Simulation system development method of electric monorail conveying system

Abstract

The invention relates to the technical field of order distribution path planning, in particular to a simulation system development method of an electric monorail conveying system. According to the invention, the three-dimensional distribution model is constructed in the simulation software by combining the three-dimensional data of the electric monorail conveying system and the factory, so that the distribution condition in a real factory environment can be accurately simulated. Data of orders to be delivered are obtained, delivery tasks are distributed in the model, training is carried out with the optimal delivery path as the target, and the finally obtained simulation system can accurately predict the optimal delivery path of each order in a factory.

Description

Technical Field

[0001] The invention relates to the technical field of order delivery path planning, and in particular to a simulation system development method for an electric monorail conveying system. Background Art

[0002] With the rapid development of modern manufacturing, production efficiency and cost control have become key factors in corporate competitiveness. As an indispensable part of the automated production line, the performance optimization and rationality of resource allocation of the electric monorail conveyor system are directly related to the overall efficiency and economic benefits of the production line. The traditional evaluation method of the electric monorail conveyor system is mainly based on past project experience and simple table calculations to evaluate the effectiveness of the system. Although this evaluation method can meet basic production needs to a certain extent, its limitations are becoming increasingly prominent.

[0003] Specifically, the traditional evaluation method first configures the operating parameters of relevant equipment, such as speed and acceleration, according to the requirements of the production process, to ensure that the trolleys in the electric monorail conveyor system can complete the material delivery orders on time and accurately. At the same time, it is also necessary to estimate the number of trolleys required and the delivery paths of the trolleys according to the production rhythm and material flow to ensure the effective delivery of the orders. However, in the actual processing process, the delivery paths of the trolleys are complex and criss-crossed, lacking a clear planning scheme, and usually adopting the strategy of which path is convenient to take. This leads to the situation that during the order delivery process, the trolleys often meet or the delivery timeout occurs due to the long detour, which further increases the chaos of the electric monorail conveyor system. Therefore, in order to avoid the above situation, enterprises often adopt a conservative strategy, setting higher equipment operating parameters and configuring more trolleys. Although this approach has improved the stability of the system to a certain extent, it has also brought high cost waste and energy consumption.

[0004] It can be seen that in response to the above problems, there is an urgent need for a more scientific, accurate and efficient distribution path planning method for electric monorail conveyor systems. Summary of the invention

[0005] In order to avoid and overcome the technical problems existing in the prior art, the present invention provides a simulation system development method for an electric monorail conveyor system, which can more accurately obtain the optimal delivery path of each order in the factory.

[0006] To achieve the above object, the present invention provides the following technical solutions: A simulation system development method for an electric monorail conveyor system includes the following development steps: S1. Combine the electric monorail system and the factory’s 3D data to build a 3D distribution model in the simulation software. S2. Obtain and distribute delivery tasks in the three-dimensional delivery model based on the order data to be delivered; S3. Based on the assigned delivery tasks and aiming at the optimal delivery path, the three-dimensional delivery model is trained. The three-dimensional delivery model after training constitutes the simulation system to be developed.

[0007] As a further solution of the present invention: the specific content of step S2 is as follows: S21. Obtain the urgency of each order to be delivered E 、Customer requirements C , Value of Goods V , Cargo volume S , and cargo weight W ; S22. Calculate the priority score of each order to be delivered based on the acquired data. The calculation formula of the priority score is as follows: ; In the formula, Indicates the priority score of the order to be delivered; Indicates urgency E The weight of Indicates customer requirements C The weight of Indicates the value of goods V The weight of Indicates cargo volume S The weight of Indicates cargo weight W The weight of S23, based on a priority determination method in which the priority is proportional to the priority score, the priority of each order to be delivered is sorted in descending order of the priority score; S24, inputting the priority sorting result into the three-dimensional distribution model, generating distribution tasks for each order to be distributed in descending order of priority, and storing them in a distribution task list; S25. Dispatching each delivery task in the delivery task list to the electric monorail conveyor system.

[0008] As a further solution of the present invention: the specific steps of step S3 are as follows: S31, obtaining the pickup location and unloading location of each delivery order when it is delivered in the factory, and based on the aerial track layout of the electric monorail conveyor system, using the time window-based Dijkstra path algorithm to obtain the optimal delivery path between the pickup location and the unloading location of each delivery task; S32, according to the obtained optimal delivery path, using the trolley in the electric monorail conveyor system to deliver each order to be delivered in sequence; S33. In the current delivery process, if no new orders to be delivered are generated, the orders are delivered in the order in the delivery task list; if new orders to be delivered are generated, the priority weight factor of the new orders to be delivered is calculated. ; If the priority trade-off factor If the priority factor is less than or equal to the set threshold, the new order to be delivered is placed at the end of the delivery task list, and then the optimal delivery path is obtained using the method in step S31, and the order is delivered in order. If it is greater than the set threshold, the target insertion position of the new order to be delivered in the current delivery task list is determined by the minimum insertion cost model, and then the optimal delivery path of the new order to be delivered is obtained using the method of step S31, and the order is waited for delivery in order.

[0009] As a further solution of the present invention: Priority Weighting Factor The calculation formula is as follows: ; In the formula, Indicates the urgency of new orders to be delivered; Indicates the total number of orders that have not been delivered in the current delivery task list; Represents the average delivery cost of each order.

[0010] As a further solution of the present invention: the steps for obtaining the optimal delivery path are as follows: S311, obtaining an aerial track layout of the electric monorail transportation system and inputting it into a three-dimensional distribution model; S312, in the three-dimensional distribution model, obtaining the pickup location, unloading location, and track intersection of each to-be-delivered order in the aerial track layout when it is delivered in the factory; S313, according to the Dijstra path algorithm based on the time window, the pickup location, the unloading location, and the track intersection are marked as nodes, and the lines between the nodes are marked as edges; at the same time, the pickup location is marked as the starting node, and the unloading location is marked as the ending node, so as to form a distribution path graph in this combination, and the weight of each edge in the distribution path graph represents the distance between the two nodes of the edge; S314: Select a starting node and an ending node of a delivery task from the delivery path diagram, and define the time window from the starting node to the ending node as ,in, Indicates the start time of the current delivery task. Indicates the end time of the current delivery task; S315. Create a distance table for the current delivery task, which is used to store the shortest distance from the starting node to each node in the current delivery task; initially, the initial distance of the starting node is set to 0, and the initial distances of other nodes are set to infinity; the initial distance at the beginning is specifically expressed as follows: ; In the formula, Indicates Nodes to the starting node The initial distance of Indicates infinity; S316, creating an access mark array containing all nodes and belonging to the current delivery task, used to mark whether each node has been visited; and initially, all nodes have not been visited; S317, select the node that is closest to the starting node from the nodes that have not been visited The nearest node is used as the current node, and the current node is marked as visited; for each neighbor node of the current node, check whether the distance from the current node to these neighbor nodes is shorter than the initial distance, and the starting node Whether the time taken to reach these neighbor nodes is within the time window of the current delivery task; if so, the current node is stored in the shortest path node set of the current delivery task, and the initial distance of the neighbor nodes is updated at the same time, and the current node is updated to the neighbor node corresponding to the minimum value of these initial distances; otherwise, the initial distance of these neighbor nodes is not updated; each neighbor node uses the following formula to update the initial distance: ; In the formula, Indicates Nodes to the starting node distance; Indicates selection and The minimum value in ; S318. Repeat step S317 until the end node of the current delivery task is accessed. At this time, each node in the shortest path node set of the current delivery task is connected from the starting node to the end node in sequence to form the shortest delivery path of the current delivery task. The shortest delivery path is the optimal delivery path of the current delivery task.

[0011] As a further solution of the present invention: the specific steps of determining the insertion position of the new to-be-delivered order in the current delivery task list by using the minimum insertion cost model are as follows: S331, marking the new order to be delivered as an urgent order, and marking all the orders to be delivered in the current delivery task list as regular orders; S332. Calculate the delivery distance cost increased by each regular order in the current delivery task list due to the insertion of the emergency order. ; The calculation formula of delivery distance cost is as follows: ; In the formula, Indicates the starting index of all regular orders that have not been delivered in the current delivery task list and are affected by the urgent order; Indicates the end index of each regular order affected by the urgent order among all regular orders that have not been delivered in the current delivery task list; Indicates the starting index of all regular orders that have been delivered but not yet completed in the current delivery task list and are affected by the urgent order; Indicates the end index of each regular order affected by the urgent order among all regular orders that have been delivered but not yet completed in the current delivery task list; To Index Each regular order of is marked as a pickup order group , the index To Index Each regular order of the order group is marked as a delivery order ; Represents a pickup order group In the index The regular order to index is The delivery distance fee required for regular orders; Represents a pickup order group In the index The unloading location of the regular order of The distance between the unloading locations of regular orders; Represents a delivery order group In the index The regular order to index is The delivery distance fee required for regular orders; Represents a delivery order group In the index The unloading location of the regular order of The distance between the unloading locations of regular orders; S333. Calculate the additional delivery time cost of the urgent order due to exceeding its corresponding time window ; The calculation formula for emergency delivery time cost is as follows: ; In the formula, represents the time penalty parameter; represents the estimated delivery arrival time of the optimal delivery route for urgent orders, Indicates the expected delivery arrival time of the urgent order expected by the customer; S334. Calculate the delivery time cost of each regular order in the current delivery task list due to the insertion of the emergency order. ; The calculation formula of delivery time cost is as follows: ; In the formula, Represents the affected order group consisting of all regular orders affected by the emergency order insertion; Indicates the affected order group The The new time window penalty cost of regular orders caused by the insertion of emergency orders; Indicates the affected order group The The original time window penalty cost of a regular order; S335. Calculate the total insertion impact cost based on the delivery distance cost and delivery time cost ; The total insertion impact cost is calculated as follows: ; S336. The calculation formula for the delivery distance cost, the calculation formula for the emergency delivery time cost, the calculation formula for the regular delivery time cost and the calculation formula for the total insertion impact cost are combined to form a minimum insertion cost model; the emergency orders are inserted in sequence between each adjacent regular order in the current delivery task list, and the minimum insertion cost model is used to calculate the total insertion impact cost of each insertion position, and the minimum value of all total insertion impact costs is selected at the same time, and the insertion position corresponding to the minimum value is the target insertion position.

[0012] As a further solution of the present invention: when executing each delivery task, the corresponding vehicle is selected to execute the corresponding delivery task through resource allocation and load balancing strategy; the specific content of resource allocation and load balancing strategy is as follows: S3211. During the delivery task, obtain the current time All idle cars are stored in the idle collection middle; ; In the formula, express The idle set consisting of all idle cars at the moment; express The first idle car at the moment; express Moment Idle cars; S3212, statistics idle set The distribution task volume assigned to each idle car in the task volume set middle; ; In the formula, express The task volume set consists of the delivery task volume assigned to all idle vehicles at the moment; express The amount of delivery tasks assigned to the first idle vehicle at the time; express Moment The amount of delivery tasks assigned to the idle vehicles; S3213, Statistical task volume collection The average , and at the same time calculate the distance between the pickup location of the first regular order in all regular orders that have not been delivered in the current delivery task list and the location of each idle cart; S3214, combined average and the distance between them, calculate the load of each idle car, and select the car with the smallest load to deliver the regular order that is ranked first among all the regular orders that have not been delivered in the current delivery task list; the calculation formula of the load is as follows: ; In the formula, express Moment The load of an idle car; Represents the task weight coefficient; Indicates the pickup location and Moment The distance between the positions of the idle cars.

[0013] As a further solution of the present invention: in the process of training the three-dimensional distribution model, when multiple vehicles meet at the same node, a conflict will occur. At this time, the conflict is resolved by a priority evaluation strategy; the priority evaluation strategy is specifically expressed as follows: S3221. Determine whether the multiple cars that meet are empty or fully loaded; when only one car is fully loaded and the rest are empty, the fully loaded car is given priority to pass, and the empty cars are then passed randomly in sequence; when two or more cars are fully loaded and the rest are empty, the passing order of the fully loaded cars is determined according to the contents of step S3222, and then after all the fully loaded cars have passed, the empty cars are then passed randomly in sequence; S3222. Calculate the priority score of the delivery task currently being performed by the fully loaded vehicle , and arrange them in order from largest to smallest, and pass through them in the order they are arranged.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention combines the electric monorail conveyor system and the three-dimensional data of the factory to build a three-dimensional distribution model in the simulation software, which can accurately simulate the distribution situation in the real factory environment. By obtaining the data of the orders to be delivered and assigning distribution tasks in the model, training is carried out with the optimal distribution path as the goal. The simulation system finally obtained can more accurately predict the optimal distribution path of each order in the factory.

[0015] 2. By obtaining data on multiple dimensions such as the urgency of the order, customer requirements, cargo value, volume and weight, the priority score is calculated and sorted according to the score, ensuring that high-priority orders are processed and delivered first, improving the flexibility and response speed of order processing, meeting the personalized needs of different customers, and enhancing customer satisfaction.

[0016] 3. The present invention not only considers the optimal path selection of the delivery task, but also introduces a new order insertion mechanism, which decides whether to insert a new order based on the priority trade-off factor and calculates the optimal insertion position, thereby ensuring the continuity and efficiency of the delivery process and reducing delivery delays and cost increases caused by the insertion of new orders.

[0017] 4. By comprehensively considering the urgency of new orders, the total number of uncompleted orders and the average delivery cost, the formula can accurately assess the impact of new orders on the overall delivery plan and provide a scientific basis for decision-making. This helps to balance overall delivery efficiency and cost while ensuring the rapid processing of high-priority orders.

[0018] The present invention introduces the Dijkstra path algorithm based on the time window to accurately calculate the optimal path of each delivery task in the three-dimensional delivery model, which not only takes into account the path length but also the time window restriction, thus ensuring that the delivery task can be completed on time and improving the reliability and accuracy of the delivery plan.

[0019] 6. Through the minimum insertion cost model, the delivery distance cost and time cost are comprehensively considered to accurately calculate the optimal insertion position of the new order in the current delivery task list, ensuring that the insertion of the new order will not have too much impact on the overall delivery plan, while maintaining the efficiency and continuity of the delivery process.

[0020] 7. When executing the delivery task, this method comprehensively considers the task volume and location information of the idle carts and selects the cart with the smallest load to perform the delivery task. This helps to balance the load of each cart, avoid the situation where some carts are overloaded while other carts are idle, and improves the overall delivery efficiency and vehicle utilization.

[0021] 8. When multiple vehicles meet and conflict at the same node, this method resolves the conflict through a priority judgment strategy, ensuring that high-priority or fully loaded vehicles can pass first, reducing delivery delays and cost increases caused by conflicts, and improving the smoothness and efficiency of the delivery process. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The figure is a flow chart of the development of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 In an embodiment of the present invention, a simulation system development method for an electric monorail conveyor system includes the following process: 1. Build a three-dimensional distribution model: Import all the 3D data of the trolleys, tracks, and elevators in the electric monorail system into the simulation software, and adjust the relevant 3D positions according to the track layout of the existing electric monorail system to ensure that the established model is consistent with the actual factory layout. This step is the basis for building a simulation environment to ensure the accuracy of all models and the consistency of actual application scenarios. Through accurate 3D models, the physical layout and dynamic behavior of the electric monorail system can be reproduced in the simulation software.

[0025] Import detailed style route data of the overhead track layout in the electric monorail system. The data can be imported in the form of a list. The list should contain key dimension information of the overhead track layout, such as length, width, height, and geometric parameters such as angles and arcs, which are essential for defining the track structure. In addition, track-related structural information such as support structures, connectors, etc. should also be included to ensure that the simulation model can fully reflect the actual electric monorail system.

[0026] In order to avoid repeated input during the simulation process and improve the efficiency of data management, data tables are set up for the relevant parameters of various devices. These tables include not only static parameters of the equipment, such as size and capacity, but also dynamic parameters, such as operating cycle and failure rate. By using data tables, parameters can be easily updated and maintained while ensuring the consistency and accuracy of the simulation model. In addition, the use of data tables also facilitates parameter sensitivity analysis and helps understand the impact of different parameter changes on system performance.

[0027] The GUI module is called in the discrete event simulation software to automatically generate the three-dimensional interface of the electric monorail conveyor system conveyor line, and combined with the three-dimensional model of the on-site equipment to form a three-dimensional distribution model of the entire structure in the factory.

[0028] Record the speed parameters, acceleration and deceleration parameters, and operation logic of the electric monorail conveyor system. Based on the operation logic of the electric monorail conveyor system, create a task dispatching module, a trolley allocation module, and an analysis module for task scheduling in the simulation software.

[0029] Build driver controls to drive the operation of the 3D distribution model, and create interfaces during the operation of the 3D distribution model to record the operation data of the simulation system.

[0030] 2. Dispatching delivery tasks: Obtain and distribute delivery tasks in the three-dimensional delivery model based on the order data to be delivered. The specific contents are as follows: First, get the urgency of each order to be delivered E 、Customer requirements C , Value of Goods V , Cargo volume S , and cargo weight W .

[0031] Then, based on the acquired data, the priority score of each order to be delivered is calculated; the calculation formula of the priority score is shown in formula (1): (1) These weights are determined through historical data analysis, expert experience, or multi-objective optimization methods to quantify the priority of orders.

[0032] Next, based on a priority determination method in which the priority is proportional to the priority score, each order to be delivered is prioritized in descending order of priority score.

[0033] Then, the priority sorting results are input into the three-dimensional distribution model, and the distribution tasks of each order to be distributed are generated in order from high to low priority, and stored in the distribution task list.

[0034] Finally, each delivery task in the delivery task list is dispatched to the electric monorail conveyor system.

[0035] 3. Training to obtain the simulation system: Based on the assigned delivery tasks, with the optimal delivery path as the goal, the 3D delivery model is trained. The 3D delivery model after training constitutes the simulation system to be developed. The specific steps are as follows: 1. Obtain the pickup location and unloading location of each delivery order when it is delivered within the factory, and based on the aerial track layout of the electric monorail conveyor system, use the time window-based Dijkstra path algorithm to obtain the optimal delivery path from the pickup location to the unloading location of each delivery task.

[0036] The steps for obtaining the optimal delivery path are as follows: 1) Obtain the aerial track layout of the electric monorail system and input it into the 3D distribution model.

[0037] 2) In the 3D distribution model, the pickup location, unloading location, and track intersection of each order to be delivered in the factory are obtained.

[0038] 3) According to the Dijstra path algorithm based on the time window, the pickup location, unloading location, and track intersection are marked as nodes, and the lines between the nodes are marked as edges; at the same time, the pickup location is marked as the starting node, and the unloading location is marked as the ending node. The combination is constructed to form a distribution path graph, and the weight of each edge in the distribution path graph represents the distance between the two nodes of the edge.

[0039] 4) S314, select a starting node and an ending node of a delivery task from the delivery path diagram, and define the time window from the starting node to the ending node as ,in, Indicates the start time of the current delivery task. Indicates the end time of the current delivery task.

[0040] 5) Create a distance table for the current delivery task to store the shortest distance from the starting node to each node in the current delivery task. Initially, the initial distance of the starting node is set to 0, and the initial distances of other nodes are set to infinity. The specific expression of the initial distance at the beginning is shown in formula (2): (2) 6) Create an array of access marks that contains all nodes and belongs to the current delivery task, which is used to mark whether each node has been visited; and initially, all nodes have not been visited.

[0041] 7) From the unvisited nodes, select the node closest to the starting node The nearest node is used as the current node, and the current node is marked as visited; for each neighbor node of the current node, check whether the distance from the current node to these neighbor nodes is shorter than the initial distance, and the starting node Is the time taken to reach these neighbor nodes within the time window of the current delivery task? If yes, store the current node in the shortest path node set of the current delivery task, update the initial distance of the neighbor nodes, and update the current node to the neighbor node corresponding to the minimum value of these initial distances; otherwise, do not update the initial distance of these neighbor nodes. Each neighbor node uses formula (3) to update the initial distance: (3) 8) Repeat step 7) until the end node of the current delivery task is accessed. At this time, each node in the shortest path node set of the current delivery task is connected from the starting node to the end node in sequence to form the shortest delivery path of the current delivery task. This shortest delivery path is the optimal delivery path of the current delivery task.

[0042] 2. According to the obtained optimal delivery route, use the trolley in the electric monorail conveyor system to deliver each order in turn.

[0043] When executing each delivery task, the corresponding vehicle is selected through resource allocation and load balancing strategy to execute the corresponding delivery task; the specific contents of resource allocation and load balancing strategy are as follows: 1) During the delivery task, get the current time All idle cars are stored in the idle collection middle.

[0044] (4) 2) Counting Idle Sets The distribution task volume assigned to each idle car in the task volume set middle.

[0045] (5) 3) Statistical task volume collection The average , and at the same time calculate the distance between the pickup location of the first regular order in all regular orders that have not been delivered in the current delivery task list and the locations of each idle cart.

[0046] 4) Combine the average value and the distance between each idle car to calculate the load of each idle car, and select the car with the smallest load to deliver the regular order that is ranked first among all the regular orders that have not been delivered in the current delivery task list; the calculation formula of the load is shown in formula (6): (6) In the process of training the 3D delivery model, when multiple vehicles meet at the same node, a conflict will occur. The conflict is resolved by the priority evaluation strategy. The priority evaluation strategy is specifically expressed as follows: 1) Determine whether the multiple cars that encounter each other are empty or full; when only one of the cars is fully loaded and the rest are empty, the fully loaded car has priority to pass, and the empty cars then pass randomly in sequence; when there are more than two cars that are fully loaded and the rest are empty, the passing order of the fully loaded cars is determined according to the content of step 1, and then after all the fully loaded cars have passed, the empty cars then pass randomly in sequence.

[0047] 2) Calculate the priority score of the delivery task currently being performed by the fully loaded vehicle , and arrange them in order from largest to smallest, and pass through them in the order they are arranged.

[0048] 3. In the current delivery process, if there is no new order to be delivered, it will be delivered in the order of the delivery task list; if there is a new order to be delivered, the priority weight factor of the new order to be delivered will be calculated. . Priority Tradeoff Factor The calculation formula is shown in formula (7): (7) If the priority factor If the priority factor is less than or equal to the set threshold, the new order to be delivered is placed at the end of the delivery task list, and then the optimal delivery path is obtained using the method in step 1 above, and the order is delivered in order. If it is greater than the set threshold, the target insertion position of the new order to be delivered in the current delivery task list is determined by the minimum insertion cost model, and then the optimal delivery path of the new order to be delivered is obtained using the method in step 1 above, and the order is waited for delivery in order.

[0049] The specific steps for determining the insertion position of a new order to be delivered in the current delivery task list using the minimum insertion cost model are as follows: 1) Mark new orders to be delivered as urgent orders, and mark all orders to be delivered in the current delivery task list as regular orders.

[0050] 2) Calculate the additional delivery distance cost of each regular order in the current delivery task list due to the insertion of the emergency order ; The calculation formula of the delivery distance cost is shown in formula (8): (8) Index To Index Each regular order of is marked as a pickup order group , the index To Index Each regular order of the order group is marked as a delivery order ; Represents a pickup order group In the index The regular order to index is The delivery distance fee required for regular orders; Represents a pickup order group In the index The unloading location of the regular order of The distance between the unloading locations of regular orders; Represents a delivery order group In the index The regular order to index is The delivery distance fee required for regular orders; Represents a delivery order group In the index The unloading location of the regular order of The distance between unloading locations for regular orders.

[0051] 3) Calculate the additional delivery time cost of urgent orders due to exceeding their corresponding time window ; The calculation formula of emergency delivery time cost is shown in formula (9): (9) 4) Calculate the additional delivery time cost of each regular order in the current delivery task list due to the insertion of the urgent order ; The calculation formula of conventional delivery time cost is shown in formula (10): (10) 5) Calculate the total insertion impact cost based on the delivery distance cost and delivery time cost ; The calculation formula of the total insertion impact cost is shown in formula (11): (11) 6) The calculation formulas for delivery distance cost, emergency delivery time cost, regular delivery time cost and total insertion impact cost are combined to form a minimum insertion cost model. The emergency orders are inserted between each adjacent regular order in the current delivery task list in sequence, and the minimum insertion cost model is used to calculate the total insertion impact cost of each insertion position. At the same time, the minimum value of all total insertion impact costs is selected, and the insertion position corresponding to the minimum value is the target insertion position.

[0052] The present invention combines the electric monorail conveyor system with the three-dimensional data of the factory to construct a three-dimensional distribution model in the simulation software, which can accurately simulate the distribution situation in the real factory environment. By obtaining the data of the orders to be delivered and assigning distribution tasks in the model, training is carried out with the optimal distribution path as the goal. The resulting simulation system can more accurately predict the optimal distribution path of each order in the factory.

[0053] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A simulation system development method for an electric monorail transportation system, characterized in that: The development steps include: S1. Combine the electric monorail system and the factory’s 3D data to build a 3D distribution model in the simulation software. S2. Obtain and distribute delivery tasks in the three-dimensional delivery model based on the order data to be delivered; S3. Based on the assigned delivery tasks and aiming at the optimal delivery path, the three-dimensional delivery model is trained. The three-dimensional delivery model after training constitutes the simulation system to be developed.

2. The method for developing a simulation system for an electric monorail transportation system according to claim 1, characterized in that: The specific content of step S2 is as follows: S21. Obtain the urgency of each order to be delivered E 、Customer requirements C , Value of Goods V , Cargo volume S , and cargo weight W ; S22. Calculate the priority score of each order to be delivered based on the acquired data; S23, based on a priority determination method in which the priority is proportional to the priority score, the priority of each order to be delivered is sorted in descending order of the priority score; S24, inputting the priority sorting result into the three-dimensional distribution model, generating distribution tasks for each order to be distributed in descending order of priority, and storing them in a distribution task list; S25. Dispatching each delivery task in the delivery task list to the electric monorail conveyor system.

3. The method for developing a simulation system for an electric monorail transportation system according to claim 2, characterized in that: The calculation formula of the priority score is as follows: ； In the formula, Indicates the priority score of the order to be delivered; Indicates urgency E The weight of Indicates customer requirements C The weight of Indicates the value of goods V The weight of Indicates cargo volume S The weight of Indicates cargo weight W The weight of .

4. The method for developing a simulation system for an electric monorail transportation system according to claim 3, characterized in that: The specific steps of step S3 are as follows: S31, obtaining the pickup location and unloading location of each delivery order when it is delivered in the factory, and based on the aerial track layout of the electric monorail conveyor system, using the time window-based Dijkstra path algorithm to obtain the optimal delivery path between the pickup location and the unloading location of each delivery task; S32, according to the obtained optimal delivery path, using the trolley in the electric monorail conveyor system to deliver each order to be delivered in sequence; S33. In the current delivery process, if no new orders to be delivered are generated, the orders are delivered in the order in the delivery task list; if new orders to be delivered are generated, the priority weight factor of the new orders to be delivered is calculated. ; If the priority factor If the priority factor is less than or equal to the set threshold, the new order to be delivered is placed at the end of the delivery task list, and then the optimal delivery path is obtained using the method in step S31, and the order is delivered in order. If it is greater than the set threshold, the target insertion position of the new order to be delivered in the current delivery task list is determined by the minimum insertion cost model, and then the optimal delivery path of the new order to be delivered is obtained using the method of step S31, and the order is waited for delivery in order.

5. The method for developing a simulation system for an electric monorail transportation system according to claim 4, characterized in that: Priority Tradeoff Factor The calculation formula is as follows: ； In the formula, Indicates the urgency of new orders to be delivered; Indicates the total number of orders that have not been delivered in the current delivery task list; Represents the average delivery cost of each order.

6. The method for developing a simulation system for an electric monorail transportation system according to claim 5, characterized in that: The steps for obtaining the optimal delivery path are as follows: S311, obtaining an aerial track layout of the electric monorail transportation system and inputting it into a three-dimensional distribution model; S312, in the three-dimensional distribution model, obtaining the pickup location, unloading location, and track intersection of each to-be-delivered order in the aerial track layout when it is delivered in the factory; S313, according to the Dijstra path algorithm based on the time window, the pickup location, the unloading location, and the track intersection are marked as nodes, and the lines between the nodes are marked as edges; at the same time, the pickup location is marked as the starting node, and the unloading location is marked as the ending node, so as to form a distribution path graph in this combination, and the weight of each edge in the distribution path graph represents the distance between the two nodes of the edge; S314: Select a starting node and an ending node of a delivery task from the delivery path diagram, and define the time window from the starting node to the ending node as ,in, Indicates the start time of the current delivery task. Indicates the end time of the current delivery task; S315. Create a distance table for the current delivery task, which is used to store the shortest distance from the starting node to each node in the current delivery task; initially, the initial distance of the starting node is set to 0, and the initial distances of other nodes are set to infinity; the initial distance at the beginning is specifically expressed as follows: ； In the formula, Indicates Nodes to the starting node The initial distance; Indicates infinity; S316, creating an access mark array containing all nodes and belonging to the current delivery task, used to mark whether each node has been visited; and initially, all nodes have not been visited; S317, select a node from the unvisited nodes that is close to the starting node The nearest node is used as the current node and marked as visited. For each neighbor node of the current node, check whether the distance from the current node to these neighbor nodes is shorter than the initial distance, and the starting node Whether the time taken to reach these neighbor nodes is within the time window of the current delivery task; If they are both, the current node is stored in the shortest path node set of the current delivery task, and the initial distances of the neighbor nodes are updated at the same time, and the current node is updated to the neighbor node corresponding to the minimum of these initial distances; otherwise, the initial distances of these neighbor nodes are not updated; each neighbor node uses the following formula to update the initial distance: ； In the formula, Indicates Nodes to the starting node distance; Indicates selection and The minimum value in ; S318. Repeat step S317 until the end node of the current delivery task is accessed. At this time, each node in the shortest path node set of the current delivery task is connected from the starting node to the end node in sequence to form the shortest delivery path of the current delivery task. The shortest delivery path is the optimal delivery path of the current delivery task.

7. The method for developing a simulation system for an electric monorail transportation system according to claim 6, characterized in that: The specific steps for determining the insertion position of a new order to be delivered in the current delivery task list using the minimum insertion cost model are as follows: S331, marking the new order to be delivered as an urgent order, and marking all the orders to be delivered in the current delivery task list as regular orders; S332. Calculate the delivery distance cost increased by each regular order in the current delivery task list due to the insertion of the emergency order. ; The calculation formula of delivery distance cost is as follows: ； In the formula, Indicates the starting index of all regular orders that have not been delivered in the current delivery task list and are affected by the urgent order; Indicates the end index of each regular order affected by the urgent order among all regular orders that have not been delivered in the current delivery task list; Indicates the starting index of all regular orders that have been delivered but not yet completed in the current delivery task list and are affected by the urgent order; Indicates the end index of each regular order affected by the urgent order among all regular orders that have been delivered but not yet completed in the current delivery task list; To Index Each regular order of is marked as a pickup order group , the index To Index Each regular order of the ; Represents a pickup order group In the index The regular order to index is The delivery distance fee required for regular orders; Represents a pickup order group In the index The unloading location of the regular order of The distance between the unloading locations of regular orders; Represents a delivery order group In the index The regular order to index is The delivery distance fee required for regular orders; Represents a delivery order group In the index The unloading location of the regular order of The distance between the unloading locations of regular orders; S333. Calculate the additional delivery time cost of the urgent order due to exceeding its corresponding time window ; The calculation formula of emergency delivery time cost is as follows: ； In the formula, represents the time penalty parameter; represents the estimated delivery arrival time of the optimal delivery route for urgent orders, Indicates the expected delivery arrival time of the urgent order expected by the customer; S334. Calculate the delivery time cost of each regular order in the current delivery task list due to the insertion of the emergency order. ; The calculation formula of delivery time cost is as follows: ； In the formula, Represents the affected order group consisting of all regular orders affected by the emergency order insertion; Indicates the affected order group The The new time window penalty cost of regular orders caused by the insertion of emergency orders; Indicates the affected order group The The original time window penalty cost of a regular order; S335. Calculate the total insertion impact cost based on the delivery distance cost and delivery time cost ; The total insertion impact cost is calculated as follows: ； S336, the calculation formula of the delivery distance cost, the calculation formula of the emergency delivery time cost, the calculation formula of the regular delivery time cost and the calculation formula of the total insertion impact cost are combined to form a minimum insertion cost model; The urgent orders are inserted in sequence between each adjacent regular order in the current delivery task list, and the total insertion impact cost of each insertion position is calculated using the minimum insertion cost model. At the same time, the minimum value of all total insertion impact costs is selected, and the insertion position corresponding to the minimum value is the target insertion position.

8. The method for developing a simulation system for an electric monorail transportation system according to claim 7, characterized in that: When executing each delivery task, the corresponding vehicle is selected through resource allocation and load balancing strategy to execute the corresponding delivery task; the specific contents of resource allocation and load balancing strategy are as follows: S3211. During the delivery task, obtain the current time All idle cars are stored in the idle collection middle; ； In the formula, express The idle set consisting of all idle cars at the moment; express The first idle car at the moment; express Moment Idle cars; S3212, statistics idle set The distribution task volume assigned to each idle car in the task volume set middle; ； In the formula, express The task volume set consists of the delivery task volume assigned to all idle vehicles at the moment; express The amount of delivery tasks assigned to the first idle vehicle at the time; express Moment The amount of delivery tasks assigned to the idle vehicles; S3213, Statistical task volume collection The average , and at the same time calculate the distance between the pickup location of the first regular order in all regular orders that have not been delivered in the current delivery task list and the location of each idle cart; S3214, combined average and the distance between them, calculate the load of each idle car, and select the car with the smallest load to deliver the first regular order among all the regular orders that have not been delivered in the current delivery task list.

9. The method for developing a simulation system for an electric monorail transportation system according to claim 8, characterized in that: The calculation formula of the load is as follows: ； In the formula, express Moment The load of an idle car; Represents the task weight coefficient; Indicates the pickup location and Moment The distance between the positions of the idle cars.

10. The method for developing a simulation system for an electric monorail transportation system according to claim 9, characterized in that: In the process of training the 3D delivery model, when multiple vehicles meet at the same node, a conflict will occur. At this time, the conflict is resolved by the priority evaluation strategy. The priority evaluation strategy is specifically expressed as follows: S3221, determine whether the encountered cars are empty or fully loaded; when only one car is fully loaded and the rest are all empty, the fully loaded car is allowed to pass first, and the empty cars are then allowed to pass randomly in sequence; when two or more cars are fully loaded and the rest are all empty, the passing order of the fully loaded cars is determined according to the contents of step S3222, and after all the fully loaded cars have passed, the empty cars are then allowed to pass randomly in sequence; S3222. Calculate the priority score of the delivery task currently being performed by the fully loaded vehicle , and arrange them in order from largest to smallest, and pass through them in the order they are arranged.

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