A method for guaranteeing and controlling a production plan realization rate of a painting workshop

Abstract

The present application belongs to the technical field of vehicle painting production, and particularly relates to a kind of painting workshop production plan realization rate guarantee control method;Vehicle sequence restoration instruction scheduling system records the actual out-painting queue, actual out-of-warehouse queue and AVI site actual queue after vehicle enters painting, and compares the actual out-painting queue, actual out-of-warehouse queue and AVI site actual queue with the predicted out-painting queue, predicted out-of-warehouse queue and predicted AVI site queue, and marks the vehicles that fall behind with priority marks, when running to the double-color vehicle control system, point vehicle supplement control system or joint scheduling system control, the double-color vehicle control system, point vehicle supplement control system or joint scheduling system processes the vehicles with priority marks first, and cancels the priority marks until the vehicles no longer fall behind;To solve the problem of long-time vehicle retention in automobile painting workshop affecting production efficiency, energy utilization rate, vehicle out-painting realization rate and high overall cost of workshop.

Description

Technical Field

[0001] This invention belongs to the field of vehicle painting production technology, specifically relating to a method for ensuring and controlling the fulfillment rate of production plans in painting workshops. Background Technology

[0002] The research on production planning and control aims to lock the painting process sequence 9 hours in advance, addressing issues such as rework at painting points, major rework, and delays in two-color painting. The traditional methods for adjusting the production sequence are as follows:

[0003] Welding workshop → Painting workshop → External vehicle body automated warehouse → Final assembly workshop

[0004] Traditional vehicle sequence adjustment methods mainly rely on production planning. If a vehicle experiences an abnormality in a certain process segment, it will affect the overall cycle time of that segment, causing the risk of vehicle delays and preventing vehicles from being painted as planned. This will affect production line efficiency, energy utilization, and vehicle painting fulfillment rate, and will also increase the overall cost of the workshop. Summary of the Invention

[0005] To overcome the above problems, this invention provides a method for ensuring and controlling the fulfillment rate of production plans in a painting workshop, which enables the actual coating sequence in the painting workshop to match the production plan, thereby solving the problems of long-term vehicle dwell time in automotive painting workshops affecting production line efficiency, energy utilization, vehicle coating fulfillment rate, and high overall workshop costs.

[0006] A method for ensuring and controlling the fulfillment rate of production plans in a painting workshop includes the following:

[0007] Step 1: The vehicle sequence restoration instruction scheduling system obtains the production plan and real-time vehicle passing information issued by the MOM system by connecting to the AVI system, and interacts with the AGV scheduling system, machine transport PLC, automated warehouse PLC, dual-color vehicle management system, point replenishment vehicle management system, and joint scheduling system.

[0008] Step 2: The vehicle sequence restoration instruction scheduling system predicts the paint exit queue, the warehouse exit queue, and each AVI station queue according to the paint entry plan, and obtains the predicted paint exit queue, the predicted warehouse exit queue, and the predicted AVI station queue.

[0009] Step 3: The vehicle sequence restoration command scheduling system adds the vehicle's entry time to the predicted exit time based on the entry plan to obtain the exit time. If this time is later than the planned exit time, the vehicle sequence restoration command scheduling system will issue an alarm.

[0010] Step four: The vehicle sequence restoration instruction scheduling system records the actual painting queue, the actual warehouse exit queue, and the actual AVI station queue after the vehicle enters the painting area. It then compares the actual painting queue, the actual warehouse exit queue, and the actual AVI station queue with the predicted painting queue, the predicted warehouse exit queue, and the predicted AVI station queue. Vehicles that have fallen behind are marked with a priority tag. When the vehicle is managed by the dual-color vehicle management system, the point-replenishment vehicle management system, or the joint scheduling system, the dual-color vehicle management system, the point-replenishment vehicle management system, or the joint scheduling system will prioritize the processing of vehicles with priority tags until the vehicle no longer falls behind, at which point the priority tag will be removed.

[0011] The prediction method for the coating queue in step two is as follows:

[0012] 1. Process in-transit time prediction:

[0013] The total time required for the vehicle to complete all process paths according to the coating plan is the predicted process transit time.

[0014] 2. Coating time prediction:

[0015] The predicted coating exit time is the time after adding the vehicle's coating entry time to the predicted process transit time.

[0016] 3. Outbound queue prediction:

[0017] The vehicles are sorted according to their predicted painting time to obtain the predicted painting queue.

[0018] The prediction method for the outbound queue in step two is as follows:

[0019] The vehicles in the automated storage and retrieval system are sorted according to their waiting time to obtain the predicted outbound queue; where the waiting time is the duration generated by the automated storage and retrieval system based on the outbound rules.

[0020] The prediction method for the AVI site queue in step two is as follows:

[0021] Based on the process path in the coating plan, determine the arrival time of vehicles at each AVI station, and sort them according to the time sequence to obtain the predicted AVI station queue.

[0022] The process for determining the lagging vehicles in step four is as follows:

[0023] The skip number is obtained by subtracting the ranking of the corresponding vehicle in the predicted queue from the ranking of the vehicle in any actual queue. When the skip number is greater than or equal to the production cycle time, it means that the vehicle has fallen out of the queue. The vehicle is marked as a priority vehicle and should be dispatched and processed with priority in the following paths until the skip number of the vehicle is less than the production cycle time. Then the priority mark is removed.

[0024] The beneficial effects of this invention are:

[0025] This invention establishes a simulation platform, filters special vehicles based on historical data, builds models at key stations, generates predicted painting queues and queues for each AVI station, rationally plans and allocates paths for special vehicles that may cause delays, issues task instructions to AGVs, enabling special vehicles to quickly overtake and complete relevant processes, and adds automated warehouse group control scheduling of vehicle departure sequences to meet the requirements of production plan management.

[0026] 1. This invention is based on a vehicle sequence restoration instruction scheduling system, which reduces the risk of vehicles being stranded en route.

[0027] 2. This invention is based on three-dimensional warehouse group control for vehicle dispatch, which maximizes the matching of the actual dispatch sequence with the production plan, thereby increasing the overall vehicle plan fulfillment rate to over 93%.

[0028] 3. This invention reduces painting operating costs by 50%, optimizes labor time, and improves energy efficiency.

[0029] 4. This invention reduces logistics costs by more than 60% in the final assembly process. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the content of the embodiments of the present invention and these drawings without creative effort.

[0031] Figure 1 This invention presents a process simulation platform for a painting workshop, built using simulation tools. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0033] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0034] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0035] In the description of this embodiment, the terms "upper," "lower," "left," and "right," etc., refer to the orientation or positional relationship shown in the accompanying drawings. They are used only for ease of description and simplification of operation, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In addition, the terms "first" and "second" are used only for distinction in description and have no special meaning.

[0036] Example 1

[0037] A method for ensuring and controlling the fulfillment rate of production plans in a painting workshop includes the following:

[0038] Step 1: The vehicle sequence restoration instruction scheduling system obtains the production plan and real-time vehicle passing information issued by the MOM system by connecting to the AVI system, and interacts with the AGV scheduling system, machine transport PLC, automated warehouse PLC, dual-color vehicle management system, point replenishment vehicle management system, and joint scheduling system.

[0039] Step 2: The vehicle sequence restoration instruction scheduling system predicts the paint exit queue, the warehouse exit queue, and each AVI station queue according to the paint entry plan, and obtains the predicted paint exit queue, the predicted warehouse exit queue, and the predicted AVI station queue.

[0040] Step 3: The vehicle sequence restoration command scheduling system adds the vehicle's entry time to the predicted exit time based on the entry plan to obtain the exit time. If this time is later than the planned exit time, the vehicle sequence restoration command scheduling system will issue an alarm.

[0041] Step four: The vehicle sequence restoration instruction scheduling system records the actual coating queue, the actual warehouse exit queue, and the actual AVI station queue after the vehicle enters the coating area. It then compares these actual coating queues, warehouse exit queues, and AVI station queues (an AVI station is set up between every two process paths and at road forks; each station scans and identifies passing vehicles, and the vehicles scanned at each AVI station are sorted chronologically to obtain the predicted AVI station queue) with the predicted coating queue, predicted warehouse exit queue, and predicted AVI station queue. Vehicles that have fallen behind are marked with a priority flag. When these vehicles are managed by the dual-color vehicle control system, the point-replenishment vehicle control system, or the joint scheduling system, the dual-color vehicle control system, the point-replenishment vehicle control system, or the joint scheduling system prioritizes vehicles with priority flags until they no longer fall behind, at which point the priority flag is removed.

[0042] The prediction method for the coating queue in step two is as follows:

[0043] 1. Process in-transit time prediction:

[0044] The total time required for the vehicle to complete all process paths according to the coating plan is the predicted process transit time.

[0045] 2. Coating time prediction:

[0046] The predicted coating exit time is the time after adding the vehicle's coating entry time to the predicted process transit time.

[0047] 3. Outbound queue prediction:

[0048] The vehicles are sorted according to their predicted painting time to obtain the predicted painting queue.

[0049] The prediction method for the outbound queue in step two is as follows:

[0050] The vehicles in each automated storage and retrieval system are sorted according to their waiting time to obtain the predicted outbound queue; where the waiting time is the duration generated by the automated storage and retrieval system based on the outbound rules.

[0051] The prediction method for the AVI site queue in step two is as follows:

[0052] Based on the process path in the coating plan, determine the arrival time of vehicles at each AVI station, and sort them according to the time sequence to obtain the predicted AVI station queue.

[0053] The process for determining the lagging vehicles in step four is as follows:

[0054] The skip number is obtained by subtracting the ranking of the corresponding vehicle in the predicted queue from the ranking of the vehicle in any actual queue. Based on this skip number, it is determined whether the queue needs to be adjusted. When the skip number is greater than or equal to the production beat (the production beat is taken as 25 here, which is the number of vehicles produced per hour), it means that the vehicle has fallen behind and needs to be adjusted. The vehicle is marked as a priority vehicle and should be dispatched with priority in the following paths until the skip number of the vehicle is less than the production beat = 25, at which point the priority mark is removed.

[0055] The dual-color vehicle control system uses the following method to make a preliminary determination of the processes that dual-color vehicles entering the workshop will undergo, and obtains the preset path for the dual-color vehicles:

[0056] The dual-color vehicle management system reads whether there is congestion at each of the eighteen processes from S1: body pre-processing to S18: waxing.

[0057] If there is no congestion, then the two-color vehicle route 3 will be used as the preset route.

[0058] If any step in the process from S13.1: dual-color masking to S18: waxing in the dual-color car path 3 is congested, while there is no congestion in the process from S15: inspection and polishing to S18: waxing in the dual-color car path 2, then the dual-color car path 2 will be used as the preset path.

[0059] If any step in the process from S13.1: dual-color masking to S18: waxing in the dual-color car path 3 is congested, and any step in the process from S15: inspection and polishing to S18: waxing in the dual-color car path 2 is also congested, then the dual-color car path 1 will be used as the preset path.

[0060] The two-tone vehicle travels along the predetermined path. During this journey, when it reaches S10: intermediate coating sanding, the two-tone vehicle control system records the actual time T taken for the two-tone vehicle to travel from S1: body pretreatment to S10: intermediate coating sanding. 实际 ;

[0061] The dual-color vehicle management system reads whether there is congestion at each step in the dual-color vehicle path 1-3, from S10: intermediate coating sanding to S18: waxing process;

[0062] If there is no congestion at any step in the two-color car path 1 from S10: intermediate coating sanding to S18: waxing, then assuming the two-color car starts traveling along the two-color car path 1 from S10: intermediate coating sanding, the estimated time required is 1770S. If there is congestion at any step in the two-color car path 1 from S10: intermediate coating sanding to S18: waxing, then the estimated time required for the two-color car to travel along the two-color car path 1 from S10: intermediate coating sanding is 1770S + 4*△t.

[0063] If there is no congestion at any step in the two-color car path 2 from S10: intermediate coating sanding to S18: waxing, then assuming the two-color car starts traveling along the two-color car path 2 from S10: intermediate coating sanding, the estimated time required is 1530 seconds. If there is congestion at any step in the two-color car path 2 from S10: intermediate coating sanding to S18: waxing, then the estimated time required for the two-color car to travel along the two-color car path 2 from S10: intermediate coating sanding is 1530 seconds + 4 * Δt.

[0064] If there is no congestion at any step in the two-color car path 3 from S10: intermediate coating sanding to S18: waxing, then assuming the two-color car starts traveling along the two-color car path 3 from S10: intermediate coating sanding, the estimated time required is 930 seconds. If there is congestion at any step in the two-color car path 3 from S10: intermediate coating sanding to S18: waxing, then the estimated time required for the two-color car to travel along the two-color car path 3 from S10: intermediate coating sanding is 930 seconds + 10 * Δt.

[0065] Calculate the actual time T taken for the two-tone vehicle to travel along the preset path from S1: body pretreatment to S10: intermediate coating sanding process. 实际 This is the sum of the times T required for the two-color car to travel along paths 1-3 from S10 (intermediate coat sanding process) in step four. 路径1 T 路径2 and T 路径3 :

[0066] When T 路径1 <T 路径2 When the time is -2*△t, the two-color vehicle control system will adjust the path of the two-color vehicle after the S10: intermediate coating grinding process to follow the two-color vehicle path 1.

[0067] When T 路径2 <T 路径3 When -2*△t, the two-color vehicle control system will adjust the path of the two-color vehicle after the S10: intermediate coating polishing process to follow the two-color vehicle path 2.

[0068] Otherwise, the two-color vehicle control system will adjust the path of the two-color vehicle after the S10: intermediate coating and sanding process to follow the two-color vehicle path 3.

[0069] The spot-repair vehicle control system receives vehicle spot-repair signals and obtains vehicle body information. The vehicle is painted in the painting workshop according to the traditional process. When the vehicle enters the S15: inspection and polishing process, the spot-repair vehicle control system determines whether there is a risk that the vehicle will not be painted as planned, and assigns an attribute label to each vehicle according to the judgment result: risk vehicle or ordinary vehicle.

[0070] The risk that vehicles may fail to be painted as planned includes the following two situations:

[0071] 1. The actual total time of all the processes that the vehicle goes through during the traditional painting process, from S1: body pretreatment process to S14: automated warehouse process 5, exceeds the planned total time of the vehicle going through these processes by more than 2 hours.

[0072] 2. The vehicle's sales code in the vehicle information obtained by the vehicle replenishment management system is less than 5;

[0073] For high-risk vehicles, the spot-repair vehicle management system controls them to travel along the AGV fast spot-repair path. That is, when they reach the S15: inspection and polishing process according to the traditional process, the spot-repair vehicle management system identifies the empty spot-repair room and assigns the high-risk vehicle to one of the empty spot-repair rooms, directly entering the S15.2: spot-repair process.

[0074] For ordinary vehicles, the vehicle is controlled to travel along the traditional procedure path, i.e., enter S15.1: fill the buffer area and wait.

[0075] Example 2

[0076] Improved method:

[0077] Welding workshop → Painting workshop → Innovative vehicle sequence adjustment method → ​​Final assembly workshop

[0078] The research on production planning and control aims to lock the painting process sequence 9 hours in advance, plan new process paths for painting point replenishment, major rework, and two-color car delays, and add a new adaptive control method for the production sequence, breaking the traditional production sequence scheduling method.

[0079] 1. Algorithm simulation platform setup:

[0080] Process path modeling:

[0081] Based on technological knowledge, a painting workshop process simulation platform was built using the Anylogic simulation tool to recreate the painting workshop's production processes. The actual production scene was recreated at a 1:1 scale based on CAD design drawings. Figure 1 As shown, it includes: each process node, each process path, automated warehouse 1-5, and AGV channel.

[0082] Vehicle sequence prediction algorithm:

[0083] For historical data, a big data processing workflow is used, including data cleaning, feature extraction, and data modeling, to model the vehicle sequence prediction algorithm.

[0084] Historical data analysis is divided into four feature gradients: material number, vehicle model + color, color, and all vehicles, to meet inference needs.

[0085] 1. Process in-transit time prediction

[0086] In historical data, observed process values ​​are recorded. Given vehicle characteristics (such as sales code, model, and color), when the inferred vehicle part number cannot be found in the historical data, feature similarity is progressively reduced for feature matching. The duration with the highest probability is used to predict the vehicle's in-transit process time.

[0087] T tech The calculation formula is as follows:

[0088] T tech =arg maxp(feature,tech,t)

[0089] Where: "feature" refers to the sales code, vehicle model, and color, in descending order; "tech" includes: electrophoresis, intermediate coat, topcoat (including offline), and inspection (including offline).

[0090] 2. Coating time prediction

[0091] The total transit time is the sum of the transit times of each subsequent process and the queuing time in the vertical warehouse. The coating time is calculated based on the latest site scan time and the total transit time. out The calculation formula is as follows:

[0092] T out =last_record_time + ∑ tech in follow time_tech(feature,tech)+∑ HB in follow

[0093] queue(HB)*interval

[0094] Where: tech in follow indicates subsequent stations, HB in follow indicates subsequent warehouses, and special vehicle queue = 0.

[0095] 3. Coating queue prediction

[0096] Based on the predicted coating time, the predicted coating queue is obtained (based on work-in-process).

[0097] Q out The calculation formula is as follows:

[0098] Q out =sort(car_in_shop,key=out_shop_time)

[0099] Algorithm data input: millions of historical data records.

[0100] Algorithm model output: On-the-go time, offline probability, and offline duration prediction model for vehicles with each sales code at each process station.

[0101] Table 1 Offline Historical Data Analysis

[0102]

[0103] Table 2 Analysis of Historical Data on Process Section On-Department Duration

[0104]

[0105] Applications of vehicle sequence prediction algorithm:

[0106] Algorithm model input:

[0107] 1. Configuration Information (Choose one):

[0108] (1) Mode 1: Predicting train sequence based on production plan (default)

[0109] (2) Mode 2: Predicting vehicle sequence based on work-in-process inventory

[0110] 2. Work-in-process and inventory information in the painting workshop (directly query the inventory database).

[0111] 3. Other algorithm configuration parameters: automated storage and retrieval system capacity, production cycle time.

[0112] Methods for obtaining work-in-process information:

[0113] process section How to obtain Electrophoresis process Includes the entry point record of BW25 in the pre-processing stage, but excludes the HB02 inbound record from the stacker crane in the automated warehouse 2. Intermediate coating process Includes outbound records for stacker crane HB02 in automated warehouse 2, but excludes inbound records for stacker crane HB03 in automated warehouse 3. Topcoat process Includes outbound records for stacker crane HB03 in automated storage and retrieval system (AS / RS) 3, but excludes inbound records for stacker crane HB05 in AS / RS 5. Maintenance process Includes outbound records for stacker crane HB05 in automated warehouse 3, but excludes inbound records for stacker crane HB04 in automated warehouse 4.

[0114] Model inference calculation:

[0115] (1) Query the current location of the vehicle and the entry time of the work-in-process query.

[0116] (2) The transit time of work-in-process is predicted based on the sales code (or vehicle characteristics if there is no sales code).

[0117] (3) Calculation of vehicle queuing time in automated warehouse = queue length × production cycle time

[0118] (4) Calculate the arrival time of each vehicle at the predicted point and sort them to obtain the vehicle sequence prediction.

[0119] Algorithm model output:

[0120] (1) Queue prediction for each key site

[0121] (2) Prediction of coating queue in the painting workshop

[0122] Vehicle sequence restoration algorithm:

[0123] Vehicle sequence restoration algorithm module 1: Early overtaking strategy: Based on historical data analysis, identify vehicles that are likely to fall behind in advance, overtake them in advance to give priority to departure and avoid the risk of falling behind.

[0124] The advance overtaking strategy is applied to automated warehouses 2, 3, and 5. As shown in Table 3, through data analysis, vehicles with a high probability of being offline and vehicles with a high probability of being replenished are sent ahead of time to ensure timely delivery, but this does not include vehicles with high production volume.

[0125] Table 3 Offline Data Analysis

[0126]

[0127] Vehicle sequence restoration algorithm section 2: Offline remediation strategy: Based on real-time offline events, trigger the 3D library to reissue the same sales code to replace the vehicle, reducing the impact of falling behind caused by offline events.

[0128] The offline remediation strategy is applied to automated warehouses 2 and 5. For vehicles that are actually offline, if there are work-in-process vehicles with the same sales code, they will be reissued to ensure timely production delivery, but this does not include vehicles with high production volume.

[0129] Factors to consider when restoring the train sequence:

[0130] (1) Color matching business: Automated warehouses 3 and 5 not only handle the production of single-color vehicles, but also the production of color matching vehicles.

[0131] (2) Material number

[0132] (3) Process duration

[0133] To reduce costs and ensure production schedules, while fully considering all constraints, a 0-1 programming model can be established. Let x... i，j，k It is a 0-1 variable, i.e., x i，j，k =1 or 0(x) i，j，k This indicates that the product with process duration i and material number j is undergoing the k-th coloring pass.

[0134]

[0135]

[0136] Therefore, the following objective function is established:

[0137]

[0138] Vehicle sequence restoration algorithm capability boundaries:

[0139] The maximum reordering capacity is the number of vehicles that can overtake and depart from each automated storage and retrieval system (AS / RS) according to the early departure strategy. The maximum overtaking capacity is the sum of the buffer queue lengths of each subsequent AS / RS.

[0140] (1) Electrophoresis process section: Length of queue 2 in 3D storage + Length of queue 3 in 3D storage + Length of queue 5 in 3D storage + Length of queue 4 / 1 in 3D storage

[0141] (2) Intermediate coating process section: Length of queue 3 in automated storage + length of queue 5 in automated storage + length of queue 4 / 1 in automated storage

[0142] (3) Topcoat process section: 5 long queues in automated storage + 4 / 1 queue length in automated storage

[0143] (4) Maintenance process section: Length of queue 4 / 1 in automated warehouse

[0144] Train sequence restoration strategy:

[0145] First priority for special vehicles

[0146] Second priority is to reissue offline vehicles.

[0147] The third priority is to send out vehicles that are likely offline and have low production volume in advance.

[0148] The fourth priority is to follow the first-in, first-out production plan.

[0149] In the following special circumstances, the restoration of the train sequence cannot be guaranteed:

[0150] (1) Human intervention in production

[0151] (2) The vehicle with the unique sales code has been offline for an abnormally long time.

[0152] (3) Vehicles with no substitute work-in-process have abnormally long offline times.

[0153] (4) Special vehicles with no substitute work-in-process: Audit vehicles, overhaul vehicles

[0154] (5) Other abnormal vehicles

[0155] This method involves the design of production planning assurance and control, with a focus on the vehicle sequence restoration instruction scheduling system.

[0156] This method uses a vehicle sequence restoration command scheduling system as the central scheduling hub. It connects to the AVI system to obtain production plans issued by the MOM system and real-time vehicle arrival information. The vehicle sequence restoration command scheduling system develops four algorithms to issue task commands, coordinating with the AGV scheduling system, AVI system, automated warehouse system, and conveyor system to achieve adaptive control of the painting vehicle sequence. During the manufacturing process, vehicles with changes in their sequence are identified as early as possible, planned in advance, and returned to their designated positions quickly, ultimately achieving the goal of maximizing the restoration of the painting sequence to the production plan.

[0157] The vehicle sequence restoration instruction scheduling system has added three scheduling modes:

[0158] This method employs three scheduling modes: automated warehouse group control, two-color vehicle branching, and point-replenishment vehicle rejoining. Eight algorithms are developed for vehicle sequence restoration. Through big data analysis, characteristic vehicle classification is used to build process models. Process paths are pre-planned for vehicles based on the process route, and the actual situation is tracked in real time. Deviations between predictions and actual results are analyzed to identify the causes of these deviations, and an analysis report is generated to facilitate continuous optimization of algorithm accuracy.

[0159] This invention extracts vehicle feature data and, based on process time and real-time traffic information, applies a scheduling algorithm to calculate and provide recommended routes.

[0160] This invention's joint scheduling system interacts in real time with the transport PLC, automated warehouse PLC, and AGVs to obtain the real-time location of vehicles and update the predicted queue information in real time. Through calculations by the joint scheduling system, it rationally plans and allocates routes, which are then distributed to the execution layer for execution.

[0161] The objective of this invention is achieved through the following technical solutions:

[0162] Based on the current queue information and the daily coating sequence in the workshop, the scheduling system applies algorithm simulation and uses big data processing to analyze past production data. Through data cleaning, feature extraction, and data modeling, it generates predicted coating queues, predicted warehouse queues, and queues for each AVI (Autopilot Image Processing) station. The algorithm identifies special vehicles (vehicles requiring spot repair or color matching) through feature extraction and plans and allocates routes to these special vehicles to reduce their transit time. The adaptive vehicle sequence restoration scheduling system obtains vehicle location information in real time through AVIs and updates the predicted queues in real time. Simultaneously, the scheduling system coordinates with the AGV scheduling system, automated warehouse system, and conveyor system to ultimately achieve adaptive control of the coating vehicle sequence.

[0163] The following is a comparison of the realization rates between simulations without and with the algorithm:

[0164] 1. No algorithm was used to verify the truth:

[0165] On a certain day, the planned number of vehicles to be painted was 140, and the actual number of vehicles painted was 140, as shown in Table 1. The fulfillment rate was 16%.

[0166] 2. Use algorithms to verify the truth:

[0167] Based on the actual production situation on the same day, the vehicle sequence was adjusted through system simulation. The actual painting sequence is shown in Table 2, with an accuracy rate of 67%.

[0168] Simulation input data:

[0169] a. Information on work-in-process and inventory at the start of production on a certain day;

[0170] b. Coating plan;

[0171] Conclusion: After using simulation, the payout rate increased significantly from 16% to 67%. This simulation was based on historical data and included vehicles with different color schemes and vehicles with large returns. If these two types of vehicles were removed, the payout rate would be 97%.

[0172] Table 4: Coating sequence based on actual production data not verified by simulation.

[0173]

[0174]

[0175]

[0176]

[0177] Table 5. Coating sequence adjusted through simulation.

[0178]

[0179]

[0180]

[0181]

[0182] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the scope of protection of the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, any person skilled in the art can make equivalent substitutions or changes based on the technical solution and inventive concept of the present invention within the scope of the technology disclosed in the present invention. These simple modifications are all within the scope of protection of the present invention.

[0183] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

[0184] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed by the present invention.

Claims

1. A method for guaranteeing and controlling the realization rate of a production plan in a painting plant, characterized in that Comprise the following contents: Step one, the vehicle sequence restoration instruction scheduling system obtains the production plan issued by the MOM system and the real-time passing point information of the vehicle through the connection of the AVI system, and interacts with the AGV scheduling system, the machine transportation PLC, the stereoscopic warehouse PLC, the double-color vehicle management and control system, the point vehicle supplement management and control system, and the joint scheduling system; Step two, the vehicle sequence restoration instruction scheduling system predicts the out-painting queue, the out-of-warehouse queue, and the queue of each AVI site according to the in-painting plan, to obtain the predicted out-painting queue, the predicted out-of-warehouse queue, and the predicted AVI site queue; Step three, the vehicle sequence restoration instruction scheduling system adds the in-painting time of the vehicle to the out-painting time predicted according to the in-painting plan to obtain the out-painting time, and if this time is later than the planned out-painting time, the vehicle sequence restoration instruction scheduling system alarms; Step four, the vehicle sequence restoration instruction scheduling system records the actual out-painting queue, the actual out-of-warehouse queue, and the actual AVI site queue after the vehicle enters the painting, and compares the actual out-painting queue, the actual out-of-warehouse queue, and the actual AVI site queue with the predicted out-painting queue, the predicted out-of-warehouse queue, and the predicted AVI site queue, marks the lagging vehicle with a priority mark, and when the double-color vehicle management and control system, the point vehicle supplement management and control system, or the joint scheduling system is running, the double-color vehicle management and control system, the point vehicle supplement management and control system, or the joint scheduling system processes the vehicle with the priority mark first, and cancels the priority mark when the vehicle no longer lags.

2. The method of claim 1, wherein The prediction method of the predicted out-painting queue in step two is as follows:

1. Process in-transit time prediction: The total time required for the vehicle to perform all process paths according to the in-painting plan is the predicted process in-transit time; 2. Out-painting time prediction: The time after adding the in-painting time of the vehicle to the predicted process in-transit time is the predicted out-painting time; 3. Out-painting queue prediction: Sort the vehicles according to the predicted out-painting time to obtain the predicted out-painting queue.

3. The method of claim 1, wherein The prediction method of the predicted out-of-warehouse queue in step two is as follows: Sort the vehicles in the stereoscopic warehouse according to the waiting time to obtain the predicted out-of-warehouse queue; wherein the waiting time is the time generated by the stereoscopic warehouse based on the out-of-warehouse rule.

4. The method of claim 1, wherein The prediction method of the predicted AVI site queue in step two is as follows: Determine the time when the vehicle reaches each AVI site according to the process path in the in-painting plan, and sort according to the time to obtain the predicted AVI site queue.

5. The method of claim 1, wherein The determination process of the lagging vehicle in step four is as follows: Subtract the ranking of the vehicle in the corresponding predicted queue from the ranking of the vehicle in the actual queue to obtain the sequence jump number, when the sequence jump number is greater than or equal to the production beat, it means that the vehicle lags, and the vehicle is marked as a priority processing vehicle, which is processed first in the following path, and the priority mark is cancelled when the sequence jump number of the vehicle is less than the production beat.

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