Control method and system for packaging box production line

Abstract

The invention relates to the technical field of electric data processing, in particular to a control method and system for a packaging box production line, and the method comprises the steps: obtaining the historical operation data of each station on the production line, the historical operation data comprises multi-dimensional indexes at different processing speeds, and the multi-dimensional indexes are used for processing the packaging box; the multi-dimensional indexes comprise the operation stability of the station, the product percent of pass, the failure rate and the cooperative efficiency; and calculating the processing effect score of each station at different processing speeds based on the multi-dimensional indexes. According to the method, multi-dimensional modeling and comprehensive evaluation are carried out on historical operation data of all stations on the packaging box production line, the optimal speed intervals of different stations at different machining speeds are accurately determined, and global speed configuration and coordinated regulation and control are carried out on the whole production line in combination with the particle swarm optimization algorithm; therefore, the overall production efficiency and yield of the production line are effectively improved on the premise that the product machining quality and the station operation stability are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of electrical data processing technology. More specifically, this invention relates to a control method and system for a packaging box production line. Background Technology

[0002] A packaging box production line typically consists of multiple interconnected workstations, including printing, slotting, die-cutting, and gluing, forming a complex automated system. Each workstation undertakes a specific processing task and is equipped with corresponding specialized equipment. For example, the printing press prints text, patterns, or logos onto the cardboard surface; the slotting machine creates creases and grooves on the cardboard for subsequent folding; the die-cutting machine cuts and shapes the cardboard according to the mold; and the gluing machine folds and glues the cardboard to ultimately form a usable packaging box. The workstations are connected sequentially according to the process flow, with strict processing logic dependencies between them. Buffer zones are also needed for material storage and transfer to ensure the continuity and stability of the production process.

[0003] The processing speed of each workstation not only determines its own production cycle time, but also directly affects the operation of upstream and downstream workstations through the transfer of materials in the buffer zone. Therefore, how to coordinate and control the processing speed of each workstation to achieve comprehensive optimization of the entire production line in multiple dimensions such as product quality, operational stability, and production efficiency is the core technical challenge in this field.

[0004] Currently, speed control on production lines often relies on manual experience or employs simple, single-objective optimization strategies, such as maximizing output speed. This approach ignores the operational stability of equipment at different speeds and the variation in product yield at each workstation, and fails to fully consider the dynamic collaborative relationships between workstations. Speed ​​parameters not only affect the processing stability, equipment failure rate, and product yield of each workstation, but also impact the material supply and receiving balance between upstream and downstream workstations. Inappropriate speed settings can lead to increased equipment failure rates, product quality fluctuations, production cycle interruptions, buffer zone congestion, or starvation, thereby reducing the efficiency and stability of the entire production line. Summary of the Invention

[0005] This invention provides a control method and system for a packaging box production line, aiming to solve the problems in related technologies where unreasonable speed settings may lead to increased equipment failure rate, product quality fluctuations, production cycle interruptions, buffer blockages or starvation, thereby reducing the efficiency and stability of the entire production line.

[0006] In a first aspect, the present invention provides a control method for a packaging box production line, comprising: acquiring historical operating data of each workstation on the production line, the historical operating data including multi-dimensional indicators at different processing speeds, the multi-dimensional indicators including the workstation's operational stability, product qualification rate, failure rate, and collaborative efficiency; calculating the processing effect score of each workstation at different processing speeds based on the multi-dimensional indicators, and determining the optimal processing speed range for each workstation based on the processing effect score, wherein the processing effect score is positively correlated with operational stability, product qualification rate, and collaborative efficiency, and negatively correlated with failure rate; randomly selecting a particle from the processing speeds of each workstation within the optimal processing speed range of each workstation to construct a particle swarm, wherein each particle represents a processing speed configuration scheme for a production line; calculating the fitness of each particle, and finding the optimal particle through a particle swarm optimization algorithm, wherein the optimal particle is the optimal speed configuration scheme for each workstation, thereby controlling the operation of the packaging box production line, wherein the fitness reflects the total output of the production line under the particle and the average value of all processing effect scores corresponding to the particle. By using multi-dimensional indicators to comprehensively evaluate different processing speeds and combining them with particle swarm optimization algorithms for global search, the uncertainty caused by relying solely on experience to set speeds can be avoided. This enables accurate identification of the optimal processing speed for each workstation and optimal speed configuration for the entire production line, thereby improving the total output and operational stability of the production line.

[0007] Furthermore, the processing effect score for each workstation at different processing speeds is calculated, including: weighted summation of the operational stability, product qualification rate, failure rate, and collaborative efficiency to obtain the processing effect score. By weighted fusion of multi-dimensional indicators, the complex multi-objective optimization problem can be transformed into a single scoring indicator, simplifying the subsequent processing flow and ensuring that the processing effect score can comprehensively reflect the overall operational performance of the workstation at different speeds.

[0008] Furthermore, the determination of operational stability includes: collecting key operational parameters at a specified workstation at a specific processing speed, including current and voltage; calculating the sum of the variances of the key operational parameters, and using the reciprocal of the sum of variances as the operational stability. By quantifying the fluctuations of key operational parameters such as current and voltage, the stability of the equipment at different processing speeds can be accurately reflected, avoiding equipment failures or operational anomalies caused by speed fluctuations, thereby improving the reliability of the evaluation results.

[0009] Furthermore, the determination of the collaborative efficiency includes: statistically analyzing the average waiting time of the workstation, its upstream workstation, and its downstream workstation during the production process; calculating the collaborative efficiency based on the average waiting time, wherein the average waiting time is negatively correlated with the collaborative efficiency. By introducing the average waiting time index, the degree of collaboration between workstations can be effectively characterized, avoiding frequent idle or overflow of the buffer zone due to uneven speed, thereby improving the overall coordination and continuity of the production line.

[0010] Furthermore, the optimal processing speed range for each workstation is determined, including: constructing a processing effect score sequence based on the historical processing speed and corresponding processing effect score of the workstation; performing curve fitting on the processing effect score sequence to obtain a processing effect score curve; identifying local maxima on the processing effect score curve; expanding outwards from each local maxima as the center until a preset condition is met to obtain the processing speed segment corresponding to the expanded range; and selecting the processing speed segment with the largest average processing effect score as the optimal processing speed range for each workstation.

[0011] Furthermore, the above is negatively correlated with the sum of the capacity anomaly indices of the buffers corresponding to each workstation, where the capacity anomaly index characterizes the degree of overflow or starvation of the buffers corresponding to each workstation. By incorporating buffer capacity anomalies into the comprehensive evaluation system, material blockage or starvation problems caused by unreasonable workstation speed settings can be effectively avoided, thereby improving the overall smoothness of the production line operation.

[0012] Furthermore, the calculation method for the buffer capacity anomaly index corresponding to each workstation includes: for any workstation, obtaining the normal capacity range of that workstation, and the buffer capacity anomaly index corresponding to that workstation is positively correlated with the degree to which the actual capacity in the buffer corresponding to that workstation deviates from its normal capacity range.

[0013] Furthermore, the preset conditions are met, including: the mean of all processing effect scores within the expanded interval is greater than a threshold, where the threshold is the dividing value of all processing effect scores in ascending order that fall within a preset proportion.

[0014] Furthermore, if the preset conditions cannot be met, the largest local maximum value in the processing effect scoring curve is selected, and the processing speed corresponding to the local maximum value is obtained. The range of speed values ​​is taken as the optimal processing speed range for that workstation.

[0015] In a second aspect, the present invention also provides a control system for a packaging box production line, comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the control method for the packaging box production line described in any of the above claims.

[0016] Beneficial effects: By performing multi-dimensional modeling and comprehensive evaluation of the historical operating data of each station on the packaging box production line, the optimal speed range of different stations at different processing speeds can be accurately determined. Combined with the particle swarm optimization algorithm, the entire production line can be configured and coordinated for speed control. This effectively improves the overall production efficiency and output of the production line while ensuring product processing quality and station operation stability. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating an optimal processing speed configuration scheme for a production line according to an embodiment of the present invention. Detailed Implementation

[0018] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] like Figure 1 As shown, S101: Obtain the operating parameters of each workstation.

[0020] Specifically, on a production line, each workstation corresponds to a specific processing task and supporting equipment. For example: a printing workstation (such as a printing press) is responsible for printing graphics on the substrate; a slotting workstation (such as a slotting machine) is responsible for cutting slots on the board or cardboard; a die-cutting workstation (such as a die-cutting machine) is responsible for cutting workpieces according to the mold pattern; and gluing / assembly workstations (such as box gluing machines and assembly machines), etc. The operating parameters of each workstation include its processing speed and its buffer capacity. The processing speed of each workstation refers to the amount of material processed by that workstation per unit time. Furthermore, each workstation is usually separated from its adjacent workstations by a buffer zone for temporarily storing semi-finished products, and the current number of workpieces in the buffer zone reflects the balance between material supply and receiving at upstream and downstream workstations.

[0021] S102: For any workstation, calculate the processing effect score of that workstation at any processing speed.

[0022] Specifically, a single processing speed not only determines the processing cycle time of a workstation but also directly affects the equipment's operational stability, failure rate, product quality, and the degree of coordination with upstream and downstream workstations. When a workstation operates at a certain processing speed, if this speed matches the equipment's optimal operating speed range, its operation is often characterized by stable equipment, minimal fluctuations, reasonable energy consumption, and low maintenance frequency. Furthermore, because the load on mechanical and electrical components is at a reasonable level, the probability of failure is significantly reduced. At the same time, the equipment's processing accuracy and production consistency are guaranteed, thereby maintaining a high product qualification rate, reducing rework and scrap, and lowering production costs.

[0023] Furthermore, in an assembly line, there is a close interdependence between the operations of each station. If the processing speed of the current station can be dynamically balanced with the material supply speed of the previous station and the material receiving capacity of the subsequent station, phenomena such as downstream blockage (forced waiting after output from the previous station) and upstream starvation (the subsequent station waiting for material to be supplied by the current station) can be avoided, significantly improving the continuity of the production cycle and the overall coordination efficiency, making the transfer of materials between stations smoother and reducing the risk of production cycle interruption.

[0024] Therefore, in order to comprehensively evaluate the overall processing level of each workstation at different processing speeds under multi-objective conditions, it is necessary to construct a processing effect score for each workstation at any processing speed based on the above content. This processing effect score is quantified through multi-dimensional indicators, comprehensively considering core factors such as equipment operating stability, product qualification rate, failure rate, and collaborative efficiency, and is constructed in a weighted summation manner. This construction method not only objectively reflects the overall performance of the workstation at a certain speed but also provides a foundation for the subsequent identification of the optimal speed range and the production line speed configuration scheme.

[0025] Specifically, the formula for constructing the processing effect score for each workstation at any processing speed is as follows: In the formula, This represents the processing performance score of the o-th station at the i-th processing speed. This indicates the stability of the o-th station at the i-th processing speed. This represents the product pass rate of the o-th station at the i-th processing speed. This represents the failure rate of the o-th station at the i-th processing speed. This represents the collaborative efficiency of the o-th workstation at the i-th processing speed.

[0026] In this embodiment, the stability of the 0th station at the i-th processing speed is obtained as follows: key parameters of the 0th station, such as current, voltage, and vibration, are selected. The variance values ​​of each key parameter at the i-th processing speed are calculated, and the reciprocal of the sum of all variances is taken as the stability of the 0th station at the i-th processing speed. The smaller the sum of variances, the smaller the fluctuation of each operating parameter, and the higher the stability of the station at that processing speed.

[0027] In this embodiment, the product qualification rate of the o-th station at the i-th processing speed is obtained by: obtaining the ratio of the number of qualified products produced by the o-th station at the i-th processing speed to the total number of products produced, and using the ratio as the product qualification rate of the station (equipment) at the i-th processing speed.

[0028] In this embodiment, the failure rate of the o-th station at the i-th processing speed is obtained by: obtaining the number of failures that occur at the o-th station at the i-th processing speed, and the cumulative running time at the i-th processing speed, and using the ratio of the number of failures to the cumulative running time as the failure rate of the o-th station at the i-th processing speed.

[0029] In this embodiment, the collaborative efficiency of the o-th station at the i-th processing speed is obtained as follows: In assembly line production, the operation of a station is not isolated; its speed must match the material supply rhythm of the preceding station and the material receiving capacity of the following station. If the processing speed of a station is not set reasonably, it will lead to: the preceding station waiting after output (downstream blockage), resulting in low processing efficiency; and the following station waiting for the current station to supply material (upstream idling), resulting in poor processing collaboration. Therefore, whether there is a waiting phenomenon between stations is the most intuitive physical indicator for measuring collaborative efficiency. In summary, a formula for calculating collaborative efficiency is provided, which is: In the formula, Let be the collaborative efficiency of the o-th station at the i-th processing speed. This represents the average waiting time for the 0th workstation and its upstream and downstream workstations at the ith processing speed. Waiting time refers to the duration during which a workstation is forced to remain idle due to a lack of material supply from upstream or downstream during production. This indicates the minimum waiting time, avoiding a denominator of 0; for example, the minimum waiting time is 1. Thus, the processing performance score for each workstation at any processing speed can be obtained.

[0030] S103: Determine the optimal processing speed range for each station based on the processing effect score at any processing speed.

[0031] Any workstation is designated as the target workstation. All historical processing speeds and processing effect scores at each speed are obtained. All historical processing speeds are sorted from smallest to largest to obtain a sorted processing speed sequence. Then, the corresponding processing effect score sequence is obtained. Next, curve fitting is performed on the processing effect score sequence to obtain a processing effect score curve. Local maxima (peak points) are identified within the processing effect score curve, and expansion is performed to the left and right of these local maxima until a preset condition is met. This yields processing speed segments corresponding to all expanded intervals. The processing speed segment with the highest average processing effect score is selected as the optimal processing speed interval for each workstation. In this embodiment, the preset condition is that the average of all processing effect scores within the expanded interval is greater than a threshold. The threshold is obtained by sorting all processing effect scores from smallest to largest, with the threshold being the boundary value of the largest 30% portion after sorting, i.e., the 70th percentile. It should be noted that if the preset condition is not met during expansion, the largest local maximum in the processing effect score curve is selected, and the processing speed corresponding to the local maximum is obtained. The range of speed values ​​is the optimal processing speed range for that station. The speed values ​​can be 5 or 8, etc., and can be adjusted according to the implementation situation.

[0032] S104: Construct initial particles based on the optimal processing speed range for each workstation.

[0033] Specifically, the processing speed of each station on the production line is defined as the set of variables to be optimized. For a production line with M stations, a particle (i.e., a candidate solution) in its solution space can be represented as: ,in Let represent the processing speed of the Mth workstation. The solution space consists of the boundaries of the optimal processing speed intervals for all workstations. This space is an M-dimensional continuous space, where the boundary of each dimension is determined by the optimal processing speed interval for the corresponding workstation.

[0034] S105: Calculate the fitness function for each particle.

[0035] Specifically, for each particle, its fitness must be calculated using the following formula: ;in, This indicates that the entire production line is in particle... fitness under the following conditions This indicates that the entire production line is in particle... The total output quantity under, This indicates that the particle The processing effect score of the i-th workstation. This indicates the total number of workstations in the production line. Indicated in the particle The abnormal index of the buffer capacity corresponding to each workstation. Among them, Including indicators such as stability, pass rate, failure rate, and collaborative efficiency. A higher value means a higher product qualification rate, reduced rework / scrap costs, lower equipment failure rate, less unplanned downtime, optimized collaboration efficiency between workstations, and shorter waiting time. The smaller the value, the closer the buffer capacity is to the ideal state (neither overflowing nor starving), reflecting the dynamic balance of the production line.

[0036] In one embodiment, a calculation is provided The calculation formula is as follows: In the formula, Indicated in the particle Abnormal index of buffer capacity for each workstation This represents the difference between the actual capacity of the buffer at the i-th workstation and the maximum value of its normal capacity range. This represents the difference between the minimum normal capacity range of the buffer at the i-th workstation and the actual capacity of that buffer. This represents the median value of the buffer capacity for all workstations. ()express function. The larger the value, the more likely it is that the particle... The more severe the overflow and starvation situations in the buffers of each workstation, the lower the coordination of the corresponding buffer capacity changes. Actual capacity refers to the quantity produced at that workstation, while buffer capacity refers to the maximum number of products that the buffer can hold. The normal capacity range for any workstation buffer is between 30% and 80% of its capacity. Indicated in particles The capacity anomaly index at the processing speed corresponding to the i-th workstation. Indicates: When The value is greater than or equal to 0. for The actual value, when When less than 0, The value is 0. It should be noted that when A value greater than or equal to 0 indicates that the actual capacity of the buffer at the i-th workstation exceeds the maximum value of its normal capacity range, meaning the buffer at that workstation is overflowing. The larger the value exceeding the limit, the more severe the overflow, and the greater the abnormality index of the buffer capacity at that workstation. Similarly, The meaning is the same as the above.

[0037] It is important to note that the particle level at each workstation can be obtained based on the prediction model. The total output quantity and any station in the particle The prediction model is constructed by collecting the following data from the historical operation records of the production line: the processing speed of each station and the average output quantity of that station at that processing speed. A neural network is used to build the prediction model, taking the processing speed of each station and the average output quantity of that station at that processing speed as input, and outputting the total number of final products. The model construction is existing technology and will not be described in detail here.

[0038] S106: Based on the fitness function of each particle, the optimal processing speed configuration scheme of the production line is obtained.

[0039] After calculating the fitness of each particle, the current particle's fitness is first compared with its historical best fitness. If the current particle's fitness is better, it is updated to a new individual best solution. Then, the particle with the highest fitness among all particles is selected as the global best solution. Next, based on the individual best solution and the global best solution, the velocity of each particle is updated. Finally, the particle's position (i.e., the processing speed of each station) is adjusted using the new velocity, ensuring that the position is within the corresponding optimal processing speed range. This process is repeated until the iteration termination condition is met, resulting in the optimal particle. The scheme consisting of the processing speeds of each station corresponding to the optimal particle is the optimal processing speed configuration scheme for the production line.

[0040] The present invention also provides a control system for a packaging box production line. The system includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the control method for a packaging box production line according to the first aspect of the present invention.

[0041] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and therefore will not be described in detail here.

[0042] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented using computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.

[0043] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A control method for a packaging box production line, characterized in that, include: Acquire historical operating data for each workstation on the production line. The historical operating data includes multi-dimensional indicators at different processing speeds, including workstation operating stability, product qualification rate, failure rate, and collaborative efficiency. Based on the aforementioned multi-dimensional indicators, the processing effect score of each workstation at different processing speeds is calculated, and the optimal processing speed range of each workstation is determined according to the processing effect score. The processing effect score is positively correlated with operational stability, product qualification rate and collaborative efficiency, and negatively correlated with failure rate. Within the optimal processing speed range of each workstation, a particle is randomly selected from the processing speeds of each workstation to construct a particle swarm, and each particle represents a processing speed configuration scheme for a production line. The fitness of each particle is calculated, and the optimal particle in the solution space is found through the particle swarm optimization algorithm to control the operation of the packaging box production line. The optimal particle is the optimal speed configuration scheme of each station, and the solution space is composed of the boundary of the optimal processing speed interval corresponding to all stations. The fitness reflects the total output of the production line under that particle and the mean value of all processing effect scores corresponding to that particle.

2. The control method for a packaging box production line according to claim 1, characterized in that, Calculate the processing effect score for each station at different processing speeds, including: The processing effect score is obtained by weighted summation of the operational stability, product qualification rate, failure rate, and collaborative efficiency.

3. The control method for a packaging box production line according to claim 1, characterized in that, The methods for determining operational stability include: Collect key operating parameters of a specified workstation at a specific processing speed, including current and voltage, etc. Calculate the sum of the variances of the key operating parameters, and take the reciprocal of the sum of variances as the operating stability.

4. The control method for a packaging box production line according to claim 1, characterized in that, The methods for determining the collaborative efficiency include: The average waiting time of the workstation, its upstream workstation, and its downstream workstation during the production process is statistically analyzed; based on the average waiting time, the collaborative efficiency is calculated, and the average waiting time is negatively correlated with the collaborative efficiency.

5. The control method for a packaging box production line according to claim 1, characterized in that, The optimal processing speed range for each workstation was determined, including: Based on the historical processing speed and corresponding processing effect scores of the workstation, a processing effect score sequence is constructed, and curve fitting is performed on the processing effect score sequence to obtain the processing effect score curve. Determine the local maxima on the processing effect scoring curve, and expand outwards from each local maxima as the center until a preset condition is met, thus obtaining the processing speed segment corresponding to the expanded interval. Select the processing speed segment with the largest average processing effect score as the optimal processing speed interval for each workstation.

6. The control method for a packaging box production line according to claim 1, characterized in that, The fitness is also negatively correlated with the sum of the capacity anomaly indices of the buffers corresponding to each workstation, where the capacity anomaly index represents the degree of overflow or starvation of the buffers corresponding to each workstation.

7. The control method for a packaging box production line according to claim 6, characterized in that, The calculation method for the abnormal index of the buffer capacity corresponding to each workstation includes: For any workstation, obtain the normal capacity range of that workstation. The abnormal capacity index of the buffer corresponding to that workstation and the degree to which the actual capacity in the buffer corresponding to that workstation deviates from its normal capacity range are positively correlated.

8. The control method for a packaging box production line according to claim 5, characterized in that, Meet the preset conditions, including: The mean of all processing effect scores within the expanded interval is greater than the threshold, where the threshold is the dividing value of the portion of all processing effect scores that are sorted from smallest to largest and fall within the preset proportion.

9. The control method for a packaging box production line according to claim 5, characterized in that, If the preset conditions cannot be met, then the largest local maximum value in the processing effect scoring curve is selected, and the processing speed corresponding to the local maximum value is obtained. The range of speed values ​​is taken as the optimal processing speed range for that workstation.

10. A control system for a packaging box production line, comprising a processor and a memory, characterized in that, The memory stores a computer program, and the processor executes the computer program to implement the control method for the packaging box production line as described in any one of claims 1-9.

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