X(t) represent the continuous value of the i-th dimension, and N represent the number of tasks.

[0018] According to a specific implementation of an embodiment of this disclosure, step 3.2 specifically includes:

[0019] Step 3.2.1: In the global and local search processes of the improved whale optimization algorithm, a dynamic inertia weight coefficient is introduced to balance the global and local searches, enhancing the adaptability and search performance of the improved whale optimization algorithm to complex problems. The formula for calculating the dynamic inertia weight coefficient is as follows:

[0020]

[0021] Among them, w min w max These represent the minimum and maximum values ​​of the inertia weight w, respectively, and f represents the fitness value of the current individual. avg f represents the current average fitness value of the population. min This represents the minimum fitness value of the current population;

[0022] Step 3.2.2: In the improved whale optimization algorithm, a feedback mechanism is introduced for random walk mutation. The formula for the feedback mechanism is:

[0023]

[0024]

[0025] Where Levy represents the walking step size that follows the Levy distribution, r is a random number between [0,1], and rand_Chaotic(t) is a random individual generated in the t-th generation using a chaotic mapping.

[0026] Step 3.2.3: For the out-of-bounds solution generated in the improved whale optimization algorithm, a value close to the corresponding boundary region is randomly generated for correction, resulting in the continuous value sequence X(t) of the crane task. The expression for the correction process is:

[0027]

[0028] X(t) i Let r1 represent the i-th dimension value of individual X(t), r1 be a random number between [0,1], and ub and lb represent the upper and lower boundaries of the individual.

[0029] According to a specific implementation of an embodiment of this disclosure, step 4 specifically includes:

[0030] Step 4.1: Based on the discrete sequence value Y(t) of the crane task and the task plan table data, obtain the starting position, target position and start time of each task, with T=0 as the starting node;

[0031] Step 4.2: Assume each transportation task has 5 sub-stages. Obtain the completion time of all cranes from time node T to their respective task sub-stages. Take the smallest time node as the next time node T+ΔT and obtain the initial trajectory of each crane within the time interval (T, T+ΔT).

[0032] Step 4.3: Determine whether a conflict occurs between multiple overhead cranes within the time interval (T, T+ΔT). If a conflict occurs, record it.

[0033] Step 4.4: Based on the pre-set task priorities, each crane obtains its corresponding static priority according to the task attributes, and then dynamically adjusts the static priority of each crane considering the urgency of the task and the importance of each crane.

[0034] Step 4.5, update time T = T + ΔT;

[0035] Step 4.6: Repeat steps 4.2 to 4.5 until all tasks are completed and obtain the optimal overhead crane running trajectory.

[0036] Secondly, embodiments of this disclosure provide a crane two-layer planning intelligent scheduling optimization system, including:

[0037] The acquisition module is used to acquire the task plan table data contained in the production scheduling plan, wherein the task plan table data includes the start position, target position and start time of each task;

[0038] The building module is used to construct a crane scheduling model with the goal of minimizing and maximizing the completion time and with the spatiotemporal characteristics of the crane as constraints.

[0039] The solver module is used in the upper-level planning to solve the crane scheduling model based on the task plan table data and use the improved whale optimization algorithm to generate the crane task sequence.

[0040] The path planning module is used in the lower-level planning to perform path planning on the crane task sequence based on the priority dynamic adjustment strategy and the dynamic time interval strategy, and to obtain the crane running trajectory.

[0041] The iterative module is used to repeat steps 2 to 4 until the termination condition is met, so as to obtain the optimal dispatching trajectory and task allocation scheme for multiple overhead cranes.

[0042] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0043] At least one processor; and,

[0044] The memory is communicatively connected to the at least one processor; wherein,

[0045] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the overhead crane two-layer planning intelligent scheduling optimization method in the first aspect or any implementation thereof.

[0046] Fourthly, embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the overhead crane two-layer planning intelligent scheduling optimization method in the first aspect or any implementation thereof.

[0047] Fifthly, embodiments of this disclosure also provide a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to execute the overhead crane two-layer planning intelligent scheduling optimization method in the first aspect or any implementation thereof.

[0048] The overhead crane two-layer planning intelligent scheduling optimization scheme in this embodiment includes: Step 1, obtaining task plan table data contained in the production scheduling plan, wherein the task plan table data includes the starting position, target position and start time of each task; Step 2, constructing an overhead crane scheduling model with the goal of minimizing and maximizing the completion time and the spatiotemporal characteristics of the overhead crane as constraints; Step 3, in the upper-layer planning, solving the overhead crane scheduling model based on the task plan table data using the improved whale optimization algorithm to generate an overhead crane task sequence; Step 4, in the lower-layer planning, performing path planning on the overhead crane task sequence based on the priority dynamic adjustment strategy and the dynamic time interval strategy to obtain the overhead crane running trajectory; Step 5, repeating steps 2 to 4 until the termination condition is reached to obtain the optimal dispatch trajectory and task allocation scheme for multiple overhead cranes.

[0049] The beneficial effects of this disclosure are: (1) Considering that the scheduling efficiency of manual experience scheduling and traditional optimization algorithm scheduling needs to be improved and it is difficult to find the optimal solution, the improved whale algorithm is used to solve the problem. Based on the traditional whale algorithm, the inertial weight factor, feedback mutation mechanism and out-of-bounds solution elastic repair mechanism are integrated to ensure that the generated task allocation is both accurate and can quickly obtain the optimal solution.

[0050] (2) Considering that the original passive transportation avoidance strategy and priority allocation mechanism may lead to problems such as poor crane coordination and low efficiency, the dynamic time interval strategy and priority dynamic adjustment strategy are introduced to improve the flexibility and speed of conflict avoidance, thereby improving overall coordination and transportation efficiency. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating an intelligent scheduling optimization method for a two-layer planning of overhead cranes, provided in an embodiment of this disclosure;

[0053] Figure 2 A schematic diagram illustrating an improved whale optimization algorithm for solving a crane scheduling model, as provided in this embodiment of the disclosure;

[0054] Figure 3 This is a schematic diagram of a crane path planning process provided in an embodiment of the present disclosure;

[0055] Figure 4 The iterative convergence result diagram provided for embodiments of this disclosure, using the present invention and other methods on a factory overhead crane dataset;

[0056] Figure 5 This disclosure provides an embodiment of an overhead crane trajectory map generated using the present invention on a factory overhead crane dataset;

[0057] Figure 6 A schematic diagram of the structure of a crane two-layer planning intelligent scheduling optimization system provided in this embodiment of the present disclosure;

[0058] Figure 7 A schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0059] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0060] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0061] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0062] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0063] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0064] This disclosure provides a method for intelligent scheduling optimization of overhead cranes with two-layer planning. The method can be applied to the scheduling optimization process of overhead cranes in industrial production scenarios such as steel gantry cranes and copper smelting.

[0065] See Figure 1 This is a flowchart illustrating an intelligent scheduling optimization method for a two-layer planning of overhead cranes provided in an embodiment of this disclosure. Figure 1 As shown, the method mainly includes the following steps:

[0066] Step 1: Obtain the task schedule data contained in the production scheduling plan, wherein the task schedule data includes the start position, target position and start time of each task;

[0067] In practice, the starting position, target position, and start time of each task can be obtained from a factory to form the task plan table data for subsequent processing.

[0068] Step 2: With the goal of minimizing and maximizing the completion time, and with the spatiotemporal characteristics of the overhead crane as constraints, construct an overhead crane scheduling model;

[0069] In practical implementation, the goal can be to minimize or maximize the completion time, with the spatiotemporal characteristics of the overhead crane as constraints. The specific process of constructing a mathematical model for overhead crane scheduling is as follows:

[0070] Step 2.1, to establish a more accurate overhead crane scheduling model, the following assumptions are made: ① The running speed of the overhead crane on the working span is constant; ② The longitudinal running time of the overhead crane is negligible; ③ After the overhead crane stops moving, the loading, unloading and other operations are calculated as fixed time; ④ There is a safe distance between the overhead cranes. When the distance between any two overhead cranes is less than the safe distance, collision avoidance should be performed. The safe distance is assumed to be 5m.

[0071] Step 2.2, Model Variable Description and Model Building:

[0072] The variable descriptions of the model are shown in Table 1:

[0073] Table 1

[0074]

[0075]

[0076] Based on the model parameters and defined decision variables, establish a model with the objective function of minimizing the total time required to complete all planned tasks:

[0077]

[0078] The constraints of the entire mathematical model are as follows:

[0079] T' i,S ≥T i,S ,T' i,E ≤T i,E ,i=1,…,N (2-2)

[0080] Formula (2-2) indicates that the actual start time of the task is greater than the planned start time, and the actual end time is less than the planned end time.

[0081]

[0082] Formula (2-3) represents the relationship between the actual start time and the actual end time;

[0083] |P x,k,t+Δt -P x,k,t |≤v x Δt,k=1,…,K,t=1,2,… (2-4)

[0084] Formula (2-4) represents the fixed distance the crane moves per unit time;

[0085]

[0086] Formula (2-5) means that at any given time, the distance between two adjacent overhead cranes is greater than or equal to the safe distance;

[0087] P x,k,t ∈L x,k ,P y,k,t ∈L y,k ,k=1,2,…,K,t=1,2,… (2-6)

[0088] Formula (2-6) indicates that at any given time all overhead cranes are within the working span;

[0089]

[0090] Formula (2-7) means that each task can only be executed by one crane and can only be assigned once;

[0091]

[0092] Formula (2-8) means that each crane can execute at most one task at any given time;

[0093]

[0094] Formula (2-9) means that all tasks must be performed.

[0095] Step 3: In the upper-level planning, based on the task plan table data, the improved whale optimization algorithm is used to solve the crane scheduling model and generate the crane task sequence.

[0096] Based on the above embodiments, step 3 specifically includes:

[0097] Step 3.1: Use the task schedule data as the data input for the overhead crane scheduling model;

[0098] Step 3.2: Solve the crane scheduling model using the improved whale optimization algorithm to obtain the continuous value sequence X(t) of the crane tasks;

[0099] Step 3.3: Discretize X(t) to obtain the discrete sequence value Y(t) of the crane task. The discretization formula is as follows:

[0100]

[0101] The formula represents X(t) i ∈[lb,ub] maps to the real number range [1,l] i Within [ ], and make the mapped real number convert to the nearest integer value and assign it to Y(t). i , where Y(t) i Let l represent the discrete value of the i-th dimension of Y(t). i Let X(t) represent the number of available overhead cranes in the i-th dimension. i Let X(t) represent the continuous value of the i-th dimension, and N represent the number of tasks.

[0102] Furthermore, step 3.2 specifically includes:

[0103] Step 3.2.1: In the global and local search processes of the improved whale optimization algorithm, a dynamic inertia weight coefficient is introduced to balance the global and local searches, enhancing the adaptability and search performance of the improved whale optimization algorithm to complex problems. The formula for calculating the dynamic inertia weight coefficient is as follows:

[0104]

[0105] Among them, w min w max These represent the minimum and maximum values ​​of the inertia weight w, respectively, and f represents the fitness value of the current individual. avg f represents the current average fitness value of the population. min This represents the minimum fitness value of the current population;

[0106] Step 3.2.2: In the improved whale optimization algorithm, a feedback mechanism is introduced for random walk mutation. The formula for the feedback mechanism is:

[0107]

[0108]

[0109] Where Levy represents the walking step size that follows the Levy distribution, r is a random number between [0,1], and rand_Chaotic(t) is a random individual generated in the t-th generation using a chaotic mapping.

[0110] Step 3.2.3: For the out-of-bounds solution generated in the improved whale optimization algorithm, a value close to the corresponding boundary region is randomly generated for correction, resulting in the continuous value sequence X(t) of the crane task. The expression for the correction process is:

[0111]

[0112] X(t) i Let r1 represent the i-th dimension value of individual X(t), r1 be a random number between [0,1], and ub and lb represent the upper and lower boundaries of the individual.

[0113] In specific implementation, such as Figure 2 As shown, step 3 specifically includes:

[0114] Step 3.1: Use the production scheduling task plan data as the data input for the overhead crane scheduling model;

[0115] Step 3.2: Solve the overhead crane scheduling model using the improved whale optimization algorithm to obtain the continuous value sequence X(t) of the overhead crane tasks;

[0116] Furthermore, step 3.2 specifically includes:

[0117] Step 3.2.1: In the global and local search processes of the improved whale optimization algorithm, a dynamic inertia weight coefficient is introduced to balance the global and local searches, enhancing the algorithm's adaptability to complex problems and its search performance. The calculation formula for the dynamic inertia weight coefficient is as follows:

[0118]

[0119] Among them, w min w max Let w represent the minimum and maximum values ​​of the inertia weight w, which are 0.4 and 0.9 respectively, and let f be the fitness value of the current individual. avg f is the current average fitness value of the population. min It is the minimum fitness value of the current population.

[0120] The whale optimization algorithm consists of three actions: encirclement hunting, bubble net hunting, and random prey search; the process is mainly determined by the random probability p∈[0,1] and the value of |A|.

[0121]

[0122]

[0123] D1 = |CX best (t)-X(t)|

[0124] D2=|X best (t)-X(t)|

[0125] D3 = |CX rand (t)-X(t)|

[0126] Among them, X best X(t) represents the best individual after iteration t, X(t) is the individual in generation t, X(t+1) is the individual in generation t+1, and X... rand (t) is a random individual generated in the t-th generation, with random number r2∈[0,1], and t is the current iteration number. max is the maximum number of iterations, b is the sum of constants used to define the shape of the logarithmic spiral, l∈[-1,1] is a random number, and C=2·r2∈[0,2] is a random number;

[0127] When p < 0.5 and |A| ≤ 1, the search is in the local encirclement predation stage; when p ≥ 0.5, the search is in the local bubble web predation stage; when p < 0.5 and |A| > 1, the search is in the global random prey search stage. During the local search process, when an individual has a low fitness value, it will increase its search range, while when an individual has a high fitness value, it will focus more on local optimization, making the search more targeted and more likely to capture the optimal solution.

[0128] Step 3.2.2 introduces a feedback mechanism into the algorithm to perform random walk mutation, thereby improving the algorithm's global search capability and helping the system escape local optima.

[0129]

[0130]

[0131] Where Levy is the walking step size following a Levy distribution, r is a random number between [0,1], and rand_Chaotic(t) is a random individual generated in generation t based on the chaotic mapping, as shown in the following formula:

[0132]

[0133] CX(t) = 4·X(t)'·(1-X(t)')

[0134] rand_Chaotic(t)=lb+CX(t)·(ub-lb)

[0135] In the specific implementation process, the individual X(t) is first normalized to obtain X(t)', then the chaotic mapping value CX(t) is calculated from X(t)' according to the Logistic chaotic mapping formula, and finally CX(t) is mapped back to the interval [lb,ub] to obtain the final random individual rand_Chaotic(t);

[0136] Step 3.2.3: For the out-of-bounds solution generated in the improved whale optimization algorithm, a value close to the corresponding boundary region is randomly generated for correction, resulting in the continuous value sequence X(t) for crane task allocation. The correction formula is as follows:

[0137]

[0138] X(t) i Let r1 be a random number between [0,1], and ub and lb represent the upper and lower boundaries of the individual X(t).

[0139] Step 3.2.4: On 23 publicly available test functions of intelligent optimization algorithms (all functions have a dimension of 30), the performance of the improved algorithm (IWOA) is compared with that of the original Whale Algorithm (WOA), Particle Swarm Optimization (PSO), and Grey Wolf Algorithm (GWO). The comparison indicators are the mean ave and standard deviation std calculated after each algorithm is run 30 times. The comparison results are shown in Table 2 ("+" indicates that both the mean ave and standard deviation std are optimal, "≈" indicates that the mean ave is optimal but the standard deviation std is not optimal, and the rest are "-"). As can be seen from Table 2, IWOA achieved "+" results on 16 test functions and "≈" results on 3 test functions, which demonstrates the algorithm's powerful exploration and utilization capabilities.

[0140] Step 3.2.5: Finally, solve the model to obtain the continuous value sequence X(t) of the crane task allocation.

[0141] Table 2

[0142]

[0143] Step 3.3: Discretize X(t) to obtain Y(t). The discretization formula is as follows:

[0144]

[0145] The formula represents X(t) i ∈[lb,ub] maps to the real number range [1,l] i Within [ ], and make the mapped real number convert to the nearest integer value and assign it to Y(t). i Wherein, Y(t) i Let l represent the discrete value of the i-th dimension of Y(t). i Let X(t) represent the number of available overhead cranes in the i-th dimension. i Let X(t) represent the continuous value of the i-th dimension, and N represent the number of tasks.

[0146] Step 4: In the lower-level planning, based on the priority dynamic adjustment strategy and the dynamic time interval strategy, the path planning of the crane task sequence is performed to obtain the crane running trajectory.

[0147] Based on the above embodiments, step 4 specifically includes:

[0148] Step 4.1: Based on the discrete sequence value Y(t) of the crane task and the task plan table data, obtain the starting position, target position and start time of each task, with T=0 as the starting node;

[0149] Step 4.2: Assume each transportation task has 5 sub-stages. Obtain the completion time of all cranes from time node T to their respective task sub-stages. Take the smallest time node as the next time node T+ΔT and obtain the initial trajectory of each crane within the time interval (T, T+ΔT).

[0150] Step 4.3: Determine whether a conflict occurs between multiple overhead cranes within the time interval (T, T+ΔT). If a conflict occurs, record it.

[0151] Step 4.4: Based on the pre-set task priorities, each crane obtains its corresponding static priority according to the task attributes, and then dynamically adjusts the static priority of each crane considering the urgency of the task and the importance of each crane.

[0152] Step 4.5, update time T = T + ΔT;

[0153] Step 4.6: Repeat steps 4.2 to 4.5 until all tasks are completed and obtain the optimal overhead crane running trajectory.

[0154] In specific implementation, such as Figure 3 As shown, the specific process of step 4 can be as follows:

[0155] Step 4.1: Based on the discrete sequence value Y(t) of the crane task and the plan table data, obtain the starting position, target position and start time of each task, with T=0 as the starting node;

[0156] Step 4.2: Each dispatching task has 5 sub-stages. Obtain the completion time of all cranes from time node T to their respective task sub-stages. Take the smallest time node as the next time node T+ΔT and obtain the initial trajectory of each crane within the time interval (T, T+ΔT).

[0157] Step 4.3: Determine whether a conflict occurs between multiple overhead cranes within the time interval (T, T+ΔT). If a conflict occurs, record it. The conflict determination formula is as follows:

[0158] P x,k,T+ΔT -P x,j,T+ΔT <(kj)δ,(k=j+1,…,K)

[0159] P x,j,T+ΔT -P x,k,T+ΔT <(jk)δ,(k=1,…,j-1)

[0160] P x,k,T+ΔT P is the x-axis coordinate position of the crane k at time T+ΔT. x,j,T+ΔTδ is the x-axis coordinate of crane j at time T+ΔT, and δ is the safety distance. When the above two formulas are satisfied, crane k and crane j will collide. It is worth noting that when judging the conflict, taking crane j as an example, it is necessary not only to predict whether crane j will collide with its adjacent crane, but also to predict whether it will collide with other cranes.

[0161] Step 4.4: Obtain the manually set task priorities according to Table 3, and the overhead crane obtains its corresponding static priority based on the task attributes.

[0162] Table 3

[0163]

[0164] When multiple overhead cranes with the same priority conflict, the static priority of each crane is first dynamically adjusted based on the urgency of the task (determined by the distance between the current position and the target position of the crane). The determination formula is as follows:

[0165]

[0166] Among them, P x,k,t G is the x-axis coordinate position of the overhead crane k at time t. k,t L is the target position of the overhead crane k at time t. x,k This refers to the operating range of the overhead crane k along the x-axis.

[0167] If there are still conflicts due to multiple cranes having the same priority, the priority will be adjusted according to the importance of each crane. The order of importance of cranes is: crane 1 > crane 2 > crane 3.

[0168] Finally, based on the final obtained crane priority ranking, in conflict scenarios, low-priority cranes will proactively give way to high-priority cranes in turn.

[0169] Step 4.5, update time T = T + ΔT;

[0170] Step 4.6: Repeat steps 4.2 to 4.5 until all tasks are completed and obtain the optimal overhead crane running trajectory.

[0171] Step 5: Repeat steps 2 to 4 until the termination condition is met, and obtain the optimal dispatch trajectory and task allocation scheme for multiple overhead cranes.

[0172] In practice, the termination condition can be a pre-set number of iterations. When iterating, steps 2 to 4 can be repeated until the training reaches the preset number of times to obtain the final optimal crane dispatching trajectory and task allocation scheme.

[0173] The overhead crane two-layer planning intelligent scheduling optimization method provided in this embodiment takes into account the problem that the scheduling efficiency of manual experience scheduling and traditional optimization algorithms needs to be improved and that it is difficult to find the optimal solution. It adopts an improved whale algorithm for solving the problem. On the basis of the traditional whale algorithm, it incorporates an inertia weight factor, a feedback mutation mechanism, and an out-of-bounds solution elastic repair mechanism to ensure that the generated task allocation is both accurate and can quickly obtain the optimal solution. Considering that the original passive transportation avoidance strategy and priority allocation mechanism may lead to poor crane coordination and low efficiency, a dynamic time interval strategy and a priority dynamic adjustment strategy are introduced to improve the flexibility and speed of conflict avoidance, thereby improving overall coordination and transportation efficiency.

[0174] The following specific embodiment will further illustrate this solution. Figure 4 The diagram shows the iterative convergence results on a dataset of overhead cranes in a factory, obtained using the present invention and other methods. The results demonstrate that the method provided by this invention can escape local optima and find the optimal solution. Figure 5 The diagram shows overhead crane trajectories on a factory overhead crane dataset, obtained using the present invention and other methods. The results demonstrate that the method provided by this invention eliminates overlap between overhead cranes and maintains sufficient safety distances between adjacent cranes, verifying the reliability of the overhead crane scheduling scheme.

[0175] To further illustrate the superiority of the avoidance strategy in the method of this disclosure, Table 4 shows the comparison results between the traditional avoidance strategy and the dynamic time interval avoidance strategy. As can be seen from Table 4, the total transportation time of the two methods is the same, but the passive movement distance of the overhead crane in the latter is reduced by 6.04% compared with the former. This shows that the conflict avoidance method proposed in this invention reduces the ineffective or repeated movement of the overhead crane and improves the overall operating efficiency.

[0176] Table 4

[0177]

[0178] For a corresponding method embodiment, see [link to relevant documentation]. Figure 6 This disclosure also provides a crane two-layer planning intelligent scheduling optimization system 60, including:

[0179] The acquisition module 601 is used to acquire the task plan table data contained in the production scheduling plan, wherein the task plan table data includes the start position, target position and start time of each task;

[0180] Module 602 is used to construct a crane scheduling model with the goal of minimizing and maximizing the completion time and with the spatiotemporal characteristics of the crane as constraints.

[0181] The solver module 603 is used in the upper-level planning to solve the crane scheduling model based on the task plan table data and use the improved whale optimization algorithm to generate the crane task sequence.

[0182] The path planning module 604 is used to perform path planning on the crane task sequence based on the priority dynamic adjustment strategy and the dynamic time interval strategy in the lower-level planning, and to obtain the crane running trajectory.

[0183] Iteration module 605 is used to repeat steps 2 to 4 until the termination condition is met, so as to obtain the optimal dispatching trajectory and task allocation scheme of multiple overhead cranes.

[0184] Figure 6 The system shown can execute the contents of the above method embodiments. For the parts not described in detail in this embodiment, please refer to the contents recorded in the above method embodiments, and they will not be repeated here.

[0185] See Figure 7 This disclosure also provides an electronic device 70, which includes at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed, enables the at least one processor to perform the overhead crane two-layer planning intelligent scheduling optimization method described in the foregoing method embodiments.

[0186] This disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the overhead crane two-layer planning intelligent scheduling optimization method in the foregoing method embodiments.

[0187] This disclosure also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the overhead crane two-layer planning intelligent scheduling optimization method in the foregoing method embodiments.

[0188] The following is for reference. Figure 7 The diagram illustrates a structural schematic of an electronic device 70 suitable for implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0189] like Figure 7 As shown, the electronic device 70 may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device 70. The processing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0190] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic device 70 to communicate wirelessly or wiredly with other devices to exchange data. Although an electronic device 70 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0191] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 709, or installed from storage device 708, or installed from ROM 702. When the computer program is executed by processing device 701, it performs the functions defined in the methods of embodiments of this disclosure.

[0192] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0193] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0194] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, enable the electronic device to perform the relevant steps of the above method embodiments.

[0195] Alternatively, the aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, enable the electronic device to perform the relevant steps of the above method embodiments.

[0196] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0197] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0198] The units described in the embodiments of this disclosure can be implemented in software or in hardware.

[0199] It should be understood that the various parts of this disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0200] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for intelligent scheduling optimization of overhead cranes with two-level planning, characterized in that, include: Step 1: Obtain the task schedule data contained in the production scheduling plan, wherein the task schedule data includes the start position, target position and start time of each task; Step 2: With the goal of minimizing the maximum completion time and the spatiotemporal characteristics of the overhead crane as constraints, construct an overhead crane scheduling model; Step 3: In the upper-level planning, based on the task plan table data, the improved whale optimization algorithm is used to solve the crane scheduling model and generate the crane task sequence. Step 3 specifically includes: Step 3.1: Use the task schedule data as the data input for the overhead crane scheduling model; Step 3.2: Solve the overhead crane scheduling model using the improved whale optimization algorithm to obtain the continuous value sequence of overhead crane tasks. ; Step 3.3, will Discretization is performed to obtain the discrete sequence values ​​of the crane task. The discretization formula is: The formula represents that Mapping to the range of real numbers Within, and makes the mapped real number convert to the nearest integer value and assign it to. ,in, Indicates the The Discrete values ​​in each dimension Indicates the first The number of overhead cranes available in each dimension. Indicates the The Continuous values ​​in each dimension Indicates the number of tasks; Step 3.2 specifically includes: Step 3.2.1: In the global and local search processes of the improved whale optimization algorithm, a dynamic inertia weight coefficient is introduced to balance the global and local searches, enhancing the adaptability and search performance of the improved whale optimization algorithm to complex problems. The formula for calculating the dynamic inertia weight coefficient is as follows: in, These respectively represent the inertial weights The minimum and maximum values, This represents the current fitness value of an individual. This represents the current average fitness value of the population. This represents the minimum fitness value of the current population; Step 3.2.2: In the improved whale optimization algorithm, a feedback mechanism is introduced for random walk mutation. The formula for the feedback mechanism is: in, This represents the walking step size that follows a Lévy distribution. For one Random numbers between In the first Random individuals generated using chaotic mapping are used in the generation; Step 3.2.3: For the out-of-bounds solutions generated in the improved whale optimization algorithm, a value close to the corresponding boundary region is randomly generated for correction, resulting in a continuous value sequence for the crane task. The expression for the correction process is: Represents an individual The Each dimension value For one Random numbers between and Indicates the upper and lower boundaries of an individual; Step 4: In the lower-level planning, based on the priority dynamic adjustment strategy and the dynamic time interval strategy, the path planning of the crane task sequence is performed to obtain the crane running trajectory. Step 5: Repeat steps 2 to 4 until the termination condition is met, and obtain the optimal dispatching trajectory and task allocation scheme for multiple overhead cranes.

2. The method according to claim 1, characterized in that... Step 4 specifically includes: Step 4.1, based on the discrete sequence values ​​of the overhead crane task Based on the task schedule data, obtain the start position, target position, and start time of each task, and then... The starting node; Step 4.2: Assuming each transportation task has 5 sub-stages, obtain all overhead crane data from the time nodes. The time from the start of each task sub-stage to its completion time is used as the next time stage. Get the time interval of each crane The initial trajectory within; Step 4.3, determine the time interval. Inside, check if there is a conflict between multiple cranes; if so, record it. Step 4.4: Based on the pre-set task priorities, each crane obtains its corresponding static priority according to the task attributes, and then dynamically adjusts the static priority of each crane considering the urgency of the task and the importance of each crane. Step 4.5, Update Time ; Step 4.6: Repeat steps 4.2 to 4.5 until all tasks are completed and obtain the optimal overhead crane running trajectory.

3. A crane two-level planning intelligent scheduling optimization system, used to execute the crane two-level planning intelligent scheduling optimization method according to any one of claims 1 and 2, characterized in that, include: The acquisition module is used to acquire the task plan table data contained in the production scheduling plan, wherein the task plan table data includes the start position, target position and start time of each task; The building module is used to construct a crane scheduling model with the goal of minimizing the maximum completion time and the spatiotemporal characteristics of the crane as constraints. The solver module is used in the upper-level planning to solve the crane scheduling model based on the task plan table data and use the improved whale optimization algorithm to generate the crane task sequence. The path planning module is used in the lower-level planning to perform path planning on the crane task sequence based on the priority dynamic adjustment strategy and the dynamic time interval strategy, and to obtain the crane running trajectory. The iteration module controls the solution module and the path planning module to repeatedly execute their corresponding processing flow until the termination condition is met, so as to obtain the optimal dispatch trajectory and task allocation scheme for multiple overhead cranes.

4. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the overhead crane two-layer planning intelligent scheduling optimization method as described in any one of claims 1-2.

5. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the overhead crane two-layer planning intelligent scheduling optimization method according to any one of claims 1-2.

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