A mold vertical warehouse automatic mold loading and carrying method and system

Abstract

The application relates to a mold vertical warehouse automatic mold feeding method, which comprises the following steps: acquiring production order sequences of each injection molding machine and residual production time of a current batch, calculating a mold leaving time of the current batch according to the residual production time, and backwardly deducing according to preset production time lengths of each order in the production order sequences to acquire a mold leaving time sequence of each order of each injection molding machine in the future; acquiring a mold temperature machine preheating time length, a cross-warehouse transportation time length, a stacker warehouse leaving estimated time length and a preset advance control time length threshold, and calculating an optimal warehouse leaving time for each mold feeding task. The mold feeding method takes the upper limit of the warehouse entrance and exit traffic capacity as a hard constraint, sorts and schedules each mold feeding task in combination with multi-dimensional priority scoring, spreads the concentrated mold changing tasks to all-time operation, overcomes the defects of morning congestion and afternoon idling caused by concentrated calls of mold feeding personnel, and effectively improves the utilization balance of the warehouse entrance and exit and the continuity of mold changing operation of the whole factory.

Description

Technical Field

[0001] This invention relates to the field of logistics scheduling technology in intelligent manufacturing workshops, specifically to an automatic mold loading and handling method and system for mold vertical warehouses. Background Technology

[0002] Injection molding is one of the main production processes for products such as toys, home appliances, and connectors. In large injection molding factories, the mold warehouse usually stores thousands of molds, and the workshop is equipped with dozens to hundreds of injection molding machines. Each injection molding machine completes 0.5 to 1 mold change per day according to the production plan. The total number of mold changes per day in the entire factory can reach 80 to 150 sets. The mold change operation is generally completed by multiple mold operators working together.

[0003] Currently, the following process is commonly used for mold outbound and handling in injection molding plants: The mold operator clicks the "Call Mold" button through the Manufacturing Operations Management System (MOM) to trigger the mold outbound command from the vertical warehouse; After receiving the call, the Warehouse Management System (WMS) adds the task to the outbound queue according to the order of the call timestamps (i.e., first-in, first-out, FIFO); The stacker crane retrieves the corresponding mold rack from the shelf according to the queue order; The Automated Guided Vehicle (AGV) transports the mold to the target injection molding machine; After completing the mold change operation, the mold operator returns the old mold to the mold vertical warehouse along the original path.

[0004] This model has the following shortcomings: On the one hand, mold operators usually click the call button in a concentrated manner during the start of the shift, and a large number of mold change tasks are issued in a short period of time, resulting in congestion at the warehouse entrance in the morning and idle operation in the afternoon; on the other hand, the timing of work assignment is only determined by the call time, and there is no correlation calculation between it and the actual time of the injection molding machine and the mold preheating time, which easily leads to time sequence misalignment such as "the mold has arrived but the machine has not stopped" or "the machine has stopped but the mold is still on its way". On the other hand, the mold warehouse has a variety of mold specifications, and different work orders correspond to different molds. The existing solution does not establish a three-dimensional mapping verification between work orders, molds and workstations at the scheduling entry point. Once the wrong mold is selected, the entire handling link will be invalid and needs to be returned and resent, which will further aggravate the congestion. Thirdly: The number of entrances and exits between the mold warehouse and the injection molding workshop is limited and they are shared. When outbound and inbound tasks request the same entrance at the same time, multiple stacker cranes are prone to path intersections and timing conflicts at that entrance. The existing solution lacks priority decision rules and can only rely on manual scheduling for temporary handling. Therefore, there is an urgent need for an automated mold handling method in a vertical mold warehouse that can achieve production planning-driven, staggered scheduling, automatic verification, and conflict resolution to solve the above problems. Summary of the Invention

[0005] This application provides an automatic mold loading and handling method and system for mold vertical warehouses to solve the above problems.

[0006] In a first aspect, embodiments of this application provide an automatic mold loading and transport method for a mold vertical warehouse, comprising the following steps: Obtain the production work order sequence of each injection molding machine and the remaining production time of the current batch. Calculate the off-time of the current batch based on the remaining production time. Then, based on the preset production time of each work order in the production work order sequence, recursively calculate the off-time sequence of each work order for the future of each injection molding machine. The preheating time of the mold temperature controller, the inter-workshop transportation time, the estimated time of the stacker crane's outbound shipment, and the preset advance control time threshold are obtained. For each mold loading task, the optimal outbound time is calculated using the following formula: Optimal outbound time = Unloading time - Mold temperature controller preheating time - Inter-workshop transportation time - Stacker crane estimated outbound shipment time - Advance control time threshold. Obtain the urgent order attributes, key product attributes, downtime risk level, and current load of the target mold installer for each mold installation task; and calculate the multi-dimensional priority score for each mold installation task based on the preset weights of each dimension. Obtain the upper limit of the passage capacity of each inlet and outlet. Using the upper limit of the passage capacity as a hard constraint, sort and schedule each upper task according to the multi-dimensional priority score of each upper task. When there is a conflict between the inlet and outlet occupancy of the outlet task and the return task, make a decision according to the preset conflict resolution rules and generate the staggered scheduling result. Based on the staggered scheduling results, before executing the outbound action, it is verified whether the production work order corresponding to the current mold task is consistent with the drawing version of the mold to be outbound, and whether the size parameters and clamping template size of the mold to be outbound are compatible with the installation space of the target injection molding machine. Based on the verification results, the outbound action is executed or the task is frozen and an alarm is issued.

[0007] In conjunction with the first aspect, in one implementation, the multidimensional priority score is calculated according to the following formula: ; in, This is the value of the "urgent order" attribute. For key product attribute values, This represents the downtime risk level value. The target module's current load value; , , , These are the corresponding weights for urgent orders, key products, downtime risk, and operator load, respectively.

[0008] In conjunction with the first aspect, in one implementation, the urgent order weighting Key product weight Downtime risk weight and the load weight of the upper module Make dynamic adjustments using one of the following methods: Switch between preset weight combinations according to preset runtime segment or runtime mode; Using at least one of the following as feedback signals—the number of concurrent users during the start of the shift, the average waiting time for the shift worker, and the standard deviation of the number of tasks the shift worker completes each day—the step size of each weight is adjusted after the daily operation ends.

[0009] In conjunction with the first aspect, in one implementation, the preset conflict resolution rules include: If both outbound and inbound tasks exist at the same inbound / outbound port during the same time period, the outbound task corresponding to the online production work order takes priority over the inbound task. If both tasks are outbound or inbound, they are sorted according to the multi-dimensional priority score, and the task with the lower score is postponed to the next idle time slot of that outbound / inbound port.

[0010] In conjunction with the first aspect, in one implementation, it further includes: When executing outbound tasks, check whether the target injection molding machine has a need to return completed old molds to the warehouse; If present, the task of returning the old mold to the warehouse and the current task of leaving the warehouse will be scheduled to be executed sequentially by the same automated guided vehicle.

[0011] In conjunction with the first aspect, in one implementation, when the verification fails, the following is also performed: Retrieve from the mold library a replacement mold that is identical to the drawing version of the current production order and whose dimensions, clamping template dimensions, and installation space of the target injection molding machine are compatible. If the alternative mold is found, it is recommended and the verification is re-executed; if it is not found, a request for manual intervention is issued.

[0012] In conjunction with the first aspect, in one implementation, the method further includes an exception handling step executed within each refresh cycle, the exception handling step comprising: Within each refresh cycle, the deviation between the predicted and actual off-time of each injection molding machine is compared. When the deviation of any injection molding machine exceeds a preset threshold, the off-time of each subsequent work order of the injection molding machine is recalculated based on the current time, the remaining production time and the sequence of unfinished work orders of the injection molding machine, and the optimal outbound time is recalculated based on the recalculated off-time. The staggered scheduling result is then regenerated, and the subsequent mold-up tasks are rescheduled. The status of the stacker crane and automated guided vehicle is detected in each refresh cycle. If no heartbeat signal is received from any device for a consecutive preset refresh cycle, the device is determined to be faulty. The handling tasks that the device has not yet performed are reassigned to other available devices in the same area, and the inlet and outlet occupied by the device are released.

[0013] In conjunction with the first aspect, in one implementation, it further includes: Detect the current load of each module member during each refresh cycle; When the current load of any operator exceeds a preset multiple of the average load of all operators and the duration exceeds a preset duration threshold, the subsequent handling tasks to be assigned will be preferentially assigned to the operator with the lowest current load.

[0014] In conjunction with the first aspect, in one implementation, it further includes: After each mold-changing task is completed, record the actual time of departure from the warehouse, the actual time of arrival at the machine, and the actual time spent changing the mold. The actual outbound time, actual arrival time at the machine, and actual mold change time are transmitted back. The self-calibration values ​​of the preset production time and the preheating time of the mold temperature controller for the corresponding injection molding machine and mold type are updated by using a sliding weighted method of measured values ​​and predicted values.

[0015] Secondly, embodiments of this application provide a system based on an automatic mold loading and transport method for mold vertical storage, including: The data acquisition module is used to obtain the production order sequence of each injection molding machine and the remaining production time of the current batch; The parsing module is used to calculate the off-time of the current batch based on the remaining production time, and to advance backward based on the preset production time of each work order in the production work order sequence to obtain the sequence of off-times of each work order in the future for each injection molding machine. The calculation module is used to obtain the preheating time of the mold temperature controller, the inter-workshop transportation time, the estimated time of the stacker crane's outbound delivery, and the preset advance control time threshold. For each mold loading task, the optimal outbound time is calculated according to the following formula: Optimal outbound time = unloading time - mold temperature controller preheating time - inter-workshop transportation time - stacker crane outbound estimated time - advance control time threshold. The scoring module is used to obtain the urgent order attributes, key product attributes, downtime risk level and current load of the target molder for each molding task, and calculate the multi-dimensional priority score of each molding task according to the preset weights of each dimension. The scheduling module is used to obtain the upper limit of the passage capacity of each inlet and outlet, use the upper limit of the passage capacity as a hard constraint, sort and schedule each upper module task according to the multi-dimensional priority score of each upper module task, and when the inlet and outlet occupancy conflict between the outlet task and the return task, it makes a decision according to the preset conflict resolution rules and generates the staggered scheduling result. The verification module is used to verify, based on the staggered scheduling results, whether the production work order corresponding to the current mold-up task is consistent with the drawing version of the mold to be shipped, and whether the size parameters and clamping template size of the mold to be shipped are compatible with the installation space of the target injection molding machine before executing the outbound action. If the verification passes, the outbound action is executed; if the verification fails, the task is frozen and an alarm is issued.

[0016] The beneficial effects of the technical solutions provided in this application include: 1. This invention uses the production work order sequence and the real-time remaining production time of the injection molding machine as input to accurately calculate the sequence of future off-times for each work order on each injection molding machine. Based on this, it calculates the optimal outbound time for each mold changing task using a backward calculation formula, ensuring precise alignment between mold outbound and injection molding machine production progress. Furthermore, using the upper limit of the access capacity at the inlet and outlet as a hard constraint, and combining multi-dimensional priority scoring, it sorts and schedules each mold changing task, spreading the centrally issued mold changing tasks across different time periods throughout the day. This overcomes the shortcomings of existing technologies where concentrated calls from mold changing personnel lead to morning congestion and afternoon idleness, effectively improving the utilization balance of the inlet and outlet and the continuity of mold changing operations throughout the plant.

[0017] 2. This invention uses a backward formula of "unloading time - mold temperature controller preheating time - inter-workshop transportation time - stacker crane outbound estimated time - advance control time threshold" to accurately calculate the optimal outbound time for each mold-loading task. This ensures that the time when the mold arrives at the machine is aligned with the time when the injection molding machine is unloaded and the time when the mold temperature controller preheating is completed. This eliminates the timing misalignment caused by the dispatching trigger timing depending solely on the manual call time in the prior art, which results in "the mold has been delivered but the machine has not yet unloaded" or "the machine has stopped waiting while the mold is still in transit". This reduces the energy consumption during the waiting period after the mold arrives and the production losses due to the injection molding machine being stopped.

[0018] 3. Before executing the outbound action, this invention automatically verifies whether the production work order and the drawing version of the mold to be outbound are consistent, and whether the mold size parameters and clamping template size are compatible with the installation space of the target injection molding machine. This eliminates the possibility of the entire handling link being scrapped and returned for resending due to the wrong mold selection, thus avoiding secondary congestion. At the same time, scheduling is based on the upper limit of the passage capacity of the inbound and outbound ports as a hard constraint. When there is a conflict between the outbound and return tasks at the inbound and outbound ports, the conflict is resolved according to the preset conflict resolution rules. This effectively solves the problem of path intersection and timing conflict caused by the shared use of inbound and outbound ports, ensuring orderly passage at the inbound and outbound ports and further improving the overall mold handling efficiency of the plant. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the main process of this method; Figure 2 This is a comparison chart of the results of Example 3 of this method. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0022] Example 1: Please see Figure 1 This application provides an automatic mold loading and transport method for a mold vertical warehouse, including the following steps: S1. Obtain the production work order sequence of each injection molding machine and the remaining production time of the current batch. Calculate the off-time of the current batch based on the remaining production time. Then, based on the preset production time of each work order in the production work order sequence, push forward to obtain the off-time sequence of each work order of each injection molding machine in the future. The production work order sequence is provided by the Advanced Production Scheduling System (APS), which contains a list of work orders for each injection molding machine arranged in chronological order for a future period. Each work order records information such as product model, planned start time, and planned production duration. The remaining production time for the current batch is obtained from the Manufacturing Operations Management (MOM) system; Calculate the off-machine time for the current batch based on the remaining production time using the following formula. : ; in, For the current moment, The remaining production time for the current batch of the i-th injection molding machine; Subsequently, based on the preset production time of each work order in the production work order sequence, the off-time of the k-th future work order of the i-th injection molding machine is calculated using the following formula. : ; in, This is the j-th work order in the work order sequence of the i-th injection molding machine. The preset production time for this work order is given. When k=1, the summation term is 0 (i.e. there is no previous work order). Through the above recursion, the sequence of the machine exit times for each future work order of each injection molding machine is obtained.

[0023] It is understood that the production work order sequence lengths of different injection molding machines are different. Those skilled in the art can determine the number of work orders to be pushed based on the actual production scheduling time window of the APS. For example, only work orders within the next 4 hours can be pushed, or all work orders for the day can be pushed. The specific push range can be set according to the scheduling needs of the workshop. In this embodiment, it is preferable to push work orders within the next 4 hours.

[0024] S2. Obtain the preheating time of the mold temperature controller, the inter-workshop transportation time, the estimated time of the stacker crane's outbound shipment, and the preset advance control time threshold. For each mold loading task, calculate the optimal outbound time. The optimal outbound time is calculated using the following formula: Optimal outbound time = Unloading time - Mold temperature controller preheating time - Inter-workshop transportation time - Stacker crane estimated outbound shipment time - Advance control time threshold. Obtain the preheating time of the mold temperature controller Inter-workshop transportation time Estimated outbound time for stacker cranes and preset pre-controlled duration threshold ; in, The time required for the mold to be preheated to a state ready for mold placement after arriving at the injection molding machine is determined by the preheating process parameters of the mold temperature controller, with a typical value of 30 to 60 minutes; The time required for the automated guided vehicle to travel from the mold vertical warehouse exit to the buffer position next to the target injection molding machine and complete the lifting action is calculated by the AGV scheduling system based on the travel path distance and average speed. The estimated time required for the stacker crane to retrieve the target mold from the mold storage room and transport it to the exit is calculated by the stacker crane control system based on the distance between the mold's location and the exit. To control the duration threshold in advance, as a safety buffer time, to deal with uncertainties during the handling process.

[0025] For each mold-up task, based on the unloading time obtained in step S1, the optimal outbound time is calculated using the following formula. : ; in, This is the off-machine time of the work order corresponding to the upper mold task.

[0026] Understandable , , , The specific value can be adaptively adjusted by those skilled in the art based on the actual equipment parameters, material handling chain length, and operational stability of the workshop. For example, for molds with high preheating efficiency of the mold temperature controller, A smaller value can be chosen; for workshops with long AGV travel paths, Take the larger value accordingly; for workshops with low operational stability, The parameters can be appropriately increased to provide more buffer margin, and the adjustment of each parameter does not change the core algorithm logic of the present invention. Perform the above calculations for each mold-up task of each injection molding machine in the plant within the next 1 to 4 hours to obtain the optimal delivery time for each task. Summarize the information such as task identifier, target injection molding machine, target workstation, optimal delivery time, and expected arrival time to form a mold-up task timetable.

[0027] S3. Obtain the urgent order attributes, key product attributes, downtime risk level, and current load of the target mold installer for each mold installation task. Calculate the multi-dimensional priority score for each mold installation task based on the preset weights of each dimension. Get the urgent order attribute of each model task. Key product attributes Downtime risk level and the current load of the target module personnel ; in, The numerical value representing the urgency of the current order can be determined based on the order priority marked in the APS system; the higher the value, the more urgent the order. The numerical value representing the current importance level of a product can be determined based on factors such as product line value and customer level. To indicate the current downtime risk level of the target injection molding machine, it can be determined based on the remaining production time of the current batch—the shorter the remaining time, the higher the downtime risk. The MOM system provides in real time the number of mold-making tasks that have been assigned to the target mold-making personnel but have not yet been completed.

[0028] Based on the preset weights W1, W2, W3, and W4 for each dimension, the multidimensional priority score for each model task is calculated using the following formula. : ; in, For urgent orders, weighting is given. As a key product weight, As a weight for downtime risk, As for the load weight of the upper mold operator, the specific values ​​of the weights of the above four dimensions can be dynamically adjusted by those skilled in the art according to actual production management needs. This invention provides the following two weight adjustment strategies: Strategy A: Rule-based weighting; The preset weight combinations can be switched according to time period or operating mode. For example, according to different operating time periods and operating modes such as day shift / night shift / weekend / urgent order period for large customers, the scheduling configuration center can preset multiple weight combinations and automatically switch the corresponding weight combinations in different time periods or modes. In one specific configuration, off-peak periods can be achieved by ( , , , The system uses a balanced configuration of (1, 1, 1, 1) to prioritize urgent orders and ensure that molds for urgent orders are shipped out first. During periods of escalating downtime risk, the system uses a configuration of (1, 1, 3, 2) to reduce the risk of unplanned equipment downtime. This means that when equipment is in poor condition, priority is given to ensuring that injection molding machines that are about to be shut down do not stop and wait. This strategy is simple to implement and highly interpretable. Those skilled in the art can flexibly configure the weight combinations for different scenarios according to the actual operating mode of the workshop.

[0029] Strategy B: Data-driven adaptive weighting; Using at least one of the following as feedback signals—the number of concurrent users during the start-up period, the average waiting time for the online simulator, and the standard deviation of the daily task count for online simulator operators—the step size of each weight is adjusted after the daily operation ends. For example, at least one of the following can be used as the back feedback signal of the objective function: "concurrency within 30 minutes of class opening", "average daily waiting time for modeling", and "standard deviation of the number of tasks per modeling staff per day". After the daily operation ends, the weights of the four items are adjusted in small increments (e.g., ±0.1) according to the gradient direction, so that the peak concurrency and waiting time of the same period the next day continue to converge. In one specific configuration, if the number of concurrent users exceeds a preset target value (e.g., 6 orders / minute) within 30 minutes of the start of the day, the downtime risk weight is slightly increased. (Give higher priority to tasks that are about to be completed, thereby reducing equipment downtime caused by waiting for molds); If the standard deviation of the daily task count of mold installers is too large (i.e., the workload of each mold installer is severely uneven), slightly increase the workload weight of the mold installers. (Enhancing load balancing effect) Through daily iterative optimization, the scheduling strategy continuously adapts to the actual operating status of the workshop on that day.

[0030] It is understood that those skilled in the art can choose one of the two strategies mentioned above according to actual needs, or they can combine the two strategies (for example, fine-tuning daily based on a preset weight combination). The specific target values ​​and step size adjustments of each feedback signal in Strategy B can be set according to the actual operating indicators of the workshop, without affecting the core scoring framework of this invention.

[0031] S4. Obtain the upper limit of the passage capacity of each inlet and outlet. Using the upper limit of the passage capacity as a hard constraint, sort and schedule each upper task according to the multi-dimensional priority score of each upper task. When there is a conflict between the inlet and outlet occupancy of the outlet task and the return task, make a decision according to the preset conflict resolution rules and generate the staggered scheduling result. Get the maximum throughput capacity of each inlet and outlet. The maximum capacity is the number of mold racks that can pass through a single inlet / outlet per unit time, which is determined by factors such as the physical size of the inlet / outlet and the operating speed of the stacker crane. The upper limit of traffic capacity is a hard constraint—that is, the number of tasks passing through any entrance or exit in any given time period shall not exceed [a certain limit]. — Select the entry / exit port with the lowest current load or the earliest available time for each task in sequence, and assign the task to the available time period of that entry / exit port to ensure that the number of tasks passing through each entry / exit port in any time period does not exceed its passage capacity limit, thereby achieving staggered scheduling.

[0032] During the scheduling process, when outbound and return tasks conflict in the use of inbound and outbound ports (i.e., the same inbound and outbound port is requested to be used by both outbound and return tasks at the same time), the conflict will be resolved according to the preset conflict resolution rules. In this embodiment, the preset conflict resolution rules specifically include: ① If there are outbound and inbound tasks at the same inbound / outbound port at the same time, the outbound task corresponding to the online production work order takes priority over the inbound task. ② If both are outbound tasks or both are inbound tasks, they are sorted according to the calculated multidimensional priority score, and the task with the lower score is postponed to the next idle time period of that outbound / inbound port.

[0033] Through the above sorting, scheduling and conflict resolution, the final staggered scheduling result is generated.

[0034] It is understandable that those skilled in the art can adjust the upper limit of passage capacity based on the actual operating conditions of the workshop. The specific value (preferably ≤ 2 units / minute per opening in this embodiment), for example, for workshops with wide inlet and outlet openings, The speed can be increased appropriately; for workshops with a large daily mold change volume, a full-day scheduling (such as an average speed of 14-18 orders / hour for 8 hours) can be adopted to avoid frequent recalculation.

[0035] S5. Based on the staggered scheduling results, before executing the outbound action, verify whether the production work order corresponding to the current mold task is consistent with the drawing version of the mold to be outbound, and whether the size parameters of the mold to be outbound, the clamping template size and the installation space of the target injection molding machine are compatible. Based on the verification results, execute the outbound action or freeze the task and issue an alarm.

[0036] S501: Based on the off-peak scheduling results generated in step S4, before the stacker crane performs the outbound action, it verifies whether the production work order corresponding to the current upper mold task is compatible with the mold to be outbound. The verification method in this embodiment specifically includes the following two items: ① Verify that the product required for the current production work order is consistent with the drawing version of the mold to be shipped; ② Verify whether the dimensions of the mold to be shipped out and the clamping template are compatible with the installation space of the target injection molding machine.

[0037] The information required for the above verification is obtained from the mold file data provided by the mold automated warehouse management system (WMS), including the drawing version number, dimensional parameters, clamping template dimensions, and other parameters for each mold set; The installation space information for the target injection molding machine is provided by the equipment file of the Manufacturing Operations Management System (MOM), including parameters such as the maximum / minimum size of the mold that can be installed on the injection molding machine and the specifications of the clamping plate; If both of the above checks pass, the outbound process is executed. The stacker crane takes the mold rack from the mold vertical warehouse, and the automated guided vehicle transports it to the buffer position next to the target injection molding machine. If any check fails, the task is frozen and an alarm is issued. The alarm information can be pushed to the mold operator's terminal or the dispatch center display screen through the Manufacturing Operations Management System (MOM) for manual handling.

[0038] It is understood that the execution order of the above two verifications can be adjusted according to actual needs, and other verification items (such as mold weight verification, mold temperature verification, etc.) can be added. As long as the compatibility between the mold and the workstation can be confirmed before leaving the warehouse, it falls within the protection scope of this invention.

[0039] S502: When executing outbound tasks, this method also includes: The system detects whether there is a need to return completed old molds to the warehouse for the target injection molding machine. The specific detection method is as follows: The system obtains the target injection molding machine number based on the scheduling results of the current outbound tasks, and queries the Manufacturing Operations Management System (MOM) to see if there are any work orders that are currently running and have been completed for the injection molding machine. If there are completed work orders and the molds corresponding to those work orders have not yet been returned to the warehouse, it is determined that there is a need to return them to the warehouse; if there are no work orders currently running for the injection molding machine or the work orders have not yet been completed, it is determined that there is no need to return them to the warehouse.

[0040] If there is a need to return the old mold to the warehouse, the task of returning the old mold to the warehouse and the current outbound task will be scheduled to be executed sequentially by the same automated guided vehicle. That is, the automated guided vehicle will first transport the new mold to the target injection molding machine and complete the unloading, and then carry the old mold replaced on the injection molding machine back to the mold warehouse to complete the warehousing.

[0041] This inbound / outbound relay mode can complete both outbound and return tasks in one trip, reducing the empty running mileage of the automated guided vehicles, thereby effectively reducing the computing power and energy consumption of AGV scheduling.

[0042] It is understood that the above detection method can be adapted by those skilled in the art based on the actual work order status management method in the workshop. For example, a "pending return to warehouse" flag can be added to the MOM system. When the injection molding machine completes a production work order and the old mold is unloaded, the system will automatically set the flag to valid, or the work order completion event can directly trigger the generation of a return to warehouse requirement for this step to query. Those skilled in the art can set it according to actual needs, without affecting the core solution of the present invention.

[0043] S503: This method also includes closed-loop feedback and calibration steps, specifically: After each mold-changing task is completed, the system records the actual time of departure from the warehouse, the actual time of arrival at the machine, and the actual mold-changing time. The system then sends back the above actual execution data and updates the preset production time for the corresponding injection molding machine and mold type using a sliding weighted average of the measured and predicted values. and mold temperature controller preheating time The self-calibration value; In this embodiment, the sliding weighting method is specifically as follows: the weighted average of the current measured value and the current predicted value is used as the benchmark value for the next prediction, and calculated according to the following formula: ; in, This is the nth predicted value. This is the measured value for the (n-1)th time. Let be the predicted value for the (n-1)th time, and α be the weighting coefficient where 0 < α < 1; In one specific embodiment, α can be set to 0.7, so that the weight of recent measured values ​​is greater than that of long-term measured values. Through this sliding weighting method, the prediction accuracy of the system continues to converge as the running data accumulates, avoiding the decline in scheduling accuracy caused by long-term changes such as equipment aging, process improvement, and mold temperature controller upgrades. This allows the invention to operate stably for a long time without frequent manual intervention after it is put into production.

[0044] It is understood that the value of the weighting coefficient α can be adjusted by those skilled in the art based on the fluctuation of the actual operating data of the workshop. For workshops with large fluctuations in operating data, a smaller α value (such as 0.5 to 0.6) can be used to smooth the data fluctuations; for workshops with stable operation, a larger α value (such as 0.8 to 0.9) can be used to accelerate the response to actual changes. The adjustment of each parameter does not change the self-calibration mechanism of the present invention.

[0045] S504: In addition, the following abnormal situations may occur during the execution of the above methods, and this application provides the following handling methods accordingly, specifically: ①Handling incompatible verifications: If the verification in step S5 fails, the system will then perform the following operations: The system retrieves alternative molds from the mold warehouse management system (WMS) that are the same drawing version as the current production work order and whose size parameters and clamping template size are compatible with the installation space of the target injection molding machine. If an alternative mold that meets the conditions is found, the alternative mold is recommended to the scheduler or mold installer, and the verification in step S5 is re-executed. If no suitable alternative mold is found, a manual intervention request is issued, which is then manually confirmed and processed by process engineers or schedulers.

[0046] ② Rescheduling when predicting beat deviation: Within each refresh cycle T (T is typically 30-60 seconds), the system compares the deviation between the predicted and actual off-time of each injection molding machine. When the deviation of any injection molding machine exceeds a preset threshold (preferably ±5 minutes in this embodiment), a rescheduling is triggered: based on the current time, according to the remaining production time and unfinished work order sequence of the injection molding machine, step S1 is re-executed to calculate the off-time sequence of subsequent work orders. According to the recalculated off-time sequence, steps S2 to S5 are re-executed for subsequent mold-up tasks that have not yet been executed (i.e. have not yet reached the optimal outbound time). The tasks that have already been issued remain unchanged. It is understood that the specific value of the above-mentioned preset threshold can be set by those skilled in the art based on the stability of workshop production. For example, for workshops with relatively stable production rhythm, the preset threshold can be set to ±3 minutes; for workshops with large production fluctuations, the preset threshold can be set to ±10 minutes.

[0047] ③ Task transfer in case of equipment failure: Within each refresh cycle T, the system detects the equipment status of the stacker crane and the automated guided vehicle; If no heartbeat signal is received from any device for a consecutive preset refresh cycle (preferably 3 consecutive cycles in this embodiment), the device is determined to be faulty. At this time, the system reassigns the unexecuted handling tasks of the device to other available devices in the same area and releases the inlet / outlet occupied by the faulty device.

[0048] It is understood that the specific number of the aforementioned continuous preset refresh cycles can be set by those skilled in the art according to the reliability requirements of device communication, for example, it can be set to 2 to 5 cycles to avoid misjudgment due to instantaneous network fluctuations.

[0049] ④ Task redistribution when the workload of the upper mold operators is uneven: Within each refresh cycle T, the system detects the current load of each loading operator. When the current load of any loading operator exceeds a preset multiple of the average load of all loading operators (preferably 1.5 times in this embodiment), and the duration of this state exceeds a preset duration threshold (preferably 30 minutes in this embodiment), the system triggers task reallocation: the subsequent unassigned handling tasks are preferentially assigned to the loading operator with the lowest current load. It is understood that the specific value of 1.5 times mentioned above can be adjusted by those skilled in the art based on the manpower configuration of the workshop mold operators. For example, it can be set to 2.0 times in a workshop with sufficient manpower, and to 1.3 times in a workshop with tight manpower, without changing the load balancing mechanism of the present invention.

[0050] In addition, the refresh cycle T is a fixed time interval preset by the system, which is used to trigger the system to perform routine scheduling refreshes periodically. In this embodiment, it is preferably 60 seconds. Those skilled in the art can make adaptive adjustments to this value according to the frequency of changes in the workshop production plan. For example, for workshops with frequent changes in production plans, a shorter refresh cycle can be used to improve the response speed; for workshops with relatively stable production plans, a longer refresh cycle can be used to reduce the system's computational load.

[0051] Example 2: This invention also provides an automated mold loading and transport system for a mold vertical warehouse, comprising the following functional modules: The data acquisition module is used to obtain the production order sequence of each injection molding machine and the remaining production time of the current batch. This module is connected to the Advanced Production Scheduling System (APS), Manufacturing Operations Management System (MOM), Mold Storage Management System (WMS), Stacker Crane Control System, and Automated Guided Vehicle (AGV) Scheduling System to obtain the above information in real time.

[0052] The parsing module is used to calculate the off-time of the current batch based on the remaining production time, and to extrapolate backwards based on the preset production time of each work order in the production work order sequence to obtain the sequence of off-times for each work order in the future for each injection molding machine. The calculation module is used to obtain the preheating time of the mold temperature controller, the inter-workshop transportation time, the estimated outbound time of the stacker crane, and the preset advance control time threshold. For each mold loading task, it calculates the optimal outbound time according to the following formula: ; The scoring module is used to obtain the urgent order attributes, key product attributes, downtime risk level and current load of the target molder for each molding task, and calculate the multi-dimensional priority score of each molding task according to the preset weights of each dimension. The scheduling module is used to obtain the upper limit of the passage capacity of each inlet and outlet. Using the upper limit of the passage capacity as a hard constraint, it sorts and schedules each upper module task according to the multi-dimensional priority score of each upper module task. When there is a conflict between the outlet task and the return task in the inlet and outlet occupancy, it makes a decision according to the preset conflict resolution rules and generates the staggered scheduling result. The verification module is used to verify, based on the staggered scheduling results, whether the production work order corresponding to the current mold-up task is consistent with the drawing version of the mold to be shipped, and whether the size parameters of the mold to be shipped, the clamping template size and the installation space of the target injection molding machine are compatible. If the verification passes, the shipment is executed; if the verification fails, the task is frozen and an alarm is issued.

[0053] The specific workflow of each of the above modules corresponds to the description of S1 to S5 in Embodiment 1 of the method of the present invention, and will not be repeated here.

[0054] It is understood that the above modules can be software modules deployed on the same server, or they can be distributed across multiple servers or edge computing nodes. Data communication between the modules can be achieved through wired or wireless networks. Those skilled in the art can make adaptive choices for the deployment method of each module according to the actual IT architecture and network conditions of the workshop, without affecting the implementation of the technical solution of the present invention.

[0055] Example 3: Please see Figure 2 Taking a day shift (8 hours) in an injection molding workshop, with 9 mold installers, 3 inbound and outbound ports, and a daily mold installation task of about 120 orders as an example, the application of the method of the present invention will be explained. First, the system obtains the daily production schedule from the APS and calculates the distribution of the 120 orders' departure times for the whole day according to step S1. Then, according to step S2, it deduces the optimal outbound time for each order. Subsequently, according to step S3, it performs multi-dimensional priority scoring on each task. According to step S4, it schedules the 120 tasks evenly across the time period of the day with the upper limit of the inbound / outbound capacity as a hard constraint (single inbound / outbound capacity ≤ 2 orders / minute), with an average speed of about 15 orders / hour throughout the day. After the scheduling is completed, according to step S5, it performs a three-dimensional verification of each order, mold, and workstation before executing the outbound. After the verification is passed, the outbound is executed. In contrast, under the existing "manual call + FIFO queuing" model, 9 operators would make calls between 8:00 and 8:30, issuing approximately 60 orders. The number of concurrent orders at the warehouse entrance / exit reached a maximum of 18 orders / minute between 8:00 and 10:00, with an average waiting time of approximately 47 minutes per order. After 14:00, the total idle time at the warehouse entrance / exit was approximately 4 hours. However, after adopting the method of this invention, the number of concurrent tasks within the first 30 minutes of the shift was reduced to no more than 6 orders / minute, the average waiting time per order / exit was reduced to approximately 8 minutes, and there were no significant congestion or idle periods at the warehouse entrance / exit throughout the day. Among them, when executing the outbound task, for work orders (about 38 orders) that require the return of old molds to the warehouse, the system arranges them as "one vehicle for two purposes" according to the inbound and outbound relay mode of step S502, and the same automated guided vehicle completes the outbound of new molds and the return of old molds to the warehouse in sequence, reducing 38 empty runs. Meanwhile, within each refresh cycle, the system performs anomaly detection and handling according to step S504: when the actual off-time of an injection molding machine deviates from the predicted value by more than ±5 minutes due to production fluctuations, the system automatically triggers rescheduling to ensure the timeliness of subsequent tasks; when a stacker crane or AGV malfunctions, the system automatically transfers its tasks to other available equipment to ensure uninterrupted handling operations; when a mold operator's load exceeds 1.5 times the average for more than 30 minutes, the system prioritizes assigning subsequent tasks to the mold operator with the lowest load to ensure a balanced workload for all mold operators. After each mold-up task is completed, the system records the actual execution data and sends it back according to step S503. The system continuously updates the self-calibration values ​​of the preset production time and mold temperature controller preheating time through a sliding weighting method, so that the prediction accuracy of the system continues to improve with the running time.

[0056] In the description of this application, it should be noted that the terms "upper," "lower," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, 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, and therefore should not be construed as a limitation of this application. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; 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 between two elements. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0057] It should be noted that in this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0058] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An automated mold loading and transport method for a mold vertical warehouse, characterized in that, Includes the following steps: Obtain the production work order sequence of each injection molding machine and the remaining production time of the current batch. Calculate the off-time of the current batch based on the remaining production time. Then, based on the preset production time of each work order in the production work order sequence, recursively calculate the off-time sequence of each work order for the future of each injection molding machine. The preheating time of the mold temperature controller, the inter-workshop transportation time, the estimated time of the stacker crane's outbound shipment, and the preset advance control time threshold are obtained. For each mold loading task, the optimal outbound time is calculated using the following formula: Optimal outbound time = Unloading time - Mold temperature controller preheating time - Inter-workshop transportation time - Stacker crane estimated outbound shipment time - Advance control time threshold. Obtain the urgent order attributes, key product attributes, downtime risk level, and current load of the target mold installer for each mold installation task; and calculate the multi-dimensional priority score for each mold installation task based on the preset weights of each dimension. Obtain the upper limit of the passage capacity of each inlet and outlet. Using the upper limit of the passage capacity as a hard constraint, sort and schedule each upper task according to the multi-dimensional priority score of each upper task. When there is a conflict between the inlet and outlet occupancy of the outlet task and the return task, make a decision according to the preset conflict resolution rules and generate the staggered scheduling result. Based on the staggered scheduling results, before executing the outbound action, it is verified whether the production work order corresponding to the current mold task is consistent with the drawing version of the mold to be outbound, and whether the size parameters and clamping template size of the mold to be outbound are compatible with the installation space of the target injection molding machine. Based on the verification results, the outbound action is executed or the task is frozen and an alarm is issued.

2. The automatic mold loading and transport method for mold vertical storage according to claim 1, characterized in that, The multidimensional priority score is calculated according to the following formula: ； in, This is the value of the "urgent order" attribute. For key product attribute values, This represents the downtime risk level value. The target module's current load value; , , , These are the corresponding weights for urgent orders, key products, downtime risk, and operator load, respectively.

3. The automatic mold loading and transport method for mold vertical storage according to claim 2, characterized in that, The weight of urgent orders Key product weight Downtime risk weight and the load weight of the upper module Make dynamic adjustments using one of the following methods: Switch between preset weight combinations according to preset runtime segment or runtime mode; Using at least one of the following as feedback signals—the number of concurrent users during the start of the shift, the average waiting time for the shift worker, and the standard deviation of the number of tasks the shift worker completes each day—the step size of each weight is adjusted after the daily operation ends.

4. The automatic mold loading and transport method for mold vertical storage according to claim 1, characterized in that, The preset conflict resolution rules include: If both outbound and inbound tasks exist at the same inbound / outbound port during the same time period, the outbound task corresponding to the online production work order takes priority over the inbound task. If both tasks are outbound or inbound, they are sorted according to the multi-dimensional priority score, and the task with the lower score is postponed to the next idle time slot of that outbound / inbound port.

5. The automatic mold loading and transport method for mold vertical storage according to claim 1, characterized in that, Also includes: When executing outbound tasks, check whether the target injection molding machine has a need to return completed old molds to the warehouse; If present, the task of returning the old mold to the warehouse and the current task of leaving the warehouse will be scheduled to be executed sequentially by the same automated guided vehicle.

6. The automatic mold loading and transport method for mold vertical storage according to claim 1, characterized in that, If the verification fails, the following also applies: Retrieve from the mold library a replacement mold that is identical to the drawing version of the current production order and whose dimensions, clamping template dimensions, and installation space of the target injection molding machine are compatible. If the alternative mold is found, it is recommended and the verification is re-executed; if it is not found, a request for manual intervention is issued.

7. The automatic mold loading and transport method for mold vertical storage according to claim 1, characterized in that, It also includes exception handling steps executed within each refresh cycle, the exception handling steps including: Within each refresh cycle, the deviation between the predicted and actual off-time of each injection molding machine is compared. When the deviation of any injection molding machine exceeds a preset threshold, the off-time of each subsequent work order of the injection molding machine is recalculated based on the current time, the remaining production time and the sequence of unfinished work orders of the injection molding machine, and the optimal outbound time is recalculated based on the recalculated off-time. The staggered scheduling result is then regenerated, and the subsequent mold-up tasks are rescheduled. The status of the stacker crane and automated guided vehicle is detected in each refresh cycle. If no heartbeat signal is received from any device for a consecutive preset refresh cycle, the device is determined to be faulty. The handling tasks that the device has not yet performed are reassigned to other available devices in the same area, and the inlet and outlet occupied by the device are released.

8. The automatic mold loading and transport method for mold vertical storage according to claim 1, characterized in that, Also includes: Detect the current load of each module member during each refresh cycle; When the current load of any operator exceeds a preset multiple of the average load of all operators and the duration exceeds a preset duration threshold, the subsequent handling tasks to be assigned will be preferentially assigned to the operator with the lowest current load.

9. The automatic mold loading and transport method for mold vertical storage according to claim 1, characterized in that, Also includes: After each mold-changing task is completed, record the actual time of departure from the warehouse, the actual time of arrival at the machine, and the actual time spent changing the mold. The actual outbound time, actual arrival time at the machine, and actual mold change time are transmitted back. The self-calibration values ​​of the preset production time and the preheating time of the mold temperature controller for the corresponding injection molding machine and mold type are updated by using a sliding weighted method of measured values ​​and predicted values.

10. A system based on the automatic mold loading and transport method of the mold vertical warehouse as described in claim 1, characterized in that, include: The data acquisition module is used to obtain the production order sequence of each injection molding machine and the remaining production time of the current batch; The parsing module is used to calculate the off-time of the current batch based on the remaining production time, and to advance backward based on the preset production time of each work order in the production work order sequence to obtain the sequence of off-times of each work order in the future for each injection molding machine. The calculation module is used to obtain the preheating time of the mold temperature controller, the inter-workshop transportation time, the estimated time of the stacker crane's outbound delivery, and the preset advance control time threshold. For each mold loading task, the optimal outbound time is calculated according to the following formula: Optimal outbound time = unloading time - mold temperature controller preheating time - inter-workshop transportation time - stacker crane outbound estimated time - advance control time threshold. The scoring module is used to obtain the urgent order attributes, key product attributes, downtime risk level and current load of the target molder for each molding task, and calculate the multi-dimensional priority score of each molding task according to the preset weights of each dimension. The scheduling module is used to obtain the upper limit of the passage capacity of each inlet and outlet, use the upper limit of the passage capacity as a hard constraint, sort and schedule each upper module task according to the multi-dimensional priority score of each upper module task, and when the inlet and outlet occupancy conflict between the outlet task and the return task, it makes a decision according to the preset conflict resolution rules and generates the staggered scheduling result. The verification module is used to verify, based on the staggered scheduling results, whether the production work order corresponding to the current mold-up task is consistent with the drawing version of the mold to be shipped, and whether the size parameters and clamping template size of the mold to be shipped are compatible with the installation space of the target injection molding machine before executing the outbound action. If the verification passes, the outbound action is executed; if the verification fails, the task is frozen and an alarm is issued.

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