Intelligent control method and system based on medicine packaging production line

Abstract

The application discloses an intelligent control method and system based on a medicine packaging production line, and relates to the technical field of medicine packaging. Through the cooperation of the production capacity extraction correction end, the efficiency curve fitting end and the energy consumption curve construction end, the unit time production capacity of the same type and specification production line under different production rates is extracted, the actual production efficiency is obtained by combining the yield evaluation correction, and the standard energy consumption index is constructed simultaneously. In a unified two-dimensional coordinate system, the production rate fluctuation broken line, the actual production efficiency fluctuation curve and the standard energy consumption fluctuation curve are fitted, and a multi-index correlation model is established. Then, the preset step process is traversed, the area of the closed region enclosed by the standard energy consumption and the actual production efficiency curve in each interval is calculated as the process characteristic, the process characteristics are sorted and screened to determine the optimal production rate interval, and the dynamic regulation and control of all production lines is implemented according to the optimal production rate interval, so that the running rate is constrained in the optimal interval.

Description

Technical Field

[0001] This invention belongs to the field of pharmaceutical packaging technology, specifically relating to intelligent control methods and systems based on pharmaceutical packaging production lines. Background Technology

[0002] As a key link in the production chain, the operational efficiency and energy consumption control level of pharmaceutical packaging directly affect the company's operating costs and drug supply capacity. In the context of large-scale continuous production, achieving synergistic optimization of production rate, output efficiency and energy consumption has become an important direction for lean management in pharmaceutical companies.

[0003] In existing pharmaceutical packaging production line control practices, most companies still rely on manual experience or simple fixed parameter settings to manage production rates, lacking the ability to systematically mine historical operating data and make cross-production line comparisons. Typically, capacity, yield rate, and energy consumption at different production rates are considered as independent indicators. For example, the production department pursues high output and blindly increases the rate, the quality department calculates the yield rate afterward, and the energy management department only records the total electricity consumption. There is no dynamic mathematical correlation model between the three, which means that when the production line is running at a certain rate, managers cannot know in real time whether there is an optimal balance between the actual production efficiency and standard energy consumption after comprehensively considering the yield rate at that rate. This often results in pseudo-optimization phenomena such as increased rate but a surge in defective products, rework costs offsetting capacity gains, or decreased rate but increased energy consumption per unit product.

[0004] Existing technologies typically adjust production rates in a discrete and lagging manner. For example, manual speed reduction is only implemented when an abnormal yield rate is detected in a batch. However, this reactive adjustment has already caused actual losses. For multiple production lines of the same type and specifications within the same workshop, existing technologies lack a unified cloud-based data extraction and collaborative analysis mechanism. Each production line operates independently, and the performance differences between them cannot be integrated into a global optimization basis. Managers can only try and fail on each production line separately, which is inefficient and inconsistent in standards. At the control execution level, fixed threshold methods are usually used instead of dynamic update closed-loop feedback adjustment, making the production line unable to adapt to changes in production conditions over time. After long-term operation, it will inevitably deviate from the true optimal state.

[0005] To address the aforementioned problems, this invention proposes an intelligent control method and system based on pharmaceutical packaging production lines. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent control method and system based on pharmaceutical packaging production lines, solving the problem that existing technologies in the pharmaceutical packaging field cannot dynamically optimize production rate, actual efficiency, and energy consumption in a coordinated manner.

[0007] The objective of this invention can be achieved through the following technical solutions: A smart control method based on a pharmaceutical packaging production line, the method comprising: Step 1: Based on the cloud database, extract the unit time capacity of several drug packaging production lines of the same type and specifications under different production rates. Combined with the yield rate obtained from the evaluation, determine the actual production efficiency of the drug packaging production line under different production rates. Step 2: Construct a production rate fluctuation line in a two-dimensional coordinate system in an incremental manner, and fit the actual production efficiency fluctuation curve in the same coordinate system based on the correspondence between the actual production efficiency value and the production rate, and perform visualization display. Step 3: Extract the real-time energy consumption of the pharmaceutical packaging production line at different production rates from the cloud database, align it to the production rate fluctuation line in the two-dimensional coordinate system, and construct a standard energy consumption fluctuation curve. Step four: Based on the numerical relationship between the standard energy consumption fluctuation curve and the actual production efficiency fluctuation curve under different progress processes, lock the optimal process characteristics associated with the pharmaceutical packaging production line, determine the optimal production rate range, and perform optimization control on all pharmaceutical packaging production lines based on the optimal production rate range.

[0008] As a further aspect of the present invention, the specific method for extracting the unit-time capacity of several pharmaceutical packaging production lines of the same type and specification under different production rates based on a cloud database is as follows: Identify any pharmaceutical packaging production line and mark it as the target pharmaceutical packaging production line; Obtain the safe production rate range [Vmin, Vmax] of the target drug packaging production line, where the safe production rate range is considered a known value; Identify all pharmaceutical packaging production lines of the same type and specifications as the target pharmaceutical packaging production line from the pharmaceutical packaging production line workshop, and record the total number as j. Then, summarize the j pharmaceutical packaging production lines as the pharmaceutical packaging production line set BZ1, BZ2, ..., BZj. Extract any pharmaceutical packaging production line BZi, where i is the counting index, 1≤i≤j; Historical operating parameters associated with the BZi pharmaceutical packaging production line were extracted from a cloud-based database. The unit time capacity of the drug packaging production line BZi is locked within the safe production rate range [Vmin, Vmax] at each production rate, where unit time is minutes and capacity is the number of packaged drugs; The safe production rate interval [Vmin, Vmax] is discretized into a production rate sequence V1, V2, ..., Vn, where V1 = Vmin, Vn = Vmax, and n is the total number of discretized production rates; By mapping the unit time capacity of the pharmaceutical packaging production line BZi at each production rate to the production rate sequence V1, V2, ..., Vn, the unit time capacity sequence C1, C2, ..., Cn is obtained. Similarly, determine the unit time capacity sequence associated with each of the j drug packaging production lines.

[0009] As a further aspect of the present invention, in step one, the specific method for determining the actual production efficiency of the pharmaceutical packaging production line at different production rates, based on the evaluated yield rate, is as follows: Obtain all historical operating parameters of the pharmaceutical packaging production line BZi at production rate V1, and obtain the total number of packaged pharmaceuticals, denoted as SUM1; The total number of defective items in the SUM1 packaged medicine is then extracted and recorded as SUM2. The yield rate QP1 of the pharmaceutical packaging production line BZi at production rate V1 is calculated using QP1 = SUM2 / SUM1 × 100%. Extract the unit time capacity C1 of the pharmaceutical packaging production line BZi at a production rate V1; The actual production efficiency PD1 of the pharmaceutical packaging production line BZi at production rate V1 is calculated using PD1=C1×QP1. Similarly, by determining the actual production efficiency of the pharmaceutical packaging production line BZi at different production rates, the actual production efficiency sequence PD1, PD2, ..., PDn can be obtained. Determine the actual production efficiency sequence associated with each of the j drug packaging production lines, extract the j actual production efficiencies corresponding to the j drug packaging production lines at production rate V1, take the average value, and denot it as the standard actual production efficiency BPD1 associated with production rate V1. By analogy, the standard actual production efficiency associated with different production rates is determined, resulting in the standard actual production efficiency sequence BPD1, BPD2, ..., BPDn.

[0010] As a further aspect of the present invention, the specific method for constructing the production rate fluctuation line in the two-dimensional coordinate system in an incremental manner in step two is as follows: Extract the discretized production rate sequence V1, V2, ..., Vn from the safe production rate interval [Vmin, Vmax]; Construct a two-dimensional coordinate system XY1, where the horizontal axis has equal-span scales and the total number of scales is n, and the vertical axis represents the production rate. Starting from the 0 mark on the horizontal axis, the n production rates in the production rate sequence V1, V2, ..., Vn are plotted on the two-dimensional coordinate system XY1, with the marked equal spans aligned with the marks on the vertical axis. This yields n data points, which are then connected by short lines to form an increasing broken line, denoted as the production rate fluctuation broken line L1.

[0011] As a further aspect of the present invention, the specific method for fitting the actual production efficiency fluctuation curve and performing visualization in step two is as follows: Extract a two-dimensional coordinate system XY1, and lock the maximum scale of the production rate fluctuation line L1 in the two-dimensional coordinate system XY1 on the vertical axis. Perform segmentation on the vertical axis at this maximum scale, and let the scale above the maximum scale represent the actual production efficiency, with the value increasing from 0 to the scale increment, to obtain the vertical axis Y2 representing the actual production efficiency. Mark the part of the vertical axis where the origin of the coordinate system and the maximum scale of the production rate fluctuation line L1 are located on the vertical axis as the vertical axis Y1. Extract the standard actual production efficiency sequence, and based on the alignment relationship between the production rate sequence and the standard actual production efficiency sequence, plot the standard actual production efficiency sequence in the region of the vertical axis Y2 in the two-dimensional coordinate system XY1 to obtain n data points. Fit the curve to obtain a complete curve, which is denoted as the actual production efficiency fluctuation curve W1. Visualize the two-dimensional coordinate system XY1.

[0012] As a further aspect of the present invention, the specific method for constructing the standard energy consumption fluctuation curve in step three is as follows: Based on the cloud database, determine the energy consumption of any pharmaceutical packaging production line BZi at different production rates, and align the production rate sequence V1, V2, ..., Vn to obtain the energy consumption sequence P1, P2, ..., Pn; Similarly, determine the energy consumption sequence associated with each of the j drug packaging production lines, and take the average energy consumption of the j drug packaging production lines at any production rate to obtain the standard energy consumption at the corresponding production rate. Align the production rate sequence and summarize it as the standard energy consumption sequence BP1, BP2, ..., BPn. Extract the two-dimensional coordinate system XY1, lock the maximum scale of the actual production efficiency fluctuation curve W1 in the two-dimensional coordinate system XY1 on the vertical axis, perform segmentation on the vertical axis at this maximum scale, let the scale above the maximum scale represent the standard energy consumption, and let the value increase from 0 on the scale to obtain the vertical axis Y3 representing the standard energy consumption, where Y1, Y2, and Y3 are segmented but not divided; Extract the standard energy consumption sequence. Based on the alignment relationship between the production rate sequence and the standard energy consumption sequence, plot the standard energy consumption sequence in the region of the vertical axis Y3 in the two-dimensional coordinate system XY1 to obtain n data points. Fit the curve to obtain a complete curve, which is denoted as the standard energy consumption fluctuation curve W2.

[0013] As a further embodiment of the present invention, the two-dimensional coordinate system XY1 in step three simultaneously includes the production rate fluctuation line L1, the actual production efficiency fluctuation curve W1, and the standard energy consumption fluctuation curve W2, with W1 located between L1 and W2 and not in contact with either L1 or W2, and W2 located above L1 and W1.

[0014] As a further aspect of the present invention, the specific method for locking the preferred process characteristics associated with the pharmaceutical packaging production line in step four is as follows: Extract the two-dimensional coordinate system XY1; Obtain the preset step process, where the span of the step process corresponds to u ticks on the horizontal axis, where u is a preset integer, u≤n; Using the 0 mark on the horizontal axis as the starting mark of the first stepping process, determine u marks in the positive direction of the horizontal axis to obtain the ending mark, and obtain the first stepping process, denoted as BG1; Then, the first stepping process BG1 is shifted one scale along the horizontal axis in the positive direction of the horizontal axis to obtain the second stepping process BG2; This process continues until the stepping process has covered n marks on the horizontal axis. The total number of marks is denoted as r, and is represented as BG1, BG2, ..., BGr. Extract the starting and ending marks of the first step of the process BG1 on the horizontal axis, and construct straight lines K1 and K2 that are perpendicular to the horizontal axis, parallel to the vertical axis, and pass through L1, W1, and W2 respectively. Extract the area values ​​of the closed region formed by K1, K2, W1, and W2, and denote them as the process feature JZ1 associated with the first forward process BG1; Similarly, determine r process characteristics, which are represented as JZ1, JZ2, ..., JZr respectively; Sort the r process features in ascending order of their numerical values, and mark the top α% of process features as preferred process features.

[0015] As a further aspect of the present invention, in step four, determining the optimal production rate range and performing optimized control on all pharmaceutical packaging production lines based on the optimal production rate range is specifically implemented as follows: In the two-dimensional coordinate system XY1, mark the stepping process corresponding to all the preferred process features on the horizontal axis and label them as preferred scales; The discrete production rate values ​​at the corresponding preferred scale on the production rate fluctuation line L1 are locked and summarized in ascending order, and recorded as the optimal production rate interval [Vgmin, Vgmax]. The optimal production rate interval [Vgmin, Vgmax] is updated once every preset update cycle. Based on the determined optimal production rate range [Vgmin, Vgmax], optimize and control the j pharmaceutical packaging production lines. If the production rate of any pharmaceutical packaging production line under normal operating conditions is less than Vgmin, then its production rate shall be adjusted to Vgmin. If the production rate is greater than Vgmax, then adjust its production rate to Vgmax; Conversely, continuous monitoring is necessary.

[0016] An intelligent control system based on a pharmaceutical packaging production line, the system comprising: The capacity extraction and calibration end extracts the unit time capacity of several drug packaging production lines of the same type and specification under different production rates based on the cloud database. Combined with the yield rate obtained from the evaluation, the actual production efficiency of the drug packaging production line under different production rates is locked. For the efficiency curve fitting end, a production rate fluctuation polygon is constructed in a two-dimensional coordinate system in an incremental manner, and the actual production efficiency fluctuation curve is fitted in the same coordinate system based on the correspondence between the actual production efficiency value and the production rate, and then visualized. On the energy consumption curve construction side, the energy consumption of the pharmaceutical packaging production line at different production rates is extracted in real time from the cloud database, aligned to the production rate fluctuation line in the two-dimensional coordinate system, and a standard energy consumption fluctuation curve is constructed. The process optimization and control unit, based on the numerical relationship between the standard energy consumption fluctuation curve and the actual production efficiency fluctuation curve under different progress processes, locks the optimal process characteristics associated with the drug packaging production line, determines the optimal production rate range, and performs optimization control on all drug packaging production lines based on the optimal production rate range.

[0017] The beneficial effects of this invention are: (1) This invention integrates historical production capacity and yield data, locks the actual production efficiency at each production rate, and synchronously associates energy consumption data to construct a dynamic fluctuation curve between production rate, actual efficiency and energy consumption. Based on the numerical relationship of process characteristics, it intelligently identifies the optimal production rate range, realizes the optimized control of the pharmaceutical packaging production line, breaks through the blindness of traditional experience-based speed adjustment, realizes data-driven refined control, improves the qualified product output rate per unit time, reduces redundant energy consumption, and achieves the optimal balance between efficiency and energy consumption under the premise of ensuring yield. At the same time, the visualized curve makes it easy for operation and maintenance personnel to intuitively grasp the production line status, and provides a reliable decision basis for multi-production line collaborative control. (2) This invention extracts historical operating parameters of multiple production lines of the same type and specifications from cloud databases and discretizes the safe production rate range to build a multi-data foundation, avoiding the influence of random errors of a single production line; on this basis, the yield rate is introduced to correct the unit time capacity, thereby obtaining the actual production efficiency, which more realistically reflects the comprehensive efficiency of the production line than simply using the capacity index, and can accurately identify the efficiency decline problem that may be caused by the increase of defective products at high speed; by calculating the average of the actual production efficiency of multiple production lines at the same production rate, a standard actual production efficiency sequence is obtained, eliminating random differences between individual production lines, providing a reliable benchmark for horizontal comparison of different production rates and selection of the optimal rate, and achieving process optimization without additional experiments, which is both economical and scientific. (3) The production rate fluctuation line constructed by the present invention in an incremental manner can clearly reflect the rate change trend; secondly, the actual production efficiency and standard energy consumption are fitted into a continuous curve and placed in different segments of the vertical axis, which ensures data independence, realizes the comparison of the same graph, facilitates the quick identification of the matching degree and abnormal fluctuations between rate and efficiency and energy consumption, and the standard energy consumption curve is always at the top, forming an intuitive reference benchmark, which helps operators to evaluate the energy-saving space and efficiency loss of the production line in real time, provides clear data support for optimizing scheduling, and significantly improves the intuitiveness of production monitoring and decision-making efficiency. (4) By dividing the process step by step, extracting process features and selecting the optimal ratio, this invention can accurately lock the optimal production rate range of the pharmaceutical packaging production line, realize the dynamic adaptive adjustment of the production rate, quantify the process features by using the area of ​​the closed region in the two-dimensional coordinate system, effectively eliminate random fluctuation interference, and ensure the objectivity and representativeness of the optimized process features; the optimal production rate range obtained by summarizing the discrete rate values ​​corresponding to the optimized scale can be dynamically adjusted with the update cycle, so that the production line always adapts to the real-time working conditions; when the production rate of any production line deviates from the range, it is automatically adjusted to the boundary value, thereby avoiding the efficiency drop caused by the low rate or the packaging defects caused by the high rate, and improving the stability of the production line operation, resource utilization and consistency of pharmaceutical packaging quality. Attached Figure Description

[0018] The invention will now be further described with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart illustrating the method described in this invention. Detailed Implementation

[0020] 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.

[0021] This invention provides an intelligent control method for pharmaceutical packaging production lines. It aims to dynamically identify the optimal production rate range through big data analysis and visualization technology, thereby adaptively optimizing and controlling the pharmaceutical packaging production line to comprehensively improve actual production efficiency and reduce energy consumption while ensuring the yield rate.

[0022] like Figure 1 As shown, this application provides an intelligent control method and system based on a pharmaceutical packaging production line; As an embodiment 1 of this application, it specifically includes: Step 1: Based on the cloud database, extract the unit time capacity of several drug packaging production lines of the same type and specifications under different production rates. Combined with the yield rate obtained from the evaluation, determine the actual production efficiency of the drug packaging production line under different production rates. Step 2: Construct a production rate fluctuation line in a two-dimensional coordinate system in an incremental manner, and fit the actual production efficiency fluctuation curve in the same coordinate system based on the correspondence between the actual production efficiency value and the production rate, and perform visualization display. Step 3: Extract the real-time energy consumption of the pharmaceutical packaging production line at different production rates from the cloud database, align it to the production rate fluctuation line in the two-dimensional coordinate system, and construct a standard energy consumption fluctuation curve. Step four: Based on the numerical relationship between the standard energy consumption fluctuation curve and the actual production efficiency fluctuation curve under different progress processes, lock the optimal process characteristics associated with the pharmaceutical packaging production line, determine the optimal production rate range, and perform optimization control on all pharmaceutical packaging production lines based on the optimal production rate range.

[0023] Example 2 This embodiment, based on embodiment 1, further discloses a method for constructing standard efficiency curves and basic control based on historical data from multiple production lines, specifically including the following: Before implementation, it should be clarified that the core process of the intelligent control method and system based on the pharmaceutical packaging production line described in this embodiment is applicable to a pharmaceutical workshop with multiple pharmaceutical packaging production lines of the same type and specifications. For example, a pharmaceutical workshop has a drug packaging production line consisting of 10 (j=10) blister packaging machines of the same model, used to package tablets of the same specification. The workshop's industrial internet platform is equipped with a cloud database to collect and store historical operating parameters of each drug packaging production line.

[0024] First, identify any one of the pharmaceutical packaging production lines, such as the production line numbered BZi (i=1,2,...,j). Obtain its safe production rate range [Vmin,Vmax] from the equipment manual for that pharmaceutical packaging line or the parameters pre-entered into the cloud database. For example, the rated rate range of pharmaceutical packaging production line BZi is 100 to 300 plates per minute, i.e., 100 to 300 plates / minute. The aforementioned safe rate range of 100 to 300 plates / minute is discretized into a production rate sequence, with a step of 20 plates per minute, resulting in the production rate sequence: V1=100, V2=120, V3=140,...,Vn=300. The discretization accuracy can be set according to actual control requirements; here, n=11.

[0025] Historical operating parameters of all 10 pharmaceutical packaging production lines (BZ1 to BZ10) are extracted from the cloud database. For any pharmaceutical packaging production line BZi, the actual number of packages completed per unit time (per minute) is queried when it is running stably at each discrete production rate, forming its unit time capacity sequence. For example, the query shows that the historical records of pharmaceutical packaging production line BZ1 at V5=180 plates / minute show that the actual average output per minute is 178 plates. Taking the pharmaceutical packaging production line BZ1 at a production rate of V1=100 plates / minute as an example, all historical batch data under this working condition are extracted from the cloud database. If a batch is found to have packaged a total of SUM1=10000 plates of medicine, and the total number of defective products detected is SUM2=50 plates, then the yield rate QP1=(1-50 / 10000)×100%=99.5%; Actual production efficiency PD1 = unit time capacity C1 × yield rate QP1. If C1 is 99 boards / minute, then PD1 = 99 × 99.5% = 98.505 boards / minute. Round down from 98.505 boards / minute to 98 boards / minute.

[0026] The above calculations were performed on all 10 pharmaceutical packaging production lines to obtain 10 actual production efficiency values ​​for each line at V1=100 plates / minute. The arithmetic mean of these values ​​was taken as the standard actual production efficiency BPD1 associated with the production rate V1 for this type of pharmaceutical packaging production line. Repeat this process to calculate the standard actual production efficiency corresponding to all discretized production rates V1 to Vn, forming a standard actual production efficiency sequence BPD1, BPD2, ..., BPDn.

[0027] Next, a two-dimensional coordinate system XY1 is constructed. The horizontal axis of the two-dimensional coordinate system XY has n equally spaced scales (1 to 11), representing the discretized production rate index. Plot the production rate values ​​100, 120, 140, ..., 300 plates / minute on the horizontal axis corresponding to the vertical axis. Then connect these data points sequentially with short lines to form an increasing broken line, namely the production rate fluctuation broken line L1. The production rate fluctuation broken line L1 shows the set production rate step. The part of the vertical axis corresponding to the production rate fluctuation broken line L1 is marked as the vertical axis Y1.

[0028] Next, the vertical axis of the coordinate system XY1 is segmented. The highest point of the production rate fluctuation line L1 is 300 plates / minute. At this highest point, the vertical axis is extended upward to create a new vertical axis Y2 with equal scale. Its scale range starts from 0 to represent the actual production efficiency. The scale interval of the vertical axis Y2 is the same as that of the vertical axis Y1. The standard actual production efficiency sequence BPD1, BPD2, ..., BPDn is plotted according to the alignment relationship (BPD1 corresponds to the horizontal axis index 1, V1=100) onto the area of ​​the vertical axis Y2. For example, BPD1=98, BPD2=118, etc. These data points are fitted with a smooth curve to form the actual production efficiency fluctuation curve W1. Finally, the two-dimensional coordinate system XY1 containing L1 and W1 is visualized on the display screen in the workshop control room. It is important to note that before actually plotting the actual production efficiency fluctuation curve W1, the standard actual production efficiency sequence BPD1, BPD2, ..., BPDn needs to be mapped to a unified numerical space using the range standardization method to eliminate the influence of the artificial scale ratio between the dimensions and the two-dimensional coordinate system XY1.

[0029] This embodiment integrates historical data from multiple similar devices, eliminating random errors in individual devices and constructing a standard actual production efficiency curve representing the overall level of the production line. The visualization allows operators to intuitively see the impact of changes in production rate on actual effective output, facilitating subsequent optimization and control.

[0030] Example 3 This embodiment further introduces energy consumption indicators based on embodiment 2, and enriches the decision-making information by constructing a three-dimensional coaxial visualization model, specifically including the following: This embodiment follows the workshop scenario in Embodiment 2 to jointly maximize actual production efficiency and optimize energy consumption costs; First, a standard energy consumption fluctuation curve is constructed. For any pharmaceutical packaging production line BZi, real-time associated energy consumption data at various discrete production rates is extracted from the cloud database. Energy consumption is defined as the total power consumption of the production line per unit time (minute). The energy consumption is collected by high-precision smart meters connected in series in the main power supply circuit of the pharmaceutical packaging production line. For example, it is found that the average power consumption of the BZ1 production line is P5=2.3kWh per minute when V5=180 plates / minute.

[0031] The arithmetic mean of the energy consumption of 10 pharmaceutical packaging production lines at the same production rate is taken to obtain the standard energy consumption at that production rate. This process is repeated to form the standard energy consumption sequence BP1, BP2, ..., BPn, corresponding to the production rate sequence 100, 120, ..., 300 plates / minute. It should be noted that the energy consumption of the 10 pharmaceutical packaging production lines at the same production rate is taken as the arithmetic average. Before obtaining the standard energy consumption at this production rate, it is necessary to ensure that the energy consumption of each pharmaceutical packaging production line is based on the pharmaceutical packaging production line under normal operating conditions. That is, abnormal energy consumption data caused by abnormal operating conditions such as equipment maintenance, idling waiting for materials, and equipment failure are excluded. The specific judgment rule is that if a single recorded value deviates from the average of the batch by more than 3 times the standard deviation, it is marked as abnormal. Based on the coordinate system XY1 constructed in Example 2, at the maximum point of the actual production efficiency fluctuation curve W1, the vertical axis is extended upward again to create a third vertical axis Y3 with equal scale. Its scale starts from 0 to represent standard energy consumption. The scale interval of the vertical axis Y3 is the same as that of the vertical axis Y2. It is important to note that the vertical axes Y1, Y2, and Y3 are visually segmented but not divided, sharing the same horizontal axis, which allows parameters with three different dimensions to be aligned and analyzed in the same coordinate system.

[0032] Next, the standard energy consumption sequence BP1, BP2, ..., BPn is plotted in the region of the vertical axis Y3 according to the alignment relationship with the production rate (BP1 corresponds to V1=100). For example, BP1=1.2kWh, BP2=1.4kWh, BP5=2.3kWh, and finally n data points are obtained. These data points are fitted into the standard energy consumption fluctuation curve W2 using a smooth curve. It is important to note that before actually plotting the standard energy consumption fluctuation curve W2, the standard energy consumption sequence BP1, BP2, ..., BPn needs to be mapped to a unified numerical space using the range standardization method to eliminate the influence of the artificial scale ratio between the dimensions and the two-dimensional coordinate system XY1.

[0033] Ultimately, the two-dimensional coordinate system XY1 simultaneously presents an increasing production rate fluctuation line L1, an actual production efficiency fluctuation curve W1, and a standard energy consumption fluctuation curve W2, with W1 located between L1 and W2 and not in contact with either L1 or W2, while W2 is located above L1 and W1. This embodiment constructs a multi-dimensional data alignment model by segmenting and integrating three key indicators—production rate, actual production efficiency, and energy consumption—in the same coordinate system. This enables operators and subsequent upper-level algorithms to intuitively analyze the dynamic relationship between rate, efficiency, and energy consumption, facilitating the subsequent search for the production rate range with optimal overall performance.

[0034] Example 4 This embodiment is based on Embodiment 3 and further discloses a dynamic optimal interval identification and closed-loop control based on step process characteristics, specifically including the following: This embodiment provides a method for determining the optimal rate range based on preset parameters. The stepping process span u = 5 scales is defined. As can be seen from Embodiment 2, the horizontal axis has a total of 11 scales (0-10, corresponding to V1-V11). Starting from the 0 mark on the horizontal axis, take 5 marks (marks 0 to 4) in the positive direction of the horizontal axis as the first step process BG1. Then shift one mark to the right to get the second step process BG2 (marks 1 to 5). Repeat this process until all marks are covered, resulting in a total of r = 11 - 5 + 1 = 7 step processes: BG1 (0-4), BG2 (1-5), BG3 (2-6), BG4 (3-7), BG5 (4-8), BG6 (5-9), BG7 (6-10). Taking the first step of the process BG1 (horizontal axis scale 0 to 4) as an example, at scale 0 and scale 4, two straight lines K1 and K2 are constructed respectively, which are perpendicular to the horizontal axis and parallel to the vertical axis. The straight lines K1 and K2 pass through the production rate fluctuation line L1, the actual production efficiency fluctuation curve W1 and the standard energy consumption fluctuation curve W2. The four lines K1, K2, W1, and W2 enclose a closed region in the two-dimensional coordinate system XY1. The area of ​​this closed region is calculated by numerical integration. In this embodiment, the composite trapezoidal method is used. The area value of the closed region is defined as the process feature JZ1 associated with the stepping process BG1; The size of the process feature JZ1 area value comprehensively reflects the trade-off between actual production efficiency and energy consumption within the rate range. The smaller the area, the smaller the energy consumption increase while achieving higher production efficiency. Similarly, calculate the process features corresponding to all 7 step processes: BG1 to BG7: JZ1 to JZ7. Multiply the calculated values ​​of the 7 process features by the preset efficiency weight coefficient λ and perform normalization to balance energy consumption sensitivity. Then, sort the 7 step processes in ascending order according to the normalized values, and take the top α%, α%=20%, that is, the two process features with the smallest values, and mark them as the preferred process features. For example, after sorting, JZ3 corresponds to step process BG3, scales 2-6 and JZ4 correspond to BG4, and the value of scale 3-7 is the smallest. Then, the horizontal scale ranges 2-6 and 3-7 covered by these two step processes are marked in the two-dimensional coordinate system XY1 and are recorded as the preferred scales. Lock the production rate values ​​corresponding to these optimal scales on the production rate fluctuation line L1. For example, scale 2 on L1 corresponds to V3=140 plates / minute, scale 6 corresponds to V7=220 plates / minute, scale 3 corresponds to V4=160 plates / minute, and scale 7 corresponds to V8=240 plates / minute. Summarize these values ​​in ascending order to obtain the optimal production rate range [Vgmin, Vgmax]. This range should be [160, 220] plates / minute. Take the minimum and maximum values ​​of all optimal scales. Next, when implementing actual optimization and control, the optimal production rate range [Vgmin, Vgmax] is obtained from the above steps, which is [160, 220] plates / minute; The system monitors the current operating production rate of all 10 pharmaceutical packaging production lines in the workshop in real time. If it is found that the current rate of any pharmaceutical packaging production line BZ5 is 140 plates / minute, which is less than Vgmin=160, an instruction is sent to the controller of that pharmaceutical packaging production line to gradually increase its production rate target value and stabilize it at 160 plates / minute.

[0035] If the current production rate of pharmaceutical packaging line BZ9 is found to be 250 plates / minute, which is greater than Vgmax=220, then its production rate target value should be reduced and stabilized at 220 plates / minute.

[0036] For production lines whose rates are already within the optimal range, such as the pharmaceutical packaging production line BZ2 with a current rate of 200 plates / minute, the system will only continuously monitor without making adjustments. This embodiment achieves automated evaluation of the comprehensive performance of rate, efficiency, and energy consumption through a step-by-step process quantitative analysis window, avoiding the drawbacks of relying on experience or single peak judgment. The resulting closed-loop control strategy can dynamically and automatically adjust all production lines to the rate range with optimal comprehensive performance, achieving the dual goals of improving overall efficiency and reducing energy consumption in actual production.

[0037] Example 5 This embodiment, based on embodiment 4, further describes a periodic dynamic update mechanism for the optimal production rate range to address long-term or short-term disturbances such as equipment aging and material batch differences, ensuring the continuous effectiveness of the control strategy. Specifically, it includes the following: Following Example 4, after the workshop has been running for a period of time, it is found that due to mold wear, the yield rate of all pharmaceutical packaging production lines at high speed (>200 plates / minute) has slightly decreased in consistency, causing the standard actual production efficiency curve W1 to change. This triggers the update mechanism, obtains the preset update cycle of 24 hours, and automatically triggers a complete recalculation process of the optimal production rate range at the end of each update cycle. Retrieve the latest historical operating parameters of all production lines within the safe production rate range over the past 24 hours from the cloud database, as these new parameters reflect the actual production capacity and yield rate after mold wear. Based on the new data, following the steps of Examples 2 to 4, the standard actual production efficiency sequence and the standard energy consumption sequence are recalculated, and the production rate fluctuation line L1, the actual production efficiency fluctuation curve W1, and the standard energy consumption fluctuation curve W2 are redrawn.

[0038] The optimal range is re-locked, and the process characteristics are recalculated, sorted and filtered using the step process method in Example 4, so as to obtain a new optimal production rate range that adapts to the current state of the equipment. For example, the new range becomes 150-200 plates / minute. The newly calculated optimal production rate range of 150-200 plates / minute will be issued and will override the original control parameters. From now on, all optimization and control of pharmaceutical packaging production lines will be based on this optimal production rate range of 150-200 plates / minute. For example, production line BZ9, which was previously adjusted to 220 plates / minute, will now be adjusted back to 200 plates / minute because its current rate of 220 plates / minute is greater than the new Vgmax200.

[0039] The dynamic periodic update mechanism introduced in this embodiment enables the application to have adaptive capabilities, effectively cope with the drift of system characteristics caused by factors such as equipment wear, environmental changes, and material differences during the production process, and ensure that the optimal production rate range always reflects the latest and most realistic production conditions. Compared with static parameter settings, it enhances the robustness and long-term effectiveness of the control system and continuously ensures that the pharmaceutical packaging production line operates at the best comprehensive performance point.

[0040] All data in the formulas described above have been calculated with dimensions removed. Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0041] The above description is merely an example and illustration of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0042] It should be stated that all user data collected in this application was collected with the user's consent and authorization. Furthermore, the uses of user data are legal and compliant, and the use and processing of user data comply with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. An intelligent control method based on a pharmaceutical packaging production line, characterized in that, The method includes: Step 1: Based on the cloud database, extract the unit time capacity of several drug packaging production lines of the same type and specifications under different production rates. Combined with the yield rate obtained from the evaluation, determine the actual production efficiency of the drug packaging production line under different production rates. Step 2: Construct a production rate fluctuation line in a two-dimensional coordinate system in an incremental manner, and fit the actual production efficiency fluctuation curve in the same coordinate system based on the correspondence between the actual production efficiency value and the production rate, and perform visualization display. Step 3: Extract the real-time energy consumption of the pharmaceutical packaging production line at different production rates from the cloud database, align it to the production rate fluctuation line in the two-dimensional coordinate system, and construct a standard energy consumption fluctuation curve. Step four: Based on the numerical relationship between the standard energy consumption fluctuation curve and the actual production efficiency fluctuation curve under different progress processes, lock the optimal process characteristics associated with the pharmaceutical packaging production line, determine the optimal production rate range, and perform optimization control on all pharmaceutical packaging production lines based on the optimal production rate range.

2. The method according to claim 1, characterized in that, In step one, the specific method for extracting the unit-time capacity of several pharmaceutical packaging production lines of the same type and specification under different production rates based on the cloud database is as follows: Identify any pharmaceutical packaging production line and mark it as the target pharmaceutical packaging production line; Obtain the safe production rate range [Vmin, Vmax] of the target drug packaging production line, where the safe production rate range is considered a known value; Identify all pharmaceutical packaging production lines of the same type and specifications as the target pharmaceutical packaging production line from the pharmaceutical packaging production line workshop, and record the total number as j. Then, summarize the j pharmaceutical packaging production lines as the pharmaceutical packaging production line set BZ1, BZ2, ..., BZj. Extract any pharmaceutical packaging production line BZi, where i is the counting index, 1≤i≤j; Historical operating parameters associated with the BZi pharmaceutical packaging production line were extracted from a cloud-based database. The unit time capacity of the drug packaging production line BZi is locked within the safe production rate range [Vmin, Vmax] at each production rate, where unit time is minutes and capacity is the number of packaged drugs; The safe production rate interval [Vmin, Vmax] is discretized into a production rate sequence V1, V2, ..., Vn, where V1 = Vmin, Vn = Vmax, and n is the total number of discretized production rates; By mapping the unit time capacity of the pharmaceutical packaging production line BZi at each production rate to the production rate sequence V1, V2, ..., Vn, the unit time capacity sequence C1, C2, ..., Cn is obtained. Similarly, determine the unit time capacity sequence associated with each of the j drug packaging production lines.

3. The method according to claim 2, characterized in that, In step one, the specific method for determining the actual production efficiency of the pharmaceutical packaging production line at different production rates, based on the evaluated yield rate, is as follows: Obtain all historical operating parameters of the pharmaceutical packaging production line BZi at production rate V1, and obtain the total number of packaged pharmaceuticals, denoted as SUM1; The total number of defective items in the SUM1 packaged medicine is then extracted and recorded as SUM2. The yield rate QP1 of the pharmaceutical packaging production line BZi at production rate V1 is calculated using QP1 = SUM2 / SUM1 × 100%. Extract the unit time capacity C1 of the pharmaceutical packaging production line BZi at a production rate V1; The actual production efficiency PD1 of the pharmaceutical packaging production line BZi at production rate V1 is calculated using PD1=C1×QP1. Similarly, by determining the actual production efficiency of the pharmaceutical packaging production line BZi at different production rates, the actual production efficiency sequence PD1, PD2, ..., PDn can be obtained. Determine the actual production efficiency sequence associated with each of the j drug packaging production lines, extract the j actual production efficiencies corresponding to the j drug packaging production lines at production rate V1, take the average value, and denot it as the standard actual production efficiency BPD1 associated with production rate V1. By analogy, the standard actual production efficiency associated with different production rates is determined, resulting in the standard actual production efficiency sequence BPD1, BPD2, ..., BPDn.

4. The method according to claim 3, characterized in that, In step two, the specific method for constructing the production rate fluctuation line in the two-dimensional coordinate system in an incremental manner is as follows: Extract the discretized production rate sequence V1, V2, ..., Vn from the safe production rate interval [Vmin, Vmax]; Construct a two-dimensional coordinate system XY1, where the horizontal axis has equal-span scales and the total number of scales is n, and the vertical axis represents the production rate. Starting from the 0 mark on the horizontal axis, the n production rates in the production rate sequence V1, V2, ..., Vn are plotted on the two-dimensional coordinate system XY1, with the marked equal spans aligned with the marks on the vertical axis. This yields n data points, which are then connected by short lines to form an increasing broken line, denoted as the production rate fluctuation broken line L1.

5. The method according to claim 4, characterized in that, In step two, the specific method for fitting the actual production efficiency fluctuation curve and performing visualization is as follows: Extract a two-dimensional coordinate system XY1, and lock the maximum scale of the production rate fluctuation line L1 in the two-dimensional coordinate system XY1 on the vertical axis. Perform segmentation on the vertical axis at this maximum scale, and let the scale above the maximum scale represent the actual production efficiency, with the value increasing from 0 to the scale increment, to obtain the vertical axis Y2 representing the actual production efficiency. Mark the part of the vertical axis where the origin of the coordinate system and the maximum scale of the production rate fluctuation line L1 are located on the vertical axis as the vertical axis Y1. Extract the standard actual production efficiency sequence, and based on the alignment relationship between the production rate sequence and the standard actual production efficiency sequence, plot the standard actual production efficiency sequence in the region of the vertical axis Y2 in the two-dimensional coordinate system XY1 to obtain n data points. Fit the curve to obtain a complete curve, which is denoted as the actual production efficiency fluctuation curve W1. Visualize the two-dimensional coordinate system XY1.

6. The method according to claim 5, characterized in that, In step three, the specific method for constructing the standard energy consumption fluctuation curve is as follows: Based on the cloud database, determine the energy consumption of any pharmaceutical packaging production line BZi at different production rates, and align the production rate sequence V1, V2, ..., Vn to obtain the energy consumption sequence P1, P2, ..., Pn; Similarly, determine the energy consumption sequence associated with each of the j drug packaging production lines, and take the average energy consumption of the j drug packaging production lines at any production rate to obtain the standard energy consumption at the corresponding production rate. Align the production rate sequence and summarize it as the standard energy consumption sequence BP1, BP2, ..., BPn. Extract the two-dimensional coordinate system XY1, lock the maximum scale of the actual production efficiency fluctuation curve W1 in the two-dimensional coordinate system XY1 on the vertical axis, perform segmentation on the vertical axis at this maximum scale, let the scale above the maximum scale represent the standard energy consumption, and let the value increase from 0 on the scale to obtain the vertical axis Y3 representing the standard energy consumption, where Y1, Y2, and Y3 are segmented but not divided; Extract the standard energy consumption sequence. Based on the alignment relationship between the production rate sequence and the standard energy consumption sequence, plot the standard energy consumption sequence in the region of the vertical axis Y3 in the two-dimensional coordinate system XY1 to obtain n data points. Fit the curve to obtain a complete curve, which is denoted as the standard energy consumption fluctuation curve W2.

7. The method according to claim 6, characterized in that, The two-dimensional coordinate system XY1 in step three includes the production rate fluctuation line L1, the actual production efficiency fluctuation curve W1, and the standard energy consumption fluctuation curve W2. W1 is located between L1 and W2 and does not contact L1 or W2. W2 is located above L1 and W1.

8. The method according to claim 7, characterized in that, In step four, the specific method for locking the preferred process characteristics associated with the drug packaging production line is as follows: Extract the two-dimensional coordinate system XY1; Obtain the preset step process, where the span of the step process corresponds to u ticks on the horizontal axis, where u is a preset integer, u≤n; Using the 0 mark on the horizontal axis as the starting mark of the first stepping process, determine u marks in the positive direction of the horizontal axis to obtain the ending mark, and obtain the first stepping process, denoted as BG1; Then, the first stepping process BG1 is shifted one scale along the horizontal axis in the positive direction of the horizontal axis to obtain the second stepping process BG2; This process continues until the stepping process has covered n marks on the horizontal axis. The total number of marks is denoted as r, and is represented as BG1, BG2, ..., BGr. Extract the starting and ending marks of the first step of the process BG1 on the horizontal axis, and construct straight lines K1 and K2 that are perpendicular to the horizontal axis, parallel to the vertical axis, and pass through L1, W1, and W2 respectively. Extract the area values ​​of the closed region formed by K1, K2, W1, and W2, and denote them as the process feature JZ1 associated with the first forward process BG1; Similarly, determine r process characteristics, which are represented as JZ1, JZ2, ..., JZr respectively; Sort the r process features in ascending order of their numerical values, and mark the top α% of process features as preferred process features.

9. The method according to claim 8, characterized in that, In step four, determining the optimal production rate range and then optimizing and controlling all pharmaceutical packaging production lines based on that range is done as follows: In the two-dimensional coordinate system XY1, mark the stepping process corresponding to all the preferred process features on the horizontal axis and label them as preferred scales; The discrete production rate values ​​at the corresponding preferred scale on the production rate fluctuation line L1 are locked and summarized in ascending order, and recorded as the optimal production rate interval [Vgmin, Vgmax]. The optimal production rate interval [Vgmin, Vgmax] is updated once every preset update cycle. Based on the determined optimal production rate range [Vgmin, Vgmax], optimize and control the j pharmaceutical packaging production lines. If the production rate of any pharmaceutical packaging production line under normal operating conditions is less than Vgmin, then its production rate shall be adjusted to Vgmin. If the production rate is greater than Vgmax, then adjust its production rate to Vgmax; Conversely, continuous monitoring is necessary.

10. An intelligent control system based on a pharmaceutical packaging production line, characterized in that: The system includes: The capacity extraction and calibration end extracts the unit time capacity of several drug packaging production lines of the same type and specification under different production rates based on the cloud database. Combined with the yield rate obtained from the evaluation, the actual production efficiency of the drug packaging production line under different production rates is locked. For the efficiency curve fitting end, a production rate fluctuation polygon is constructed in a two-dimensional coordinate system in an incremental manner, and the actual production efficiency fluctuation curve is fitted in the same coordinate system based on the correspondence between the actual production efficiency value and the production rate, and then visualized. On the energy consumption curve construction side, the energy consumption of the pharmaceutical packaging production line at different production rates is extracted in real time from the cloud database, aligned to the production rate fluctuation line in the two-dimensional coordinate system, and a standard energy consumption fluctuation curve is constructed. The process optimization and control unit, based on the numerical relationship between the standard energy consumption fluctuation curve and the actual production efficiency fluctuation curve under different progress processes, locks the optimal process characteristics associated with the drug packaging production line, determines the optimal production rate range, and performs optimization control on all drug packaging production lines based on the optimal production rate range.

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