Aluminum alloy profile aging heat treatment multi-furnace collaborative scheduling control method and equipment

Abstract

The application discloses an aging heat treatment multi-furnace collaborative scheduling control method and equipment for aluminum alloy profiles, which comprises the following steps: global state sensing and time window analysis; three-dimensional space verification based on profile physical characteristics, furnace space and hot air circulation flow field constraints, intelligent grouping combined with process compatibility and flexible holding time window rules to generate physically feasible batches to be processed; constructing a multi-furnace collaborative scheduling model, introducing heat attenuation and waste heat utilization and logistics equipment time sequence mutual exclusion constraints, and globally solving the optimal scheduling scheme; performing heat field simulation pre-rehearsal based on digital twinning before execution, adaptively correcting the bottom controller process curve and issuing; collaboratively executing handling and heat treatment, real-time monitoring through edge computing, and triggering local self-healing rescheduling in case of abnormality. The application effectively solves the problems of complex profile loading limitation, high energy consumption of cold furnace, logistics conflict and disconnection between plan and execution, and realizes efficient, low-consumption and stable operation of multi-furnace collaboration.

Description

Technical Field

[0001] This invention relates to the field of aluminum alloy heat treatment technology, and more specifically, to a method and equipment for coordinated scheduling and control of multiple furnaces in the aging heat treatment of aluminum alloy profiles. Background Technology

[0002] Aging heat treatment is a crucial process that determines the final mechanical properties (such as strength, hardness, and toughness) of aluminum alloy profiles. Modern aluminum profile production workshops typically employ multiple aging furnaces to meet the demands of large-volume, multi-grade production. However, the coordinated scheduling of multiple furnaces in actual production faces numerous technical bottlenecks. Traditional scheduling methods either rely on manual experience or employ simple rule-based production scheduling, making it difficult to adapt to the complex and ever-changing constraints of the factory environment. The main problems include: batching strategies deviating from physical reality, leading to low furnace loading efficiency: Traditional batching only verifies the total weight and maximum length of the profiles. Due to the diverse cross-sections of aluminum alloy profiles (such as radiator profiles and industrial aluminum profiles), dense stacking can severely obstruct the hot air circulation within the furnace, resulting in localized "under-aging" or "over-burning." This often leads to a discrepancy where "the production schedule shows 10 tons can be loaded, but due to wind resistance, only 6 tons can be loaded on-site," and it is difficult to ensure temperature uniformity. Furthermore, there are rigid differences in holding times between different orders, making it difficult to achieve cross-order furnace merging using traditional methods, resulting in low equipment utilization. The lack of consideration for equipment thermal inertia and logistical bottlenecks leads to poor plan executability: The energy consumption for heating an aging furnace from a cold state to 200℃ is extremely high. Traditional scheduling ignores the residual heat decay characteristics of the furnace body, resulting in frequent cold furnace starts and significant energy waste. Simultaneously, multiple aging furnaces share workshop cranes / AGVs, and traditional scheduling does not incorporate time-sequence constraints for logistical equipment. This often results in multiple furnaces simultaneously requiring loading / unloading, causing crane congestion and lock-ups, and disrupting the on-site production rhythm. There is a disconnect between theoretical process curves and actual thermal response: Theoretical scheduling typically calculates nodes based on standard holding times, but actual heating times are greatly affected by furnace load and profile specifications. The lack of pre-scheduling verification of the furnace's thermal field often leads to delayed actual heating, insufficient or excessive holding times, not only causing quality issues but also rendering the entire production line's Gantt chart ineffective.

[0003] In summary, there is an urgent need for a multi-furnace collaborative scheduling and control method that can deeply integrate process physical constraints, energy consumption and logistics coordination, digital twin pre-simulation and edge self-healing mechanism to solve the problems of disconnect between planning and execution, high energy consumption and weak anti-interference ability in the existing technology. Summary of the Invention

[0004] A method for coordinated scheduling and control of multiple furnaces in the aging heat treatment of aluminum alloy profiles includes the following steps: S1. Global Status Awareness and Time Window Analysis: Through communication interfaces with the MES system, PLC control system and WMS system, acquire data on orders awaiting processing, real-time status data of each aging furnace and status data of workshop logistics equipment; extract the physical characteristics and process requirements of profiles based on the data on orders awaiting processing, calculate the latest furnace entry time for each order, and form a strict time window constraint; S2. Three-dimensional spatial verification and flexible rule intelligent batching: Based on the physical characteristics of the profile (maximum circumcircle, wall thickness, etc.) combined with the internal space dimensions of the aging furnace and the constraints of the hot air circulation flow field, three-dimensional spatial verification is performed. According to the process compatibility and flexible heat preservation time window rules, for the problem of different heat preservation times for different orders, a flexible range is given to the heat preservation time by utilizing the alloy over-aging sensitivity, and orders that could not be combined into the furnace are reasonably merged to generate a batch to be processed that meets both physical space and process flexibility. S3. Multi-constraint collaborative modeling and global optimization solution: Construct a multi-furnace collaborative scheduling model, take the batch to be processed as input, aim to minimize the maximum completion time, energy consumption and order overdue penalty, and introduce constraints on the utilization of waste heat from the heat decay of the aging furnace and the time sequence mutual exclusion constraints of logistics equipment, and solve to generate the globally optimal scheduling scheme; S4. Digital Twin Thermal Field Pre-simulation and Curve Adaptive Correction: Before executing the scheduling scheme, the furnace loading scheme in the global optimal scheduling scheme is simulated and pre-simulated based on digital twin technology. The set process curve of the underlying controller is adaptively corrected and issued according to the actual thermal response curve predicted by the simulation. S5. Collaborative Execution and Edge-Level Self-Healing Rescheduling: Workshop logistics equipment performs handling and furnace loading according to the issued scheduling plan, and the bottom controller of the aging furnace performs heat treatment according to the modified set process curve; during the execution, the equipment status is monitored in real time through edge computing, and a local self-healing rescheduling mechanism is triggered when an anomaly occurs (such as thermocouple failure or over-temperature).

[0005] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. In step S2, a three-dimensional spatial verification is performed based on the physical characteristics of the profile, combined with the internal space dimensions of the aging furnace and the constraints of the hot air circulation flow field. The specific three-dimensional verification is as follows: S21. Parametric modeling: Spatial modeling of the effective furnace chamber size, material rack layer spacing and rated load of the aging furnace, and extraction of the length of the profiles to be batched, the maximum outer circle diameter (which determines the single layer height of the profile stack), the wall thickness (thin-walled parts are more easily deformed and have extremely high wind resistance when densely packed) and the cross-sectional complexity coefficient. S22. Geometric and load-bearing verification: Verify whether the profile length exceeds the effective length of the furnace, calculate whether the total volume of the stacked profiles occupies the effective volume of the furnace exceeding the first set threshold (e.g., 65%), and verify whether the total weight of the profiles exceeds the rated load-bearing capacity. S23. Flow field resistance assessment: Calculate the flow field blockage coefficient by combining the profile cross-sectional complexity coefficient, the maximum outer circle diameter and the stacking density; verify whether the proposed furnace installation plan has reserved the top air duct gap and side return air channel to meet the second set threshold (e.g., 200mm); and verify whether the gap between the bundled profiles meets the minimum hot air penetration threshold (e.g., 5mm). S24. Dynamic capacity reduction adjustment: When the geometry and load-bearing verification pass but the flow field resistance assessment fails, the system does not reject the current order combination, but triggers the capacity reduction mechanism to dynamically reduce the maximum allowable furnace loading limit of the batch scheme, and / or generate a three-dimensional arrangement strategy of forced layered charging and adding interlayer pads. S25. Output Results: The output includes the final upper limit of furnace loading, three-dimensional arrangement method and wind resistance warning level of the batch to be processed, which will serve as the input parameters for subsequent multi-furnace collaborative scheduling modeling.

[0006] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. Step S2, which aggregates orders based on process compatibility and flexible insulation time window rules, specifically includes: Flexible range definition: Obtain the standard heat preservation time for each order profile based on alloy grade and mechanical performance requirements, and combine it with the pre-stored alloy over-aging sensitivity database to convert the standard heat preservation time from a rigid fixed value into a flexible heat preservation time window range with an upper limit (3.5h) and a lower limit (2.5h). Baseline time extraction: During the batch aggregation stage, when it is determined that the target insulation temperature of multiple orders is within the preset temperature difference compatibility range, but the standard insulation time is different, the longest standard insulation time among the candidate orders for the combined furnace is extracted as the baseline insulation time. Attenuation risk quantification assessment: For orders to be combined with furnaces with a short standard holding time, calculate the excess holding time between the benchmark holding time and the standard holding time, and determine whether the benchmark holding time exceeds the upper limit of the flexible holding time window range for the short-time order. Furnace Combination Decision and Isolated Furnace Output Plan Generation: If the reference holding time does not exceed the upper limit of the flexible holding time window range for shorter time orders, the performance degradation risk is determined to be within a safe range, and the batch is allowed to be combined into the furnace and uniformly output according to the reference holding time; If the baseline insulation time exceeds the upper limit of the flexible insulation time window range for shorter time orders, it is determined that the safe range has been exceeded. The system automatically selects available equipment, such as double-door walking beam aging furnaces, for fusion and generates a partial isolation unloading plan in the production scheduling scheme. The partial isolation unloading plan includes: marking the specific material frame position and level of the shorter time order, setting a temporary unloading time node in the middle, and a logistics control instruction to control the corresponding side furnace door of the double-door walking beam aging furnace to open at the time node to remove the marked material frame separately.

[0007] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. Step S3 introduces constraints on the utilization of waste heat from the aging furnace and the time-series mutual exclusion constraints on logistics equipment, and solves to generate the globally optimal scheduling scheme, as detailed below: Special constraints are constructed: a constraint on the utilization of waste heat from the thermal decay of the aging furnace is introduced, and the difference between the current furnace temperature of the idle aging furnace and the target process temperature of the batch to be processed is calculated in real time and converted into a heating energy consumption penalty. The smaller the difference, the higher the fitness weight assigned to the batch. A mutual exclusion constraint on the timing of logistics equipment is introduced, setting that the loading or unloading time windows of any two aging furnaces cannot overlap, and that there must be an interval between their start times that is greater than or equal to the buffer time of the logistics equipment. At the same time, the number of tasks occupying the same logistics equipment at the same time is limited to no more than the total number of physical equipment in the workshop. Algorithm initialization and iterative solution: A fitness evaluation model is constructed with the multi-objective function of minimizing the maximum completion time, total energy consumption, and order overdue penalty. An improved heuristic algorithm is adopted, combining "Earliest Delivery Date (EDD) + Shortest Processing Time (SPT) + Residual Heat Matching" as rules to generate the initial population. Based on the fitness evaluation model (comprehensive evaluation of time, energy consumption, and overdue status) and the constraints, the individuals in the population are evaluated, and global iterative optimization is performed through crossover and mutation operations. Solution Output: After iteration to the termination condition, the optimal individual is output. The solution decodes and generates a globally optimal production scheduling Gantt chart that includes batch allocation sequence, specified time furnace number, planned furnace entry time, planned furnace exit time, and logistics equipment scheduling sequence.

[0008] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. In step S4, the set process curve of the underlying controller is adaptively corrected and distributed based on the actual thermal response curve predicted by simulation. The specific steps are as follows: S41. Curve Comparison and Deviation Quantification: Compare the actual thermal response curve predicted by the digital twin thermal field simulation with the standard set process curve, and extract the deviation of key feature nodes. The deviation includes at least the temperature lag in the heating stage (actually 20℃ / min slower than the set value), the target temperature achievement delay time in the temperature equalization stage (15 minutes later than the set value), and the maximum overshoot in the heat preservation stage. S42. Adaptive compensation for setting curve: The setting process curve of the underlying controller is feedforward compensated and corrected according to the deviation amount. The specific correction strategies include: increasing the upper limit of power output or the setting value of the heating slope in the heating stage to compensate for temperature lag; extending the setting holding time of the uniform temperature section to ensure that the actual temperature of the furnace core profile reaches the target value; and dynamically adjusting the PID control parameters of the heat preservation section to suppress overshoot caused by thermal inertia. S43. Production schedule linkage update: Based on the corrected set process curve, recalculate the actual total heat treatment time required for this batch, and synchronously transmit the actual total heat treatment time back to the global scheduling model to dynamically update the planned furnace exit time of the aging furnace and the production schedule time nodes of subsequent batches to be processed. S44. Command Issuance and Execution: The corrected set process curve parameters and PID control parameters are sent to the corresponding aging furnace's underlying PLC controller via the industrial communication protocol, so that the PLC drives the heating element and circulating fan to perform heat treatment operations according to the corrected curve.

[0009] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. The workshop logistics equipment performs handling and furnace loading according to the issued scheduling plan. The handling and furnace loading specifically includes: The logistics control system (WMS / WCS) receives dispatch instructions and dispatches overhead cranes or AGVs to transport material frames to the designated aging furnace; Before loading the furnace, the identity of the material frame is identified by RFID and barcode scanning equipment and compared with the production schedule. When the materials do not match, the system refuses to issue a furnace door closing command and triggers an alarm; when they match, the system automatically controls the furnace door to close and sends back a heating ready signal.

[0010] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. In step S5, a local self-healing rescheduling mechanism is triggered when an anomaly occurs. The mechanism is as follows: The edge computing gateway deployed next to the aging furnace collects temperature and air pressure data at multiple points in the furnace in real time at a millisecond frequency. If it is determined that the temperature of the insulation section exceeds the safety threshold, the heating rate is abnormal, or the sensor fails, the edge gateway executes local hard interlock control, instantly cuts off the heating source, and simultaneously reports the abnormal event code and fault type to the scheduling system. Upon receiving the abnormal event, the scheduling system immediately marks the status of the faulty aging furnace as unavailable, automatically freezes all subsequent production schedules for the furnace, and suspends the issuance of handling tasks involving the furnace to the logistics equipment. For batches affected by an anomaly and still inside the faulty furnace, the system extracts the actual temperature-time history curve before the interruption and calculates the completed equivalent holding time based on the heat treatment kinetic model. If it is determined that the fault can be eliminated in a short time, the subsequent holding section is adaptively extended according to the remaining equivalent time after the equipment is repaired. If it is determined that the material needs to be transferred, a cooling reset command is generated and the nearest idle hot furnace is prioritized for re-entry into the furnace. For queued batches that were originally scheduled to be assigned to faulty furnaces but have not yet been put into the furnace, their time windows and process constraints are extracted, and the scheduling algorithm is re-triggered in the remaining m-1 available aging furnaces to perform local right shift rearrangement; during the rearrangement process, the timing mutual exclusion constraints of logistics equipment are checked simultaneously to avoid traffic conflicts, and an updated local production scheduling plan is generated and sent to the logistics control system and on-site dashboard to complete the self-healing closed loop.

[0011] A multi-furnace coordinated scheduling and control system for aging heat treatment of aluminum alloy profiles, including: Memory, used to store computer programs; A processor is configured to implement the steps of the multi-furnace coordinated scheduling control method for aging heat treatment of aluminum alloy profiles as described in any one of the computer programs.

[0012] A computer-readable storage medium storing a computer program, wherein when executed by a processor, the computer program implements the steps of the multi-furnace collaborative scheduling and control method for aging heat treatment of aluminum alloy profiles as described in any one of the claims.

[0013] The beneficial effects of this invention are as follows: Intelligent batching based on three-dimensional flow field verification and flexible time windows significantly improves furnace loading efficiency and product quality. This invention breaks through the limitations of traditional batching based solely on weight and length, introducing assessments of profile cross-sectional complexity and hot air circulation flow field resistance. This fundamentally avoids local overheating / under-aging, ensuring furnace temperature uniformity. Simultaneously, a flexible holding time window is established using the alloy's over-aging sensitivity. Combined with the local isolation unloading plan of the double-door walking beam furnace, safe furnace merging of orders with different holding times is achieved, greatly improving equipment space utilization and furnace merging rate. The introduction of dual constraints of thermal decay and material flow mutual exclusion achieves cost reduction, efficiency improvement, and deadlock prevention. The innovative introduction of waste heat utilization constraints from aging furnace thermal decay in the scheduling model prioritizes batch allocation to furnaces with small waste heat temperature differences, significantly reducing natural gas / electricity consumption for cold furnace heating. Digital twin simulation and adaptive curve correction eliminate deviations between planning and execution: Before the scheduling plan is issued, a lightweight digital twin model is used to conduct thermal field simulation to detect temperature rise lag or overshoot caused by furnace loading in advance. Based on this, the system performs feedforward compensation correction on the setpoint curve (SP) and PID parameters of the underlying PLC, realizing precise temperature control of "one furnace, one curve". This ensures the consistency of mechanical properties under different furnace loading, and feeds back the actual time consumption to the global production scheduling model, ensuring the accuracy of the overall timing. Attached Figure Description

[0014] Figure 1 A flowchart of a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles; Detailed Implementation This invention provides a method for coordinated scheduling and control of multiple furnaces in the aging heat treatment of aluminum alloy profiles. The specific process is as follows: Figure 1 As shown, a complete closed-loop control from production scheduling to self-healing is achieved through a combination of hardware and software, including the following steps: S1. Global Status Awareness and Time Window Analysis: In actual factory operation, the system first captures real-time workshop-level data through communication interfaces with the MES system, PLC control system, and WMS system. Data for orders awaiting aging includes order number, alloy grade (e.g., 6063, 6061), specifications, cross-sectional area, etc.; the real-time status of the aging furnace includes current furnace temperature and idle / running status; the status of logistics equipment includes the current position and occupancy status of the crane or AGV. At the same time, aluminum alloy profiles cannot wait indefinitely for aging after extrusion, otherwise excessive natural aging will occur, resulting in substandard mechanical properties. Therefore, the system calculates the "latest furnace entry time" for each order based on the extrusion time plus a preset upper limit of allowed waiting time (e.g., 12 hours), forming a strict time window constraint. S2. Three-dimensional spatial verification and flexible rule intelligent batching: Traditional batching only verifies the total weight and maximum length. However, in actual production, if complex cross-section profiles such as radiator profiles are densely stacked, they will seriously obstruct the hot air circulation, resulting in local "under-aging" or "over-burning". The system not only performs weight verification, but also introduces the physical characteristics of the profiles (maximum circumcircle, wall thickness, etc.) and spatial matching with the three-dimensional dimensions inside the aging furnace, and calculates the hot air circulation flow field resistance. In response to the problem of different holding times for different orders, the system uses the alloy over-aging sensitivity to give the holding time a flexible range, and reasonably merges orders that cannot be combined into one furnace, generating batches that meet both physical space and process flexibility requirements. S3. Multi-constraint collaborative modeling and global optimization solution: The batched tasks are assigned to multiple aging furnaces. Considering that the energy consumption of aging furnaces from cold to 180℃-200℃ is extremely high, the system introduces "thermal decay and waste heat utilization constraints" to prioritize assigning low-temperature aging tasks to furnaces that have just finished high-temperature aging and still have residual heat, which greatly reduces the energy consumption of heating. In order to prevent multiple furnaces from requiring the crane to load materials at the same time, which would cause on-site congestion and deadlock, "logistics equipment timing mutual exclusion constraints" are introduced. On this basis, with the goal of minimizing completion time, energy consumption and overdue penalties, a heuristic algorithm (such as genetic algorithm) is used to solve the optimal production scheduling Gantt chart. S4. Digital Twin Thermal Field Prediction and Curve Adaptive Correction: There is often a deviation between theoretical production time and actual heating time. The heating curves of 10 tons of charge and 6 tons of charge are completely different. Before execution, the system calls the digital twin lightweight model, inputs the furnace charge and arrangement, and simulates and predicts the actual thermal response curve inside the furnace. If the simulation finds that the heating is lagging, the system automatically corrects the set process curve of the underlying PLC (such as increasing the heating power) and dynamically updates the subsequent production nodes according to the corrected total consumption time, so as to achieve perfect alignment between planning and execution. S5. Collaborative Execution and Edge-Level Self-Healing Rescheduling: Logistics equipment moves material frames according to instructions, and the PLC executes heating according to the correction curve. During the execution process, in order to deal with sudden failures (such as thermocouple failure or overheating), the edge computing gateway deployed next to the furnace monitors data at millisecond speeds. Once an anomaly is detected, the heating source is immediately cut off locally to prevent accidents, and the information is reported to the scheduling system. The scheduling system quickly isolates the faulty furnace, calculates the insulation equivalent for the batches in the furnace, and performs a local right-shift rearrangement of the batches not in the furnace in the remaining available furnaces, thereby realizing dynamic self-healing of the production process.

[0015] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. In step S2, a three-dimensional spatial verification is performed based on the physical characteristics of the profile, combined with the internal space dimensions of the aging furnace and the constraints of the hot air circulation flow field. The specific three-dimensional verification is as follows: S21. Parametric modeling: Spatial modeling of the effective furnace chamber size, material rack layer spacing and rated load of the aging furnace, and extraction of the length of the profiles to be batched, the maximum outer circle diameter (which determines the single layer height of the profile stack), the wall thickness (thin-walled parts are more easily deformed and have extremely high wind resistance when densely packed) and the cross-sectional complexity coefficient. S22. Geometric and load-bearing verification: Verify whether the profile length exceeds the effective length of the furnace, calculate whether the total volume of the stacked profiles occupies the effective volume of the furnace exceeding the first set threshold (e.g., 65%), and verify whether the total weight of the profiles exceeds the rated load-bearing capacity. S23. Flow field resistance assessment: Calculate the flow field blockage coefficient by combining the profile cross-sectional complexity coefficient, the maximum outer circle diameter and the stacking density; verify whether the proposed furnace installation plan has reserved the top air duct gap and side return air channel to meet the second set threshold (e.g., 200mm); and verify whether the gap between the bundled profiles meets the minimum hot air penetration threshold (e.g., 5mm). S24. Dynamic capacity reduction adjustment: When the geometry and load-bearing verification pass but the flow field resistance assessment fails, the system does not reject the current order combination, but triggers the capacity reduction mechanism to dynamically reduce the maximum allowable furnace loading limit of the batch scheme, and / or generate a three-dimensional arrangement strategy of forced layered charging and adding interlayer pads. S25. Output Results: The output includes the final upper limit of furnace loading, three-dimensional arrangement method and wind resistance warning level of the batch to be processed, which will serve as the input parameters for subsequent multi-furnace collaborative scheduling modeling.

[0016] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. Step S2, which aggregates orders based on process compatibility and flexible insulation time window rules, specifically includes: Flexible range definition: Obtain the standard heat preservation time for each order profile based on alloy grade and mechanical performance requirements, and combine it with the pre-stored alloy over-aging sensitivity database to convert the standard heat preservation time from a rigid fixed value into a flexible heat preservation time window range with an upper limit (3.5h) and a lower limit (2.5h). Baseline time extraction: During the batch aggregation stage, when it is determined that the target insulation temperature of multiple orders is within the preset temperature difference compatibility range, but the standard insulation time is different, the longest standard insulation time among the candidate orders for the combined furnace is extracted as the baseline insulation time. Attenuation risk quantification assessment: For orders to be combined with furnaces with a short standard holding time, calculate the excess holding time between the benchmark holding time and the standard holding time, and determine whether the benchmark holding time exceeds the upper limit of the flexible holding time window range for the short-time order. Furnace Combination Decision and Isolated Furnace Output Plan Generation: If the reference holding time does not exceed the upper limit of the flexible holding time window range for shorter time orders, the performance degradation risk is determined to be within a safe range, and the batch is allowed to be combined into the furnace and uniformly output according to the reference holding time; If the baseline insulation time exceeds the upper limit of the flexible insulation time window range for shorter time orders, it is determined that the safe range has been exceeded. The system automatically selects available equipment, such as double-door walking beam aging furnaces, for fusion and generates a partial isolation unloading plan in the production scheduling scheme. The partial isolation unloading plan includes: marking the specific material frame position and level of the shorter time order, setting a temporary unloading time node in the middle, and a logistics control instruction to control the corresponding side furnace door of the double-door walking beam aging furnace to open at the time node to remove the marked material frame separately.

[0017] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. Step S3 introduces constraints on the utilization of waste heat from the aging furnace and the time-series mutual exclusion constraints on logistics equipment, and solves to generate the globally optimal scheduling scheme, as detailed below: Special constraints are established: thermal decay and waste heat utilization constraints: The system reads the current furnace temperature of the idle furnace in real time. If the current furnace temperature is 180℃ and the target temperature of the batch to be processed is 200℃, the temperature difference is only 20℃. The system converts this into an extremely low "heating energy consumption penalty term", which significantly increases the fitness weight of the batch assigned to the furnace. Conversely, if the 200℃ batch is assigned to the cold furnace (temperature difference of 200℃), the penalty term is extremely high. This mechanism guides the algorithm to prioritize the utilization of waste heat. Logistics equipment timing mutual exclusion constraint: If there are only 2 overhead cranes in the workshop, the system sets the constraint that the loading or unloading time windows of any two furnaces cannot completely overlap, and an interval greater than or equal to the handling buffer time (such as 5 minutes) must be reserved. On any time slice, the total number of tasks occupying the overhead cranes is ≤2. This constraint fundamentally eliminates the deadlock phenomenon of "seamless on the production schedule, but the on-site overhead cranes collide and queue". Algorithm initialization and iterative solution: A fitness evaluation model is constructed with the multi-objective function of minimizing the maximum completion time, total energy consumption, and order overdue penalty. An improved heuristic algorithm is adopted, combining "Earliest Delivery Date (EDD) + Shortest Processing Time (SPT) + Residual Heat Matching" as rules to generate the initial population. Based on the fitness evaluation model (comprehensive evaluation of time, energy consumption, and overdue status) and the constraints, the individuals in the population are evaluated, and global iterative optimization is performed through crossover and mutation operations. Solution Output: After iteration to the termination condition, the optimal individual is output. The solution decodes and generates a globally optimal production scheduling Gantt chart that includes batch allocation sequence, specified time furnace number, planned furnace entry time, planned furnace exit time, and logistics equipment scheduling sequence.

[0018] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. In step S4, the set process curve of the underlying controller is adaptively corrected and distributed based on the actual thermal response curve predicted by simulation. The specific steps are as follows: S41. Curve Comparison and Deviation Quantification: Compare the actual thermal response curve predicted by the digital twin thermal field simulation with the standard set process curve, and extract the deviation of key feature nodes. The deviation includes at least the temperature lag in the heating stage (actually 20℃ / min slower than the set value), the target temperature achievement delay time in the temperature equalization stage (15 minutes later than the set value), and the maximum overshoot in the heat preservation stage. S42. Adaptive compensation for setting curve: The setting process curve of the underlying controller is corrected by feedforward compensation based on the deviation. Specific correction strategies include: increasing the upper limit of power output in the heating stage (e.g., from 80% to 100%) or the setting value of the heating slope to compensate for temperature lag; extending the setting holding time in the uniform temperature section to ensure that the actual temperature of the furnace core profile reaches the target value; and dynamically adjusting the PID control parameters in the heat preservation section to suppress overshoot caused by thermal inertia. S43. Production schedule linkage update: Based on the corrected set process curve, recalculate the actual total heat treatment time required for this batch, and synchronously transmit the actual total heat treatment time back to the global scheduling model to dynamically update the planned furnace exit time of the aging furnace and the production schedule time nodes of subsequent batches to be processed. S44. Command Issuance and Execution: The corrected set process curve parameters and PID control parameters are sent to the corresponding aging furnace's underlying PLC controller via the industrial communication protocol, so that the PLC drives the heating element and circulating fan to perform heat treatment operations according to the corrected curve.

[0019] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. The workshop logistics equipment performs handling and furnace loading according to the issued scheduling plan. The handling and furnace loading specifically includes: The logistics control system (WMS / WCS) receives dispatch instructions and dispatches overhead cranes or AGVs to transport material frames to the designated aging furnace; Before loading the furnace, the identity of the material frame is identified by RFID and barcode scanning equipment and compared with the production schedule. When the materials do not match, the system refuses to issue a furnace door closing command and triggers an alarm; when they match, the system automatically controls the furnace door to close and sends back a heating ready signal.

[0020] Furthermore, a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles is proposed. In step S5, a local self-healing rescheduling mechanism is triggered when an anomaly occurs. The mechanism is as follows: The edge computing gateway deployed next to the aging furnace collects temperature and air pressure data at multiple points in the furnace in real time at a millisecond frequency. If it is determined that the temperature of the insulation section exceeds the safety threshold, the heating rate is abnormal, or the sensor fails, the edge gateway executes local hard interlock control, instantly cuts off the heating source, and simultaneously reports the abnormal event code and fault type to the scheduling system. Upon receiving the abnormal event, the scheduling system immediately marks the status of the faulty aging furnace as unavailable, automatically freezes all subsequent production schedules for the furnace, and suspends the issuance of handling tasks involving the furnace to the logistics equipment. For batches affected by an anomaly and still inside the faulty furnace, the system extracts the actual temperature-time history curve before the interruption and calculates the completed equivalent holding time based on the heat treatment kinetic model. If it is determined that the fault can be eliminated in a short time, the subsequent holding section is adaptively extended according to the remaining equivalent time after the equipment is repaired. If it is determined that the material needs to be transferred, a cooling reset command is generated and the nearest idle hot furnace is prioritized for re-entry into the furnace. For queued batches that were originally scheduled to be assigned to faulty furnaces but have not yet been put into the furnace, their time windows and process constraints are extracted, and the scheduling algorithm is re-triggered in the remaining m-1 available aging furnaces to perform local right shift rearrangement; during the rearrangement process, the timing mutual exclusion constraints of logistics equipment are checked simultaneously to avoid traffic conflicts, and an updated local production scheduling plan is generated and sent to the logistics control system and on-site dashboard to complete the self-healing closed loop.

[0021] A multi-furnace coordinated scheduling and control system for aging heat treatment of aluminum alloy profiles, including: Memory, used to store computer programs; A processor is configured to implement the steps of the multi-furnace coordinated scheduling control method for aging heat treatment of aluminum alloy profiles as described in any one of the computer programs.

[0022] A computer-readable storage medium storing a computer program, wherein when executed by a processor, the computer program implements the steps of the multi-furnace collaborative scheduling and control method for aging heat treatment of aluminum alloy profiles as described in any one of the claims.

[0023] Example 2 This embodiment provides a multi-furnace coordinated scheduling and control method for aging heat treatment of aluminum alloy profiles. It is applied to an aluminum profile production workshop with four aging furnaces (denoted as F1, F2, F3, and F4, where F4 is a double-door walking beam aging furnace) and two overhead cranes. The implementation steps are as follows: S1. Global Status Awareness and Time Window Analysis: At 8:00 AM, the scheduling system retrieves the list of pending orders that were pushed offline that morning from the MES system, including: Order A: 6063-T5 alloy heat sink profile, wall thickness 1.2mm, complex cross-section, total weight 8 tons, required insulation temperature 200℃, standard insulation time 3 hours, delivery date 18:00 the next day; Order B: 6061-T6 alloy general building profiles, total weight 6 tons, required insulation temperature 200℃, standard insulation time 4 hours, delivery date is 12:00 the next day; The system simultaneously collected data showing that aging furnace F1 had just completed a 200℃ aging treatment, and the current residual heat temperature was 185℃ (hot furnace); F2 was in a cold furnace state (30℃); F3 was running; F4 was idle; and both overhead cranes No. 1 and No. 2 in the workshop were in standby mode. The system performed time window analysis: orders A and B were both offline at 8:00. Based on the characteristics of 6063 / 6061 alloys, the maximum allowable waiting time is 12 hours, meaning the latest furnace entry time is 20:00 on the same day. S2. Three-dimensional spatial verification and flexible rule-based intelligent batching: The system attempts to combine Order A and Order B into one furnace. First, a three-dimensional spatial verification is performed: Order A is a radiator profile with a large maximum outer circle diameter and thin walls. The system establishes a virtual furnace (F1 effective size 15m×3m×2m). Calculations show that if the conventional bundled loading is used, the volume occupancy of Order A and B, totaling 14 tons, is only 55%. However, the dense stacking of Order A causes the flow field blockage coefficient to exceed the standard, and the top air duct gap is only 100mm (below the 200mm threshold). The system triggers dynamic capacity reduction adjustment. Instead of rejecting the furnace combination, it forces Order A to use layered loading with spacers between layers and lowers the maximum allowable loading capacity of this furnace to 10 tons. The actual arrangement is Order A (5 tons) + Order B (5 tons), totaling 10 tons. The air duct gap is restored to 220mm, and the verification is passed. Subsequently, flexible rule-based intelligent batching is performed: Order A is kept at a standard temperature for 3 hours, and Order B is kept at a standard temperature for 4 hours. The system retrieves the alloy over-aging sensitivity database. The flexible temperature holding time window range for Order A is [3 hours, 3.5 hours]. The system extracts the longest baseline time of 4 hours. After evaluation, 4 hours exceeds the upper limit of 3.5 hours for Order A, posing a risk of over-aging. The system decides to use flexible isolation for unloading: the double-door walking beam aging furnace F4 is automatically selected for fusion. In the layout strategy, the material frame of Order A is marked and arranged at material position 1 near the left furnace door of F4, generating a partial isolation unloading plan: the entire furnace is kept at a temperature of 4 hours. At the 3.5-hour mark, the left furnace door of F4 is opened, and the walking beam mechanism moves Order A from material position 1 out separately. Then the furnace door is closed, and Order B continues to be kept at a temperature until the 4-hour mark before unloading. This generates batch C to be processed (containing Orders A and B). S3. Multi-Constraint Collaborative Modeling and Global Optimization Solution: The scheduling system inputs batch C and other queued batches in the workshop into the multi-furnace collaborative scheduling model. The model aims to minimize the total completion time and energy consumption. When allocating equipment, the constraint of waste heat utilization due to heat decay is introduced: F1 currently has a waste heat of 185℃, the target temperature is 200℃, and the temperature difference is only 15℃. The system assigns F1 a very high "waste heat matching degree" weight; while F2 has a temperature difference as high as 170℃, and the energy consumption penalty for heating is extremely large. The algorithm prioritizes allocating subsequent low-temperature batches to F1, while also introducing logistics design constraints. Preparing timing mutual exclusion constraints: The system sets constraints on the loading time of batch C (expected to start at 9:00) and the unloading time of another furnace F2 (expected to start at 9:10), forcing that the time difference between the two must be greater than the overhead crane handling buffer time (5 minutes), and ensuring that no more than 2 tasks occupy the overhead crane at the same time. Finally, an improved genetic algorithm is used to solve the problem and output the globally optimal production scheduling Gantt chart: Batch C is assigned to F4, scheduled to enter the furnace at 9:00, and end at 13:00 (mid-process unloading of furnace A) to 13:30 (unloading of furnace B); S4. Digital Twin Thermal Field Simulation and Adaptive Curve Correction: Before the execution command is issued at 8:50, the system calls the digital twin thermal field model to simulate the loading of batch C (10 tons of complex cross-section profile) into F4. The simulation prediction shows that due to the large loading volume and high radiator resistance, the actual heating curve is severely delayed, reaching 200℃ 12 minutes later than the standard setting curve, and there may be an 8℃ overshoot in the initial stage of the heat preservation section. Based on this, the system performs adaptive correction: increasing the upper limit of the heating section power output of the bottom PLC of F4 from 80% to 100%; extending the set holding time of the temperature equalization section by 12 minutes; and dynamically reducing the proportional coefficient of the PID of the heat preservation section to suppress overshoot. After correction, the system recalculates the actual total consumption time of batch C, which has increased by 12 minutes. The planned furnace exit time of F4 is then updated to 13:42, and subsequent production scheduling nodes are postponed. The corrected process curve is sent to the PLC of F4 through OPC UA. S5. Collaborative Execution and Edge-Level Self-Healing Rescheduling: At 9:00, the logistics control system receives instructions and dispatches crane 1 to lift the material frame of batch C to the F4 loading platform according to the system's specified 3D layout (order A is at material position 1). Before loading, the RFID reader scans the material frame label and compares it with the production schedule. The system controls the F4 furnace door to close, and the PLC starts heating according to the corrected curve. At 11:30, the edge computing gateway next to the F4 furnace detects that the thermocouple temperature data in the middle of the furnace suddenly drops to room temperature (thermocouple failure). The edge gateway executes a local hard interlock within 5 milliseconds, instantly cutting off the F4 heating source and reporting a "sensor thermocouple failure" exception code to the scheduling system. The scheduling system immediately triggers local self-healing rescheduling. Mark F4 as unavailable and freeze its subsequent tasks; For batch C still inside furnace F4, the system extracts its temperature-time curve before 11:30 and calculates that the equivalent holding time has been completed as 1.5 hours. The system determined that the thermocouple was broken and needed maintenance, and the materials had to be transferred. The system searched among the remaining available furnaces and found that F1 had just been emptied and still had residual heat. The system generated a reset command and dispatched crane No. 2 to urgently move batch C from F4 to F1. Based on the remaining 2.5 hours of insulation equivalent, the system reissued the insulation curve for F1. For another queue, D, which was originally scheduled to enter F4 at 11:50, the system partially shifted and rearranged the queue to the right in F2 and F3, reassigning it to F2, and simultaneously updated the Gantt chart on the site dashboard, completing the self-healing closed loop and avoiding a complete shutdown.

Claims

1. A method for coordinated scheduling and control of multiple furnaces in the aging heat treatment of aluminum alloy profiles, characterized in that, Includes the following steps: S1. Global Status Awareness and Time Window Analysis: Acquire data on orders awaiting processing, real-time status data of each aging furnace, and status data of workshop logistics equipment; extract the physical characteristics and process requirements of profiles based on the data on orders awaiting processing, and calculate the time window constraints for each order; S2. Three-dimensional spatial verification and flexible rule intelligent batching: Based on the physical characteristics of the profile, combined with the internal space dimensions of the aging furnace and the constraints of the hot air circulation flow field, three-dimensional spatial verification is performed, and according to process compatibility and flexible heat preservation time window rules, scattered orders are aggregated to generate physically feasible batches to be processed. S3. Multi-constraint collaborative modeling and global optimization solution: Construct a multi-furnace collaborative scheduling model, take the batch to be processed as input, aim to minimize the maximum completion time, energy consumption and order overdue penalty, and introduce constraints on the utilization of waste heat from the heat decay of the aging furnace and the time sequence mutual exclusion constraints of logistics equipment, and solve to generate the globally optimal scheduling scheme; S4. Digital Twin Thermal Field Pre-simulation and Curve Adaptive Correction: Before executing the scheduling scheme, the furnace loading scheme in the global optimal scheduling scheme is simulated and pre-simulated based on digital twin technology. The set process curve of the underlying controller is adaptively corrected and issued according to the actual thermal response curve predicted by the simulation. S5. Collaborative Execution and Edge-Level Self-Healing Rescheduling: Workshop logistics equipment performs handling and furnace loading according to the issued scheduling plan, and the aging furnace bottom controller performs heat treatment according to the modified set process curve; during the execution process, the equipment status is monitored in real time through edge computing, and a local self-healing rescheduling mechanism is triggered when an anomaly occurs.

2. The method for coordinated scheduling and control of multiple furnaces for aging heat treatment of aluminum alloy profiles as described in claim 1, characterized in that, In step S2, a three-dimensional spatial verification is performed based on the physical characteristics of the profile, combined with the internal space dimensions of the aging furnace and the constraints of the hot air circulation flow field. The specific three-dimensional verification is as follows: S21. Parametric modeling: Spatial modeling of the effective furnace chamber dimensions, material rack layer spacing and rated load of the aging furnace, and extraction of the length, maximum circumscribed circle diameter, wall thickness and cross-sectional complexity coefficient of the profiles to be batched; S22. Geometric and load-bearing verification: Verify whether the profile length exceeds the effective length of the furnace, calculate whether the total volume of the stacked profiles occupies the effective volume of the furnace exceeding the first set threshold, and verify whether the total weight of the profiles exceeds the rated load-bearing capacity. S23. Flow field resistance assessment: Calculate the flow field blockage coefficient by combining the profile cross-sectional complexity coefficient, the maximum circumscribed circle diameter and the stacking density; verify whether the proposed furnace installation scheme has reserved the top air duct gap and side return air channel to meet the second set threshold; and verify whether the gap between the bundled profiles meets the minimum threshold for hot air penetration. S24. Dynamic capacity reduction adjustment: When the geometry and load-bearing verification pass but the flow field resistance assessment fails, the system does not reject the current order combination, but triggers the capacity reduction mechanism to dynamically reduce the maximum allowable furnace loading limit of the batch scheme, and / or generate a three-dimensional arrangement strategy of forced layered charging and adding interlayer pads. S25. Output Results: The output includes the final upper limit of furnace loading, three-dimensional arrangement method and wind resistance warning level of the batch to be processed, which will serve as the input parameters for subsequent multi-furnace collaborative scheduling modeling.

3. The method for coordinated scheduling and control of multiple furnaces for aging heat treatment of aluminum alloy profiles as described in claim 1, characterized in that, Step S2, which aggregates orders based on process compatibility and flexible insulation time window rules, specifically includes: Flexible range definition: Obtain the standard heat preservation time for each order profile based on alloy grade and mechanical performance requirements, and combine it with the pre-stored alloy over-aging sensitivity database to convert the standard heat preservation time from a rigid fixed value into a flexible heat preservation time window range with upper and lower limits. Baseline time extraction: During the batch aggregation stage, when it is determined that the target insulation temperature of multiple orders is within the preset temperature difference compatibility range, but the standard insulation time is different, the longest standard insulation time among the candidate orders for the combined furnace is extracted as the baseline insulation time. Attenuation risk quantification assessment: For orders to be combined with furnaces with a short standard holding time, calculate the excess holding time between the benchmark holding time and the standard holding time, and determine whether the benchmark holding time exceeds the upper limit of the flexible holding time window range for the short-time order. Furnace merging decision and isolated unloading plan generation: If the reference holding time does not exceed the upper limit of the flexible holding time window range of the shorter time order, furnace merging is allowed and unloading is carried out uniformly according to the reference holding time; When the upper limit is exceeded, the double-door walking beam aging furnace will be automatically selected for merging and a local isolation unloading plan will be generated. The plan includes: marking the material frame position of the shorter time order, setting the temporary unloading time node in the middle, and controlling the corresponding side furnace door to open to remove the marked material frame separately.

4. The method for coordinated scheduling and control of multiple furnaces in the aging heat treatment of aluminum alloy profiles as described in claim 1, characterized in that, Step S3 introduces constraints on the utilization of waste heat from the aging furnace and the time-series mutual exclusion constraints on logistics equipment, and solves to generate the globally optimal scheduling scheme, as detailed below: Special constraints are constructed: a constraint on the utilization of waste heat from the thermal decay of the aging furnace is introduced, and the difference between the current furnace temperature of the idle aging furnace and the target process temperature of the batch to be processed is calculated in real time and converted into a heating energy consumption penalty. The smaller the difference, the higher the fitness weight is assigned to the batch. A mutual exclusion constraint on the timing of logistics equipment is introduced, which sets that the loading and unloading time windows of any two aging furnaces cannot overlap, and there must be an interval between their start times that is greater than or equal to the buffer time of the logistics equipment. At the same time, the number of tasks occupying the same logistics equipment at the same time is limited to no more than the total number of physical equipment in the workshop. Algorithm initialization and iterative solution: A fitness evaluation model with multiple objective functions, including minimizing the maximum completion time, total energy consumption, and order overdue penalty, is constructed. An improved heuristic algorithm is adopted, combining the earliest delivery date priority, the shortest processing time priority, and the residual heat matching degree as rules to generate the initial population. Based on the fitness evaluation model and the constraints, the individuals in the population are evaluated, and global iterative optimization is performed through crossover and mutation operations. Solution Output: After iteration to the termination condition, the optimal individual is output. The solution decodes and generates a globally optimal production scheduling Gantt chart that includes batch allocation sequence, specified time furnace number, planned furnace entry time, planned furnace exit time, and logistics equipment scheduling sequence.

5. The method for coordinated scheduling and control of multiple furnaces in the aging heat treatment of aluminum alloy profiles as described in claim 1, characterized in that, In step S4, the set process curve of the underlying controller is adaptively corrected and distributed based on the actual thermal response curve predicted by simulation. The specific steps are as follows: S41. Curve Comparison and Deviation Quantification: The actual thermal response curve predicted by the digital twin thermal field simulation is compared with the standard set process curve, and the deviation of key feature nodes is extracted. The deviation includes the temperature lag in the heating stage, the target temperature attainment delay time in the temperature equalization stage, and the maximum overshoot in the heat preservation stage. S42. Adaptive compensation for setting curve: Feedforward compensation correction is performed on the setting process curve of the bottom controller according to the deviation amount. The specific correction strategies include: increasing the upper limit of power output and the setting value of heating slope in the heating stage to compensate for temperature lag; extending the setting holding time of the uniform temperature section to ensure that the actual temperature of the furnace core profile reaches the target value; and dynamically adjusting the PID control parameters of the heat preservation section to suppress overshoot caused by thermal inertia. S43. Production schedule linkage update: Based on the corrected set process curve, recalculate the actual total heat treatment time required for this batch, and synchronously transmit the actual total heat treatment time back to the global scheduling model to dynamically update the planned furnace exit time of the aging furnace and the production schedule time nodes of subsequent batches to be processed. S44. Command Issuance and Execution: The corrected set process curve parameters and PID control parameters are sent to the corresponding aging furnace's underlying PLC controller via the industrial communication protocol, so that the PLC drives the heating element and circulating fan to perform heat treatment operations according to the corrected curve.

6. The method for coordinated scheduling and control of multiple furnaces in the aging heat treatment of aluminum alloy profiles as described in claim 1, characterized in that, The workshop logistics equipment performs handling and furnace loading according to the issued scheduling plan. The handling and furnace loading specifically includes: The logistics control system receives dispatch instructions and dispatches the overhead crane to transport the material frame to the designated aging furnace; Before loading the furnace, the identity of the material frame is identified by RFID and barcode scanning equipment and compared with the production schedule. When the materials do not match, the system refuses to issue a furnace door closing command and triggers an alarm; when they match, the system automatically controls the furnace door to close and sends back a heating ready signal.

7. The method for coordinated scheduling and control of multiple furnaces for aging heat treatment of aluminum alloy profiles as described in claim 1, characterized in that, In step S5, a local self-healing rescheduling mechanism is triggered when an anomaly occurs. The mechanism is as follows: The edge computing gateway deployed next to the aging furnace collects temperature and air pressure data at multiple points in the furnace in real time at a millisecond frequency. When it is determined that the temperature of the insulation section exceeds the safety threshold, the heating rate is abnormal, or the sensor is disconnected, the edge gateway executes local hard interlock control, instantly cuts off the heating source, and simultaneously reports the abnormal event code and fault type to the scheduling system. Upon receiving the abnormal event, the scheduling system immediately marks the status of the faulty aging furnace as unavailable, automatically freezes all subsequent production schedules for the furnace, and suspends the issuance of handling tasks involving the furnace to the logistics equipment. For batches affected by the anomaly and still inside the faulty furnace, the system extracts the actual temperature-time history curves before the interruption and calculates the equivalent holding time based on the heat treatment kinetic model. If it is determined that the fault can be eliminated in a short time, the subsequent insulation section will be extended adaptively according to the remaining equivalent time after the equipment is repaired. If it is determined that materials need to be transferred, a cooling reset command is generated and the nearest available hot furnace is prioritized for reloading. For queued batches that were originally scheduled to be assigned to faulty furnaces but have not yet been put into the furnace, their time windows and process constraints are extracted, and the scheduling algorithm is re-triggered in the remaining m-1 available aging furnaces to perform local right shift rearrangement; during the rearrangement process, the timing mutual exclusion constraints of logistics equipment are checked simultaneously to avoid traffic conflicts, and an updated local production scheduling plan is generated and sent to the logistics control system and on-site dashboard to complete the self-healing closed loop.

8. A multi-furnace coordinated scheduling and control equipment for aging heat treatment of aluminum alloy profiles, characterized in that, include: Memory, used to store computer programs; A processor is configured to implement the steps of the multi-furnace collaborative scheduling control method for aging heat treatment of aluminum alloy profiles as described in any one of claims 1 to 7 when executing the computer program.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the multi-furnace collaborative scheduling and control method for aging heat treatment of aluminum alloy profiles as described in any one of claims 1 to 7.

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