A Development Method for a Simulation System of an Electrical Monorail Conveyor System

Abstract

The present invention relates to the technical field of order delivery route planning, and specifically to a method for developing a simulation system of an electric monorail conveying system. By combining the three-dimensional data of the electric monorail conveying system and the factory, a three-dimensional delivery model is constructed in the simulation software, which can accurately simulate the delivery situation in the real factory environment. By obtaining the order data to be delivered and dispatching delivery tasks in the model, training is carried out with the optimal delivery route as the goal, and the finally obtained simulation system can relatively accurately predict the optimal delivery route of each order in the factory.

Description

Technical Field

[0001] The present invention relates to the technical field of order delivery path planning, and specifically to a method for developing a simulation system of an electric single-rail conveying system. Background Art

[0002] In the rapid development of modern manufacturing, production efficiency and cost control have become key factors in the competitiveness of enterprises. As an indispensable part of the automated production line, the performance optimization and rational resource allocation of the electric single-rail conveying system are directly related to the overall efficiency and economic benefits of the production line. Traditional evaluation methods for electric single-rail conveying systems mainly evaluate the system's effectiveness based on past project experience and simple tabular calculations. Although this evaluation method can meet basic production requirements 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, acceleration, etc., according to the requirements of the production process to ensure that the trolleys in the electric single-rail conveying system can complete the material delivery orders on time and accurately. At the same time, it is also necessary to estimate the required number of trolleys and the delivery paths of the trolleys based on the production rhythm and material flow volume to ensure the effective completion of order delivery. However, in the actual processing process, the delivery paths of the trolleys are complex and changeable, criss-crossing each other, lacking a clear planning scheme, and usually adopting the strategy of taking the path that is convenient to walk. This leads to the situation that the trolleys often meet or the delivery is timed out due to taking a detour during the order delivery process, further increasing the chaos degree of the electric single-rail conveying system. Therefore, in order to avoid the occurrence of the above situation, enterprises often adopt a conservative strategy, setting higher equipment operating parameters and configuring more trolleys. Although this approach improves the stability of the system to a certain extent, it also brings high cost waste and energy consumption.

[0004] It can be seen from this that in view of the above problems, there is an urgent need for a more scientific, accurate and efficient method for planning the delivery path of an electric single-rail conveying system. Summary of the Invention

[0005] In order to avoid and overcome the technical problems existing in the prior art, the present invention provides a method for developing a simulation system of an electric single-rail conveying system. The present invention can relatively accurately obtain the optimal delivery paths of each order in the factory.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for developing a simulation system of an electric single-rail conveying system, including the following development steps:

[0008] S1. Combine the three-dimensional data of the electric monorail conveying system and the factory to construct a three-dimensional distribution model in the simulation software;

[0009] S2. Obtain and, according to the data of the orders to be distributed, dispatch distribution tasks in the three-dimensional distribution model;

[0010] S3. Based on the dispatched distribution tasks, train the three-dimensional distribution model with the optimal distribution path as the goal. After the training is completed, the three-dimensional distribution model constitutes the required developed simulation system.

[0011] As a further solution of the present invention: The specific content of step S2 is as follows:

[0012] S21. Obtain the urgency E , customer requirements C , value of goods V , volume of goods S , and weight of goods W ;

[0013] S22. According to the obtained data, calculate the priority scores of each order to be distributed; The calculation formula for the priority score is as follows:

[0014] ;

[0015] In the formula, represents the priority score of the order to be distributed; represents the weight of the urgency E ; represents the weight of the customer requirements C ;

[0016] represents the weight of the value of goods V ; represents the weight of the volume of goods S ; represents the weight of the weight of goods W ;

[0017] S23. Based on the priority judgment method where the priority is proportional to the priority score, sort the priorities of each order to be distributed in descending order of the priority score;

[0018] S24. Input the priority sorting result into the three-dimensional distribution model, and generate the distribution tasks of each order to be distributed in descending order of priority in sequence, and store them in the distribution task list;

[0019] S25. Dispatch each distribution task in the distribution task list to the electric monorail conveying system.

[0020] As a further aspect of the present invention: The specific steps of step S3 are as follows:

[0021] S31. Obtain the pick-up location and unloading location of each order to be delivered during the in-plant delivery, and based on the aerial track layout of the electric monorail conveyor system, use the Dijkstra path algorithm based on time windows to obtain the optimal delivery path between the pick-up location and the unloading location of each delivery task;

[0022] S32. According to the obtained optimal delivery path, use the trolleys in the electric monorail conveyor system to deliver each order to be delivered in sequence;

[0023] S33. During the current delivery process, if no new order to be delivered is generated, deliver them in sequence according to the order in the delivery task list; if a new order to be delivered is generated, calculate the priority trade-off factor of the new order to be delivered ; if the priority trade-off factor is less than or equal to the set threshold, place the new order to be delivered at the end of the delivery task list, then use the method of step S31 to obtain its optimal delivery path, and wait for delivery in sequence; if the priority trade-off factor is greater than the set threshold, determine the target insertion position of the new order to be delivered in the current delivery task list through the minimum insertion cost model, then use the method of step S31 to obtain the optimal delivery path of the new order to be delivered, and wait for delivery in sequence.

[0024] As a further aspect of the present invention: The calculation formula of the priority trade-off factor is expressed as follows:

[0025] ;

[0026] In the formula, represents the urgency of the new order to be delivered; represents the total number of undelivered orders in the current delivery task list; represents the average delivery cost of each order.

[0027] As a further aspect of the present invention: The specific steps for obtaining the optimal delivery path are as follows:

[0028] S311. Obtain the aerial track layout of the electric monorail conveyor system and input it into the three-dimensional delivery model;

[0029] S312. In the three-dimensional delivery model, obtain the pick-up location, unloading location, and track intersection points of each order to be delivered in the aerial track layout during the in-plant delivery;

[0030] S313. According to the Dijstra path algorithm based on the time window, mark the pick-up location, the unloading location, and the track intersections as nodes, and mark the connections between each node as edges; at the same time, mark the pick-up location as the starting node and the unloading location as the ending node, and combine them to construct a distribution path graph. The weight of each edge in the distribution path graph represents the distance between the two nodes of this edge.

[0031] S314. Select the starting node and the ending node of a distribution task from the distribution path graph, and at the same time define the time window from this starting node to this ending node as , where represents the start time of executing the current distribution task, and represents the end time of executing the current distribution task.

[0032] S315. Create a distance table for the current distribution task to store the shortest distance from the starting node to each node in the current distribution task; initially, set the initial distance of the starting node to 0, and the initial distances of other nodes to infinity; the specific expressions of the initial distances at the beginning are as follows:

[0033] ;

[0034] In the formula, represents the initial distance from the -th node to the starting node ; represents infinity;

[0035] S316. Create an access flag array that contains all nodes and belongs to the current distribution task to mark whether each node has been visited; and initially, all nodes have not been visited.

[0036] S317. Among the unvisited nodes, select the node closest to the starting node as the current node, and mark this current node 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 whether the time taken for the starting node to reach these neighbor nodes is within the time window of the current distribution task; if both are satisfied, store the current node in the set of shortest path nodes of the current distribution task, and at the same time 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 distances of these neighbor nodes; the initial distances of each neighbor node are updated using the following formula:

[0037] ;

[0038] In the formula, Indicates the distance from the th node to the starting node . Indicates the selection of and the minimum value among them;

[0039] S318. Repeat step S317 until the termination node of the current delivery task is accessed. At this time, connect each node in the shortest path node set of the current delivery task from the starting node to the termination node in sequence to form the shortest delivery path of the current delivery task, and this shortest delivery path is the optimal delivery path of the current delivery task.

[0040] As a further aspect of the present invention: The specific steps to determine the insertion position of a new order to be delivered in the current delivery task list through the minimum insertion cost model are as follows:

[0041] S331. Mark the new order to be delivered as an urgent order, and at the same time mark all orders to be delivered in the current delivery task list as regular orders;

[0042] S332. Calculate the increased delivery distance cost of each regular order in the current delivery task list due to the insertion of the urgent order . The calculation formula of the delivery distance cost is expressed as follows:

[0043] ;

[0044] In the formula, represents the starting index of each regular order affected by the urgent order among all the regular orders that have not been delivered in the current delivery task list; represents the termination index of each regular order affected by the urgent order among all the regular orders that have not been delivered in the current delivery task list; represents the starting index of each regular order affected by the urgent order among all the regular orders that have been delivered but not yet completed in the current delivery task list; represents the termination index of each regular order affected by the urgent order among all the regular orders that have been delivered but not yet completed in the current delivery task list; Mark each regular order from index to index as a pick-up order group , and mark each regular order from index to index as a delivery order group ; represents the regular order with index in the pick-up order group to the regular order with index The delivery distance cost required for a regular order;

[0045] Indicates a pick-up order group Among them, the index is The distance between the unloading location of the regular order with the index and the unloading location of the regular order with the index

[0046] Indicates a delivery order group Among them, the index is The delivery distance cost required for the regular order with the index to the regular order with the index

[0047] Indicates a delivery order group Among them, the index is The distance between the unloading location of the regular order with the index and the unloading location of the regular order with the index

[0048] S333. Calculate the increased delivery time cost of an emergency order due to exceeding its corresponding time window ; The calculation formula for the emergency delivery time cost is as follows:

[0049] ;

[0050] In the formula, Indicates the time penalty parameter; Indicates the estimated delivery arrival time of the optimal delivery path of the emergency order, Indicates the expected delivery arrival time of the emergency order expected by the customer;

[0051] S334. Calculate the increased delivery time cost of each regular order in the current delivery task list due to the insertion of an emergency order ; The calculation formula for the delivery time cost is as follows:

[0052] ;

[0053] In the formula, Indicates the affected order group composed of all regular orders affected by the insertion of the emergency order; Indicates the affected order group Among them, the th new time window penalty cost generated by the regular order due to the insertion of the emergency order; Indicates the affected order group Among them, the th original time window penalty cost of the regular order;

[0054] S335. Calculate the total insertion impact cost based on the distribution distance cost and the distribution time cost. ; The calculation formula for the total insertion impact cost is as follows:

[0055] ;

[0056] S336. The calculation formula for the distribution distance cost, the calculation formula for the emergency distribution time cost, the calculation formula for the regular distribution time cost, and the calculation formula for the total insertion impact cost cooperate to form the minimum insertion cost model; Insert the emergency orders into the adjacent regular orders in the current distribution task list in turn, and use the minimum insertion cost model to calculate the total insertion impact cost of each insertion position. At the same time, select the minimum value of all the total insertion impact costs, and the insertion position corresponding to this minimum value is the target insertion position.

[0057] As a further solution of the present invention: When executing each distribution task, select the corresponding trolley to execute the corresponding distribution task through the resource allocation and load balancing strategy; The specific content of the resource allocation and load balancing strategy is as follows:

[0058] S3211. During the execution of the distribution task, obtain all the idle trolleys at the current moment and store all the idle trolleys in the idle set ;

[0059] ;

[0060] In the formula, represents the idle set composed of all the idle trolleys at the moment; represents the first idle trolley at the moment; represents the th idle trolley at the moment;

[0061] S3212. Count the distribution task volumes assigned to each idle trolley in the idle set and store them in the task volume set ;

[0062] ;

[0063] In the formula, represents the task volume set composed of the distribution task volumes assigned to all the idle trolleys at the moment; represents the distribution task volume assigned to the first idle trolley at the moment; represents the The delivery task volume assigned to an idle trolley;

[0064] S3213. Statistic the task volume set of the average value , and at the same time calculate the distance between the pick-up location of the earliest regular order among all the undelivered regular orders in the current delivery task list and the locations of each idle trolley;

[0065] S3214. Combine the average value and the distance, calculate the load of each idle trolley, and select the trolley with the smallest load to deliver the earliest regular order among all the undelivered regular orders in the current delivery task list; The calculation formula of the load is as follows:

[0066] ;

[0067] In the formula, represents the load of the th idle trolley at time ; represents the task volume weight coefficient; represents the distance between the pick-up location of the earliest regular order among all the undelivered regular orders in the current delivery task list and the location of the th idle trolley at time

[0068] As a further solution of the present invention: During the process of training the three-dimensional delivery model, when multiple trolleys meet at the same node, a conflict will occur, and at this time, the conflict is resolved through a priority evaluation strategy; The priority evaluation strategy is specifically as follows:

[0069] S3221. Determine whether the multiple trolleys that meet are empty or full; When only one of the trolleys is full and the rest are empty, the full trolley passes first, and the empty trolleys pass randomly in sequence later; When two or more trolleys are full and the rest are empty, then determine the passing order of the full trolleys through the content of step S3222, and after all the full trolleys have passed, the empty trolleys pass randomly in sequence later;

[0070] S3222. Calculate the priority score of the delivery task currently being executed by the full trolleys, arrange them in descending order, and pass in the arranged order in sequence.

[0071] Compared with the prior art, the beneficial effects of the present invention are:

[0072] 1. By combining the electric monorail conveyor system with the 3D data of the factory, a 3D distribution model is constructed 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 distributed and dispatching distribution tasks in the model, and training with the goal of the optimal distribution path, the final obtained simulation system can relatively accurately predict the optimal distribution path of each order in the factory.

[0073] 2. By obtaining multi-dimensional data such as the urgency of the order, customer requirements, goods value, volume and weight, calculating the priority score, and sorting according to the score, it ensures that high-priority orders can be processed and distributed first, improves the flexibility and response speed of order processing, meets the personalized needs of different customers, and enhances customer satisfaction.

[0074] 3. The present invention not only considers the selection of the optimal path for distribution tasks, but also introduces a new order insertion mechanism. It decides whether to insert a new order according to the priority trade-off factor and calculates the optimal insertion position, ensuring the continuity and efficiency of the distribution process, and reducing the distribution delay and cost increase caused by the insertion of new orders.

[0075] 4. By comprehensively considering the urgency of the new order, the total number of unfinished orders and the average distribution cost, this formula can accurately evaluate the impact of the new order on the overall distribution plan, providing a scientific basis for decision-making. This helps to balance the overall distribution efficiency and cost while ensuring the rapid processing of high-priority orders.

[0076] 5. The present invention accurately calculates the optimal path of each distribution task in the 3D distribution model by introducing the Dijkstra path algorithm based on time windows. It not only considers the path length but also the time window limit, ensuring that the distribution tasks can be completed on time and improving the reliability and accuracy of the distribution plan.

[0077] 6. Through the minimum insertion cost model, comprehensively considering the distribution distance cost and time cost, accurately calculates the optimal insertion position of the new order in the current distribution task list, ensuring that the insertion of the new order will not have too much impact on the overall distribution plan, while maintaining the efficiency and continuity of the distribution process.

[0078] 7. When executing the distribution task, this method selects the trolley with the minimum load to execute the distribution task by comprehensively considering the task volume and position information of the idle trolleys, which helps to balance the load of each trolley, avoid the situation of some trolleys being overloaded while others are idle, and improve the overall distribution efficiency and trolley utilization rate.

[0079] 8. When multiple small vehicles encounter conflicts at the same node, this method resolves the conflicts through a priority evaluation strategy, ensuring that high-priority or fully loaded small vehicles can pass first, reducing delivery delays and cost increases caused by conflicts, and improving the fluency and efficiency of the delivery process. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 It is a development flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0081] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0082] Please refer to Figure 1 , in the embodiments of the present invention, a method for developing a simulation system of an electric single-rail conveying system includes the following processes:

[0083] I. Construct a three-dimensional delivery model:

[0084] Import all three-dimensional data of small vehicles, tracks, elevators, etc. in the electric single-rail conveying system into the simulation software, and adjust the relevant three-dimensional positions according to the track layout of the existing electric single-rail conveying system to ensure that the established model is consistent with the actual factory layout. This step is the basis for constructing the simulation environment, ensuring the accuracy of all models and the consistency of the actual application scenarios. Through the accurate three-dimensional model, reproduce the physical layout and dynamic behavior of the electric single-rail conveying system in the simulation software.

[0085] Import the detailed style route data of the aerial track layout in the electric single-rail conveying system, and this data supports import in the form of a list. The list should include key dimension information of the aerial track layout, such as length, width, height, as well as geometric parameters such as angles and radians, which are crucial for defining the track structure. In addition, it should also include structure information related to the track, such as support structures, connectors, etc., to ensure that the simulation model can comprehensively reflect the actual electric single-rail conveying system.

[0086] To avoid repeated input during the simulation process and improve the efficiency of data management, set data tables for the relevant parameters of various devices. These tables include not only the static parameters of the devices, such as dimensions and capacities, but also dynamic parameters, such as operation cycles and failure rates. By using data tables, it is easy to update and maintain the parameters, while ensuring the consistency and accuracy of the simulation model. In addition, the use of data tables is also convenient for performing parameter sensitivity analysis to help understand the impact of different parameter changes on the system performance.

[0087] In the discrete event simulation software, call the GUI module to automatically generate the 3D interface of the conveying line of the electric monorail conveying system, and combine the 3D models of on-site equipment to form the 3D distribution model of all structures in the factory.

[0088] Record data such as the speed parameters, acceleration and deceleration parameters, and operation logic of the electric monorail conveying system. Based on the operation logic of the electric monorail conveying system, create a task dispatching module, a trolley allocation module, and an analysis module for task scheduling in the simulation software.

[0089] Construct a driving control to drive the operation of the 3D distribution model, and create an interface during the operation of the 3D distribution model to record the operation data of the simulation system.

[0090] II. Dispatching distribution tasks:

[0091] Obtain and dispatch distribution tasks in the 3D distribution model according to the data of the orders to be distributed. The specific content is as follows:

[0092] First, obtain the urgency E , customer requirements C , value of goods V , volume of goods S , and weight of goods W .

[0093] Then, calculate the priority scores of each order to be distributed according to the obtained data; the calculation formula of the priority score is shown in formula (1):

[0094] (1)

[0095] These weights are determined through historical data analysis, expert experience, or multi-objective optimization methods to quantify the priority of the orders.

[0096] Next, based on the priority judgment method where the priority is proportional to the priority score, sort the priorities of each order to be distributed in descending order of the priority score.

[0097] Then, input the priority sorting result into the 3D distribution model, and generate the distribution tasks of each order to be distributed in descending order of priority in sequence, and store them in the distribution task list.

[0098] Finally, dispatch each distribution task in the distribution task list to the electric monorail conveying system.

[0099] III. Training to obtain the simulation system:

[0100] Based on the dispatched distribution tasks, aiming at the optimal distribution path, a three-dimensional distribution model is trained, and the trained three-dimensional distribution model constitutes the simulation system to be developed. The specific steps are as follows:

[0101] 1. Obtain the picking locations and unloading locations of each order to be distributed during the in-plant distribution, and based on the aerial track layout of the electric monorail conveying system, use the Dijkstra path algorithm based on time windows to obtain the optimal distribution path between the picking location and the unloading location of each distribution task.

[0102] The specific steps for obtaining the optimal distribution path are as follows:

[0103] 1) Obtain the aerial track layout of the electric monorail conveying system and input it into the three-dimensional distribution model.

[0104] 2) In the three-dimensional distribution model, obtain the picking locations, unloading locations, and track intersections of each order to be distributed in the aerial track layout during the in-plant distribution.

[0105] 3) According to the Dijstra path algorithm based on time windows, mark the picking location, unloading location, and track intersections as nodes, and mark the connections between each node as edges; at the same time, mark the picking location as the starting node and the unloading location as the ending node, and combine them to construct 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.

[0106] 4) S314. Select the starting node and ending node of a distribution task from the distribution path graph, and at the same time define the time window from the starting node to the ending node as , where represents the start time of executing the current distribution task, represents the end time of executing the current distribution task.

[0107] 5) Create a distance table for the current distribution task to store the shortest distance from the starting node to each node in the current distribution task; initially, set the initial distance of the starting node to 0 and the initial distances of other nodes to infinity; the specific expression of the initial distance at the beginning is shown in formula (2):

[0108] (2)

[0109] 6) Create an access marker array that includes all nodes and belongs to the current distribution task to mark whether each node has been visited; and initially, all nodes have not been visited.

[0110] 7) From the unvisited nodes, select the node that is the distance from the starting node The nearest node is taken as the current node, and this 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 whether the time taken for the starting node to reach these neighbor nodes is within the time window of the current delivery task; if both conditions are met, store the current node in the set of shortest path nodes of the current delivery task, and at the same time 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 distances of these neighbor nodes. Each neighbor node updates the initial distance using formula (3):

[0111] (3)

[0112] 8) Repeat step 7) until the termination node of the current delivery task is visited. At this time, connect the nodes in the set of shortest path nodes of the current delivery task from the starting node to the termination node in sequence to form the shortest delivery path of the current delivery task, and this shortest delivery path is the optimal delivery path of the current delivery task.

[0113] 2. According to the obtained optimal delivery path, use the trolleys in the electric monorail conveying system to deliver each order to be delivered in sequence.

[0114] When executing each delivery task, select the corresponding trolley to execute the corresponding delivery task through the resource allocation and load balancing strategy; the specific content of the resource allocation and load balancing strategy is as follows:

[0115] 1) During the execution of the delivery task, obtain all the idle trolleys at the current moment and store all the idle trolleys in the idle set .

[0116] (4)

[0117] 2) Count the amount of delivery tasks assigned to each idle trolley in the idle set and store it in the task amount set .

[0118] (5)

[0119] 3) Count the average value of the task amount set, and at the same time calculate the distance between the pick-up location of the top-ranked regular order among all the undelivered regular orders in the current delivery task list and the locations of each idle trolley.

[0120] 4) Combine the average value and the distance apart, calculate the loads of each idle vehicle, and select the vehicle with the smallest load to deliver the top-ranked regular order among all the undelivered regular orders in the current delivery task list; the calculation formula for the load is shown in formula (6):

[0121] (6)

[0122] During the process of training the 3D delivery model, when multiple vehicles meet at the same node, a conflict will occur, and the conflict is resolved through a priority evaluation strategy; the priority evaluation strategy is specifically expressed as follows:

[0123] 1) Determine whether the multiple vehicles meeting are empty or full; when only one of the vehicles is full and the rest are empty, the full vehicle passes first, and the empty vehicles then pass randomly in sequence; when there are two or more full vehicles and the rest are empty, the passing order of the full vehicles is determined by the content of step 1), and then after all the full vehicles have passed, the empty vehicles then pass randomly in sequence.

[0124] 2) Calculate the priority score of the delivery task that the full vehicle is currently executing , and arrange them in descending order, and pass in the arranged order.

[0125] 3. During the current delivery process, if no new orders to be delivered are generated, deliver them in sequence according to the order in the delivery task list; if new orders to be delivered are generated, calculate the priority trade-off factor of the new order to be delivered . The priority trade-off factor is calculated as shown in formula (7):

[0126] (7)

[0127] If the priority trade-off factor is less than or equal to the set threshold, then rank the new order to be delivered at the end of the delivery task list, then use the method in step 1 above to obtain its optimal delivery path, and wait for delivery in order; if the priority trade-off factor is greater than the set threshold, determine the target insertion position of the new order to be delivered in the current delivery task list through the minimum insertion cost model, then use the method in step 1 above to obtain the optimal delivery path of the new order to be delivered, and wait for delivery in order.

[0128] The specific steps to determine the insertion position of the new order to be delivered in the current delivery task list through the minimum insertion cost model are as follows:

[0129] 1) Mark the new order to be delivered as an urgent order, and mark all orders to be delivered in the current delivery task list as regular orders.

[0130] 2) Calculate the increased delivery distance cost for each regular order in the current delivery task list due to the insertion of the urgent order ; The calculation formula for the delivery distance cost is shown in formula (8):

[0131] (8)

[0132] Mark the regular orders from index to index as the pick-up order group and mark the regular orders from index to index as the delivery order group ; represents the delivery distance cost required for the regular order with index in the pick-up order group from the regular order with index to the regular order with index

[0133] represents the distance between the unloading locations of the regular order with index in the pick-up order group from the regular order with index to the regular order with index

[0134] represents the delivery distance cost required for the regular order with index in the delivery order group from the regular order with index to the regular order with index

[0135] represents the distance between the unloading locations of the regular order with index in the delivery order group from the regular order with index to the regular order with index

[0136] 3) Calculate the increased delivery time cost for the urgent order due to exceeding its corresponding time window ; The calculation formula for the urgent delivery time cost is shown in formula (9):

[0137] (9)

[0138] 4) Calculate the increased delivery time cost for each regular order in the current delivery task list due to the insertion of the urgent order ; The calculation formula for the conventional delivery time cost is shown as in Formula (10):

[0139] (10)

[0140] 5) Calculate the total insertion impact cost based on the delivery distance cost and the delivery time cost ; The calculation formula for the total insertion impact cost is shown as in Formula (11):

[0141] (11)

[0142] 6) The calculation formula for the delivery distance cost, the calculation formula for the emergency delivery time cost, the calculation formula for the conventional delivery time cost, and the calculation formula for the total insertion impact cost cooperate to form a minimum insertion cost model; insert the emergency orders successively between each adjacent conventional order in the current delivery task list, and use the minimum insertion cost model to calculate the total insertion impact cost at each insertion position, and at the same time select the minimum value among all the total insertion impact costs. The insertion position corresponding to this minimum value is the target insertion position.

[0143] By combining the electric monorail conveyor system and the 3D data of the factory, the present invention constructs a 3D delivery model in the simulation software, which can accurately simulate the delivery situation in the real factory environment. By obtaining the data of the orders to be delivered and dispatching delivery tasks in the model, and training with the optimal delivery path as the goal, the finally obtained simulation system can relatively accurately predict the optimal delivery path of each order in the factory.

[0144] As described above, it is only the preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within 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; 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 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 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.

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 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.

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 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.

7. The method for developing a simulation system for an electric monorail transportation system according to claim 6, 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.

8. The method for developing a simulation system for an electric monorail transportation system according to claim 7, characterized in that: The calculation formula of the load is as follows: ； In the formula, express Moment The load of an idle trolley; Represents the task weight coefficient; Indicates the pickup location and Moment The distance between the positions of the idle cars.

9. The method for developing a simulation system for an electric monorail transportation system according to claim 8, 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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