OHT trolley motion control optimization method, device, equipment and medium

Abstract

The invention discloses an OHT trolley motion control optimization method, device and equipment and a medium, and the method comprises the steps: obtaining the guide rail energy consumption distribution of an OHT trolley, carrying out the motion control optimization of the OHT trolley according to the guide rail energy consumption distribution, so as to generate an optimization control strategy of the OHT trolley, and transmitting the optimization control strategy to a corresponding OHT trolley for execution. A more refined control strategy of the OHT trolley is realized, the power loss of the OHT trolley is optimized, and the operation efficiency of the whole crown block carrying system is improved.

Description

Technical Field

[0001] The present invention relates to the field of automated handling task scheduling, and particularly to an OHT cart motion control optimization method, device, equipment and medium. Background Art

[0002] AMHS (Automated Material Handling System) is widely used in current semiconductor wafer fabs. Under the control of MES (Manufacturing Execution System), AMHS can replace manual handling. Due to its stable characteristics, it largely avoids the quality problems of materials caused by uncontrollable factors during the transportation process.

[0003] For a semiconductor manufacturing factory, the purpose of introducing AMHS is to improve production efficiency and achieve maximum production capacity. And the handling efficiency of AMHS itself directly affects the production efficiency of the factory. A complete AMHS system includes transmission hardware devices (OHT, OHT cart / AMR, Lifter, etc.), storage devices (Foup&Reticle Stocker, Reticle Cabinet, NTB, OHCV, OHB), purification devices (TLP, OPS, FPS, etc.) and software systems (MCS, OHTC, STKC, etc.). Among them, the OHT cart is an important part of the automated material handling system. Multiple material handling carts (Overhead Hoist Transport, OHT) provide high-altitude handling operation support for the transmission device by hoisting and walking on the guide rail under the ceiling of the factory. The transmission device can move on the walking guide rail and transport wafer boxes according to actual needs, thereby improving the production efficiency of the Fab. In the prior art, there is no corresponding solution for the running speed control of the OHT cart. Most tend to simply optimize the path and scheduling control, and cannot solve the variable speed optimization problem of the OHT cart speed control. Summary of the Invention

[0004] In view of this, the present invention provides an OHT cart motion control optimization method to solve the variable speed optimization problem of the OHT cart speed control in the prior art.

[0005] According to the first aspect of the present invention, an OHT cart motion control optimization method is provided, including:

[0006] Obtaining the guide rail energy consumption distribution of the OHT cart;

[0007] According to the energy consumption distribution of the guide rail, optimize the motion control of the OHT cart to generate an optimized control strategy for the OHT cart, wherein the motion control optimization includes motion path optimization and motion speed optimization;

[0008] Send the optimized control strategy to the corresponding OHT cart for execution.

[0009] According to the second aspect of the present invention, there is provided an OHT cart motion control optimization device, including:

[0010] An acquisition module for acquiring the energy consumption distribution of the guide rail of the OHT cart;

[0011] A processing module for optimizing the motion control of the OHT cart according to the energy consumption distribution of the guide rail to generate an optimized control strategy for the OHT cart, wherein the motion control optimization includes motion path optimization and motion speed optimization;

[0012] A sending module for sending the optimized control strategy to the corresponding OHT cart for execution.

[0013] According to the third aspect of the present invention, there is provided a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned OHT cart motion control optimization method are implemented.

[0014] According to the fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned OHT cart motion control optimization method are implemented.

[0015] By means of the above technical solutions, an OHT cart motion control optimization method, device, equipment and medium provided by the present invention obtain the energy consumption distribution of the guide rail of the OHT cart, optimize the motion control of the OHT cart according to the energy consumption distribution of the guide rail to generate an optimized control strategy for the OHT cart, and send the optimized control strategy to the corresponding OHT cart for execution. It realizes a more refined control strategy for the OHT cart, optimizes the power loss of the OHT cart, and improves the operation efficiency of the entire overhead crane handling system.

[0016] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly, and to be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following specifically illustrates the specific embodiments of the present invention. Description of the Drawings

[0017] The accompanying drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 FIG. shows a schematic diagram of an application scenario for optimizing the motion control of an OHT cart provided in an embodiment of the present invention;

[0019] Figure 2 FIG. shows a schematic flowchart of a method for optimizing the motion control of an OHT cart provided in an embodiment of the present invention;

[0020] Figure 3 FIG. shows a schematic flowchart of another method for optimizing the motion control of an OHT cart provided in an embodiment of the present invention;

[0021] Figure 4 FIG. shows a schematic flowchart of yet another method for optimizing the motion control of an OHT cart provided in an embodiment of the present invention;

[0022] Figure 5 FIG. shows a schematic diagram of a hierarchical multi-objective model for optimizing the motion control of an OHT cart provided in an embodiment of the present invention;

[0023] Figure 6 FIG. shows a schematic structural diagram of a device for optimizing the motion control of an OHT cart provided in an embodiment of the present invention; Detailed Embodiments

[0024] The following will refer to the accompanying drawings and describe in detail the specific embodiments of the present invention in combination with the embodiments. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0025] A method for optimizing the motion control of an OHT cart provided in an embodiment of the present invention can be applied to an automatic material handling system in a semiconductor wafer fab, such as Figure 1As shown in the figure, taking the automatic material handling system of a 300mm wafer fab as an example, the OHT system is extended to the inter-bay area to form a unified layout mode. The transfer between different bays no longer requires the stocker as a transfer station. This mode is the direct transfer mode of Tool-to-Tool, which can achieve direct transfer between different bays, with faster speed and higher efficiency. The OHB is set on the aisle system for temporary caching of wafer boxes. The OHT cart shuttles between any two of the OHB, EQP Port (machine loading and unloading point), and Stocker. The guide rails include linear guide rails, arc guide rails, and shortcut guide rails. The guide rail base is fixed at the connection of the front linear guide rail and the rear linear guide rail. The base is provided with a straight guide rail for docking with the front linear guide rail and the rear linear guide rail, a left-turn guide rail for docking with the front linear guide rail and the left-turn rear rail, and a right-turn guide rail for docking with the front linear guide rail and the right-turn rear rail. It should be noted here that the guide rail types do not distinguish between Intra-Bay and Inter-Bay in the process area. In the implementation of the present invention, in order to illustrate the principle of optimization, it should be noted that when the OHT cart moves on the same guide rail system, the power consumption per unit distance is not lower when the speed is faster. In the embodiment of the present invention, it is necessary to find the relationship between the guide rail type T (linear guide rail or arc guide rail), the guide rail friction coefficient, and the minimum power consumption speed. However, since the guide rail friction coefficient is difficult to measure, an indirect measurement method is adopted. When moving at a fixed speed V0, the functional relationship between the power P of the OHT cart and the guide rail type and friction coefficient μ is μ = f(V0, T, P). Under the same guide rail, for any speed V, there is μ = f(V, T, P). That is, for guide rails with the same guide rail type and friction coefficient, a certain speed V must only correspond to one power P. When the friction coefficient changes and V remains unchanged, P will also change accordingly. Therefore, when the OHT cart moves on the guide rail, only by measuring the current moving speed, the corresponding power, and the guide rail type can the minimum power consumption speed be obtained at this time.

[0026] Method for Detecting the Lowest Power Consumption Speed To consider the lowest power consumption speed of the trolley under different guide rails, adaptive adjustment is carried out by previously measuring the optimal moving speed under different guide rails. The speed-power data of the OHT trolley during movement under the same type of guide rail is measured (the power data can be the average power of different guide rail segments or the instantaneous power of the OHT trolley at the measurement time point. Since the acquisition of relevant data belongs to the prior art, it will not be elaborated here again), and the power consumption per unit distance is obtained. The parameter (P / V) characterizes the operating power consumption of the OHT trolley. The smaller its value, the less power is consumed per unit distance by the trolley. The measured data is fitted to obtain the expression and curve graph of the power per unit distance and speed under this guide rail. Thus, the optimal operating speed under this guide rail is obtained. Then, tests are carried out on various different guide rails to measure the relationships among multiple speeds V, times T, the power P of the OHT trolley, and V0, and a standard curve is obtained. That is to say, when the OHT trolley runs on the guide rail, the lowest power consumption speed at this time is obtained based on the speed V of the trolley and the detected power P at this time. Applying this method avoids the problem that the friction coefficient is difficult to measure during the detection process, and realizes the real-time adjustment of the lowest power consumption speed. An energy consumption map of the guide rail can be generated according to the corresponding relationship between V, T, P, V0 and the guide rail position. In the embodiment of the present invention, multi-objective optimization is carried out according to the generated energy consumption map of the guide rail. Factors such as the handling path length, energy consumption, handling task duration, and OHT trolley loss need to be considered simultaneously. The optimization algorithm can adopt particle swarm algorithm, simulated annealing algorithm, ant colony algorithm, slime mold algorithm, immune algorithm, etc. In the embodiment of the present invention, the ant colony algorithm is preferably used for optimization. An OHT trolley motion control optimization method provided by the embodiment of the present invention includes obtaining the energy consumption distribution of the guide rail of the OHT trolley; according to the energy consumption distribution of the guide rail, optimizing the motion control of the OHT trolley to generate an optimized control strategy for the OHT trolley. The optimization strategy simultaneously takes one or more of the handling path length, energy consumption, and handling task duration as optimization objectives; the optimized control strategy is sent to the corresponding OHT trolley for execution, realizing the comprehensive control optimization of the OHT trolley and the variable-speed optimization of speed control.

[0027] The present invention will be described in detail below through specific embodiments.

[0028] Embodiment 1

[0029] As Figure 2 shown, an OHT trolley motion control optimization method provided in an embodiment of the present invention includes:

[0030] Step 201, obtain the energy consumption distribution of the guide rail of the OHT trolley;

[0031] Among them, the corresponding relationship between V, T, P, V0 and the guide rail position can be obtained through historical data analysis to generate an energy consumption map of the guide rail, or the data can be collected in real time by the OHT trolley patrolling the field to generate an energy consumption distribution map of the guide rail.

[0032] Step 202: Optimize the motion control of the OHT trolley according to the energy consumption distribution of the guide rail to generate an optimized control strategy for the OHT trolley.

[0033] Among them, the optimization strategy needs to simultaneously consider one or more of the handling path length, energy consumption, and handling task duration as optimization objectives.

[0034] Step 203: Send the optimized control strategy to the corresponding OHT trolley for execution.

[0035] An OHT trolley motion control optimization method provided by an embodiment of the present invention obtains the energy consumption distribution of the guide rail of the OHT trolley; optimizes the motion control of the OHT trolley according to the energy consumption distribution of the guide rail to generate an optimized control strategy for the OHT trolley. The optimization strategy simultaneously considers one or more of the handling path length, energy consumption, and handling task duration as optimization objectives; sends the optimized control strategy to the corresponding OHT trolley for execution, realizing comprehensive control optimization of the OHT trolley and variable-speed optimization of speed control.

[0036] Embodiment 2

[0037] In Embodiment 1 of the present invention, in order to efficiently find the motion curve with the lowest power consumption, Embodiment 2 of the present invention uses an optimized ant colony algorithm to optimize the decision-making of multiple objectives, and combines the requirements of low-energy operation to improve its low power consumption, thereby designing an optimal control algorithm. The optimized control algorithm proposed based on the ant colony algorithm is an extended application based on the ant colony algorithm.

[0038] To evaluate the effect of the optimized control strategy, the minimum value of the objective function is introduced in Embodiment 2 of the present invention as Among them, are the respective weights of the handling distance length, handling time, and handling energy consumption of the OHT trolley. The weight can be 0, that is, it corresponds to not considering this factor as an optimization objective. m is the number of OHT trolleys. is the total travel distance of the kth OHT trolley. is the total handling time of the kth OHT trolley. is the total power consumption of the kth OHT trolley.

[0039] Adopt the grid method for two-dimensional space planning in combination with the topological distribution of the guide rails. The planned space is divided into grids, and the running trajectory is formed by connecting adjacent grid nodes. Accordingly, construct a "T" centered on the current node as the data structure, with a total of 3 adjacent nodes. Take the position of the OHT cart as the current node, and determine the traveling direction according to the topological constraints of the guide rails. When there is no shortcut guide rail at this node, there are two adjacent nodes in the front and back.

[0040] Based on the guide rail energy consumption distribution map, assume that the probability of ant k transferring from node i to node j during the process of searching for nodes is expressed as:

[0041]

[0042] where N is the number of iterations, and path k The number of times represents the set of path nodes of the candidate guide rail segments path that ant k has not passed in the next step. k . Assume that the power consumption per unit distance on the section from node i to node j is W. Then, the pheromone concentration on the guide rail from node i to node j at the Nth iteration is τ ij (N) / W. There is no difference in the pheromones on each initial path. Ants determine the direction according to the pheromone concentration during the process of searching for nodes, that is, the greater the pheromone concentration, the greater the probability that the ant will move in this direction. Assume that the probability of ant k transferring from node i to node j during the process of searching for nodes is

[0043] α and β respectively represent the relative importance of pheromones and expected heuristic factors. When all ants complete a tour, the pheromones on each guide rail segment path are updated according to τ ij (N + 1) = ρη(N) + (1 - ρ)Δτ ij (N) ijUpdate, where N is the number of iterations, ρ is the evaporation coefficient of pheromone on the path, ρ ∈ (0, 1). Optionally, the evaporation coefficient ρ of different guide rail segments can be set according to the guide rail energy consumption distribution map, and ρ is set in an inverse proportion to the energy consumption. α should always be in the dominant position to prevent local optimality, and β should not be too large. In the second embodiment of the present invention, it can be determined by various methods, such as: Empirical setting: In many cases, the weights of α and β are set based on experience or previous research results, and the initial values can be set according to the nature of the problem and previous research results; Parameter tuning: The values of α and β can be adjusted by trial and error to find the best combination, so that the algorithm reaches the best performance. It is necessary to run multiple simulations, each time using different parameter values, and then compare the results; Adaptive method: In some advanced variants of the ant colony algorithm, α and β will be dynamically adjusted during the algorithm operation to adapt to different situations in the search process; Optimization algorithm: Other optimization techniques (such as genetic algorithm, particle swarm optimization, etc.) can be used to automatically find the optimal α and β. Preferably, the value of ρ is adaptively changed in the second embodiment of the present invention. When the problem scale is relatively large (more tracks), due to the existence of the evaporation coefficient ρ of the information amount, the information amount that has never been searched will be reduced to close to 0, reducing the global search ability of the algorithm; when ρ is too large and the information amount of the solution increases, the probability of selecting the previously searched solution is too large, which will also affect the global search ability of the algorithm: although reducing ρ can improve the global search ability of the algorithm, it will reduce the convergence speed of the algorithm. Therefore, the value of ρ can be changed adaptively. The initial value of ρ, ρ0 = 1; when the optimal value obtained by the algorithm has no obvious improvement within N cycles, ρ is reduced to N is the number of iterations, ρ min is the preset minimum evaporation coefficient, which can be set according to experience or obtained by continuous attempts.

[0044] Heuristic factor η ij is a heuristic factor, which represents the expected degree of the OHT cart moving from node i to node j. In this embodiment, it can take the ε of the handling cost (or one or more factors of the distance length to node j, the time to node j, the energy consumption to node j) from node i to node j ij The reciprocal is where ε ij = δ1·l ij + δ2·t ij + δ3·P ij , δ1, δ2, δ3 are the weights of the distance length l ij from node i to node j, the handling time t ij , and the energy consumption P ij respectively.

[0045] Correspondingly, such as Figure 3As shown in the figure, in the second embodiment of the present invention, an optimized ant colony algorithm is used to optimize multi-objective decision-making, including:

[0046] Step 301: Load the guide rail energy consumption distribution map;

[0047] Step 302: According to the guide rail topology, all ants are assigned to the initial nodes;

[0048] Step 303: According to the pheromone on each section path of the guide rail, the ant selects the next node;

[0049] Among them, the probability of the next node is

[0050] Step 304: Update the global pheromone of the guide rail;

[0051] Among them, the pheromone on each section path of the guide rail is based on τ ij (N + 1) = ρη(N) + (1 - ρ)Δη ij for updating, where N is the number of iterations and ρ is the evaporation coefficient of the pheromone on the path. ij

[0052] Step 305: Determine whether the number of iterations N > N max or the objective function value remains unchanged. When the number of iterations N is less than N max and the objective function value changes, jump to Step 303 to continue execution; when the number of iterations N > N max or the objective function value remains unchanged, jump to Step 306 to continue execution;

[0053] Among them, N is the current number of iterations. It should be noted here that the number of ants is m, that is, the number of OHT cars. In practical applications, it is equivalent to m ants executing the method flow of the second embodiment of the present invention in parallel. The objective function value is When the function value remains unchanged or the difference from the function value of the previous iteration is less than the preset threshold, it means that the algorithm converges or when the number of iterations reaches the maximum number N max the iterative process is forcibly terminated.

[0054] Step 306: Output the optimized control strategy.

[0055] In the second embodiment of the present invention, combined with the guide rail energy consumption distribution map, the application of the ant colony algorithm is optimized and adjusted, so that the applicability of the algorithm is enhanced, and it can optimize multiple target strategies at the same time. While achieving fast convergence, it takes into account the global search range and avoids falling into the local optimal solution.

[0056] Embodiment 3

[0057] Compared with the optimization objective of Embodiment 2 of the present invention, Embodiment 3 of the present invention considers the weights of multiple criteria, making the optimization control strategy more flexible and the optimization result more in line with the actual application. For example, if the user is most concerned about energy consumption, the weight of the energy consumption index can be increased. The implementation of the method is optimized as follows:

[0058] Correspondingly, as Figure 4 shown, Embodiment 3 of the present invention uses an optimized ant colony algorithm with hierarchical analysis to make decision optimization for multiple objectives, including:

[0059] Step 401: Load the energy consumption distribution map of the guide rail;

[0060] Step 402: According to the optimization objective, conduct multi-objective hierarchical analysis to obtain the weights of each optimization objective;

[0061] Among them, Embodiment 3 of the present invention introduces the hierarchical analysis process (AHP): by analyzing the hierarchical process, the path length, handling time, and handling energy consumption are converted into comprehensive weight influencing factors. Among them, the analytic hierarchy process, abbreviated as AHP (Analytic Hierarchy Process), is a decision-making method that decomposes the elements related to the decision into levels such as objectives, criteria, and solutions, and conducts qualitative and quantitative analysis on this basis. The analytic hierarchy process regards a complex multi-objective decision-making problem as a system, decomposes the objective into multiple objectives or criteria, and then decomposes it into several levels of multiple indicators (or criteria, constraints). Through the qualitative index fuzzy quantification method, the single-level ranking (weights) and the total ranking are calculated to be used as a systematic method for optimizing decisions of objectives (multiple indicators) and multiple solutions. The decision-making problem is decomposed into different hierarchical structures in the order of the overall objective, evaluation criteria, and specific alternative investment plans, and then by using the method of solving the eigenvector of the judgment matrix, the priority weights of each element at each level with respect to a certain element at the previous level are obtained. According to the nature of the problem and the overall objective to be achieved, this method decomposes the problem into different constituent factors, and according to the mutual correlation and subordination relationship between the factors, the factors are aggregated and combined at different levels to form a multi-level analysis structure model, so that the problem is finally reduced to the determination of the relative importance weights or the ranking of the relative advantages and disadvantages of the lowest level (solutions, measures, etc. for decision-making) with respect to the overall objective.

[0062] The hierarchical analysis process in this step includes:

[0063] Step 1: Establish a hierarchical model;

[0064] Among them, as Figure 5 shown, the structure of the model is divided into two layers, namely the objective layer A and the criterion layer B. The optimal handling strategy is the objective, and the evaluation criteria include path length, handling time, and handling energy consumption. The weight of path optimization in the objective layer is w ij, the weights of path length, handling time, and handling energy consumption in the criterion layer are w1, w2, and w3 respectively.

[0065] Step 2: Construct the judgment matrix;

[0066] Among them, constructing the judgment matrix is to compare each element pairwise and determine the weight of each criterion layer with respect to the target layer. Simply put, it is to make pairwise judgments on the indicators of the criterion layer, usually using Santy's 1-9 scale method.

[0067] Step 3: Hierarchical single sorting;

[0068] Among them, hierarchical single sorting means pairwise comparing all elements in this layer for a certain element in the upper layer and performing hierarchical sorting to arrange the order of importance. The specific calculation can be based on the judgment matrix A, ensuring that it meets the eigenvalue and eigenvector conditions of AW =. The largest eigenvalue of A is λ max , corresponding to λ max The normalized eigenvector is W, and w1, w2, and w3 are the components of W, referring to the weights, corresponding to the single sorting of their respective elements. Calculate the weights (weight coefficients) of each factor with respect to the target layer using the judgment matrix. The calculation steps of the weight vector (W) and the largest eigenvalue (λ max ) are as follows:

[0069] Step (1): Calculate the product of each row element in matrix A, that is

[0070] Step (2): Calculate the nth root of M i of where n is the number of criteria;

[0071] Step (3): If is normalized to then w j is the eigenvector;

[0072] Step (4): Calculate the largest eigenvalue

[0073] Step 4: Solve the largest eigenvalue and CI value, and solve the CR value based on the CI and RI values to determine whether its consistency passes;

[0074] Among them, the consistency test is used to determine the acceptable range of inconsistency in the judgment matrix A. CR is the consistency ratio. When its value is less than 0.1, the judgment matrix passes the consistency test; otherwise, it needs to be corrected. CI is the consistency index, CI = λ max-n / n-1. RI is the random consistency index, which is determined by the order of the judgment matrix. Satty simulated 1000 times to obtain the values of the random consistency index RI. If inconsistency occurs, the judgment matrix A needs to be corrected. If consistent, the weight values corresponding to different criteria are output. For example, the weights of path length, handling time, and handling energy consumption in the criterion layer are w1, w2, and w3 respectively.

[0075] Step 403: According to the guide rail topology, all ants are assigned to the initial nodes.

[0076] Step 404: According to the weights of each optimization objective and the pheromone on each section path of the guide rail, the ants select the next node.

[0077] Among them, in the improved ant colony algorithm, a direction guidance and dynamic optimization mechanism is introduced, which improves the efficiency and accuracy of path search. In the ant colony algorithm, the heuristic factor η ij is a heuristic factor, which represents the expected degree of the OHT cart moving from node i to node j. Among them, the heuristic factor is generally the reciprocal of the distance d ij between adjacent nodes. The smaller d ij , the larger . The smaller the distance, the greater the possibility for the ant to select the next node. This definition is applicable to the unordered TSP problem, but for the optimization problem of the ordered guide rail, this search method will reduce the search efficiency. The A* algorithm is introduced to improve the heuristic factor, and the modification is as follows:

[0078]

[0079] Among them, η ij (N) is the heuristic function, d jD is the dimensionless value of the length between node j and the end point D, d ij is the dimensionless value of the length between adjacent nodes i and j. If the path length, handling time, and handling energy consumption are considered simultaneously, the comprehensive improved heuristic factor η ij is as follows:

[0080]

[0081] Among them, s ij = w1·d ij + w2·t ij + w3·p ij , where w1, w2, and w3 are the weights of path length, handling time, and handling energy consumption in the criterion layer respectively.

[0082] In addition, Embodiment 3 of the present invention further optimizes the transition probability as follows:

[0083]

[0084] Among them, q is a random variable uniformly distributed on [0, 1], and q0 is a parameter controlling the movement rule, where q0 ∈ [0, 1]. According to the scale of the guide rail, the value of q0 can be divided into three types, usually large, medium, and small, with values of 0.6, 0.4, and 0.2 respectively. When q < q0, the ant temporarily ignores the existence of a better next node and accumulates pheromone on the path from node i to all candidate nodes j. When q ≥ q0, the ant will select the next node according to the original selection to prevent the ant from choosing a path with a large pheromone concentration at the beginning, which is beneficial to global search and avoids local convergence.

[0085] Step 405: Update the global pheromone of the guide rail;

[0086] Among them, when the information of the guide rail path changes, it should be re-optimized according to the changed information. Therefore, when the ant selects the next node, it checks whether the information in the guide rail path has changed. If it has changed, the pheromone concentration τ ij (N + 1) = ρη(N) + (1 - ρ)Δτ ij (N) needs to be updated. ij .

[0087] Step 406: Determine whether the number of iterations N > N max or the objective function value remains unchanged. When the number of iterations N is less than N max and the objective function value changes, jump to step 303 to continue execution; when the number of iterations N > N max or the objective function value remains unchanged, jump to step 306 to continue execution;

[0088] Step 407: Output the optimized control strategy.

[0089] In the third embodiment of the present invention, by analyzing the hierarchical process, multiple factors such as path length, handling time, and handling energy consumption are scientifically considered, providing a more comprehensive optimization solution for the movement control of the OHT cart, rather than just optimizing based on a single criterion (such as the shortest path or the lowest energy consumption). By introducing a direction guidance and dynamic optimization mechanism, the efficiency of path search is improved, unnecessary exploration is reduced, thereby accelerating the algorithm convergence speed and speeding up the optimization process. The improved algorithm can dynamically adapt to changes in the wafer processing workload, and by real-time updating pheromone and heuristic information, it provides more accurate and current-state-adaptive optimization suggestions for the OHT cart.

[0090] Embodiment Four

[0091] To take into account cost factors such as mechanical wear and consumable consumption of the OHT cart, in the OHT in the non-full-load overall working state, through a specific constant-speed patrol plan, energy consumption and consumable (wheels, bearings, etc.) losses are reduced. Some limiting conditions need to be determined to reduce energy consumption and consumable losses. For example, in combination with the comparison relationship between the wheel loss caused by acceleration and the wheel loss caused by constant speed, the speed is controlled.

[0092] The following is an example of the implementation optimization algorithm of the present invention realized by Python, as follows:

[0093] Step 1: Define the path network

[0094] import networkx as nx

[0095] # Create a graph to represent the path network

[0096] G = nx.Graph()

[0097] # Assume there are 5 nodes and the guide rail connections between them

[0098] nodes = range(5)

[0099] G.add_nodes_from(nodes)

[0100] edges = [(0,1),(1,2),(2,3),(3,4),(4,0),(1,3)]

[0101] G.add_edges_from(edges)

[0102] # Initialize the pheromone of each edge

[0103] pheromone = {edge: 1.0 for edge in G.edges}

[0104] Step 2: Initialize the parameters of the ant colony algorithm

[0105] # Ant colony algorithm parameters

[0106] num_ants = 10

[0107] alpha = 1.0

[0108] beta = 2.0

[0109] evaporation_rate = 0.5

[0110] max_iterations = 100

[0111] Step 3: The logic for ants to select paths and update pheromones

[0112]

[0113]

[0114] Step 4: Obtain the optimization result

[0115] Determine the recommended speed based on the usage frequency of the path: For example, paths that are used more frequently may require a higher speed to improve efficiency, while paths that are used less frequently can have their speed reduced to improve safety and reduce wear and tear.

[0116] Consider the type of guide rail: Different types of guide rails (such as linear guide rails, shortcut guide rails, turning guide rails) may have different optimal speeds. For example, linear guide rails may allow a higher speed, while turning guide rails may require a lower speed to ensure safety.

[0117]

[0118]

[0119] Further, as Figure 2 a specific implementation of the method, an OHT cart motion control optimization device is provided in an embodiment of the present invention, as Figure 6 shown, the device includes:

[0120] An acquisition module 610, configured to acquire the guide rail energy consumption distribution of the OHT cart;

[0121] A processing module 620, configured to perform motion control optimization on the OHT cart according to the guide rail energy consumption distribution to generate an optimized control strategy for the OHT cart, where the motion control optimization includes motion path optimization and motion speed optimization;

[0122] A sending module 630, configured to send the optimized control strategy to the corresponding OHT cart for execution.

[0123] An embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0124] Acquire the guide rail energy consumption distribution of the OHT cart;

[0125] Perform motion control optimization on the OHT cart according to the guide rail energy consumption distribution to generate an optimized control strategy for the OHT cart;

[0126] Send the optimized control strategy to the corresponding OHT cart for execution.

[0127] In an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0128] Obtain the energy consumption distribution of the guide rail of the OHT cart;

[0129] According to the energy consumption distribution of the guide rail, optimize the motion control of the OHT cart to generate an optimized control strategy for the OHT cart;

[0130] Send the optimized control strategy to the corresponding OHT cart for execution.

[0131] It should be noted that for the functions or steps that can be realized by the above computer-readable storage medium or computer device, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described in detail here.

[0132] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0133] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.

[0134] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An optimization method for the motion control of an OHT cart, characterized in that, Including: Obtain the energy consumption distribution of the guide rails of the OHT trolley; According to the energy consumption distribution of the guide rails, optimize the motion control of the OHT trolley to generate an optimized control strategy for the OHT trolley, wherein the motion control optimization includes motion path optimization and / or motion speed optimization; Send the optimized control strategy to the corresponding OHT trolley for execution.

2. The OHT cart motion control optimization method according to claim 1, wherein The step of optimizing the motion control of the OHT trolley according to the energy consumption distribution of the guide rails to generate an optimized control strategy for the OHT trolley includes: Step 1: Load the energy consumption distribution map of the guide rails; Step 2: Allocate all ants to the initial nodes according to the guide rail topology; Step 3: The ants select the next node according to the pheromone on each segmented path of the guide rail; Step 4: Update the global pheromone of the guide rail. Among them, the pheromone on the segmented path of the guide rail is updated according to τ ij (N + 1)= ρτ ij (N)+(1 - ρ)Δτ ij where N is the number of iterations and ρ is the evaporation coefficient of the pheromone on the segmented path of the guide rail; Step 5: Determine whether the number of iterations N > N max or the objective function value remains unchanged when the number of iterations N is less than N max and the objective function value changes, then jump to Step 3 and continue to execute; when the number of iterations N > N max or the objective function value remains unchanged, then jump to Step 6 and continue to execute; Step 6: Output the optimized control strategy of the OHT trolley.

3. The OHT cart motion control optimization method according to claim 2, wherein, The step that the ants select the next node according to the pheromone on each segmented path of the guide rail includes: Calculate the transition probability according to the pheromone on each segmented path of the guide rail where N is the number of iterations, α represents the relative importance of pheromone, β represents the relative importance of the expected heuristic factor, and path k The number of times represents the set of nodes path on the candidate guide rail segments that have not been passed by ant k in the next step k , η ij (N) is the heuristic factor from node i to node j, and τ ij (N) is the pheromone from node i to node j; Based on the calculated transition probability The ant selects the next node.

4. The optimized method for controlling the movement of the OHT cart according to claim 3, characterized in that, Heuristic factor from node i to node j where ε ij = δ1·l ij + δ2·t ij + δ3·P ij , and δ1, δ2, and δ3 are the corresponding weights of the path length l ij from node i to node j, the handling time t ij , and the energy consumption P ij respectively.

5. The OHT cart motion control optimization method according to claim 2, wherein Volatilization coefficient of pheromone on the segmented path of the guide rail Where N is the number of iterations.

6. The optimized method for OHT cart motion control according to claim 2, characterized in that Before step 3, there is a step of performing multi-objective hierarchical analysis according to the optimization objectives to obtain the weights of each optimization objective. Step 3 is that the ants select the next node according to the weights of each optimization objective and the pheromone on each segmented path of the guide rail.

7. The optimized method for controlling the movement of the OHT cart according to claim 6, wherein, The step that the ants select the next node according to the weights of each optimization objective and the pheromone on each segmented path of the guide rail includes: Calculate the transition probability based on the pheromone on each segmented path of the guide rail where N is the number of iterations, α represents the relative importance of pheromone, β represents the relative importance of the expected heuristic factor, path k The number of times represents the set of nodes path on the candidate guide rail segments not yet passed by ant k in the next step k , η ij (N) is the heuristic factor from node i to node j, τ ij (N) is the pheromone from node i to node j; Based on the calculated transition probability The ant selects the next node.

8. An OHT cart motion control optimization device, characterized in that, Including: An acquisition module for obtaining the energy consumption distribution of the guide rails of the OHT trolley; A processing module for optimizing the motion control of the OHT trolley according to the energy consumption distribution of the guide rails to generate an optimized control strategy for the OHT trolley, wherein the motion control optimization includes motion path optimization and motion speed optimization; A sending module for sending the optimized control strategy to the corresponding OHT trolley for execution.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the OHT trolley motion control optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the OHT trolley motion control optimization method as described in any one of claims 1 to 7.

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