Aluminum bar production line automation scheduling method and system

Abstract

This invention discloses an automated scheduling method and system for an aluminum rod production line, relating to the field of aluminum profile extrusion manufacturing. The method includes: acquiring production orders, equipment status, and process standards; dynamically batching orders based on a hot-state-changeover cost joint evaluation model to generate a pre-arranged sequence; establishing a hot-state lifecycle model for each aluminum rod in the pre-arranged sequence to generate an elastic discharge time window with attached hot-state constraint fingerprints; utilizing the elastic discharge time window with attached hot-state constraint fingerprints to implement multi-level hot-state emergency interlock control on the heating furnace outlet mechanism to obtain a pre-arranged sequence with hot-state safety attributes; detecting the physical idle status of the heating furnace outlet, extruder receiving port, and die installation position to generate an original resource dataset. This invention achieves automated, high-precision, and low-risk scheduling of the entire aluminum rod production line process through discrete control pulse sequence distribution and hot-state safety margin monitoring.

Description

Technical Field

[0001] This invention relates to the field of aluminum profile extrusion manufacturing, and in particular to an automated scheduling method and system for an aluminum rod production line. Background Technology

[0002] In the aluminum profile extrusion manufacturing industry, production scheduling technology has gradually evolved from manual experience-based decision-making to automated scheduling systems based on operations research algorithms. Existing advanced planning and scheduling typically treat order delivery dates, equipment occupancy times, and die changeover times as deterministic parameters, using heuristic rules or intelligent optimization algorithms for global optimization to improve equipment utilization and production efficiency. Relying on industrial Internet of Things (IoT) technology, modern scheduling systems have the ability to collect real-time data on the operating status of equipment such as heating furnaces and extruders, and can passively adjust the established plan based on equipment failures or idle status to maintain the basic continuity of the production line.

[0003] Existing technologies have significant limitations in handling the spatiotemporal coupling constraints of heat-sensitive materials. Traditional scheduling models, when dealing with the aluminum rod heating process, often treat the heating furnace as a simple time buffer, focusing only on the busy / idle status of the equipment while neglecting refined modeling of the thermal lifecycle of individual aluminum rods. Specifically, existing technologies struggle to quantify the temperature drop curve of the aluminum rod from furnace exit to extrusion and its impact on material plasticity. This results in the physical accessibility verification stage where the system can only determine whether the equipment is available, but not whether the thermal condition permits it. This can easily lead to temperatures dropping below the minimum extrudable temperature threshold due to transport delays, resulting in unnecessary remelting or scrapping losses. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an automated scheduling method for aluminum rod production lines to solve the problem that existing scheduling technologies, due to the lack of refined modeling of the thermal life cycle during the transfer of heat-sensitive materials, cannot accurately determine the legality of the thermal state under spatiotemporal coupling constraints, thus causing losses such as remelting or scrapping.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides an automated scheduling method for an aluminum rod production line, comprising: acquiring production orders, equipment status and process standards; dynamically batching orders based on a hot-change-cost joint evaluation model; and generating a pre-arranged sequence.

[0008] A hot life cycle model is established for each aluminum rod in the pre-arranged sequence, and an elastic discharge time window with hot constraint fingerprint is generated.

[0009] By utilizing the elastic discharge time window with attached thermal constraint fingerprint, multi-level thermal emergency interlock control is implemented on the heating furnace outlet mechanism to obtain a pre-arranged sequence of thermal safety attributes, detect the physical idle status of the heating furnace outlet, extruder receiving port and die mounting position, and generate the original resource dataset.

[0010] For each aluminum rod in the pre-arranged sequence, physical space reachability is checked, and three-stage latching is performed based on thermal constraint fingerprints to generate an executable contract set. The optimal combination of spatial connectivity with the goal of minimizing thermal loss is selected to generate a scheduling instruction table.

[0011] The scheduling instruction table is converted into equipment control signals and sent to the heating furnace, extruder, and mold storage management unit to monitor the production progress.

[0012] As a preferred embodiment of the automated scheduling method for the aluminum rod production line described in this invention, the pre-arranged sequence includes:

[0013] The production order, equipment status, and process standards are input into the hot-change cost joint evaluation model to obtain the hot-change cost joint evaluation result. The orders are then dynamically batched. By quantifying the hot-change connection benefits and changeover operation losses between different orders, the optimal aggregation scheme is determined, and the dynamic batching result is obtained.

[0014] Based on the dynamic batching results, the thermal life cycle of materials within the group and the processing capacity of equipment are used as sequence constraints to adjust the execution order of production orders within the group, resulting in a pre-arranged sequence.

[0015] As a preferred embodiment of the automated scheduling method for the aluminum rod production line described in this invention, the elastic discharge time window with attached thermal constraint fingerprint includes,

[0016] Based on each aluminum rod in the pre-arranged sequence, the material parameters, cross-sectional specifications and current position information of the aluminum rod in the heating furnace section are collected to construct a high-energy state evolution field with micro-lattice vibration frequency and electron thermal motion intensity as variables.

[0017] The high-energy state evolution field uses a virtual heat source superposition algorithm to deploy virtual heat sources with multi-frequency oscillation characteristics along the axial and radial directions inside the aluminum rod to drive the thermal energy to oscillate back and forth. Based on the anti-entropy heat flow tracking mechanism, the heat flow is forced to converge in the opposite direction along the preset negative thermodynamic gradient direction.

[0018] The thermal state evolution trajectory of the aluminum rod in the time dimension, axial position dimension and radial position dimension is mapped to a high-dimensional feature space by using the multidimensional phase space mapping method, and the transient thermal potential energy distribution of the aluminum rod in the entire heating cycle is reconstructed to obtain the thermal life cycle model.

[0019] The thermal life cycle model analyzes the nonlinear coupling characteristics of latent heat accumulation inside the aluminum rod and heat dissipation on the surface, and inversely reconstructs the heat energy accumulation pattern and dissipation path during different heating periods to obtain a thermal potential energy distribution cloud map of the aluminum rod that characterizes the instantaneous thermal energy distribution state of the entire aluminum rod.

[0020] The thermal potential energy distribution cloud map of the aluminum rod is matched with the target extrusion temperature range in the process standard in a high-dimensional topology. The continuous residence time of the aluminum rod in the topological isomorphic region is extracted. The thermal decay trajectory of the aluminum rod in a non-ideal conveying environment is deduced by constructing a virtual cooling channel, and the thermal constraint fingerprint is obtained.

[0021] Tensor fusion is performed on the thermal potential energy distribution data in the thermal constraint fingerprint and thermal life cycle model. By applying nonlinear stretching transformation of the time axis, the thermal energy collapse process under abnormal delay of the heating furnace outlet mechanism and conveying obstruction is simulated. The extended range of discharge time for aluminum rod thermal potential energy not lower than the critical threshold is defined, and the elastic discharge time window with thermal constraint fingerprint is obtained.

[0022] As a preferred embodiment of the automated scheduling method for the aluminum rod production line described in this invention, the pre-arranged sequence of the thermal safety attributes includes:

[0023] By utilizing the elastic discharge time window with attached thermal constraint fingerprint, the degree of thermal deviation is determined by comparing the difference between the lower limit threshold of the elastic discharge time window and the action response delay of the heating furnace outlet mechanism, and the thermal deviation determination result is obtained.

[0024] The hot deviation determination result and the elastic discharge time window with attached hot constraint fingerprint are jointly input into the multi-level hot emergency interlock control logic. By triggering the start, stop and speed adjustment commands of the heating furnace outlet mechanism in stages, a pre-arranged sequence of hot safety attributes is obtained.

[0025] As a preferred embodiment of the automated scheduling method for the aluminum rod production line described in this invention, the original resource dataset includes:

[0026] The pre-arranged sequence of thermal safety attributes and the status information of the heating furnace outlet mechanism are used together as the detection benchmark. By scanning the occupancy and release events of the heating furnace outlet, the extruder inlet, and the die mounting position, the physical idle status record is obtained. After spatiotemporal alignment processing, the original resource dataset is generated.

[0027] As a preferred embodiment of the automated scheduling method for the aluminum rod production line described in this invention, the scheduling instruction table includes:

[0028] For each aluminum rod in the pre-arranged sequence based on the thermal safety attribute, the elastic discharge time window with the attached thermal constraint fingerprint of the aluminum rod is called, and the elastic discharge time window is nonlinearly topologically stretched on the high-dimensional time axis to obtain the topologically stretched elastic discharge time window.

[0029] The elastic discharge time window after topological stretching is compared with the conveying path time from the furnace outlet to the extruder inlet using quantum entanglement. The probability amplitude of the conveying path time collapsing into the time window is calculated to obtain the physical space reachability verification result.

[0030] The physical space accessibility verification result is matched with the lowest extrudable temperature threshold in the thermal constraint fingerprint using anti-causal locking. The causal chain branches that cause the temperature threshold to be breached are removed in reverse along the time axis. The first locking screening is performed to obtain the first-level screening result.

[0031] The first-level screening results are compared with the idle time periods of the heating furnace outlet, extruder inlet, and die installation position in the original resource dataset. A multi-dimensional spatiotemporal convolution limit operation is performed. At the spatiotemporal folds, the second-stage locking screening is performed to remove aluminum bars with hidden spatiotemporal conflicts, thus obtaining the second-level screening results.

[0032] The second-level screening results are compared with the mold-alloy adaptation rules in the process standard using non-Boolean logic fuzzy membership determination. The third-stage locking screening is then performed to remove aluminum bars that are in the process taboo edge state, thus obtaining the third-level screening results.

[0033] The results of the third-level screening are recombined and summarized through spatiotemporal slicing to form an executable contract set, thus obtaining the executable contract set;

[0034] Each contract item in the executable contract set is integrated with the thermal potential energy decay curve in the thermal constraint fingerprint using a complex plane loop. The singularity energy value enclosed by the integration path is extracted as a quantitative index to obtain the thermal loss evaluation value.

[0035] The thermal loss assessment value is matched with the device idle time period in the original resource dataset to find the optimal combination of contract terms with the highest cross-dimensional connectivity under the condition of minimizing the total edge weight of the hypergraph.

[0036] The optimal combination of spatial connectivity is sorted by time axis renormalization and then arranged in sequence to obtain the scheduling instruction table.

[0037] As a preferred embodiment of the automated scheduling method for the aluminum rod production line described in this invention, the monitoring of production progress includes:

[0038] The scheduling instruction table is reordered and arranged sequentially after time axis renormalization to obtain the scheduling instruction table. The timing action logic in the scheduling instruction table is mapped to the discrete control pulse sequence of the heating furnace, extruder and mold library management unit to obtain the equipment control signal.

[0039] The equipment control signals are sent to the heating furnace, extruder, and mold library management unit to drive the physical actuators and obtain physical execution feedback status.

[0040] The physical execution feedback status is dynamically compared with the contract entries in the scheduling instruction table to obtain the production progress deviation value.

[0041] The production progress deviation value is cross-validated in real time with the elastic discharge time window in the thermal constraint fingerprint to obtain the thermal safety margin monitoring result.

[0042] Secondly, the present invention provides an automated scheduling system for an aluminum rod production line, including a batching module, which acquires production orders, equipment status and process standards, dynamically batches orders based on a hot-change cost joint evaluation model, and generates a pre-arranged sequence;

[0043] A module is established to create a hot life cycle model for each aluminum rod in the pre-arranged sequence, generating an elastic discharge time window with hot constraint fingerprints.

[0044] The detection module utilizes the elastic discharge time window with attached thermal constraint fingerprint to implement multi-level thermal emergency interlock control of the heating furnace outlet mechanism, obtains the pre-arranged sequence of thermal safety attributes, detects the physical idle status of the heating furnace outlet, extruder receiving port and die mounting position, and generates the original resource dataset.

[0045] The filtering module performs physical space reachability verification on each aluminum rod in the pre-arranged sequence, performs three-stage latch filtering based on thermal constraint fingerprint, generates an executable contract set, selects the optimal combination of spatial connectivity with the goal of minimizing thermal loss, and generates a scheduling instruction table.

[0046] The monitoring module converts the scheduling instruction table into equipment control signals and sends them to the heating furnace, extruder, and mold storage management unit to monitor the production progress.

[0047] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the automated scheduling method for aluminum rod production lines as described in the first aspect of the present invention.

[0048] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the automated scheduling method for an aluminum rod production line as described in the first aspect of the present invention.

[0049] The beneficial effects of this invention are as follows: By constructing a joint evaluation model for hot-state and changeover costs, orders are dynamically batched, achieving an optimal combination that balances the benefits of hot-state connection and the losses of changeover, thus avoiding energy waste caused by ineffective heating and frequent changeovers at the source; by introducing microscopic lattice vibration and electronic thermal motion variables to establish a hot-state life cycle model and generating an elastic discharge time window with hot-state constraint fingerprints, the transient thermal potential energy distribution of a single aluminum rod is accurately quantified, solving the problem that traditional models cannot characterize nonlinear thermal coupling; and by using the elastic discharge time window to implement multi-level hot-state emergency interlock control, the heating furnace outlet mechanism is gradedly blocked under abnormal operating conditions. With thermal preservation, the risk of low-temperature scrapping caused by transport obstruction is reduced; by performing a three-stage locking and screening based on quantum entangled state comparison and multidimensional spatiotemporal convolution active limit operation on the pre-arranged sequence, the deep coupling of physical space accessibility and thermal legitimacy is realized, eliminating implicit spatiotemporal conflicts and process edge states, and ensuring the executability of the contract set; by quantizing thermal loss through complex plane loop integration and selecting the optimal combination of spatial connectivity through hypergraph matching, the optimal resource allocation under the goal of minimizing thermal loss is realized; by distributing discrete control pulse sequences and monitoring thermal safety margin, the entire process of aluminum rod production line is automated, highly accurate and low-risk scheduled. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart of an automated scheduling method for an aluminum rod production line.

[0052] Figure 2 This is a schematic diagram of an automated scheduling system for an aluminum rod production line. Detailed Implementation

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0054] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0055] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0056] Reference Figures 1-2 As one embodiment of the present invention, this embodiment provides an automated scheduling method for an aluminum rod production line, comprising the following steps:

[0057] S1. Obtain production orders, equipment status, and process standards. Dynamically batch the orders based on the hot-change cost joint evaluation model to generate a pre-arranged sequence.

[0058] S1.1 Input the production order, equipment status and process standard into the hot-change cost joint evaluation model to obtain the hot-change cost joint evaluation result, and dynamically batch the orders. By quantifying the hot-change connection benefits and changeover operation losses between different orders, the optimal aggregation scheme is determined and the dynamic batching result is obtained.

[0059] Furthermore, the information on aluminum rod material, cross-sectional specifications, order quantity, and delivery sequence in the production orders is summarized along with the extruder operating status, die occupancy status, and available furnace capacity information in the equipment status. This information is then combined with the target extrusion temperature range, die adaptation rules, and changeover requirements from the process standards and input into the hot-changeover cost joint evaluation model. This model is used to calculate the hot-state continuity benefit and changeover operation loss for two production orders during continuous production. The hot-state continuity benefit characterizes the degree to which adjacent production orders maintain the hot continuity of aluminum rods during continuous production, while the changeover operation loss is used to represent… The model assesses the downtime, mold replacement, and process adjustment costs incurred during production order switching. A joint evaluation model of hot-state and changeover costs is used to generate a corresponding joint evaluation result based on the benefits of hot-state connection and the losses from changeover operations. Based on this joint evaluation result, production orders are dynamically batched, grouping those with higher hot-state connection benefits and lower changeover operation losses into the same batch. Different production order combinations are continuously iterated and compared to select the combination with higher cumulative hot-state connection benefits and lower cumulative changeover operation losses, thus determining the optimal aggregation scheme and obtaining the dynamic batching result.

[0060] The expression for the benefit of hot connection is:

[0061] ;

[0062] in, For the hot-state connection benefit when two production orders are produced consecutively, This refers to the temperature range that can be connected after the previous production order has been heated or unloaded. This is the target extrusion temperature range for the next production order. This refers to the range of alloy materials or processes compatible with the previous production order. For the range of alloy materials or processes to be adapted for the next production order, For the previous production order, This indicates the next production order.

[0063] The changeover operation loss formula is:

[0064] ;

[0065] in, This refers to the changeover operation losses when two production orders are produced consecutively. This refers to the mold changeover losses incurred when switching from one production order to another. This refers to the process parameter adjustment losses incurred when switching from one production order to another. This refers to the waiting or downtime losses incurred when switching from one production order to another.

[0066] The joint evaluation results of hot-state and replacement costs are as follows:

[0067] ;

[0068] in, The results represent the combined assessment of hot-state and replacement costs.

[0069] S1.2 Based on the dynamic batching results, the thermal life cycle of materials within the group and the processing capacity of equipment are used as sequence constraints to adjust the execution order of production orders within the group, thereby obtaining a pre-arranged sequence.

[0070] Furthermore, based on the dynamic batching results, the hot holding time of aluminum bars, target extrusion temperature range, and expected discharge time of each production order within each batch are sorted and analyzed to obtain the extrusion press processing cycle, heating furnace discharge capacity, and die switching preparation status. Production orders with shorter hot holding times are prioritized before those with longer hot holding times. Production orders whose corresponding equipment processing capacity can continuously handle are arranged adjacently. Production orders with conflicting equipment processing capacity or intermittent hot holding times are adjusted in position until the order of production orders within the group simultaneously meets the hot life cycle requirements and equipment processing capacity requirements, generating an initial processing sequence and obtaining a pre-arranged sequence.

[0071] S2. Establish a thermal life cycle model for each aluminum rod in the pre-arranged sequence and generate an elastic discharge time window with thermal constraint fingerprint.

[0072] S2.1. Based on each aluminum rod in the pre-arranged sequence, collect the aluminum rod material parameters, cross-sectional specifications and current position information of the heating furnace section, and construct a high-energy state evolution field with micro-lattice vibration frequency and electron thermal motion intensity as variables.

[0073] Furthermore, the material parameters of the aluminum rod are used to characterize the differences in thermal conductivity, heat storage, and temperature rise response after the aluminum rod is heated. The cross-sectional specifications are used to characterize the axial and radial heat transfer distance of the aluminum rod. The current location information of the heating furnace section is used to characterize the stage state of the aluminum rod receiving heat in different furnace zones. The internal temperature transfer process of the aluminum rod is discretized and solved based on finite element thermal analysis technology. The heat diffusion state at different locations is calculated based on heat conduction theory. The lattice vibration energy change law during the heating process of the aluminum rod is characterized based on lattice dynamics analysis method. The internal electron thermal motion state and heat transfer capacity of the aluminum rod are characterized based on free electron thermal conduction theory. The micro-lattice vibration frequency reflects the energy activity of the lattice level inside the aluminum rod after heating. The electron thermal motion intensity reflects the internal thermal excitation state and heat transfer capacity of the aluminum rod. Thus, the thermal response of the aluminum rod during the heating process is jointly characterized by material parameters, cross-sectional specifications, current location information of the heating furnace section, micro-lattice vibration frequency, and electron thermal motion intensity, constructing a high-energy state evolution field with micro-lattice vibration frequency and electron thermal motion intensity as variables.

[0074] Specifically, a correlation is established between the externally collectable information of the aluminum rod and its internal thermal activation state, transforming the thermal state expression of the aluminum rod from a single temperature judgment to an energy evolution judgment. The material parameters of the aluminum rod determine its thermal conductivity, the cross-sectional specifications determine the heat diffusion path, and the current location in the heating furnace section determines the heating stage. By variableizing these differences through the microscopic lattice vibration frequency and the intensity of electron thermal motion, the thermal state differences generated by different aluminum rods under the same heating conditions can be more accurately described. For example, aluminum rods of different materials will produce different thermal responses even in the same furnace zone; aluminum rods with different cross-sectional specifications may have different internal heat storage states even if their surface temperatures are similar.

[0075] S2.2 The high-energy state evolution field uses a virtual heat source superposition algorithm to deploy virtual heat sources with multi-frequency oscillation characteristics along the axial and radial directions inside the aluminum rod to drive the thermal energy to oscillate back and forth. Based on the anti-entropy heat flow tracking mechanism, the heat flow is forced to converge in the opposite direction along the preset negative thermodynamic gradient direction.

[0076] Furthermore, the high-energy state evolution field, through a virtual heat source superposition algorithm, determines the heating stage corresponding to each position along the axial direction of the aluminum rod based on the current location information of the heating furnace section; determines the heat transfer path length corresponding to each position in the radial direction of the aluminum rod based on the cross-sectional specifications; determines the thermal conductivity and heat storage capacity corresponding to different positions based on the aluminum rod material parameters; and determines the energy activity level corresponding to different positions based on the microlattice vibration frequency and electron thermal motion intensity. Based on the heating stage, heat transfer path length, thermal conductivity, heat storage capacity, and energy activity level, virtual heat sources with multi-frequency oscillation characteristics are arranged along the axial and radial directions inside the aluminum rod. Positions in the continuously heated region correspond to virtual heat sources in the heat input state, positions in the heat diffusion region correspond to virtual heat sources in the heat transfer state, and positions in the heat diffusion region correspond to virtual heat sources in the heat transfer state. The location of the heat accumulation region corresponds to the virtual heat source in the heat energy retention state. The virtual heat source superposition algorithm drives multiple virtual heat sources to superimpose according to their respective heat energy input state, heat energy transfer state and heat energy retention state. The energy coupling relationship between multiple virtual heat sources simulates the reciprocating oscillation process of the internal heat energy of the aluminum rod between the axial and radial directions, forming the dynamic evolution trajectory of the internal heat energy of the aluminum rod. Based on the anti-entropy heat flow tracking mechanism, the heat flow migration path in the dynamic evolution trajectory is tracked in reverse. The heat flow is converged to the heat energy accumulation region along the preset negative thermodynamic gradient direction. The heat energy accumulation characteristics and heat flow migration characteristics corresponding to the heat energy accumulation region are extracted to obtain the high-energy state evolution field processing results that can reflect the heat energy accumulation trend and heat flow migration direction inside the aluminum rod.

[0077] Specifically, the thermal energy differences between different regions inside the aluminum rod are represented by the arrangement of virtual heat sources along the axial and radial axes. Virtual heat sources with multi-frequency oscillation characteristics can correspond to different heat transfer rhythms, allowing for differentiated representation of fast-response, slow-response, and stagnant regions within the aluminum rod. The value of the anti-entropy heat flow tracking mechanism lies in enhancing the identifiability of heat accumulation areas, preventing surface heat dissipation or localized temperature fluctuations from masking the true heat storage state inside the aluminum rod. The heating rhythms of the aluminum rod's ends, center, and surface regions are not consistent; the virtual heat source superposition algorithm can transform these differences into a traceable thermal energy oscillation process. The anti-entropy heat flow tracking mechanism highlights the concentrated heat energy areas that have a constraining effect on the quality of the output material.

[0078] S2.3. Using the multidimensional phase space mapping method, the thermal state evolution trajectory of the aluminum rod in the time dimension, axial position dimension and radial position dimension is mapped to a high-dimensional feature space, and the transient thermal potential energy distribution of the aluminum rod in the entire heating cycle is reconstructed to obtain the thermal life cycle model.

[0079] Furthermore, the high-energy state evolution field processing results are reconstructed using a multi-dimensional phase space mapping method. The thermal energy accumulation and heat flow migration characteristics at different time points in the high-energy state evolution field processing results are jointly expanded according to the time dimension, axial position dimension, and radial position dimension to form the corresponding thermal state evolution trajectory. The time evolution information, axial heat migration information, and radial heat diffusion information in the thermal state evolution trajectory are mapped to a high-dimensional feature space using the multi-dimensional phase space mapping method, so that the thermal response relationship formed by the aluminum rod at different heating stages, different spatial positions, and different thermal energy states is... The thermal state evolution trajectory is uniformly expressed within the same feature space. In the high-dimensional feature space, continuous correlation analysis is performed on the thermal state evolution trajectory. The thermal energy accumulation area, heat flow migration path and heat diffusion state formed at different time nodes are correlated and recombined to restore the thermal state change process of the aluminum rod from entering the heating furnace to completing the heating process. Based on the correlated and recombined thermal state evolution trajectory, the distribution state of the internal thermal energy of the aluminum rod in the time dimension, axial position dimension and radial position dimension is inverted and reconstructed to obtain the transient thermal potential energy distribution of the aluminum rod at each moment in the entire heating cycle, thus obtaining the thermal life cycle model.

[0080] Specifically, the multidimensional phase space mapping method incorporates the time dimension, axial position dimension, and radial position dimension into the thermal state expression process. This prevents the analysis of heat accumulation, heat flow migration, and heat diffusion as independent information, forming a continuous and interconnected thermal state evolution trajectory within a unified high-dimensional feature space. For example, for location regions with the same temperature value but at different heating stages, the multidimensional phase space mapping method can distinguish between states where heat energy is accumulating and states where heat energy is decaying using the thermal state evolution trajectory. Similarly, for location regions with similar axial temperature distributions but different radial heat diffusion levels, the multidimensional phase space mapping method can identify different thermal potential energy states through trajectory differences in the high-dimensional feature space. By mapping the thermal state evolution trajectory to a high-dimensional feature space and then reconstructing the transient thermal potential energy distribution, not only is the information about the thermal state changing over time preserved, but also the continuous relationship of the thermal state during spatial migration is maintained. This allows the thermal life cycle model to fully reflect the thermal potential energy evolution process of the aluminum rod throughout the entire heating cycle.

[0081] S2.4 The thermal life cycle model analyzes the nonlinear coupling characteristics of latent heat accumulation inside the aluminum rod and heat dissipation on the surface, and inversely reconstructs the heat energy accumulation pattern and dissipation path in different heating periods to obtain the aluminum rod thermal potential energy distribution cloud map that characterizes the instantaneous thermal energy distribution state of the entire aluminum rod.

[0082] Furthermore, by analyzing the nonlinear coupling characteristics of latent heat accumulation inside the aluminum rod and surface heat dissipation using a thermal life cycle model, the mutual influence between the internal heat storage process and the surface heat dissipation process of the aluminum rod is identified. The latent heat accumulation inside the aluminum rod represents the energy reserve formed during the heating process, while the surface heat dissipation represents the heat loss between the aluminum rod and the external environment. By reversibly reconstructing the heat accumulation pattern and dissipation path during different heating periods, the instantaneous thermal energy distribution state of the aluminum rod in different regions and at different time points can be obtained, resulting in a thermal potential energy distribution cloud map characterizing the instantaneous thermal energy distribution state of the entire aluminum rod.

[0083] Specifically, the internal latent heat accumulation and surface heat dissipation are treated as coupled objects for unified expression, avoiding misjudgments caused by judging the thermal state of the aluminum rod solely based on surface temperature. The actual extrusion quality of the aluminum rod depends not only on the surface temperature but also on the sufficiency of internal heat storage and the stability of the heat dissipation path. A decrease in the surface temperature of the aluminum rod does not necessarily mean that the aluminum rod as a whole is not suitable for extrusion; the latent heat inside the aluminum rod may still maintain an extrudable state. Conversely, a surface temperature that meets the standard does not necessarily indicate that the internal thermal state of the aluminum rod is sufficient. This is achieved by reconstructing the heat accumulation pattern and dissipation path in reverse.

[0084] S2.5. The thermal potential energy distribution cloud map of the aluminum rod is matched with the target extrusion temperature range in the process standard in a high-dimensional topology. The continuous residence time of the aluminum rod in the topological isomorphic region is extracted. The thermal decay trajectory of the aluminum rod in a non-ideal conveying environment is deduced by constructing a virtual cooling channel, and the thermal constraint fingerprint is obtained.

[0085] Furthermore, by comparing the correspondence between the thermal energy distribution states of different regions in the aluminum rod's thermal potential energy distribution cloud map and the target extrusion temperature range, the topologically isomorphic regions where the aluminum rod meets the extrusion requirements are identified. By statistically analyzing the duration at which the aluminum rod remains within the target extrusion temperature range at each continuous time point in the thermal potential energy distribution cloud map, the continuous residence time of the aluminum rod within the topologically isomorphic region is extracted to determine the time range within which the aluminum rod can stably maintain a suitable extrusion state. By constructing a virtual cooling channel, the waiting, conveying obstruction, and external heat dissipation under non-ideal conveying environments are respectively converted into heat loss paths after the aluminum rod leaves the heating furnace, where waiting corresponds to a static heat dissipation path, and conveying obstruction corresponds to extended exposure heat dissipation. The path is defined as the external heat dissipation path corresponding to the environmental heat exchange path. Based on the thermal energy distribution state at each continuous time point in the thermal potential energy distribution cloud map of the aluminum rod, the thermal potential energy change state after heat loss is calculated point by point along the time continuity direction, forming the thermal decay trajectory of the aluminum rod changing from the current thermal potential energy state to a low thermal potential energy state. The matching result of the target extrusion temperature range is used as the thermal available boundary. The continuous trajectory segments in the thermal decay trajectory that are still within the target extrusion temperature range are determined as extrudable trajectory segments. The positions in the thermal decay trajectory that are outside the target extrusion temperature range are determined as thermal failure points. The extrudable trajectory segments, thermal failure points, and the continuous residence time in the topological isomorphic region are correlated to obtain the thermal constraint fingerprint.

[0086] S2.6. Tensor fusion is performed on the thermal potential energy distribution data in the thermal constraint fingerprint and thermal life cycle model. By applying nonlinear stretching transformation of the time axis, the thermal energy collapse process under abnormal delay of the heating furnace outlet mechanism and conveying obstruction is simulated. The extended range of the discharge time when the thermal potential energy of the aluminum rod is not lower than the critical threshold is defined, and the elastic discharge time window with thermal constraint fingerprint is obtained.

[0087] Furthermore, by performing tensor fusion on the thermal potential energy distribution data in the thermal constraint fingerprint and the thermal life cycle model, the target extrusion temperature range maintenance capability reflected in the thermal constraint fingerprint and the thermal potential energy distribution process reflected in the thermal life cycle model are unified. The tensor fusion process uses time series, axial position, radial position and thermal potential energy state as fusion dimensions, and maps the extrudable trajectory segment, thermal failure point and continuous residence time in the topological isomorphic region in the thermal constraint fingerprint to the thermal potential energy distribution data in the thermal life cycle model, so that each time node and each spatial location has a corresponding thermally available boundary. A nonlinear time-axis stretching transformation is applied to simulate the thermal collapse process of aluminum bars under abnormal delays in the furnace outlet mechanism and conveying stagnation conditions. This nonlinear time-axis stretching transformation can be achieved using an exponential time stretching method, transforming the time progression under normal discharge conditions into a non-uniform time progression under delayed discharge conditions. This stretches the time periods near the discharge waiting stage and conveying stagnation stage for longer periods. The thermal potential energy changes of each unit are iteratively extracted along the time series and spatial path, mapping the coupling relationship between local energy dissipation and global heat flux into the virtual environment. Predict the contraction and collapse trends of thermal energy distribution; during the prediction process, the thermal potential energy state that is still within the target extrusion temperature range after tensor fusion is determined as the extrudable state, and the thermal potential energy state that is below the critical threshold after nonlinear stretching transformation along the time axis is determined as the inextrudable state. The boundary of the change of aluminum rod thermal potential energy from the extrudable state to the inextrudable state is determined along the time series; based on the continuous time range before the boundary where the aluminum rod thermal potential energy is not lower than the critical threshold, the extrusion time range where the aluminum rod thermal potential energy is not lower than the critical threshold is defined, and the elastic extrusion time window with thermal constraint fingerprint is obtained.

[0088] S3. Utilize the elastic discharge time window with attached thermal constraint fingerprint to implement multi-level thermal emergency interlock control on the heating furnace outlet mechanism, obtain the pre-arranged sequence of thermal safety attributes, detect the physical idle status of the heating furnace outlet, extruder receiving port and die installation position, and generate the original resource dataset.

[0089] S3.1. Using the elastic discharge time window with attached thermal constraint fingerprint, the degree of thermal deviation is determined by comparing the difference between the lower limit threshold of the elastic discharge time window and the action response delay of the heating furnace outlet mechanism, and the thermal deviation determination result is obtained.

[0090] Furthermore, utilizing the elastic discharge time window with attached thermal constraint fingerprint, the time boundary information corresponding to the elastic discharge time window is extracted, and the action response delay of the heating furnace outlet mechanism in the current operating state is obtained. The action response delay is mapped to the time coordinate system corresponding to the elastic discharge time window. By extracting the time difference between the action response delay and the lower limit threshold of the elastic discharge time window, the degree of deviation of the aluminum rod from the thermal safety boundary during the actual discharge process is analyzed. When the action response delay continuously approaches the lower limit threshold of the elastic discharge time window, the lower limit threshold refers to the minimum safe value allowed by any index or parameter (such as temperature, pressure, thermal energy, discharge time), indicating that the aluminum rod is approaching the boundary area defined by the thermal constraint fingerprint. When the action response delay is far from the lower limit threshold of the elastic discharge time window, it indicates that the aluminum rod is still in a relatively stable thermal state maintenance stage. Combining the thermal decay characteristics recorded in the attached thermal constraint fingerprint, the thermal change trend corresponding to different differences is correlated and analyzed to obtain the thermal deviation judgment result that reflects the current thermal safety level of the aluminum rod.

[0091] S3.2 The hot deviation judgment result and the elastic discharge time window with attached hot constraint fingerprint are jointly input into the multi-level hot emergency interlock control logic. By triggering the start-stop and speed adjustment commands of the heating furnace outlet mechanism in stages, the pre-arranged sequence of hot safety attributes is obtained.

[0092] Furthermore, the multi-level thermal emergency interlock control logic dynamically evaluates the operating status of the heating furnace outlet mechanism based on the deviation level corresponding to the thermal deviation judgment result and the remaining available time interval corresponding to the flexible discharge time window. When the thermal deviation is within the allowable range, the multi-level thermal emergency interlock control logic maintains the current discharge rhythm; when the thermal deviation shows an increasing trend, the multi-level thermal emergency interlock control logic adjusts the action frequency and operating speed of the heating furnace outlet mechanism; when the thermal deviation further expands and approaches the thermal constraint fingerprint limit boundary, the multi-level thermal emergency interlock control logic triggers priority discharge action or suppresses unnecessary waiting action. By rearranging the control priorities corresponding to different aluminum bars, aluminum bars that meet the thermal safety requirements are given priority for discharge opportunities, and a new execution order is formed according to the thermal risk level, resulting in a pre-arranged sequence of thermal safety attributes.

[0093] S3.3 The pre-arranged sequence of hot safety attributes and the status information of the heating furnace outlet mechanism are used together as the detection benchmark. By scanning the occupancy and release events of the heating furnace outlet, extruder inlet, and die mounting position, the physical idle status record is obtained. After spatiotemporal alignment processing, the original resource dataset is generated.

[0094] Furthermore, according to the expected discharge sequence corresponding to each aluminum bar in the pre-arranged sequence of thermal safety attributes, the occupancy and release events of the heating furnace discharge port, extruder receiving port, and die mounting position are continuously scanned. During the scanning process, the state change information such as the start of occupancy and end of release of the heating furnace discharge port, the start of receiving and end of receiving of the extruder receiving port, and the start of use and completion of release of the die mounting position are recorded. Correspondence is established according to the occurrence time of the events, and the physical idle state records formed at each location are uniformly time-calibrated and spatially correlated, so that the state records generated at different locations can be mapped to the same time base. The time difference and state misalignment between different locations are eliminated through spatiotemporal alignment processing, forming a unified resource state set reflecting the actual idle status of the heating furnace discharge port, extruder receiving port, and die mounting position, generating the original resource dataset.

[0095] S4. Perform physical space reachability verification on each aluminum rod in the pre-arranged sequence, perform three-stage latching screening based on thermal constraint fingerprint, generate an executable contract set, select the optimal combination of spatial connectivity with the goal of minimizing thermal loss, and generate a scheduling instruction table.

[0096] S4.1 For each aluminum rod in the pre-arranged sequence based on the thermal safety attribute, call the elastic discharge time window with attached thermal constraint fingerprint corresponding to the aluminum rod, and perform nonlinear topological stretching on the high-dimensional time axis to obtain the topologically stretched elastic discharge time window.

[0097] Furthermore, based on the thermal stability capability corresponding to each time position within the elastic discharge time window, the time axis is divided into several sub-segments, and differentiated scaling is applied according to the thermal decay rate of each sub-segment. For example, for time periods with strong thermal stability capability, the sub-segment can be nonlinearly stretched on the high-dimensional time axis to occupy a larger proportion in the topological space, thereby enhancing the expressive ability of thermal stability at that stage; for time periods with rapid thermal decay, the high-dimensional time axis can be compressed to reduce its topological proportion, thus reflecting the rapid thermal change characteristics on the overall time axis. This can be achieved using an exponential time scale transformation function to map the original time point to the stretched time position, ensuring that the thermally stable segment expands and the thermal decay segment is compressed in the time series. The thermal potential energy value and corresponding topological coordinates are iteratively updated at each mapped point to form the topologically stretched elastic discharge time window. Through this processing, the structure of the original elastic discharge time window in the high-dimensional topological space is nonlinearly reshaped, ultimately yielding a topologically stretched elastic discharge time window that can characterize the thermal risk distribution characteristics.

[0098] Specifically, the logic of nonlinear topological stretching lies in changing the expression weight of each time position within a time window, so that the time window not only reflects the length of time but also the distribution of thermal value. Traditional time windows can only express the allowable discharge range, but cannot reflect the differences in thermal quality corresponding to different time positions. Through nonlinear topological stretching on a high-dimensional time axis, thermally stable regions gain higher expressive power, and thermally risky regions gain stronger identification ability. For example, two time positions located within the same elastic discharge time window may correspond to completely different thermal retention capabilities; or, for example, different aluminum rods may have similar time window ranges, but their thermal potential decay characteristics may differ.

[0099] S4.2 The elastic discharge time window after topological stretching is compared with the conveying path time from the furnace outlet to the extruder receiving port using quantum entanglement. The probability amplitude of the conveying path time collapsing into the time window is calculated to obtain the physical space reachability verification result.

[0100] Furthermore, the conveying path time from the furnace outlet to the extruder inlet is obtained, and this conveying path time is mapped to the time coordinate space corresponding to the elastic discharge time window after topological stretching. Then, using the physical space accessibility verification result expression, the matching relationship between the wave function of the elastic discharge time window after topological stretching and the conveying path time is calculated. The correlation between ambient temperature and the minimum extrudable temperature threshold is analyzed through the thermal constraint compatibility judgment function, and the thermal constraint compatibility judgment result is used as a constraint condition in the probability amplitude calculation. When the time position corresponding to the conveying path time can stably fall within the elastic discharge time window after topological stretching, the corresponding probability amplitude increases; when the time position corresponding to the conveying path time deviates from the elastic discharge time window after topological stretching, the corresponding probability amplitude decreases. Based on the probability amplitude, the ability of the aluminum rod to maintain thermal effectiveness during the transportation from the furnace outlet to the extruder inlet is judged, and the physical space accessibility verification result is obtained.

[0101] Specifically, the essence of quantum entangled state comparison lies in establishing a correlation evaluation mechanism between transport behavior and thermal state maintenance behavior. This ensures that transport path time is no longer treated as an independent transport parameter in scheduling, but rather, together with the topologically stretched elastic discharge time window, it constitutes the basis for evaluating thermal reachability. Quantum entangled state comparison considers the coupling relationship between time position and thermal state. For example, the same transport path time may correspond to different thermal state results under different thermal potential energy decay conditions; similarly, the same elastic discharge time window may have different thermal compatibility under different ambient temperature conditions. The probability amplitude calculation simultaneously reflects the degree of time matching and the degree of thermal compatibility.

[0102] The expression for the physical space reachability verification result is:

[0103] ;

[0104] in, This is the result of the physical space accessibility verification. The wave function of the elastic discharge time window after topological stretching. For delivery path time, For the aluminum rod index in the presorted sequence, This is the thermal constraint compatibility determination function. For ambient temperature, This is the minimum extrudable temperature threshold.

[0105] The expression for calculating temperature deviation is:

[0106] ;

[0107] in, For the first Temperature deviation at each point in time, The actual ambient temperature at that point in time. This refers to the upper limit of the allowable temperature or the safety threshold.

[0108] Quantitative indicators for thermal constraint exceedance

[0109] ;

[0110] Different time positions within the elastic discharge time window after topological stretching correspond to different levels of thermal risk. The thermally stable region corresponds to a higher wavefunction amplitude, the thermally critical transition region corresponds to a gradually decreasing wavefunction amplitude, and the time positions outside the elastic discharge time window after topological stretching correspond to wavefunction amplitudes approaching zero. Used to express the time reachability amplitude corresponding to the elastic discharge time window after the conveying path time falls into the topological stretching.

[0111] when Not less than When the hot-state constraint compatibility determination function outputs a high compatibility result; when near At that time, the hot-state constraint compatibility determination function outputs the critical compatibility result. Below When the hot-state constraint compatibility determination function outputs a low compatibility result, the hot-state constraint compatibility determination function is used to determine whether the aluminum rod is still in the extrudable hot state range after the conveying path time is completed.

[0112] This represents the probability amplitude of the conveying path time falling within the elastic discharge time window after topological stretching; the probability amplitude itself is simply an expression of the amplitude of the time-matching state; through The probability amplitude is converted into a non-negative reachability probability intensity. The closer the conveying path time is to the thermally stable region, the higher the physical space reachability verification result; conversely, the closer the conveying path time is to the thermally critical transition region or beyond the elastic discharge time window after topological stretching, the lower the physical space reachability verification result. The thermal constraint compatibility judgment function uses a square to transform the thermal constraint compatibility judgment from linear to strong constraint judgment. This more strictly suppresses cases approaching or below the minimum extrudable temperature threshold, preventing aluminum bars that only meet time-reachability requirements but have already failed thermally from being mistakenly judged as executable objects. The physical space reachability verification result simultaneously reflects the degree of matching between the conveying path time and the elastic discharge time window after topological stretching, as well as the degree to which the aluminum bar meets the minimum extrudable temperature threshold after the conveying path time.

[0113] S4.3 The physical space accessibility verification result is matched with the lowest extrudable temperature threshold in the thermal constraint fingerprint using anti-causal locking. The causal chain branches that cause the temperature threshold to be breached are removed in reverse along the time axis. The first locking screening is performed to obtain the first-level screening result.

[0114] Furthermore, based on the physical space accessibility verification results, the thermal evolution path of the aluminum rod during the transportation process is determined, and a correspondence is established between the thermal evolution path and the minimum extrudable temperature threshold. The thermal change process is traced backward along the time axis to locate the time node that leads to the failure of the minimum extrudable temperature threshold, and the corresponding thermal change links before each time node are analyzed. When a certain thermal change link ultimately leads to the failure of the minimum extrudable temperature threshold, the corresponding causal chain branch is marked as a failed branch; when a certain thermal change link can ensure that the minimum extrudable temperature threshold is always satisfied, the corresponding causal chain branch is retained. By continuously backward eliminating the causal chain branches that lead to the failure of the minimum extrudable temperature threshold, only the set of thermal paths that can maintain the effectiveness of the minimum extrudable temperature threshold is retained, and the first stage of locking and screening is completed based on the retention results to obtain the first-level screening results.

[0115] Specifically, starting from whether the minimum extrudable temperature threshold is met, the process traces the thermal path leading to the result, enabling early elimination of thermal risks. Causal locking matching further analyzes the formation process behind the current state. For example, two aluminum bars may have similar physical accessibility verification results, but the thermal path corresponding to one bar shows a rapid temperature decay trend, while the thermal path corresponding to the other bar remains stable. Alternatively, some thermal paths may currently meet the minimum extrudable temperature threshold, but are highly susceptible to threshold breach later. By eliminating causal chain branches leading to the breach of the minimum extrudable temperature threshold along the time axis, potential failure targets are filtered out in advance, ensuring that all aluminum bars entering the next stage of screening have a stable thermal foundation.

[0116] S4.4. The first-level screening results are compared with the idle time periods of the heating furnace outlet, extruder inlet, and mold installation position in the original resource dataset. Multidimensional spatiotemporal convolution and limit operation are performed. At the spatiotemporal folds, the second-stage locking screening is performed to remove aluminum bars with hidden spatiotemporal conflicts, and the second-level screening results are obtained.

[0117] Furthermore, for the suggested discharge start time of each aluminum bar in the first-level screening results, combined with the spatiotemporal occupancy trajectory function and time interval of each resource within the surrounding infinitesimal neighborhood, the Dirac function is used to perform convolution operation on the possible occupancy conflicts of the aluminum bar at each position, forming a multidimensional spatiotemporal convolution active limit result. In the convolution active limit operation, it is detected whether there is a potential resource conflict in any infinitesimal neighborhood. When a conflict is detected, the corresponding aluminum bar is removed from the feasible discharge list to ensure that there is no hidden conflict at the spatiotemporal fold. After completing the multidimensional spatiotemporal convolution active limit operation, aluminum bars with hidden spatiotemporal conflicts are removed to obtain the second-level screening results.

[0118] The expression for the multidimensional spatiotemporal convolution active limit operation is:

[0119] ;

[0120] in, For the results of multidimensional spatiotemporal convolution active limit operation, For an infinitesimal neighborhood, The first-level screening result Recommended starting time for discharging aluminum rods. For an infinitesimal time interval, For the original resource dataset, For the first The spatiotemporal occupancy trajectory function of the aluminum rod. For Dirac function, For resource status matching operators.

[0121] Specifically, the original resource dataset is used to characterize the availability of physical resources during the process of aluminum bars being discharged from the heating furnace, received by the extruder, and accepted by the die. The original resource dataset includes resource data for the heating furnace discharge port, the extruder receiving port, and the die mounting position. The heating furnace discharge port resource data includes the operating status of the heating furnace outlet mechanism, the port occupancy status, the release time, and the available time period. The extruder receiving port resource data includes the port occupancy status, the release time, the available time period, and the extruder processing cycle. The die mounting position resource data includes the die mounting position occupancy status, the release time, the die switching completion status, and the compatibility status between the die and the corresponding process standards of the aluminum bar. By performing spatiotemporal alignment processing on the occupancy and release events of the heating furnace discharge port, the extruder receiving port, and the die mounting position, the availability of corresponding physical resources can be queried for each aluminum bar at the candidate discharge time, generating the original resource dataset.

[0122] };

[0123] in, For the first aluminum rods during scheduling time The position attribute below, For the first aluminum rods during scheduling time The following resource attributes, For the first aluminum rods during scheduling time The resource consumption persistence attribute under the following For the first aluminum rods during scheduling time The resource conflict attribute under.

[0124] For the first aluminum rods during scheduling time The spatiotemporal occupancy attribute below is used to characterize the first The occupancy status of aluminum rods on production line physical resources at the corresponding scheduling time; the spatiotemporal occupancy attribute is not simply a position coordinate, but rather determined by the first... aluminum rods during scheduling time The spatiotemporal occupancy attribute is composed of the physical location of the resource, the resource object it occupies, the duration of resource occupancy, and whether there is any overlap with other resource states. This attribute includes at least location, resource, duration, and conflict attributes. The location attribute is used to represent the... The aluminum bar is located at the specific position of the furnace outlet, conveyor path, extruder inlet, or die receiving position; resource attributes are used to indicate the first... The aluminum bar currently occupies the heating furnace outlet resources, conveying path resources, extrusion press inlet resources, or die mounting position resources; the persistent attribute is used to represent the first... The start and end times or remaining time of occupation of the corresponding resource by the aluminum rod; the conflict attribute is used to indicate the first Does the current occupancy status of the aluminum rod overlap in time or space with the occupancy status of other aluminum rods or other production line resources? As the first The dedicated spatiotemporal occupancy attribute of the aluminum rod is used in subsequent resource conflict judgment, enabling the determination of the first material discharge moment. Does the aluminum rod support have the conditions for continuous discharge, conveying and receiving?

[0125] S4.5. The second-level screening results are compared with the mold-alloy adaptation rules in the process standard using non-Boolean logic fuzzy membership determination. The third-stage locking screening is then performed to remove aluminum rods that are in the process taboo edge state, thus obtaining the third-level screening results.

[0126] Furthermore, for each aluminum rod, the compatibility between its alloy type and the candidate die is analyzed. The alloy type, cross-sectional specifications, target extrusion temperature range, and process standard requirements of each aluminum rod are compared with the alloy range, specification range, and applicable temperature range allowed by the candidate die. Membership values ​​are calculated to reflect the degree of deviation within the allowable process range. Based on the membership threshold, aluminum rods on the edge of process prohibitions are eliminated, ensuring the remaining aluminum rods meet the optimal compatibility range of the process standard. This third-stage locking screening operation yields the third-level screening results, ensuring that the aluminum rods are hot-ready and that the alloy compatibility between the aluminum rod and the die meets the process standard requirements, thus achieving the screening objective of process safety.

[0127] The membership value expression is:

[0128] ;

[0129] in, For the first aluminum rod and the first Membership value of each candidate mold. For the first aluminum rod and the first The membership degree of the alloy type of each candidate mold. For the first aluminum rod and the first Membership degree of the cross-sectional specifications of each candidate mold. For the first aluminum rod and the first Membership degree of the temperature range of each candidate mold.

[0130] Specifically, the degree of compatibility is not determined solely by whether the alloy types are the same, but rather by the... Aluminum rod in use When selecting a candidate mold, does it simultaneously fall within the allowed alloy type range, specification compatibility range, and process temperature range for the candidate mold? If the first... If the alloy type of the aluminum rod belongs to the alloy type that the candidate die allows to process, and the cross-sectional specifications and target extrusion temperature range are all within the allowable range of the process standard, then the first... aluminum rod and the first The first candidate mold has a high degree of compatibility; if the second... Although the alloy type of the aluminum rod can be processed, if the cross-sectional specifications or the target extrusion temperature range are close to the process standard boundary, then the first... aluminum rod and the first The fit of the candidate mold decreases; if the first If the alloy type of the aluminum support rod is not within the allowable processing range of the candidate mold, then the... aluminum rod and the first The candidate mold is not compatible.

[0131] S4.6 The third-level screening results are recombined and summarized through spatiotemporal slices to form an executable contract set, thus obtaining the executable contract set.

[0132] Furthermore, leveraging the continuity of the time axis and the consistency of spatial distribution, each contract item is sorted and reorganized according to feasible output time periods to generate an executable contract set. This unifies the scheduling information of multiple stages, resources, and aluminum rods, ensuring that the executable contract set reflects both thermal feasibility and resource accessibility. The spatiotemporal slice reorganization forms the executable contract set by uniformly summarizing the results of different screening stages, integrating thermal safety, resource accessibility, and process adaptability into a single set of executable objects, achieving the collaborative expression of multi-dimensional constraints. Through spatiotemporal slice reorganization, the feasible output range, occupied resources, and thermal constraints of different aluminum rods are simultaneously incorporated into the executable contract set, providing a complete data foundation for subsequent complex plane integration and hypergraph matching optimization, thereby improving the global executability and optimization capability of the scheduling strategy.

[0133] S4.7. Each contract item in the executable contract set is integrated with the thermal potential energy decay curve in the thermal constraint fingerprint using a complex plane loop. The singularity energy value enclosed by the integration path is extracted as a quantitative index to obtain the thermal loss evaluation value.

[0134] The expression for the singularity energy value is:

[0135] ;

[0136] in, This is the value for assessing thermal loss. Let be the set of singularities enclosed by the integration path. For singularity index, For the first The thermal energy values ​​corresponding to each singularity. The integration path is formed by equipotential lines or gradient lines in the high-energy state evolution field or thermal potential energy distribution cloud map. The region enclosed by these lines contains local thermal energy extrema (singularities). The energy values ​​of these singularities reflect the local energy dissipation of the aluminum rod under non-ideal thermal conditions.

[0137] Furthermore, for each aluminum rod, its temporal-spatial distribution within the executable contract set is mapped to the complex plane, and a closed integral loop is formed along the thermal potential decay path. The singularity energy value within the region enclosed by the integral path is extracted. This singularity energy value serves as a quantitative indicator to assess the risk of thermal displacement of the aluminum rod due to thermal energy loss during the unloading process. By comparing the singularity energy values ​​of each aluminum rod, the thermal loss assessment value is evaluated, thereby achieving a quantitative assessment of the thermal stability of each aluminum rod.

[0138] Specifically, the complex-plane loop integral maps temporal-spatial thermal information into a complex-plane geometric representation. By extracting singularity energy values ​​through integration, it quantifies heat loss. The complex-plane loop integral can simultaneously consider the magnitude and duration of thermal energy changes, as well as path continuity, achieving a global assessment of heat loss. For example, two aluminum rods may have the same discharge time window length, but different rates of thermal potential energy decay along their paths. The integral loop can accurately distinguish their thermal stability, identify potential high heat loss risks in advance, and provide reliable indicators for scheduling optimization.

[0139] S4.8. The thermal loss assessment value and the idle time period of the equipment in the original resource dataset are used to perform hypergraph matching optimization. The combination of contract terms with the highest cross-dimensional connectivity under the condition of minimizing the total sum of edge weights of the hypergraph is selected to obtain the optimal combination of spatial connectivity.

[0140] Furthermore, the executable contract set, thermal loss assessment value, and idle time periods of each device are constructed as hypergraph nodes and hyperedges, where hyperedges represent the multidimensional matching relationship between aluminum rod output and resource availability. Subsequently, the sum of edge weights is calculated on the hypergraph, using the thermal loss assessment value as a weighting index, to perform cross-dimensional matching optimization for all possible combinations of contract terms. By iteratively comparing the sum of thermal losses and resource reachability of different combinations of contract terms, the combination of contract terms with the highest cross-dimensional connectivity under the condition of minimizing the sum of edge weights is selected, forming the spatially optimal connectivity combination.

[0141] Specifically, hypergraph matching optimization incorporates thermal loss assessment and multi-resource reachability into a unified graph structure for global optimization, rather than optimizing each resource or individual contract item. Through hypergraph matching, both thermal loss minimization and multi-resource connectivity can be considered simultaneously, ensuring that the scheduling combination is both thermally safe and spatially feasible. For example, some aluminum rods may have the minimum thermal loss on a single equipment path, but the overall resource utilization is uneven; hypergraph optimization can coordinate the idle time of all aluminum rods and equipment from a global perspective to achieve the globally optimal scheduling combination.

[0142] S4.9 The optimal combination of spatial connectivity is sorted by time axis renormalization and arranged in sequence to obtain the scheduling instruction table.

[0143] Furthermore, the optimal combination of spatial connectivity is reordered along the time axis, rearranging each contract item according to its output time and resource occupancy priority, and adjusting its order based on temporal continuity and resource dependencies. The sorted contract items are then sequentially arranged to generate a final scheduling instruction table. Each instruction corresponds to the output action and time node of the aluminum rod in the heating furnace, extruder, and die storage management unit, thus making the scheduling sequence executable. This scheduling instruction table reflects both thermal safety constraints and ensures multi-resource spatial reachability.

[0144] S5. Convert the scheduling instruction table into equipment control signals and send them to the heating furnace, extruder, and mold storage management unit to monitor the production progress.

[0145] S5.1 The scheduling instruction table is sorted sequentially after being renormalized along the time axis to obtain the scheduling instruction table. The timing action logic in the scheduling instruction table is mapped to the discrete control pulse sequence of the heating furnace, extruder, and mold library management unit to obtain the equipment control signal.

[0146] Furthermore, the scheduling instruction table is reordered and arranged sequentially after time-axis reorganization to ensure that the aluminum rod discharge time and resource occupation order corresponding to each scheduling item are clear. The timing action logic in the scheduling instruction table is mapped to discrete control pulse sequences of the heating furnace, extruder, and die storage management unit. Each pulse sequence contains discharge start, duration, and end action information. By encoding the discharge action signals of each aluminum rod, equipment control signals are generated, enabling the control instructions to be accurately executed by the heating furnace, extruder, and die storage management unit, achieving precise triggering and coordination of the physical actuators.

[0147] S5.2 Send the equipment control signal to the heating furnace, extruder and mold library management unit to drive the physical actuator to move and obtain the physical execution feedback status.

[0148] Furthermore, the generated equipment control signals are sent to the heating furnace, extruder, and die storage management units, enabling the physical actuators to complete the material discharge, transfer, and storage actions according to the instructions. During equipment execution, the physical feedback status of the mechanism actions is collected, including the response of the heating furnace outlet action, the occupancy of the extruder receiving port, and the changes in the die storage retrieval position. The actual action time and resource occupancy status of each aluminum bar are recorded to achieve closed-loop monitoring of the executed actions.

[0149] S5.3 The physical execution feedback status is dynamically compared with the contract item in the scheduling instruction table to obtain the production progress deviation value.

[0150] Furthermore, the physical execution feedback status is dynamically compared with each contract item in the scheduling instruction table to calculate the actual deviation of each aluminum rod's output time and resource usage, thus obtaining the production progress deviation value. The deviation value can be quantified as a time difference, action sequence misalignment, or resource usage conflict index to reflect the degree of deviation between production execution and planned scheduling, providing basic data for thermal safety assessment and production optimization, and realizing real-time quantification of production deviation.

[0151] S5.4 The production progress deviation value is cross-validated in real time with the elastic discharge time window in the thermal constraint fingerprint to obtain the thermal safety margin monitoring result.

[0152] Furthermore, the production schedule deviation value is cross-validated in real time with the elastic discharge time window in the thermal constraint fingerprint to calculate the thermal safety margin of the aluminum rods in actual operation. By comparing the deviation value with the upper and lower limits of the time window, it is determined whether the discharge thermal potential energy of each aluminum rod is kept within the safe range, generating thermal safety margin monitoring results, and realizing real-time monitoring of the thermal safety of the entire production line.

[0153] This embodiment also provides an automated scheduling system for an aluminum rod production line, including: a batching module, which acquires production orders, equipment status and process standards, dynamically batches orders based on a hot-change-cost joint evaluation model, and generates a pre-arranged sequence;

[0154] A module is established to create a hot life cycle model for each aluminum rod in the pre-arranged sequence, generating an elastic discharge time window with hot constraint fingerprints.

[0155] The detection module utilizes the elastic discharge time window with attached thermal constraint fingerprint to implement multi-level thermal emergency interlock control of the heating furnace outlet mechanism, obtains the pre-arranged sequence of thermal safety attributes, detects the physical idle status of the heating furnace outlet, extruder receiving port and die mounting position, and generates the original resource dataset.

[0156] The filtering module performs physical space reachability verification on each aluminum rod in the pre-arranged sequence, performs three-stage latch filtering based on thermal constraint fingerprint, generates an executable contract set, selects the optimal combination of spatial connectivity with the goal of minimizing thermal loss, and generates a scheduling instruction table.

[0157] The monitoring module converts the scheduling instruction table into equipment control signals and sends them to the heating furnace, extruder, and mold storage management unit to monitor the production progress.

[0158] This embodiment also provides a computer device applicable to the automated scheduling method for an aluminum rod production line, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the automated scheduling method for an aluminum rod production line as proposed in the above embodiment.

[0159] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0160] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the automated scheduling method for an aluminum rod production line as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0161] In summary, this invention achieves optimal aggregation that balances the benefits of hot-state connection and the losses of changeover by constructing a joint evaluation model for hot-state-changeover costs, thus avoiding energy waste caused by ineffective heating and frequent changeovers at the source. By introducing microscopic lattice vibrations and electronic thermal motion variables to establish a hot-state lifecycle model and generating an elastic discharge time window with accompanying hot-state constraint fingerprints, it achieves accurate quantification of the transient thermal potential energy distribution of a single aluminum rod, solving the problem that traditional models cannot characterize nonlinear thermal coupling. Furthermore, by utilizing the elastic discharge time window to implement multi-level hot-state emergency interlock control, it achieves graded blocking of the heating furnace outlet mechanism under abnormal operating conditions. Thermal preservation reduces the risk of low-temperature scrapping due to transport obstruction; by performing a three-stage locking and screening process based on quantum entanglement state comparison and multidimensional spatiotemporal convolution limit operation on the pre-arranged sequence, deep coupling between physical space accessibility and thermal legitimacy is achieved, eliminating implicit spatiotemporal conflicts and process edge states, and ensuring the executability of the contract set; by quantizing thermal loss through complex plane loop integration and selecting the optimal combination of spatial connectivity through hypergraph matching, optimal resource allocation under the goal of minimizing thermal loss is achieved; by distributing discrete control pulse sequences and monitoring thermal safety margins, automation, high precision and low-risk scheduling of the entire aluminum rod production line process are achieved.

[0162] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automated scheduling method for an aluminum rod production line, characterized in that: This includes acquiring production orders, equipment status, and process standards; dynamically batching orders based on a hot-change-cost joint evaluation model; and generating pre-arranged sequences. A hot life cycle model is established for each aluminum rod in the pre-arranged sequence, and an elastic discharge time window with hot constraint fingerprint is generated. By utilizing the elastic discharge time window with attached thermal constraint fingerprint, multi-level thermal emergency interlock control is implemented on the heating furnace outlet mechanism to obtain a pre-arranged sequence of thermal safety attributes, detect the physical idle status of the heating furnace outlet, extruder receiving port and die mounting position, and generate the original resource dataset. For each aluminum rod in the pre-arranged sequence, physical space reachability is checked, and three-stage latching is performed based on thermal constraint fingerprints to generate an executable contract set. The optimal combination of spatial connectivity with the goal of minimizing thermal loss is selected to generate a scheduling instruction table. The scheduling instruction table is converted into equipment control signals and sent to the heating furnace, extruder, and mold storage management unit to monitor the production progress.

2. The automated scheduling method for an aluminum rod production line as described in claim 1, characterized in that: The pre-arranged sequence includes, The production order, equipment status, and process standards are input into the hot-change cost joint evaluation model to obtain the hot-change cost joint evaluation result. The orders are then dynamically batched. By quantifying the hot-change connection benefits and changeover operation losses between different orders, the optimal aggregation scheme is determined, and the dynamic batching result is obtained. Based on the dynamic batching results, the thermal life cycle of materials within the group and the processing capacity of equipment are used as sequence constraints to adjust the execution order of production orders within the group, resulting in a pre-arranged sequence.

3. The automated scheduling method for an aluminum rod production line as described in claim 2, characterized in that: The elastic discharge time window with attached thermal constraint fingerprint includes, Based on each aluminum rod in the pre-arranged sequence, the material parameters, cross-sectional specifications and current position information of the aluminum rod in the heating furnace section are collected to construct a high-energy state evolution field with micro-lattice vibration frequency and electron thermal motion intensity as variables. The high-energy state evolution field uses a virtual heat source superposition algorithm to deploy virtual heat sources with multi-frequency oscillation characteristics along the axial and radial directions inside the aluminum rod to drive the thermal energy to oscillate back and forth. Based on the anti-entropy heat flow tracking mechanism, the heat flow is forced to converge in the opposite direction along the preset negative thermodynamic gradient direction. The thermal state evolution trajectory of the aluminum rod in the time dimension, axial position dimension and radial position dimension is mapped to a high-dimensional feature space by using the multidimensional phase space mapping method, and the transient thermal potential energy distribution of the aluminum rod in the entire heating cycle is reconstructed to obtain the thermal life cycle model. The thermal life cycle model analyzes the nonlinear coupling characteristics of latent heat accumulation inside the aluminum rod and heat dissipation on the surface, and inversely reconstructs the heat energy accumulation pattern and dissipation path during different heating periods to obtain a thermal potential energy distribution cloud map of the aluminum rod that characterizes the instantaneous thermal energy distribution state of the entire aluminum rod. The thermal potential energy distribution cloud map of the aluminum rod is matched with the target extrusion temperature range in the process standard in a high-dimensional topology. The continuous residence time of the aluminum rod in the topological isomorphic region is extracted. The thermal decay trajectory of the aluminum rod in a non-ideal conveying environment is deduced by constructing a virtual cooling channel, and the thermal constraint fingerprint is obtained. Tensor fusion is performed on the thermal potential energy distribution data in the thermal constraint fingerprint and thermal life cycle model. By applying nonlinear stretching transformation of the time axis, the thermal energy collapse process under abnormal delay of the heating furnace outlet mechanism and conveying obstruction is simulated. The extended range of discharge time for aluminum rod thermal potential energy not lower than the critical threshold is defined, and the elastic discharge time window with thermal constraint fingerprint is obtained.

4. The automated scheduling method for an aluminum rod production line as described in claim 3, characterized in that: The pre-arranged sequence of the thermal security attributes includes, By utilizing the elastic discharge time window with attached thermal constraint fingerprint, the degree of thermal deviation is determined by comparing the difference between the lower limit threshold of the elastic discharge time window and the action response delay of the heating furnace outlet mechanism, and the thermal deviation determination result is obtained. The hot deviation determination result and the elastic discharge time window with attached hot constraint fingerprint are jointly input into the multi-level hot emergency interlock control logic. By triggering the start, stop and speed adjustment commands of the heating furnace outlet mechanism in stages, a pre-arranged sequence of hot safety attributes is obtained.

5. The automated scheduling method for an aluminum rod production line as described in claim 4, characterized in that: The original resource dataset includes, The pre-arranged sequence of thermal safety attributes and the status information of the heating furnace outlet mechanism are used together as the detection benchmark. By scanning the occupancy and release events of the heating furnace outlet, the extruder inlet, and the die mounting position, the physical idle status record is obtained. After spatiotemporal alignment processing, the original resource dataset is generated.

6. The automated scheduling method for an aluminum rod production line as described in claim 5, characterized in that: The scheduling instruction table includes, For each aluminum rod in the pre-arranged sequence based on the thermal safety attribute, the elastic discharge time window with the attached thermal constraint fingerprint of the aluminum rod is called, and the elastic discharge time window is nonlinearly topologically stretched on the high-dimensional time axis to obtain the topologically stretched elastic discharge time window. The elastic discharge time window after topological stretching is compared with the conveying path time from the furnace outlet to the extruder inlet using quantum entanglement. The probability amplitude of the conveying path time collapsing into the time window is calculated to obtain the physical space reachability verification result. The physical space accessibility verification result is matched with the lowest extrudable temperature threshold in the thermal constraint fingerprint using anti-causal locking. The causal chain branches that cause the temperature threshold to be breached are removed in reverse along the time axis. The first locking screening is performed to obtain the first-level screening result. The first-level screening results are compared with the idle time periods of the heating furnace outlet, extruder inlet, and die installation position in the original resource dataset. A multi-dimensional spatiotemporal convolution limit operation is performed. At the spatiotemporal folds, the second-stage locking screening is performed to remove aluminum bars with hidden spatiotemporal conflicts, thus obtaining the second-level screening results. The second-level screening results are compared with the mold-alloy adaptation rules in the process standard using non-Boolean logic fuzzy membership determination. The third-stage locking screening is then performed to remove aluminum bars that are in the process taboo edge state, thus obtaining the third-level screening results. The results of the third-level screening are recombined and summarized through spatiotemporal slicing to form an executable contract set, thus obtaining the executable contract set; Each contract item in the executable contract set is integrated with the thermal potential energy decay curve in the thermal constraint fingerprint using a complex plane loop. The singularity energy value enclosed by the integration path is extracted as a quantitative index to obtain the thermal loss evaluation value. The thermal loss assessment value is matched with the device idle time period in the original resource dataset to find the optimal combination of contract terms with the highest cross-dimensional connectivity under the condition of minimizing the total edge weight of the hypergraph. The optimal combination of spatial connectivity is sorted by time axis renormalization and then arranged in sequence to obtain the scheduling instruction table.

7. The automated scheduling method for an aluminum rod production line as described in claim 6, characterized in that: The monitoring of production progress includes, The scheduling instruction table is reordered and arranged sequentially after time axis renormalization to obtain the scheduling instruction table. The timing action logic in the scheduling instruction table is mapped to the discrete control pulse sequence of the heating furnace, extruder and mold library management unit to obtain the equipment control signal. The equipment control signals are sent to the heating furnace, extruder, and mold library management unit to drive the physical actuators and obtain physical execution feedback status. The physical execution feedback status is dynamically compared with the contract entries in the scheduling instruction table to obtain the production progress deviation value. The production progress deviation value is cross-validated in real time with the elastic discharge time window in the thermal constraint fingerprint to obtain the thermal safety margin monitoring result.

8. An automated scheduling system for an aluminum rod production line, based on the automated scheduling method for an aluminum rod production line according to any one of claims 1 to 7, characterized in that: This includes a batching module that acquires production orders, equipment status, and process standards, and dynamically batches orders based on a hot-change-cost joint evaluation model to generate pre-arranged sequences; A module is established to create a hot life cycle model for each aluminum rod in the pre-arranged sequence, generating an elastic discharge time window with hot constraint fingerprints. The detection module utilizes the elastic discharge time window with attached thermal constraint fingerprint to implement multi-level thermal emergency interlock control of the heating furnace outlet mechanism, obtains the pre-arranged sequence of thermal safety attributes, detects the physical idle status of the heating furnace outlet, extruder receiving port and die mounting position, and generates the original resource dataset. The filtering module performs physical space reachability verification on each aluminum rod in the pre-arranged sequence, performs three-stage latch filtering based on thermal constraint fingerprint, generates an executable contract set, selects the optimal combination of spatial connectivity with the goal of minimizing thermal loss, and generates a scheduling instruction table. The monitoring module converts the scheduling instruction table into equipment control signals and sends them to the heating furnace, extruder, and mold storage management unit to monitor the production progress.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the automated scheduling method for the aluminum rod production line according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the automated scheduling method for the aluminum rod production line according to any one of claims 1 to 7.

Images