A method and system for digital management of a conductive foam production line

Abstract

This invention relates to the field of conductive foam manufacturing technology, and discloses a digital management method and system for conductive foam production lines. The method includes: fusing historical order data and equipment operation data based on time-series characteristics to obtain the future load status of the equipment; acquiring real-time processing tasks, and determining the imbalance status of production line resources if there are sudden faults or urgent orders; acquiring material flow paths and evaluating the load of process nodes to obtain backlogged process nodes; acquiring a set of tasks to be processed and idle equipment, performing mapping encoding and iterative optimization to obtain reallocation instructions; acquiring the completion time of the preceding process and reconstructing the processing flow time sequence to obtain the adjusted processing flow; and acquiring the waiting time of the following process and adjusting equipment allocation and integrating scheduling schemes to obtain the target production task allocation sequence. This method can achieve dynamic scheduling and balanced resource utilization in abnormal situations of conductive foam production lines.

Description

Technical Field

[0001] This invention relates to the field of conductive foam manufacturing technology, and in particular to a digital management method and system for conductive foam production lines. Background Technology

[0002] Currently, conductive foam, as a key material for electromagnetic shielding and conductive connections in electronic devices, is widely used in communication equipment, computers, automotive electronics, medical devices, and other fields. With the accelerating pace of iteration in consumer electronics, conductive foam manufacturers generally face market pressures of small order batches, diverse product types, and tight delivery schedules, placing higher demands on the flexibility and responsiveness of the production process. Currently, relying on industrial control software to achieve digital management and control of the production process has become an industry trend, and industrial software such as manufacturing execution systems (MES) and data acquisition and monitoring systems are gradually being applied to equipment monitoring and data management in conductive foam production workshops.

[0003] In one existing technology, production scheduling and management in conductive foam production workshops primarily rely on manual experience. Managers manually arrange the processing sequence and equipment allocation for each process by reviewing order lists and the current operating status of each piece of equipment, combined with their personal experience. When the workshop faces production scenarios with multiple orders running concurrently and multiple processes overlapping, this manual scheduling method lacks the ability to quantitatively predict equipment load status in future periods. Managers can only make decisions based on the current equipment status, making it difficult to identify potential overload or idle capacity at each workstation in the future. Furthermore, frequent equipment failures or urgent customer orders during workshop operation can render existing production arrangements ineffective. When relying on manual experience for rescheduling, the lack of global optimization capabilities for material flow paths and processing sequences across production lines often results in only localized repair measures, making it difficult to achieve rapid rebalancing of production resources at an overall level.

[0004] In summary, existing technologies have the problem of failing to perform dynamic scheduling with global optimization in the event of sudden equipment failure or emergency order insertion. Summary of the Invention

[0005] This invention provides a digital management method and system for conductive foam production lines to solve the problem of dynamic scheduling with global optimization in the event of sudden equipment failure or emergency order insertion.

[0006] Firstly, in order to solve the above-mentioned technical problems, the present invention provides a digital management method for conductive foam production lines, comprising: Acquire historical order data, equipment operation data, and real-time processing tasks for conductive foam; perform time-series feature fusion on the historical order data and the equipment operation data to obtain the future load status of the equipment. The real-time processing task is detected based on the future load status of the equipment. If there is a sudden failure or emergency order that causes production line imbalance, the current production line is judged to be unbalanced, and the production line resource imbalance status is obtained. Based on the imbalance of production line resources, the material flow path is obtained, and the process node load of the material flow path is evaluated to obtain the process backlog nodes. Obtain the set of tasks to be processed and idle equipment at the backlog node of the process, and combine them to obtain an initial allocation scheme. Iterate and optimize the initial allocation scheme to obtain a reallocation instruction. The completion time of the preceding process of the task to be processed is obtained according to the reallocation instruction. The processing flow sequence is reconstructed according to the completion time of the preceding process to obtain the adjusted processing flow. The waiting time for subsequent processes is obtained based on the adjusted processing flow, and the scheme scheduling is integrated to generate a target production task allocation sequence.

[0007] Secondly, the present invention provides a digital management system for a conductive foam production line, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described above.

[0008] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0009] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention obtains the future load status of equipment by fusing historical order data of conductive foam with equipment operation data based on time-series features, thereby identifying potential resource bottleneck areas before production anomalies occur. Since this method does not rely on human experience to make subjective judgments about the future status of equipment, but rather makes quantitative predictions based on the time-series evolution patterns of historical data, the system can quickly locate the affected workstations and related production lines when sudden failures or emergency orders occur, thereby avoiding blind scanning of all equipment in the workshop, and providing accurate decision-making basis for subsequent resource reallocation, effectively improving the anomaly response speed.

[0010] (2) This invention identifies backlogged process nodes by comparing material flow paths with preset thresholds, and then uses a genetic algorithm to perform crossover and mutation operations on the tasks to be processed and the set of idle equipment to generate redistribution instructions, thereby realizing intelligent matching between backlogged tasks and idle resources. Since this method no longer relies on the manual experience of managers to split tasks and assign equipment after identifying specific backlogged processes, but instead uses the global search capability of the genetic algorithm to iteratively optimize multiple allocation schemes, the tasks to be processed can be reasonably distributed among idle equipment, thereby avoiding the structural imbalance caused by some production lines being overloaded and others being idle due to the traditional local repair method, and improving the balance of production line resource utilization.

[0011] (3) This invention obtains the available time window of each device and the waiting time of the subsequent process by matching the available time window with the adjusted processing flow to obtain a global optimized schedule. Then, it judges and outputs the target production task allocation sequence based on the target order delivery date requirements, thereby ensuring that the dynamically adjusted production plan can still meet the delivery date constraints. Since this method further incorporates the time window constraints of each device and the waiting status of the subsequent process into the scheduling optimization process after completing the device reallocation, and filters out the scheduling scheme that meets the delivery requirements through the delivery date deviation check, the reallocated processing flow not only has the feasibility at the device level, but also meets the urgency requirements of the order in the time dimension, avoiding the situation where the overall delivery date is delayed due to local scheduling optimization, and ensuring the timely achievement of the production plan. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of a digital management method for a conductive foam production line provided in the first embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Reference Figure 1 The first embodiment of the present invention provides a digital management method for a conductive foam production line, comprising the following steps: S11, acquire historical order data, equipment operation data and real-time processing tasks of conductive foam, and perform time-series feature fusion on the historical order data and the equipment operation data to obtain the future load status of the equipment; S12, the real-time processing task is detected according to the future load status of the equipment. If there is a sudden failure or emergency order that causes the production line to become unbalanced, the current production line is judged to be unbalanced, and the production line resource imbalance status is obtained. S13, obtain the material flow path based on the production line resource imbalance state, and perform process node load assessment on the material flow path to obtain process backlog nodes; S14, obtain the set of tasks to be processed and idle equipment at the backlog node of the process, and combine them to obtain an initial allocation scheme. Iterate and optimize the initial allocation scheme to obtain a reallocation instruction. S15, obtain the completion time of the previous process of the task to be processed according to the reallocation instruction, and reconstruct the processing flow sequence according to the completion time of the previous process to obtain the adjusted processing flow. S16, based on the adjusted processing flow, obtain the waiting time of the subsequent process, and perform scheme scheduling fusion processing to generate the target production task allocation sequence.

[0015] In step S11, historical order data, equipment operation data, and real-time processing tasks for conductive foam are acquired. The historical order data and the equipment operation data are then fused using time-series features to obtain the future load status of the equipment, including: The historical order data and the equipment operation data are aligned and concatenated to construct an initial multidimensional data matrix; Extract the time series features of the initial multidimensional data matrix to obtain the first hidden state sequence; The feature values ​​in the first hidden state sequence that are greater than the feature threshold are subjected to nonlinear activation processing to obtain the second hidden state sequence; The second hidden state sequence is dimensionality-reduced to obtain the future load state of the device.

[0016] First, historical order data for conductive foam from multiple statistical periods is retrieved from the database of the Production Execution System (MES). This historical order data includes order quantity and delivery urgency level. Order quantity refers to the number of finished conductive foam units required for a single order, recorded in units of pieces. Delivery urgency level is a priority marker determined by the MES based on the difference between the order's required delivery date and the current date. When the difference is less than or equal to 24 hours, it is marked as the highest urgency level, coded as 3; when the difference is greater than 24 hours but less than or equal to 72 hours, it is marked as the medium urgency level, coded as 2; and when the difference is greater than 72 hours, it is marked as the normal level, coded as 1. Simultaneously, operating temperature parameters are collected by a temperature sensor deployed on the die-cutting machine's spindle, and motor speed parameters are read via the inverter communication protocol to obtain equipment operating data. The operating temperature parameter refers to the surface temperature of the die-cutting machine spindle bearing housing, recorded in degrees Celsius; the speed parameter refers to the rotational speed of the die-cutting machine spindle, recorded in revolutions per minute (rpm). Then, using the sampling timestamp as the alignment reference, the order demand, delivery urgency level, temperature parameters, and rotation speed parameters collected at the same time are arranged in a fixed field order and written into a continuously addressed storage space to form a two-dimensional data array with the number of rows equal to the total number of sampling points and the number of columns equal to the feature dimension. This array is the initial multidimensional data matrix.

[0017] Simultaneously, all processing tasks currently being executed and awaiting execution are retrieved from the real-time task management module of the production execution system. These tasks are arranged in chronological order of their issuance time. The task identifier, target product specifications, planned processing time, and current execution status fields are extracted from each task and organized into a task queue in chronological order. The current execution status field indicates that the task is currently in one of three states: pending processing, processing, or completed. These real-time processing tasks are only used in subsequent steps for determining production line imbalance and do not participate in the time-series feature fusion and the generation of future equipment load states.

[0018] Next, a sliding window statistical process is performed on the initial multidimensional data matrix to extract time series features. Specifically, a fixed-length sliding window is set. This window length is determined by collecting order placement time interval data from the conductive foam production workshop over a number of past natural days. The time intervals between adjacent orders are statistically analyzed, and the average of all time intervals is calculated. This average is then converted to the same time unit as the data sampling period and divided. The result is rounded up to obtain the window length. For each sliding window position, the average value and slope of each feature column within the window are calculated. The slope is calculated by subtracting the feature value of the first sampling moment from the feature value of the last sampling moment within the window, and then dividing by the time span covered by the window. The time span is the difference between the first and last sampling moments within the window. The average value and slope of each feature column calculated for each window position are arranged in chronological order to form a feature sequence, which is the first hidden state sequence.

[0019] Subsequently, the feature values ​​in the first hidden state sequence are filtered and nonlinearly enhanced. Specifically, each feature value in the first hidden state sequence is read and compared with a preset feature threshold. The preset feature threshold is determined by collecting sensor data from the acquisition device during a period of time when the equipment is operating under normal load and the production is stable with no abnormal records. The standard deviation of this set of data is calculated, and the feature threshold is set to three times this standard deviation. That is, for data that follows a normal distribution, data within the range of the mean plus or minus three standard deviations account for 99.73% of the total data, and values ​​exceeding this range are statistically significant outliers. Those skilled in the art can also set the threshold to two or four times the standard deviation according to the distribution characteristics of the actual signal and the same statistical principles to adapt to the sensitivity requirements under different operating conditions. If the current feature value is greater than the feature threshold, the value is determined to be a significant fluctuation signal, and nonlinear activation processing is performed on the value. The nonlinear activation processing employs a leaky modified linear unit function. This function is calculated by outputting the input value itself when it is positive, and multiplying the input value by a small positive coefficient, such as 0.01, when the input value is less than or equal to zero. This operation is implemented through a comparator and multiplier circuit, preserving the original magnitude of positive values ​​while slightly retaining negative signals instead of setting them to zero, thus avoiding the loss of negative change information such as load decline trends. Those skilled in the art can also adjust the small positive coefficient within the range of 0.001 to 0.1 based on the signal-to-noise ratio characteristics of the actual signal. If the current feature value is less than or equal to the feature threshold, the feature value is retained as is without further processing. After completing the above comparison and processing for all values ​​in the first hidden state sequence, the processed values ​​are rearranged according to their original row and column positions to form the second hidden state sequence.

[0020] Finally, the second hidden state sequence is subjected to dimensionality reduction mapping to obtain the future load state of the equipment. The feature vector corresponding to the latest time window in the second hidden state sequence is read. This feature vector contains the mean and slope of each feature column. A weighted summation dimensionality reduction method is used, that is, each element in the feature vector is multiplied by its corresponding weight coefficient and then summed to obtain a scalar value. The weight coefficients are determined using the entropy weight method. Sensor data for each feature column over the past 90 natural days is collected. After Z-score standardization of temperature and rotation speed parameters to eliminate the influence of dimensions, the information entropy redundancy of each feature column is determined according to the standard calculation process of the entropy weight method. After normalization, the weight coefficient of each feature is obtained. For example, the weight of order demand is 0.35, the weight of delivery urgency level is 0.20, the weight of temperature parameter is 0.25, the weight of rotation speed parameter is 0.20, and the sum of the above weight coefficients is 1.0. Those skilled in the art can adjust the length of the historical data collection period (e.g., 60 days or 120 days) according to the distribution characteristics of actual production data. The entropy weight method will automatically adapt the weighting results without affecting the core logic of the method. Specifically, the closer the entropy value is to 1, the smaller the dispersion of the indicator, and the smaller the weight should be assigned; conversely, the smaller the entropy value, the larger the weight. To adapt to data distribution drift caused by equipment aging or product switching, the weight coefficients are updated every 30 calendar days, or at least 30 calendar days of data are recollected and the weights recalculated after a product switching. The scalar value represents the future load state of the equipment, and its value range is a normalized dimensionless index, ranging from 0 to 100. This value characterizes the predicted relative load level of the equipment in the future period; the larger the value, the higher the predicted load of the equipment in the future period. The specific physical meaning of the value is a comprehensive score of the equipment's processing demand in the future period. When the value exceeds a preset threshold, it indicates that the equipment may experience processing capacity overload in the future period.

[0021] In step S12, the real-time processing task is detected based on the future load status of the equipment. If there is a sudden failure or emergency order insertion that could cause production line imbalance, the current production line is assessed for imbalance, resulting in the production line resource imbalance status, including: Extract the task queue from the real-time processing task, and perform feature extraction processing on the task queue to obtain an initial task feature vector; Collect the current operating status parameters of the equipment, and concatenate the initial task feature vector with the operating status parameters to construct a multi-dimensional production status vector; If there is a value in the multidimensional production state vector that is greater than the preset fluctuation threshold, it is determined that the real-time processing task has a sudden failure or an emergency order insertion, and a scheduling instruction is generated according to the multidimensional production state vector. The current production line is marked as unbalanced according to the scheduling instructions to obtain the production line resource imbalance status.

[0022] In this step, the sensitivity of the imbalance detection is dynamically adjusted by the future load status of the equipment, specifically by adjusting the triggering conditions of the preset fluctuation threshold. First, all processing tasks currently being executed and awaiting execution are retrieved from the real-time task management module of the manufacturing execution system. These processing tasks are an objectively existing task list issued by the manufacturing execution system, and their acquisition does not depend on the equipment operating status or load prediction results. The retrieved processing tasks are arranged in chronological order of their issuance time. The task identifier, target product specification, planned processing time, and current execution status fields are extracted from each processing task, and this information is organized into a task queue in chronological order. The current execution status field is used to mark that the task is currently in one of three states: pending processing, processing, or completed. Subsequently, the future load status of the equipment output in the previous step is read, and this future load status is used as the criterion for subsequent detection of whether there are sudden failures or emergency orders in the real-time processing tasks.

[0023] Specifically, when the predicted future load state of the equipment exceeds a preset high load threshold, it indicates that the equipment is already operating at or near full load in the future. In this case, the triggering condition of the preset fluctuation threshold is adaptively increased, meaning that a sudden fault is only determined when the fluctuation amplitude of the current equipment operating status parameters significantly exceeds the normal level, in order to avoid false triggering due to normal load fluctuations. When the predicted future load state of the equipment is lower than a preset low load threshold, it indicates that the equipment has sufficient processing margin in the future. In this case, the triggering condition of the preset fluctuation threshold is adaptively decreased, meaning that an imbalance judgment is triggered when the current equipment operating status parameters show slight fluctuations, in order to make full use of idle capacity to cope with potential emergency orders in advance. The high load threshold and low load threshold are set according to 70% and 30% of the rated capacity of the equipment, respectively. These percentages can be adaptively adjusted by those skilled in the art according to the actual operating characteristics of different equipment.

[0024] Next, feature extraction processing is performed on the task queue to obtain the initial task feature vector. Each task in the task queue is traversed, and the target product specification code and planned processing time value for that task are read. The planned processing times of all tasks are sorted in descending order, and the planned processing time corresponding to the task in the middle position after sorting is extracted as the median time. The planned processing times of all tasks are summed and divided by the total number of tasks to obtain the average time. The number of tasks marked as pending processing in the task queue is counted. Then, the median time, average time, and number of pending processing tasks are arranged in a fixed order and combined into a three-dimensional feature vector, which is the initial task feature vector.

[0025] Simultaneously, real-time operating status parameters of the equipment are collected through sensors deployed on the current equipment. These real-time operating status parameters include at least the current vibration frequency of the equipment's main shaft, the equipment's operating temperature, and the current load current. Specifically, the collection involves acquiring vibration signals using a piezoelectric accelerometer mounted on the main shaft bearing housing, which is then amplified by a charge amplifier and converted from analog to digital to obtain the vibration frequency value in Hertz; acquiring the equipment's surface temperature using a thermocouple temperature sensor, which is then converted from analog to digital to obtain the temperature value in degrees Celsius; and acquiring the current load current using a current transformer connected in series with the equipment's power supply line, which is then converted from analog to digital to obtain the current value in Ampersands. The collected vibration frequency, temperature, and current values ​​are arranged in a fixed field order to form a three-dimensional operating status vector. Subsequently, the initial task feature vector and the operating status vector are concatenated end-to-end, merging the elements of the two vectors into a longer vector. The merged vector has six dimensions, which is the multi-dimensional production status vector.

[0026] Then, the values ​​of each dimension in the multidimensional production state vector are compared with preset fluctuation thresholds to detect whether there is a sudden failure or an emergency order insertion. The preset fluctuation thresholds are set by collecting operating status data of each dimension for 24 consecutive hours during which the equipment operates continuously without failure records, covering different operating conditions of the equipment and at least one complete production product switching cycle. The average value and standard deviation of the data for each dimension within this time period are calculated, and the fluctuation threshold corresponding to each dimension is set as the average value of the data for that dimension plus three times the standard deviation. The values ​​of the three dimensions representing vibration frequency, temperature, and load current in the multidimensional production state vector are compared with their respective preset fluctuation thresholds. If the value of any dimension is greater than its corresponding preset fluctuation threshold, it is determined that the current equipment has a sudden failure or the production line has received a high-priority emergency order insertion task. The feature dimensions of the multidimensional production state vector derived from the task queue do not participate in the fluctuation threshold comparison, but are only used as the basis for task allocation decisions when generating subsequent scheduling instructions.

[0027] If a sudden fault or emergency order is detected, a scheduling instruction is generated based on the multi-dimensional production state vector. If a sudden fault is detected, the specific values ​​of equipment vibration frequency, temperature, and load current are extracted from the multi-dimensional production state vector and matched with a preset equipment fault code mapping table. The fault code mapping table is stored in read-only memory and contains the mapping relationship between the numerical ranges of vibration frequency, temperature, and current and the corresponding fault types. It is obtained by acquiring historical data of the equipment operating under different fault modes, including at least the vibration frequency, temperature, and current values ​​under normal operating conditions, bearing wear conditions, rotor imbalance conditions, temperature overload conditions, and current overload conditions. The historical data is extracted from the alarm logs of the equipment operation and maintenance system and the time-series data of the corresponding sensors. The fault codes recorded in the alarm logs serve as the fault type labels corresponding to the data set. For each set of historical data corresponding to a fault mode, the numerical distribution range of each parameter under that fault mode is calculated, and the maximum and minimum values ​​of each parameter are determined as the upper and lower boundaries of the numerical range corresponding to that fault mode. Then, the parameter numerical ranges corresponding to each fault mode and the text description of the fault type are stored in the memory one by one in the form of key-value pairs to form the fault code mapping table. The currently collected 3D numerical values ​​are compared with the numerical ranges in the fault code mapping table to locate the matching fault type. A shutdown and maintenance instruction containing the fault type and equipment identifier is output in text form as a scheduling instruction. If an emergency order is determined to exist, the median duration, average duration, and number of pending tasks are extracted from the multidimensional production status vector. The estimated clearing time of the current task queue is estimated based on the number of pending tasks, and information containing the estimated clearing time and suggested task insertion position is output in text form as a scheduling instruction.

[0028] Finally, the current production line is marked as unbalanced according to the scheduling instruction. The instruction type field in the scheduling instruction is read. If the instruction type is "shutdown for maintenance," the current production line is marked as fault-related and unbalanced; this marking information includes the faulty equipment number and fault type. If the instruction type is "emergency order insertion," the current production line is marked as "load-related and unbalanced"; this marking information includes the total number of tasks awaiting processing and the expected insertion position of the emergency order. The above marking information is written into the production line status register according to a predetermined format. The marking information after writing is the production line resource imbalance state.

[0029] In step S13, the material flow path is obtained based on the production line resource imbalance status, and the process node load of the material flow path is evaluated to obtain the process backlog nodes, including: The material flow path is broken down to obtain each initial path node; Collect the flow time parameters of the devices corresponding to each initial path node, and construct a node load matrix based on the flow time parameters; Anomaly detection is performed on the node load matrix to obtain the abnormal load value corresponding to each initial path node; The initial path node whose abnormal load value is greater than the preset backlog threshold is identified as a process backlog node.

[0030] First, based on the production line resource imbalance status output in the previous steps, the complete material flow path of the current batch of conductive foam is retrieved from the workshop manufacturing execution system. The material flow path refers to the ordered set of all process nodes that the conductive foam must pass through from raw material input to finished product output, with each process node arranged in series according to its processing sequence. Specifically, the process route query interface of the manufacturing execution system is called, using the current batch number as the input parameter to obtain the process route identifier corresponding to that batch. Then, based on the process route identifier, all process names and execution sequences contained in the route are retrieved from the process parameter database, forming an ordered process sequence, which is the material flow path. Next, according to the arrangement order of the processes in the material flow path, each process is defined as an independent path node, that is, a complete process route is decomposed into multiple discrete nodes at the process granularity, with each node retaining its original sequential relationship. The set of nodes obtained after decomposition is the initial path node.

[0031] Next, the processing time parameters of the equipment corresponding to each initial path node are collected, and a node load matrix is ​​constructed based on these parameters. For each initial path node, the single-piece processing cycle parameter of the corresponding processing equipment in the current batch, as well as the quantity of material currently accumulated at the feed inlet of the equipment, are read from the manufacturing execution system. The single-piece processing cycle parameter refers to the standard time required for the equipment to process one piece of conductive foam, recorded in seconds. This parameter is entered into the system by the process engineer according to the rated speed and process requirements of the equipment during equipment import. The quantity of material to be processed at each initial path node is used as row data, and the single-piece processing cycle parameter of the equipment corresponding to each node is used as column data. The row index is the arrangement order of the initial path nodes, and the data is written row by row according to the node order, forming a two-dimensional data array with the number of rows equal to the total number of initial path nodes and the number of columns being 2. This array is the node load matrix.

[0032] Subsequently, anomaly detection is performed on the node load matrix to obtain the abnormal load values ​​corresponding to each initial path node. The anomaly detection employs a statistical outlier-based method. Specifically, for each row in the node load matrix, the quantity of materials to be processed in that row is multiplied by the corresponding single-piece processing cycle parameter to obtain the estimated total processing time required for that node to complete all currently accumulated materials. The estimated total processing times of each initial path node are arranged in node order to form a sequence. The arithmetic mean of this sequence is calculated, and then the square of the difference between each value in the sequence and the mean is calculated. All squared values ​​are summed, divided by the total number of nodes, and the square root is taken to obtain the standard deviation of the sequence. For each initial path node, its estimated total processing time is subtracted from the arithmetic mean, and then divided by the standard deviation to obtain the abnormal load value of that node. A positive abnormal load value indicates that the total processing time of that node is higher than the average level, while a negative value indicates that the total processing time of that node is lower than the average level.

[0033] Finally, the initial path nodes whose abnormal load values ​​exceed the preset backlog threshold are identified as process backlog nodes. The preset backlog threshold is determined based on statistical quality control principles, specifically using the conventional anomaly detection principle of the Shewhart control chart. This involves collecting historical flow data for each node within 30 consecutive natural days of normal production line operation without backlog records, calculating a historical abnormal load value sample set using the aforementioned anomaly detection method, calculating the mean and standard deviation of this sample set, and setting the preset backlog threshold as the mean plus twice the standard deviation. Those skilled in the art can also make adaptive adjustments based on the anomaly detection criteria of the GB / T 4091 conventional control chart standard to adapt to backlog detection scenarios with different sensitivity requirements. The abnormal load values ​​of each initial path node are sequentially compared with the preset backlog threshold. If the abnormal load value of a node exceeds the preset backlog threshold, the node is identified as a process backlog node, and its name and corresponding abnormal load value are output as the determination result.

[0034] In step S14, the set of tasks to be processed and idle equipment at the backlog node of the process is obtained, and a combination configuration is performed to obtain an initial allocation scheme. The initial allocation scheme is iteratively optimized to obtain a reallocation instruction, including: The initial allocation scheme is encoded as chromosomes, and the initial population is composed of multiple chromosomes; The initial population is subjected to fitness calculation to obtain the fitness value of each chromosome; The initial population is iteratively optimized based on the fitness value to obtain the optimal chromosome. The optimal chromosome is decoded to obtain a redistribution instruction.

[0035] First, based on the backlog nodes output from the previous steps, all currently backlogged tasks to be processed are extracted from the equipment buffer corresponding to that node. These tasks refer to batches of conductive foam materials that have reached the process node but have not yet started processing; each batch includes a batch number, product specifications, and quantity information. Simultaneously, the real-time status indicators of all equipment in the current production line are scanned. These indicators are periodically updated by the control systems of each device and written to shared memory, with values ​​ranging from "running," "standby," or "faulty." Devices with the status indicator "standby" are selected, and their device numbers and type information are extracted to form a set of idle devices.

[0036] Next, the tasks to be processed and the set of idle equipment are combined and configured to generate an initial allocation scheme. The tasks to be processed are divided into multiple task batches. During the division, all tasks to be processed are first grouped according to the product specifications of the conductive foam (including thickness, length, and adhesive type) to ensure that the specifications of the conductive foam in the same batch are exactly the same. Then, for the tasks to be processed in the same specification group, they are evenly divided according to the principle that the number of conductive foam contained in each batch is equal. If the total quantity in the group cannot be divided evenly by the number of equipment allocated to the group, the remainder is allocated to the corresponding batch in ascending order of the number of equipment already allocated. For each task batch, one processing equipment is allocated to it from the set of idle equipment, forming a "task batch-equipment" allocation mapping record. All task batches are traversed to complete the generation of all mapping records, and the set of these mapping records is used as an allocation scheme. The above allocation process is repeated multiple times, each time using a random allocation method to generate different combinations of mapping records, resulting in multiple allocation schemes. The set of these allocation schemes is the initial allocation scheme.

[0037] Subsequently, the initial allocation scheme is encoded as chromosomes. Each allocation scheme corresponds to one chromosome, using real number encoding. This means that the task batches in the allocation scheme are arranged sequentially as gene loci on the chromosome, and the value stored at each gene locus is the number of the idle device allocated to that task batch. For example, if there are 5 task batches and 3 idle devices, then one chromosome consists of 5 gene loci, each with a value between 1 and 3, corresponding to the numbers of the 3 devices. Each allocation scheme in the initial allocation scheme is encoded in the above manner to obtain multiple corresponding chromosomes, which constitute the initial population. The size of the initial population is determined based on the total number of task batches to be processed, specifically by multiplying the total number of task batches by two and rounding up; if the calculated value is less than twenty, it is rounded up to twenty.

[0038] Then, the initial population is subjected to fitness calculation to obtain the fitness value of each chromosome. For each chromosome in the initial population, based on the equipment allocation scheme encoded by the chromosome and combined with the single-piece processing cycle parameters of each idle device, the processing process of all task batches is simulated. Specifically, for each device assigned a task, the number of all task batches assigned to that device is multiplied by the single-piece processing cycle parameter of that device to obtain the processing time required for that device to complete all assigned tasks. The maximum processing time among all devices is taken as the maximum completion time of the allocation scheme. The reciprocal of the maximum completion time is taken as the original fitness value of the chromosome, and then multiplied by a preset scaling factor to obtain the final fitness value. The larger the fitness value, the shorter the processing time of the allocation scheme. The preset scaling factor is set by dividing the total number of tasks to be processed by ten and rounding up to obtain the coefficient value, which is used to adjust the fitness value to a numerical range that is easy to compare; those skilled in the art can adjust the scaling factor according to the actual order of magnitude of the fitness value, and its specific value does not affect the optimization direction.

[0039] Finally, the initial population is iteratively optimized based on the fitness value to obtain the optimal chromosome. The iterative optimization process uses a genetic algorithm, specifically including selection, crossover, and mutation operations. The selection operation employs a roulette wheel selection method, where the probability of each individual being selected is proportional to its fitness value relative to the total fitness value of the population. Two individuals are selected from the current population as parents based on the aforementioned probabilities. The crossover operation performs single-point crossover on the two selected parent chromosomes, randomly selecting a crossover position and exchanging all genes after that position between the two parent chromosomes to generate two offspring chromosomes. The mutation operation performs mutation on gene loci in the offspring chromosomes at a preset mutation probability, randomly changing the device number at that locus to another device number from the set of idle devices.

[0040] It should be noted that the preset mutation probability was set by testing the population convergence performance in an offline simulation environment with mutation probabilities of 0.01, 0.03, 0.05, 0.07, and 0.10. Specifically, multiple test cases of different sizes were constructed, with the total number of task batches covering 10, 20, 50, and 100 batches, and the number of idle devices covering 3, 5, 10, and 20 units, forming a total of 16 test scenarios to cover the typical scheduling scale of a conductive foam production workshop. For each test scenario and each mutation probability value, the genetic algorithm was run independently 50 times, and the iteration generation and final fitness value were recorded at convergence in each run. The average of the convergence generation in the 50 runs was used as the convergence speed index, and the average of the final fitness values ​​in the 50 runs was used as the solution quality index. The weighted sum of the normalized values ​​of the convergence speed index and the solution quality index for each mutation probability is calculated as the comprehensive performance score, where the weight of the solution quality index is 0.6 and the weight of the convergence speed index is 0.4. The mutation probability with the highest comprehensive performance score is used as the preset value, and the mutation probability is set to 0.05 after testing. The normalization process is achieved by subtracting the minimum value of the original index value under each mutation probability and then dividing by the range of the index, i.e., the maximum value minus the minimum value, to eliminate the dimensional differences between different indices. The above selection, crossover, and mutation operations constitute an iteration. The above iteration is repeated until the preset maximum iteration is reached. The preset maximum iteration is determined by testing the convergence curves of the fitness function when the maximum iteration is 50, 100, 200, and 500 in an offline simulation environment. The minimum iteration corresponding to the fitness value change rate being less than 1% is selected as the preset value, and the maximum iteration is set to 200 after testing. When the iteration terminates, the chromosome with the highest fitness value is selected from the final population, and this chromosome is the optimal chromosome.

[0041] The optimal chromosome is decoded to obtain a reallocation instruction. The decoding method involves reading the device number at each gene locus in the optimal chromosome, combining this device number with the corresponding task batch number to generate a mapping list of "task batch number - device number". This mapping list is then encapsulated into a text instruction according to a predetermined format. This text instruction contains the device number, a list of task batch numbers assigned to that device, and the quantity of each batch. After being sent to the production line control system, it guides material distribution and equipment task allocation; this text instruction is the reallocation instruction.

[0042] In step S15, the completion time of the preceding process of the task to be processed is obtained according to the reallocation instruction, and the processing flow timing is reconstructed according to the completion time of the preceding process to obtain the adjusted processing flow, including: According to the reallocation instruction, obtain the current processing sequence of each device corresponding to the task to be processed and the completion time of the previous process; Calculate the process connection delay based on the current processing sequence and the completion time of the previous process; The processing timing is reconstructed by performing processing sequence reconstruction on the process connection delay to obtain the process flow nodes; Based on each process flow node and the reallocation instruction, an adjusted processing flow is generated.

[0043] First, the reallocation instruction output from the preceding steps is parsed to extract the task batch list assigned to each device. For each device assigned a task, the existing processing tasks and their processing order in its internal production scheduling queue are read. The production scheduling queue is stored in the controller cache of each device, recording all tasks currently scheduled for production by that device and their order. The new task batches from the reallocation instruction are appended sequentially to the end of each device's existing production scheduling queue to obtain the updated task arrangement order for each device. This order is the current processing order for each device.

[0044] Next, the process connection delay is calculated based on the current processing sequence and the completion time of the preceding process. The completion time of the preceding process refers to the moment when each task batch completes processing in the upstream process before entering the current process. This moment is read from the process completion record of the manufacturing execution system and stored as an absolute timestamp. For each task batch, its preceding process completion time is read, and then the estimated start time of the task batch on the current equipment is determined according to the current processing sequence. The estimated start time is calculated as follows: for the first task in the equipment scheduling queue, its estimated start time is the larger value between the current moment and the equipment ready moment; for subsequent tasks in the queue, its estimated start time is the estimated completion time of the previous task. The estimated start time of a task batch is subtracted from its preceding process completion time to obtain the difference. If the difference is greater than or equal to zero, the difference is used as the process connection delay of the task batch, and its value reflects the time span of materials waiting for processing after flowing from the upstream process to the current process. If the difference is less than zero, it indicates that the current equipment has been scheduled to start work before the previous process has been completed. In this case, the estimated start time of the task batch is adjusted to the completion time of the previous process. The adjusted estimated start time is then subtracted from the completion time of the previous process, and the process connection delay is recalculated to be zero. The estimated start time of the subsequent task batches affected in the current processing sequence is then postponed in sequence.

[0045] Then, the processing sequence is reconstructed based on the process connection delay to obtain the process flow nodes. First, all task batches are sorted in ascending order of process connection delay value from low to high. The lower the process connection delay, the shorter the material waiting time, and the earlier it should be scheduled to start to eliminate the risk of production line stagnation caused by waiting. For task batches with the same process connection delay, they are sorted in lexicographical order according to the target product specification code of each task batch. Second, the processing order of each task batch is determined according to the above sorting results. For the first task batch after sorting, its expected start time is set to the greater of the current time and the completion time of the previous process of the task batch. For subsequent task batches after sorting, its expected start time is set to the greater of the completion time of the previous process of the task batch and the expected completion time of the previous task batch, ensuring that the material arrival time and equipment availability time both meet the constraints. Finally, for each task batch, the processing time is calculated based on the single-piece processing cycle parameters of the equipment and the number of conductive foams contained in the task batch. The expected start time is added to the processing time to obtain the expected completion time of the task batch on the equipment. Record the equipment number, task batch number, estimated start time and estimated completion time of each task batch in a fixed format to form a time sequence record. The estimated completion time in each time sequence record is a process flow node.

[0046] Finally, based on the process flow nodes and the reallocation instructions, an adjusted processing flow is generated. All the process flow nodes obtained above are organized according to the dimension of each device. For each device, the process flow nodes of all task batches on that device are arranged in chronological order to form a timeline scheduling sequence for that device. The scheduling sequences of each device are merged and associated according to the device allocation relationship recorded in the reallocation instructions, forming a two-dimensional table containing four columns of information: device number, task batch number, estimated start time, and estimated completion time. Each row in this two-dimensional table represents the processing time arrangement of a task batch on a specified device, and adjacent rows maintain a strict temporal constraint relationship according to their chronological order. This two-dimensional table is the adjusted processing flow, which clearly defines the specific processing time period and corresponding processing device for each task batch after reallocation in a structured data form.

[0047] In step S16, the waiting time of the subsequent process is obtained according to the adjusted processing flow, and the scheme scheduling fusion process is performed to generate the target production task allocation sequence, including: Based on the adjusted processing flow, the available time window for the equipment in the subsequent process is obtained by identifying the equipment status. The waiting time for the subsequent process is then calculated based on the available time window and the adjusted processing flow. When the waiting time of the subsequent process exceeds a preset conflict threshold, a resource allocation instruction is generated. The adjusted processing flow is scheduled and merged according to the resource allocation instruction to obtain a globally optimized schedule. The completion time of each node is extracted based on the global optimized schedule. When the completion time of each node meets the preset delivery date requirement, the target production task allocation sequence is output.

[0048] First, based on the adjusted processing flow output from the preceding steps, identify the downstream equipment corresponding to each task batch and determine the available time window for each downstream equipment. The downstream equipment refers to the equipment corresponding to the next processing step that the conductive foam needs to enter after completing the current process. Extract the estimated completion time of each task batch and the equipment number corresponding to its subsequent process from the adjusted processing flow. For each downstream equipment, read the estimated completion time of each task in its current production queue, mark the completion time intervals of these existing tasks as occupied time intervals, and then determine the scan start time. The scan start time is determined by first determining whether the current time is within any occupied time interval of an existing task. If the current time is not within any occupied time interval, then the current time is used as the scan start time. If the current time is within an occupied time interval, read the actual processed quantity or actual running time of the task, combine it with the planned total processing time of the task, estimate the remaining processing time of the task, and use the current time plus the remaining processing time as the scan start time. If the actual progress data of the task cannot be obtained, the end time of the occupied time interval shall be used as the scan start time.

[0049] Starting from the initial scan time, the system scans backwards to identify idle periods not occupied by any existing tasks, and consecutive idle periods are designated as the available time window for the equipment. Next, the waiting time for subsequent processes is calculated based on the available time window and the adjusted processing flow. For each task batch, the estimated completion time recorded in the adjusted processing flow is taken as its arrival time at the subsequent process, and this arrival time is compared with the start time of the next available time window for the subsequent process equipment. If the arrival time is earlier than or equal to the start time of the available time window, the waiting time for the subsequent process is zero, indicating that the subsequent process equipment was idle or just available when the batch arrived. If the arrival time is later than the start time of the available time window, the arrival time is subtracted from the start time of the available time window, and the difference is the waiting time for the subsequent process, representing the queuing time required for the task batch to arrive at the subsequent process.

[0050] Subsequently, when the waiting time of the downstream process exceeds a preset conflict threshold, a resource allocation instruction is generated. The preset conflict threshold is determined based on the material buffer capacity of the conductive foam production workshop. Specifically, it is calculated by obtaining the maximum material capacity of the material buffer set in front of the downstream process equipment, dividing this quantity by the single-piece processing cycle time parameter of the downstream process equipment, and obtaining the buffer's maximum waiting time. This waiting time is used as the preset conflict threshold. The downstream process waiting time of each task batch is compared one by one with the preset conflict threshold. If the downstream process waiting time of a task batch exceeds the preset conflict threshold, it is determined that the task batch has a risk of material backlog, triggering the generation of a resource allocation instruction. If the downstream process waiting time of a task batch is less than or equal to the preset conflict threshold, it is determined that the task batch does not have a risk of material backlog, no resource allocation instruction is generated, the current equipment allocation scheme remains unchanged, and the current equipment allocation scheme is directly used as the equipment allocation scheme in subsequent scheduling fusion processing. Resource allocation instructions include instructions to wake up backup equipment or task splitting instructions. The "wake-up standby equipment" command refers to a scheme where, when the available time window of the downstream process equipment is insufficient to accommodate the currently queued materials, a start signal is sent to a standby equipment in standby mode with the same processing capacity, activating the standby equipment and incorporating it into the current schedule. The "task splitting" command refers to a scheme where the currently backlogged tasks are divided into two parts according to a preset splitting ratio, with one part maintaining its original equipment allocation and the other part allocated to the standby equipment for parallel processing.

[0051] Then, the adjusted processing flow is fused according to the resource allocation instruction to obtain a globally optimized schedule. Specifically, the resource allocation instruction is parsed to obtain the task batch number, the number of splits, and the target backup equipment number. The original record of the task batch is located from the adjusted processing flow, and the total number of tasks in the batch is divided into two parts according to the number of splits. One part maintains the original equipment allocation, and the other part is allocated to the target backup equipment. The estimated start time of the materials allocated to the target backup equipment is set to the tail time of the current production queue of the target backup equipment. The estimated completion time of this part of the materials is recalculated based on this time and the single-piece processing cycle parameter of the target backup equipment. The time sequence records of the two split parts are updated to the adjusted processing flow, replacing the original single records to form the updated processing flow. All split tasks involved in the resource allocation instruction are processed in sequence, and the globally optimized schedule is obtained after all updates are completed.

[0052] Finally, the completion time of each node is extracted based on the global optimized schedule. When the completion time of a node meets the preset delivery date requirement, the target production task allocation sequence is output. The preset delivery date requirement refers to the final delivery time required by the target order, which is written into the order information table by the production execution system when the order is placed. The estimated completion time of the task batches on each equipment corresponding to the last processing step is extracted from the global optimized schedule, and the maximum value is taken as the final completion time. The final completion time is compared with the preset delivery date requirement. If the final completion time is earlier than or equal to the preset delivery date requirement, it is determined that the global optimized schedule meets the delivery date requirement. The task batches in the global optimized schedule are organized into an ordered sequence format according to the equipment number, batch number, estimated start time, and estimated completion time. This sequence is the target production task allocation sequence, which is output to the production line control system for execution.

[0053] If the final completion time is later than the preset delivery date requirement, further iterative adjustments will be performed. The strategy selection logic for iterative adjustments is to first determine whether the current scheduling scheme has invoked the splitting mechanism. If no splitting mechanism has been invoked, strategy two (invoking the splitting mechanism to increase the allocation ratio of spare equipment) will be executed first. If the splitting mechanism has been invoked but the splitting ratio has reached the maximum allocation capacity of the spare equipment available for this process, strategy one will be executed. Strategy one is to reduce the search space for chromosome coding in step S14, specifically by removing the slowest processing cycle equipment from the set of idle equipment, reducing the range of values ​​for the equipment number coding, and re-executing steps S14 to S16. Strategy two is to invoke the splitting mechanism in step S16 to increase the allocation ratio of spare equipment, specifically by increasing the current splitting ratio by 20%, and re-executing the scheduling fusion process in step S16. Each time strategy one or strategy two is executed, the iterative adjustment count is incremented by one, and the iterative adjustment count is globally valid within the processing cycle of the current batch of tasks. If the final completion time is still later than the delivery date requirement after three iterations, the iteration adjustment is terminated. The global optimized schedule with the smallest difference between the final completion time and the delivery date requirement among the three iterations is output as the current optimal schedule, and the risk of delivery delay is marked.

[0054] In summary, this invention achieves rapid dynamic scheduling and balanced resource utilization of conductive foam production lines under sudden abnormal situations by predicting future equipment load status, identifying backlog nodes in processes, and using genetic algorithms for intelligent task redistribution and global scheduling optimization.

[0055] The second embodiment of the present invention provides a digital management system for a conductive foam production line, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described above.

[0056] It should be noted that the conductive foam production line digital management system provided in this embodiment of the invention is used to execute all process steps of the conductive foam production line digital management method in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0057] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0058] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A digital management method for a conductive foam production line, characterized in that, include: Acquire historical order data, equipment operation data, and real-time processing tasks for conductive foam; perform time-series feature fusion on the historical order data and the equipment operation data to obtain the future load status of the equipment. The real-time processing task is detected based on the future load status of the equipment. If there is a sudden failure or emergency order insertion that causes the production line to become unbalanced, the current production line is judged to be unbalanced, and the production line resource imbalance status is obtained. Based on the imbalance of production line resources, the material flow path is obtained, and the process node load of the material flow path is evaluated to obtain the process backlog nodes. Obtain the set of tasks to be processed and idle equipment at the backlog node of the process, and combine them to obtain an initial allocation scheme. Iterate and optimize the initial allocation scheme to obtain a reallocation instruction. The completion time of the preceding process of the task to be processed is obtained according to the reallocation instruction. The processing flow sequence is reconstructed according to the completion time of the preceding process to obtain the adjusted processing flow. The waiting time for subsequent processes is obtained based on the adjusted processing flow, and the scheme scheduling is integrated to generate a target production task allocation sequence.

2. The digital management method for conductive foam production line according to claim 1, characterized in that, The step of fusing the historical order data and the equipment operation data based on time-series features to obtain the future load status of the equipment includes: The historical order data and the equipment operation data are aligned and concatenated to construct an initial multidimensional data matrix; Extract the time series features of the initial multidimensional data matrix to obtain the first hidden state sequence; The feature values ​​in the first hidden state sequence that are greater than the feature threshold are subjected to nonlinear activation processing to obtain the second hidden state sequence; The second hidden state sequence is dimensionality-reduced to obtain the future load state of the device.

3. The digital management method for conductive foam production line according to claim 1, characterized in that, The process of determining the current production line's imbalance status to obtain the production line resource imbalance status includes: Extract the task queue from the real-time processing task, and perform feature extraction processing on the task queue to obtain an initial task feature vector; Collect the current operating status parameters of the equipment, and concatenate the initial task feature vector with the operating status parameters to construct a multi-dimensional production status vector; If there is a value in the multidimensional production state vector that is greater than the preset fluctuation threshold, it is determined that the real-time processing task has a sudden failure or an emergency order insertion, and a scheduling instruction is generated according to the multidimensional production state vector. The current production line is marked as unbalanced according to the scheduling instructions to obtain the production line resource imbalance status.

4. The digital management method for conductive foam production line according to claim 1, characterized in that, The process node load assessment of the material flow path to obtain process backlog nodes includes: The material flow path is broken down to obtain each initial path node; Collect the flow time parameters of the devices corresponding to each initial path node, and construct a node load matrix based on the flow time parameters; Anomaly detection is performed on the node load matrix to obtain the abnormal load value corresponding to each initial path node; The initial path node whose abnormal load value is greater than the preset backlog threshold is identified as a process backlog node.

5. The digital management method for conductive foam production line according to claim 1, characterized in that, The iterative optimization of the initial allocation scheme to obtain the reallocation instruction includes: The initial allocation scheme is encoded as chromosomes, and the initial population is composed of multiple chromosomes; The initial population is subjected to fitness calculation to obtain the fitness value of each chromosome; The initial population is iteratively optimized based on the fitness value to obtain the optimal chromosome. The optimal chromosome is decoded to obtain a redistribution instruction.

6. The digital management method for conductive foam production line according to claim 1, characterized in that, The step of obtaining the completion time of the preceding process of the task to be processed according to the reallocation instruction, and reconstructing the processing flow sequence according to the completion time of the preceding process to obtain the adjusted processing flow includes: According to the reallocation instruction, obtain the current processing sequence of each device corresponding to the task to be processed and the completion time of the previous process; Calculate the process connection delay based on the current processing sequence and the completion time of the previous process; The processing timing is reconstructed by performing processing sequence reconstruction on the process connection delay to obtain the process flow nodes; Based on each of the process flow nodes and the reallocation instructions, an adjusted processing flow is generated.

7. The digital management method for conductive foam production line according to claim 1, characterized in that, The step of obtaining the waiting time of the subsequent process according to the adjusted processing flow, and performing scheme scheduling fusion processing to generate the target production task allocation sequence includes: Based on the adjusted processing flow, the available time window for the equipment in the subsequent process is obtained by identifying the equipment status. The waiting time for the subsequent process is then calculated based on the available time window and the adjusted processing flow. When the waiting time of the subsequent process exceeds a preset conflict threshold, a resource allocation instruction is generated. The adjusted processing flow is scheduled and merged according to the resource allocation instruction to obtain a globally optimized schedule. The completion time of each node is extracted based on the global optimized schedule. When the completion time of each node meets the preset delivery date requirement, the target production task allocation sequence is output.

8. The digital management method for conductive foam production line according to claim 7, characterized in that, The resource allocation instructions include instructions to wake up backup devices or instructions to split tasks.

9. A digital management system for a conductive foam production line, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method described in any one of claims 1 to 8.

Images