An intelligent medicine production line management system based on multi-dosage form quick switching

Abstract

The application discloses an intelligent medicine production line management system based on multi-dosage form quick switching, and relates to the technical field of automatic control of medicine production lines. The system effectively improves production efficiency, equipment utilization and resource conservation through real-time monitoring and optimization of production paths. First, the system pre-models and real-time monitors the production processes of different injections through process modeling and deployment time monitoring modules, which can timely identify problems such as production delay or low equipment efficiency. Second, the quick switching module automatically adjusts the production path according to the deployment waiting time difference, mixing speed time difference and equipment energy consumption data, reducing manual intervention and improving the flexibility and response speed of the production line. In addition, the production path optimization module accurately adjusts the production path according to factors such as equipment energy consumption and production rate to ensure that each injection is produced with the optimal path, maximizing the utilization of equipment resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control of pharmaceutical production lines, in particular to an intelligent pharmaceutical production line management system based on rapid switching of multiple dosage forms. BACKGROUND

[0002] With the continuous development of the pharmaceutical industry and the increasing demand, the production of injections has become a crucial link in pharmaceutical manufacturing. The production process of injections is complex and involves multiple links such as batching, mixing, sterilization, and filling, etc. These processes have very high requirements for equipment performance, process parameters, production efficiency, and safety. However, in traditional injection production, there are some shortcomings that need to be solved urgently.

[0003] Traditional production systems lack effective monitoring and scheduling mechanisms, especially in real-time data monitoring of aspects such as batching waiting time, mixing speed, and energy consumption. In the production process, when there are large differences in process parameters such as batching waiting time and mixing speed, manual intervention is usually required for adjustment, which wastes a lot of time and resources. And lack of ability to optimize production paths based on real-time data, making the production process have great uncertainty and delay.

[0004] Furthermore, with the increase in the variety of injections and the diversification of production demands, traditional production lines are difficult to achieve rapid switching and efficient management. The production of different dosage forms often requires a long debugging time, and there is often waste of raw materials and low production efficiency during the batching process. Due to the complexity of the process, the switching of the production path is not only affected by the running state of the equipment, but also needs to be optimized comprehensively in terms of equipment scheduling, energy consumption control, and production efficiency. Traditional methods often lack comprehensive consideration of these factors, resulting in inflexible production path switching and inability to achieve fine management and optimization. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides an intelligent pharmaceutical production line management system based on rapid switching of multiple dosage forms to solve the problems mentioned in the background art.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: an intelligent pharmaceutical production line management system based on rapid switching of multiple dosage forms, comprising a process modeling module, a batching time monitoring module, a rapid switching module, and a production path optimization module;

[0007] The process modeling module is used to pre-model the production process of different injections. According to the batching process of each injection, the batching waiting time of each raw material is collected and modeled to form a batching waiting time model for different injection types. The working cycle of each injection production equipment is modeled, the time required for the production equipment from start to completion of production is collected and calculated, and an initial production path is preliminarily selected;

[0008] a dispensing time monitoring module for recording the dispensing waiting time Twait of the i-th injection in real time i and the mixing speed Vmix i , and calculating the dispensing waiting time difference ATwait of the i-th injection by comparing with the preset standard value i and the mixing speed time difference AVmix i ; and presetting the first time difference threshold X1 and the second time difference threshold X2, when ATwait i >X1 or AVmix i >X2, triggering the production path switching instruction;

[0009] a fast switching module for, when receiving the production path switching instruction, collecting the energy consumption data of the production equipment based on the dispensing waiting time difference ATwait i of the i-th injection and the mixing speed time difference AVmix i , constructing the switching coefficient Qhxs; presetting the switching energy consumption threshold Y, if the switching coefficient Qhxs is lower than the switching energy consumption threshold Y, triggering the first path switching instruction, and generating the second optimal production path;

[0010] a production path optimization module for, after executing the second optimal production path, collecting the predicted total time Zsj of the i-th injection from the second optimal production path to the production completion, the energy consumption ratio Nh j of the j-th production equipment and the production rate Scs j of the j-th production equipment, constructing the production path state coefficient Ljzt i of the i-th injection, and presetting the comprehensive threshold Z, if the production path state coefficient Ljzt i of the i-th injection exceeds the comprehensive threshold Z, triggering the second path switching instruction, and generating the third optimal production path; if the production path state coefficient Ljzt i of the i-th injection does not exceed the comprehensive threshold Z, continuously producing according to the second optimal production path.

[0011] Preferably, the process modeling module comprises a batching dispensing modeling unit and a production equipment cycle modeling unit;

[0012] The batching dispensing modeling unit is used for collecting the dispensing waiting time of each raw material of different types of injections and related process parameters according to the production requirements of each injection, establishing a raw material dispensing time data set, and pre-establishing a dispensing waiting time model of each injection by using neural network technology according to the raw material dispensing time data set;

[0013] The raw material deployment time dataset includes: injection ID, raw material label, raw material name, deployment waiting time, raw material physical property, process condition and equipment type;

[0014] The raw material physical property includes: raw material density, dissolution rate, dissolution temperature;

[0015] The process condition includes: stirring speed, temperature and humidity;

[0016] The equipment type includes: batching tank, mixing tank, vortex mixer, stirrer, sterilization kettle, lamp inspection machine and sealing machine.

[0017] Preferably, the production equipment cycle modeling unit is used to establish a working cycle model of each injection production equipment, collect the time T cycle required for the production equipment to complete production from start to finish, and preliminarily select an initial production path; the specific operation steps are as follows:

[0018] The equipment type, injection type and the time T cycle,j required for the jth production equipment to complete production from start to finish are recorded for each injection production equipment, and a working cycle dataset is established; the time T cycle,j required for the jth production equipment to complete production from start to finish includes the preheating time T preheat,j , the running time T opreation,j and the sterilization and cleaning time T cleaning,j ;

[0019] The time T cycle,j required for the jth production equipment to complete production from start to finish is obtained through the following summation formula:

[0020] T cycle,j = T preheat,j + T opreation,j + T cleaning,j ;

[0021] Based on the working cycle dataset, an initial production path is selected for each injection, and the initial production path is: preferentially selecting the time T cycle,j required for the jth production equipment to complete production from start to finish to be combined.

[0022] Preferably, the deployment time monitoring module includes a deployment time monitoring unit, a mixing speed monitoring unit and a first comparison unit;

[0023] The deployment time monitoring unit is used to record the deployment waiting time Twait i of the ith injection in real time; and the deployment waiting time difference ΔTwait i of the ith injection is calculated through the following formula:

[0024] ΔTwait i =Twait i -Tstandard i ;

[0025] Among them, Tstandard i This represents the preset standard preparation time value for the i-th type of injectable drug.

[0026] Preferably, the mixing speed monitoring unit is used to acquire the mixing speed Vmix of the i-th injectable agent in real time. i The mixing rate time difference ΔVmix of the i-th injectable agent is calculated using the following formula. i ;

[0027] ΔVmix i =Vmix-Tv i ;

[0028] Among them, TV i This represents the standard time value for the preset mixing speed of the i-th type of injectable drug;

[0029] The first comparison unit is used to preset a first time difference threshold X1 and a second time difference threshold X2, when ΔTwait i >X1 indicates that excessively long waiting times will affect the production efficiency of the i-th type of injection; when ΔVmix i When the speed is greater than X2, it indicates that the mixing speed is too slow, which will cause the production progress to lag and trigger the production path switching instruction. If ΔTwait i ≤X1 and ΔVmix i If the value is less than or equal to X2, it means that the current initial production path is qualified, and production will continue on the initial production path.

[0030] Preferably, the fast switching module includes a production energy consumption acquisition unit, a switching coefficient calculation unit, and a second comparison unit;

[0031] The production energy consumption acquisition unit is used to collect energy consumption data of production equipment when receiving a production path switching command. The production equipment energy consumption data includes: the additional working time Cswitch of the j-th production equipment during the switching process. j and additional power consumption Eswitch j ;

[0032] The switching coefficient calculation unit is used to calculate the dispensing waiting time difference ΔTwait based on the i-th injectable drug. i Mixing speed time difference ΔVmix i The additional working time Cswitch during the switchover process of the j-th production equipment. j Additional power consumption Eswitch j The time T required for the j-th production equipment to complete production from start-up.cycle，j After dimensionless processing, the switching coefficient Qhxs is generated by the following formula:

[0033]

[0034] In the formula, T total represents the standard production time, E pridl represents the normal production energy consumption of the production equipment per unit time, a1, a2, a3, and a4 represent weight coefficients, U equip represents the equipment utilization rate, which is calculated by the following formula:

[0035]

[0036] In the formula, T effective，j is the actual production time of the jth production equipment, T cycle，j represents the time required for the jth production equipment to start and complete production.

[0037] Preferably, the second comparison unit is used to preset a switching energy consumption threshold Y, and compare the switching coefficient Qhxs with the switching energy consumption threshold Y to obtain a second evaluation result, including:

[0038] If the switching coefficient Qhxs is less than the switching energy consumption threshold Y, it indicates that the switching process energy consumption is qualified, and a first path switching instruction is triggered, the first path switching instruction includes: increasing the stirring speed of the current mixing equipment by 3%-5%, adding 1-2 parallel standby devices to reduce the serial waiting between processes, and increasing the raw material feeding rate by 3%-5%, and generating a second optimal production path and a first production path switching report;

[0039] If the switching coefficient Qhxs is greater than or equal to the switching energy consumption threshold Y, it indicates that the switching process energy consumption is abnormal, and no path switching is performed, and the initial production path is maintained for production.

[0040] Preferably, the production path optimization module includes a prediction unit and a third comparison unit;

[0041] The prediction unit is used to collect the total predicted time Zsj required for the i-th injection to complete production in the second optimal production path, the energy consumption ratio Nh j of the jth production equipment, and the production rate Scs j of the jth production equipment after the second optimal production path is executed. i After dimensionless processing, the production path state coefficient Ljzt of the i-th injection is calculated by the following formula:

[0042]

[0043] In the formula, M represents the total number of production equipment in the second optimal production path, Zsj_max represents the maximum total time under all production paths, Nh max represents the maximum energy consumption ratio of the jth device among all production devices, Scs max represents the maximum average production rate of all production devices; and alpha, beta and delta represent weight coefficients, and alpha + beta + delta = 1. represents a time factor term, specifically, the shorter the time required for production, the better; represents an energy efficiency factor term, specifically, the lower the energy consumption of the production equipment, the better; represents a production efficiency factor term, specifically, the higher the production rate, the better, so the term is in the denominator;

[0044] The third comparison unit is used to preset a comprehensive threshold Z, and the production path state coefficient Ljzt i of the i-th injection is compared with the comprehensive threshold Z to obtain a third evaluation result, including:

[0045] If the production path state coefficient Ljzt i of the i-th injection exceeds the comprehensive threshold Z, it indicates that the path has not reached the optimization target, and a second path switching instruction is triggered, including increasing the stirring speed of the current mixing equipment by 6%-9%, adding 3-4 parallel standby devices to reduce the serial waiting time between processes, and increasing the raw material feeding rate by 6%-9%, and generating a third optimal production path and a second production path switching report.

[0046] If the production path state coefficient Ljzt i of the i-th injection does not exceed the comprehensive threshold Z, it indicates that the path has reached the optimization target, and production is continued according to the second optimal production path.

[0047] The present application provides an intelligent medicine production line management system based on multi-dosage form rapid switching. It has the following beneficial effects:

[0048] (1) The intelligent medicine production line management system based on multi-dosage form rapid switching realizes intelligent optimization and rapid switching of production paths, and can dynamically adjust the production path according to real-time data during the production process of different injections, reducing unnecessary production waiting time. Specifically, by increasing the raw material feeding rate, adding parallel devices, optimizing the mixing speed and other means, the production efficiency is significantly improved. For example, in the second optimal production path, by reducing the raw material deployment time and improving the mixing efficiency, a faster production process can be realized, thereby effectively reducing the total production cycle and ultimately improving the overall production capacity of the medicine production line.

[0049] (2) The intelligent medicine production line management system based on multi-dosage form rapid switching can minimize energy consumption while ensuring production efficiency by introducing switching coefficient (Qhxs) and energy consumption threshold (Y). The judgment basis for path switching includes energy consumption and working time data to ensure compliance of equipment energy consumption during production. If the energy consumption of the switching process is low, the system will execute path optimization instructions, which helps to reduce unnecessary energy consumption overhead, thereby reducing overall production cost. For example, by improving equipment utilization and reducing serial waiting time between devices, energy consumption can be effectively controlled, especially in the case of multi-device cooperation, which optimizes the energy use efficiency of production equipment.

[0050] (3) The intelligent medicine production line management system based on multi-dosage form rapid switching can quickly respond to production requirements and process changes of different types of injections, ensuring that the production line can flexibly respond to the switching of multi-dosage form products. By real-time monitoring, predicting and adjusting the production path, the system can generate different optimal production paths for different injection types, which provides strong support for the flexibility and response speed of the production line. For example, when facing new production tasks or needing to adjust the production process, the system can automatically adjust the production path to adapt to new production goals, thereby ensuring the stability and sustainability of medicine production. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 The flowchart of the intelligent medicine production line management system based on multi-dosage form rapid switching. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0053] Embodiment 1

[0054] Please refer to Figure 1 The present application provides an intelligent medicine production line management system based on multi-dosage form rapid switching, which includes a process modeling module, a deployment time monitoring module, a rapid switching module and a production path optimization module.

[0055] The process modeling module is used for pre-modeling the production process of different injections, collecting and modeling the blending waiting time of each raw material according to the batching process of each injection, and forming a blending waiting time model for different injection types; the working cycle of the production equipment for each injection is modeled, the time required for the production equipment from starting to completing production is collected and calculated, and an initial production path is preliminarily selected;

[0056] The blending time monitoring module is used for real-time recording of the blending waiting time Twait i and the mixing speed Vmix i of the i-th injection, and comparing with the preset standard value to calculate the blending waiting time difference ATwait i and the mixing speed time difference AVmix i of the i-th injection; and presetting a first time difference threshold X1 and a second time difference threshold X2, when ATwait i >X1 or AVmix i >X2, a production path switching instruction is triggered;

[0057] The rapid switching module is used for, when receiving the production path switching instruction, based on the blending waiting time difference ATwait i and the mixing speed time difference AVmix i of the i-th injection, collecting the energy consumption data of the production equipment, and constructing a switching coefficient Qhxs; presetting a switching energy consumption threshold Y, if the switching coefficient Qhxs is lower than the switching energy consumption threshold Y, a first path switching instruction is triggered, and a second optimal production path is generated;

[0058] The production path optimization module is used for, after the second optimal production path is executed, collecting the predicted total time Zsj required for the i-th injection to complete production in the second optimal production path, the energy consumption ratio Nh j of the j-th production equipment, and the production rate Scs j of the j-th production equipment, to construct a production path state coefficient Ljzt i of the i-th injection, and presetting a comprehensive threshold Z, if the production path state coefficient Ljzt i of the i-th injection exceeds the comprehensive threshold Z, a second path switching instruction is triggered, and a third optimal production path is generated; if the production path state coefficient Ljzt i of the i-th injection does not exceed the comprehensive threshold Z, the production is continued according to the second optimal production path.

[0059] In this embodiment, the system can identify potential problems in the production process, such as production delays or low equipment efficiency, by monitoring the blending waiting time and mixing speed in real time, and automatically adjust the production process. This real-time data monitoring not only reduces manual intervention, but also reduces time and resource waste. Through process modeling and blending time monitoring module, the system can develop personalized production paths for different types of injections, and realize efficient and rapid switching according to the load of the production line and the production needs of various drugs. This function not only improves production flexibility, but also maximizes the use of equipment resources and improves production efficiency. By setting the switching coefficient Qhxs and comparing it with the energy consumption threshold Y, the system can minimize energy consumption while ensuring production efficiency, avoid unnecessary energy waste, and ensure the economic and environmental performance of the production process. The production path optimization module can accurately adjust the production path based on equipment energy consumption, production rate and other data to ensure that each injection can be produced with the optimal path. In the production process, adjusting the production path based on real-time data can help improve production efficiency and reduce production delays. Because the system can dynamically adjust the production path and switch paths at critical moments, it reduces the production delays and uncertainties caused by manual intervention or improper production path adjustment in traditional production.

[0060] Embodiment 2

[0061] This embodiment is an explanation and illustration in Embodiment 1, please refer to Figure 1 , specifically, the process modeling module includes a batching blending modeling unit and a production equipment cycle modeling unit;

[0062] The batching blending modeling unit is used to collect the blending waiting time of each raw material of different types of injections and related process parameters according to the production requirements of each injection, establish a raw material blending time data set, and pre-establish a blending waiting time model for each injection using neural network technology according to the raw material blending time data set;

[0063] The raw material blending time data set includes injection ID, raw material label, raw material name, blending waiting time, raw material physical properties, process conditions and equipment types;

[0064] The raw material physical properties include: raw material density, dissolution rate, dissolution temperature;

[0065] The process conditions include: stirring speed, temperature and humidity;

[0066] The equipment types include: batching tank, mixing tank, vortex mixer, stirrer, sterilization kettle, lamp inspection machine and sealing machine.

[0067] The collection of raw material dispensing time data set, according to the specific production requirements of each injection, accurately predicts the dispensing waiting time, promotes the efficiency and accuracy of the dispensing process;

[0068] Raw material dispensing time data set example chart:

[0069]

[0070]

[0071] The production equipment cycle modeling unit is used to establish the working cycle model of each injection production equipment, collect and calculate the time T required for the production equipment to complete production from start to finish cycle , and preliminarily select the initial production path; the specific operation steps are as follows:

[0072] Collect and record the equipment type, injection type, and time T required for the jth production equipment to complete production from start to finish cycle，j , and establish the working cycle data set; the time T required for the jth production equipment to complete production from start to finish cycle，j includes the preheating time T of the ith production equipment preheat，j , the running time T opreation，j , and the disinfection and cleaning time T cleaning，j ;

[0073] The time T required for the jth production equipment to complete production from start to finish cycle，j is obtained through the following summation formula:

[0074] T cycle，j = T preheat，j + T opreation，j + T cleaning，j ;

[0075] Based on the working cycle data set, select the initial production path for each injection, and the initial production path is: preferentially select the minimum value of the time T required for the jth production equipment to complete production from start to finish cycle，j to combine.

[0076] The initial production path chart example of the ith injection:

[0077]

[0078] In this embodiment, the system utilizes the working cycle dataset to prioritize the shortest working cycle of the equipment when selecting the production path, thereby achieving optimal allocation of equipment resources and optimization of the production path. By prioritizing the selection of equipment combinations with the shortest time required from start to completion of production, the system can reduce waiting time and non-productive time such as equipment setup time during production, avoid delays caused by frequent equipment switching or excessive setup time, and improve production efficiency and production flexibility. When selecting the initial production path, the system is based on actual working cycle data, reducing human adjustment and unnecessary intervention, thereby improving the stability and consistency of the entire production process. This also helps to ensure the stability of product quality.

[0079] Embodiment 3

[0080] This embodiment is an explanation and illustration in Embodiment 1, please refer to Figure 1 , specifically, the deployment time monitoring module includes a deployment time monitoring unit, a mixing speed monitoring unit, and a first comparison unit;

[0081] The deployment time monitoring unit is used to record the deployment waiting time Twait i of the i-th injection in real time i ; and the deployment waiting time difference ATwait i of the i-th injection is calculated by the following formula:

[0082] ATwait i = Twait i - Tstandard i ;

[0083] Where Tstandard i represents the preset deployment standard time value of the i-th injection.

[0084] The mixing speed monitoring unit is used to obtain the mixing speed Vmix i of the i-th injection in real time i ; and the mixing speed time difference AVmix i of the i-th injection is calculated by the following formula:

[0085] AVmix i = Vmix - Tv i ;

[0086] Where Tv i represents the preset mixing speed standard time value of the i-th injection;

[0087] The first comparison unit is used to preset a first time difference threshold X1 and a second time difference threshold X2, when ATwait i > X1, it indicates that the waiting time is too long and will affect the production efficiency of the i-th injection; when AVmixi X2, indicating that the mixing speed is too slow, which will cause the production progress to lag, triggering the production path switching instruction, if ΔTwait i ≤ X1 and ΔVmix i ≤ X2, indicating that the current initial production path is qualified, and the initial production path is maintained for production.

[0088] In this embodiment, through the real-time data acquisition and calculation of the deployment time monitoring unit and the mixing speed monitoring unit, the system can monitor the key parameters in the production process in real time, ensuring that the deployment waiting time and the mixing speed are within the standard range. This real-time monitoring mechanism greatly reduces the need for manual intervention and improves production efficiency. When the monitoring unit finds that the deployment waiting time or the mixing speed deviates (such as exceeding the preset threshold), the system can timely trigger the production path switching instruction and automatically adjust the production path to optimize the production efficiency. This ensures that the production process will not be delayed due to the lag of a certain link. By accurately calculating and comparing the actual deployment waiting time and the mixing speed time difference, potential problems can be found in advance, avoiding efficiency loss due to production lag. When the deployment waiting time is too long or the mixing speed is too slow, the system can quickly respond and optimize the production path to reduce the time waste caused by equipment or process problems in the production process. The preset threshold of the first comparison unit provides a clear basis for the system, avoiding system instability caused by excessive adjustment or too frequent path switching. The strategy of maintaining the initial production path ensures the stability of the production process and avoids unnecessary path switching, improving the stability and consistency of the production line.

[0089] Embodiment 4

[0090] This embodiment is an explanation and description in Embodiment 1, please refer to Figure 1 , specifically, the fast switching module includes a production energy consumption acquisition unit, a switching coefficient calculation unit and a second comparison unit;

[0091] The production energy consumption acquisition unit is used to acquire production device energy consumption data when receiving the production path switching instruction, and the production device energy consumption data includes: the additional working time Cswitch j and the additional energy consumption Eswitch i of the jth production device in the switching process;

[0092] The switching coefficient calculation unit is used to calculate the switching coefficient based on the deployment waiting time difference ΔTwait i of the ith injection, the mixing speed time difference ΔVmix i , the additional working time Cswitch j of the jth production device in the switching process, and the additional energy consumption Eswitch jTj represents the time required for the jth production device to complete production from start-up cycle，j After non-dimensional processing, the switching coefficient Qhxs is generated by the following formula:

[0093]

[0094] In the formula, T total represents the standard production time, E pridl represents the energy consumption of the production device per unit time during normal production, a1, a2, a3, and a4 represent weight coefficients, and U equip represents the device utilization rate, which is calculated by the following formula:

[0095]

[0096] In the formula, T effective，j is the actual production time of the jth production device, T cycle，j represents the time required for the jth production device to complete production from start-up.

[0097] The second comparison unit is used to preset a switching energy consumption threshold Y and compare the switching coefficient Qhxs with the switching energy consumption threshold Y to obtain a second evaluation result, including:

[0098] If the switching coefficient Qhxs is less than the switching energy consumption threshold Y, it indicates that the switching process energy consumption is qualified, and a first path switching instruction is triggered, which includes increasing the stirring speed of the current hybrid device by 3%-5%, adding 1-2 parallel standby devices to reduce the serial waiting between processes, and increasing the raw material feeding rate by 3%-5%, and generating a second optimal production path and a first production path switching report.

[0099] If the switching coefficient Qhxs is greater than or equal to the switching energy consumption threshold Y, it indicates that the switching process energy consumption is abnormal, and no path switching is performed, and the initial production path is maintained for production.

[0100] In this embodiment, by collecting and evaluating the energy consumption, working time and changes in the production process of the production equipment in real time, the system can timely determine whether the production path needs to be optimized, and avoid production delay caused by excessive energy consumption or time of the equipment. The system evaluates whether the energy consumption of path switching is reasonable according to the switching coefficient Qhxs. If the switching coefficient is lower than the energy consumption threshold Y, it means that the path switching is qualified in energy efficiency, and the system will trigger the optimization path instruction, thereby improving the production efficiency. Through accurate monitoring of energy consumption, the system avoids unnecessary additional energy consumption caused by inappropriate path switching. For example, if the switching coefficient Qhxs is greater than or equal to the switching energy consumption threshold Y, the system will maintain the initial production path to avoid unnecessary energy consumption caused by path switching. When the switching coefficient meets the preset condition, the system adjusts the stirring speed, increases the standby equipment, increases the raw material feeding rate and other measures by triggering the "first path switching instruction", to further optimize the production path. In this way, not only the production speed can be improved, but also the waiting time can be effectively reduced, and the overall production benefit can be improved. By generating the second optimal production path of the i-th injection and the first production path switching report, the system can record and analyze the path switching in detail, and provide data support for subsequent optimization.

[0101] The second optimal production path diagram of the i-th injection is as follows:

[0102]

[0103]

[0104] Embodiment 5

[0105] This embodiment is an explanation and description in embodiment 1, please refer to Figure 1 The production path optimization module includes a prediction unit and a third comparison unit;

[0106] The prediction unit is used to collect the total predicted time Zsj of the i-th injection from the second optimal production path to the completion of production, the energy consumption ratio Nh j and the production rate Scs j of the j-th production equipment after the execution of the second optimal production path of the i-th injection, and then the production path state coefficient Ljzt of the i-th injection is calculated by the following formula: i

[0107]

[0108] In the formula, M represents the total number of production equipment in the second optimal production path, Zsj_max represents the maximum total time under all production paths, Nh max represents the maximum energy consumption ratio of the j-th equipment among all production equipment, and Scs max ​This represents the maximum average production rate of all production equipment; α, β, and δ represent weighting coefficients, and α + β + δ = 1; This indicates a time factor, specifically that the shorter the time required to complete production, the better. This indicates the energy efficiency factor, specifically that the lower the energy consumption of production equipment, the better. This represents the production efficiency factor, specifically, the higher the production rate, the better; therefore, this term is placed in the denominator.

[0109] The third comparison unit is used to preset the comprehensive threshold Z, and to set the production path state coefficient Ljzt of the i-th injectable drug. i The results are compared with the comprehensive threshold Z to obtain the third evaluation result, including:

[0110] If the production path state coefficient of the i-th type of injection is Ljzt i If the overall threshold Z is exceeded, it indicates that the path has not reached the optimization target, and a second path switching instruction is triggered. The second path switching instruction includes: increasing the stirring speed of the current mixing equipment by 6%-9%, adding 3-4 parallel backup equipment to reduce the serial waiting between processes, and increasing the raw material feeding rate by 6%-9%, and generating a third optimal production path and a second production path switching report.

[0111] If the production path state coefficient of the i-th type of injection is Ljzt i If the overall threshold Z is not exceeded, it means that the path has reached the optimization goal, and production will continue according to the second optimal production path.

[0112] In this embodiment, the prediction unit collects relevant data and calculates the production path state coefficient based on the execution status of the second optimal production path. When the state coefficient fails to meet the optimization target, the system automatically triggers a second path switching command. By changing the operating parameters of the production equipment (such as increasing the mixing speed of the mixing equipment, adding backup equipment, increasing the raw material feeding rate, etc.), the production path is dynamically adjusted to meet the requirements of more efficient and low-energy production. By comparing the set comprehensive threshold Z with the production path state coefficient, the system can automatically determine whether the current production path has met the optimization target. If the target has not been met, the system can trigger an optimization command in a timely manner, reducing waiting and waste between processes and improving production continuity. This flexibility and adaptability enable the system to effectively adjust under different production needs, equipment states, and production conditions to ensure the best production path and process efficiency.

[0113] Example of the second optimal production path diagram for the i-th type of injectable drug:

[0114]

[0115]

[0116] In the initial path, the batching tank required 30 minutes; the second optimal path reduced this to 28 minutes; and the optimized path further reduced it to 26 minutes. The time reduction in the optimized path was mainly due to the increased raw material feeding rate, which accelerated the batching process. In the initial path, the mixer required 35 minutes; the second optimal path required 33 minutes; and the optimized path reduced this to 30 minutes. Increasing the mixing speed effectively improved production efficiency and reduced the time required for the mixing process. The initial path took 25 minutes; the second optimal path reduced this to 18 minutes; and the optimized path further reduced it to 16 minutes. Increasing the number of parallel backup devices (from 1-2 to 3-4) effectively reduced serial waiting and significantly shortened the filling time. The light inspection stage time remained at 20 minutes and was not optimized because the quality inspection process is usually fixed and depends on the equipment's inspection capabilities. The sealing stage time remained unchanged at 10 minutes, and the sealing process was already close to the minimum. By optimizing the production path, especially in key stages such as batching, mixing, and filling, the overall production time was significantly reduced from the initial 175 minutes to the optimized 155 minutes, saving 20 minutes. This time reduction translates to increased production capacity and efficiency. Optimization measures such as adding parallel backup equipment and increasing mixing speed significantly improve equipment utilization during production. Reduced equipment downtime and more even workload distribution enhance the overall efficiency of the production line. Through intelligent path optimization and adjustment, the system can flexibly respond to different production demands and changes. Optimization measures (such as increasing raw material feed rates and adding backup equipment) can be adjusted according to actual production conditions, thereby maintaining the high efficiency and stability of the production process. Despite the optimized production process, the quality inspection time during the light inspection and packaging stages remains unchanged, ensuring the stability and consistency of product quality.

[0117] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0118] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. An intelligent medicine production line management system based on multi-dosage form rapid switching, characterized in that, The process modeling module, the dispensing time monitoring module, the quick switching module and the production path optimization module are included. The process modeling module is used for pre-modeling the production process of different injections, collecting and modeling the dispensing waiting time of each raw material according to the batching process of each injection, forming a dispensing waiting time model for different injection types, modeling the working cycle of the production equipment of each injection, collecting and calculating the time required for the production equipment from starting to completing production, and preliminarily selecting an initial production path. The dispensing time monitoring module is configured to record the dispensing waiting time Twait of the i-th injection in real time i and the mixing speed Vmix i , and compare the dispensing waiting time Twait of the i-th injection with a preset standard value to obtain a dispensing waiting time difference ATwait i and the mixing speed time difference AVmix i ; and preset a first time difference threshold X1 and a second time difference threshold X2, when ΔTwait i > X1 or ΔVmix i > X2, a production path switch instruction is triggered; The rapid switching module is configured to, when receiving a production path switching instruction, based on the dispensing waiting time difference ATwait of the i-th injection i And the mixing speed time difference AVmix i Collect energy consumption data of the production equipment, and construct a switching coefficient Qhxs; a switching energy consumption threshold Y is preset, and if the switching coefficient Qhxs is lower than the switching energy consumption threshold Y, a first path switching instruction is triggered to generate a second optimal production path. The production path optimization module is configured to collect the predicted total time Zsj of the i-th injection in the second optimal production path to the completion of production, the energy consumption ratio Nh of the j-th production device j and the production rate Scs of the j-th production device j to construct the production path state coefficient Ljzt of the i-th injection i and preset a comprehensive threshold Z. If the production path state coefficient Ljzt of the i-th injection i exceeds the comprehensive threshold Z, a second path switching instruction is triggered to generate a third optimal production path. If the production path state coefficient Ljzt of the i-th injection i does not exceed the comprehensive threshold Z, the production is continued according to the second optimal production path.

2. The intelligent medicine production line management system based on multi-dosage form quick switching according to claim 1, characterized in that, The process modeling module includes a batching dispensing modeling unit and a production equipment cycle modeling unit. The batching dispensing modeling unit is used for collecting the dispensing waiting time of each raw material of different types of injections and related process parameters according to the production requirements of each injection, establishing a raw material dispensing time data set, and pre-establishing a dispensing waiting time model for each injection using neural network technology according to the raw material dispensing time data set. The raw material dispensing time data set includes injection ID, raw material label, raw material name, dispensing waiting time, raw material physical properties, process conditions and equipment type. The equipment type includes a batching tank, a mixing tank, a vortex mixer, a stirrer, a sterilization kettle, a lamp inspection machine and a sealing machine.

3. The intelligent medicine production line management system based on multi-dosage form quick switching according to claim 2, characterized in that, The production equipment cycle modeling unit is used to establish a work cycle model of each injection production equipment, collect the time T required for the production equipment to start and complete production cycle and preliminarily select an initial production path; the specific operation steps are as follows: Collecting records of the equipment type, injection type and the time T required for the jth production equipment to complete production from start to finish cycle,j and establishing a work cycle dataset; the time T required for the jth production equipment to complete production from start to finish cycle,j including the preheating time T of the ith production equipment preheat,j , the running time T opreation,j and the sterilization and cleaning time T cleaning,j ; Tj = time needed by the jth production plant to complete production from start-up cycle,j By the following summation formula: T cycle,j = T preheat,j + T opreation,j + T cleaning,j ; Based on the work cycle dataset, an initial production path is selected for each injection, the initial production path being: the time T required for the jth production device to go from start to completion of production is preferentially chosen cycle,j The minimum values are combined.

4. The intelligent medicine production line management system based on multi-dosage form quick switching according to claim 1, characterized in that, The dispensing time monitoring module includes a dispensing time monitoring unit, a mixing speed monitoring unit and a first comparison unit. The dispensing time monitoring unit is used to record the dispensing waiting time Twait of the i-th injection in real time i ; and the dispensing waiting time difference ATwait of the i-th injection is calculated by the following formula i : ΔTwait i = Twait i - Tstandard i ; wherein Tstandard i represents the preset standard time value of the i-th injection.

5. The intelligent medicine production line management system based on multi-dosage form quick switching according to claim 1, characterized in that, The quick switching module includes a production energy consumption collection unit, a switching coefficient calculation unit and a second comparison unit. The production energy consumption collection unit is configured to collect production equipment energy consumption data when receiving a production path switching instruction, the production equipment energy consumption data comprising: additional working time Cswitch of the jth production equipment in the switching process j and additional energy consumption Eswitch j ; The switching coefficient calculation unit is configured to calculate a switching coefficient Qhxs based on a difference ΔTwait in the preparation waiting time of the i-th injection i , a difference ΔVmix in the mixing speed i , an additional working time Cswitch of the j-th production device during the switching process j , an additional energy consumption Eswitch j , and a time T required for the j-th production device to complete production from start cycle,j After dimensionless processing, the switching coefficient Qhxs is generated by the following formula: In the formula, T total represents the standard production time, E pridl represents the normal production energy consumption of the production equipment per unit of time, a1, a2, a3, and a4 represent weight coefficients, and U equip represents the equipment utilization rate, which is obtained by calculation through the following formula: where T effective,j is the actual production time of the jth production device, T cycle,j represents the time required for the jth production device to complete production from start-up.

6. The intelligent medicine production line management system based on multi-dosage form quick switching according to claim 5, characterized in that, The second comparison unit is used for presetting a switching energy consumption threshold Y and comparing the switching coefficient Qhxs with the switching energy consumption threshold Y to obtain a second evaluation result, including: If the switching coefficient Qhxs is less than the switching energy consumption threshold Y, it indicates that the switching process energy consumption is qualified, a first path switching instruction is triggered, the first path switching instruction includes increasing the stirring speed of the current mixing equipment by 3%-5%, increasing 1-2 parallel standby devices to reduce the serial waiting time between processes, and increasing the raw material feeding rate by 3%-5%, and a second optimal production path and a first production path switching report are generated. If the switching coefficient Qhxs is greater than or equal to the switching energy consumption threshold Y, it indicates that the switching process energy consumption is abnormal, no path switching is performed, and the initial production path is maintained for production.

7. The intelligent medicine production line management system based on multi-dosage form quick switching according to claim 1, characterized in that, The production path optimization module includes a prediction unit and a third comparison unit. The prediction unit is used to collect the predicted total time Zsj of the i-th injection in the second optimal production path to the production completion, the energy consumption ratio Nh of the j-th production equipment j and the production rate Scs of the j-th production equipment after the second optimal production path execution j After dimensionless processing, the production path state coefficient Ljzt of the i-th injection is calculated by the following formula i : where M represents the total number of production equipment in the second optimal production path, Zsj_max represents the maximum total time under all production paths, Nh max represents the maximum energy consumption ratio of the jth equipment among all production equipment, Scs max represents the maximum average production rate of all production equipment; α, β and δ represent weight coefficients, and α+β+δ=1; represents the time factor term, which specifically means that the shorter the time required for production completion is the better; represents the energy efficiency factor term, which specifically means that the lower the energy consumption of the production equipment is the better; represents the production efficiency factor term, which specifically means that the higher the production rate is the better, so the term is placed in the denominator; The third comparison unit is used to preset the comprehensive threshold Z, and to set the production path state coefficient Ljzt of the i-th injectable drug. i The results are compared with the comprehensive threshold Z to obtain the third evaluation result, including: If the production path state coefficient Ljzt of the i-th injection is less than the comprehensive threshold value Z, indicating that the path has reached the optimization target, a first path switching instruction is triggered, which includes: reducing the stirring speed of the current mixing device by 6%-9%, reducing the number of parallel standby devices by 3-4 to reduce the serial waiting time between processes, and reducing the raw material feeding rate by 6%-9%, and generating a first optimal production path and a first production path switching report. i If the production path state coefficient Ljzt of the i-th injection is less than the comprehensive threshold value Z, indicating that the path has reached the optimization target, a first path switching instruction is triggered, which includes: reducing the stirring speed of the current mixing device by 6%-9%, reducing the number of parallel standby devices by 3-4 to reduce the serial waiting time between processes, and reducing the raw material feeding rate by 6%-9%, and generating a first optimal production path and a first production path switching report. If the production path state coefficient Ljzt of the i-th injection is greater than the threshold value Z i If the comprehensive threshold value Z is not exceeded, indicating that the path has reached the optimization goal, production continues according to the second optimal production path.

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