Intelligent medicine production line management system based on rapid switching of multiple dosage forms

Abstract

The invention discloses an intelligent medicine production line management system based on multi-dosage form rapid switching, relates to the technical field of medicine production line automatic control, and effectively improves the production efficiency and equipment utilization rate and saves resources through real-time monitoring and optimization of a production path. Firstly, the system carries out pre-modeling and real-time monitoring on production processes of different injections through a process modeling and allocation time monitoring module, and the problem of production delay or low equipment efficiency can be recognized in time. And secondly, the rapid switching module automatically adjusts the production path according to the allocation waiting time difference, the mixing speed time difference and the equipment energy consumption data, so that the manual intervention is reduced, and the flexibility and the reaction speed of the production line are improved. Besides, a production path optimization module accurately adjusts a production path according to factors such as equipment energy consumption and a production rate, so that each injection is enabled to complete production with an optimal path, and the utilization of equipment resources is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of automated control of pharmaceutical production lines, and in particular to an intelligent pharmaceutical production line management system based on rapid switching of multiple dosage forms. Background Art

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

[0003] Traditional production systems lack effective monitoring and scheduling mechanisms, especially in terms of real-time data monitoring of preparation waiting time, mixing speed, and energy consumption. During the production process, when process parameters such as preparation waiting time and mixing speed vary greatly, manual intervention and adjustment are usually required, wasting a lot of time and resources. In addition, the lack of the ability to optimize the production path based on real-time data leads to great uncertainty and delays in the production process.

[0004] Furthermore, with the increase in the variety of injections and the diversification of production needs, traditional production lines are difficult to switch quickly and manage efficiently. 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 preparation process. Due to the complexity of the process flow, the switching of production paths is not only affected by the operating status of the equipment, but also requires comprehensive optimization of equipment scheduling, energy consumption control, and production efficiency. Traditional methods often lack comprehensive consideration of these factors, resulting in inflexible switching of production paths and the inability to achieve refined management and optimization. Summary of the invention

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

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent drug production line management system based on rapid switching of multiple dosage forms, including a process modeling module, a dispensing 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 processes of different injections. According to the batching process of each injection, the preparation waiting time of each raw material is collected and modeled to form a preparation 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-up to completion of production is collected and calculated, and the initial production path is preliminarily selected;

[0008] A dispensing time monitoring module for real - time recording of the dispensing waiting time Twait of the i - th injection i and the mixing speed Vmix i , comparing with preset standard values, calculating and obtaining the dispensing waiting time difference ΔTwait of the i - th injection i and the mixing speed time difference ΔVmix i ; and presetting a first time - difference threshold X 1 and a second time - difference threshold X 2 , when ΔTwait i > X 1 or ΔVmix i > X 2 , triggering a production path switching instruction;

[0009] A fast - switching module for, when receiving a production path switching instruction, based on the dispensing waiting time difference ΔTwait of the i - th injection i and the mixing speed time difference ΔVmix i , collecting production equipment energy - consumption data, 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, then triggering a first - path switching instruction and generating a second - optimal production path;

[0010] A production path optimization module for, after executing the second - optimal production path, collecting the total predicted time Zsj required for the i - th injection from the second - optimal production path to 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 j , to construct a production path state coefficient Ljzt of the i - th injection i , and presetting a comprehensive threshold Z, if the production path state coefficient Ljzt of the i - th injection i exceeds the comprehensive threshold Z, then triggering a second - path switching instruction and generating 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, then continuing production according to the second - optimal production path.

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

[0012] The batching and dispensing modeling unit is used to collect 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, establish a raw material dispensing time data set, and use neural network technology to pre - establish a dispensing waiting time model for each injection;

[0013] The raw material preparation time dataset includes: injection ID, raw material label, raw material name, preparation waiting time, raw material physical properties, process conditions, and equipment type;

[0014] The raw material physical properties include: raw material density, dissolution rate, and dissolution temperature;

[0015] The process conditions include: stirring speed, temperature, and humidity;

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

[0017] Preferably, 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 startup; cycle , and preliminarily select the initial production path; the specific operation steps are as follows:

[0018] Collect and record the equipment type of each injection production equipment, the injection type, and the time T required for the jth production equipment to complete production from startup; cycle,j , and establish a working cycle dataset; the time T required for the jth production equipment to complete production from startup 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 ;

[0019] The time T required for the jth production equipment to complete production from startup cycle,j 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, select the initial production path for each injection. The initial production path is: preferentially select the combination with the minimum time T required for the jth production equipment to complete production from startup. cycle,j

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

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

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

[0025] Among them, Tstandard i represents the preset dispensing standard time value of the i-th injection.

[0026] Preferably, the mixing speed monitoring unit is used to obtain the mixing speed Vmix of the i-th injection in real time i , and obtain the mixing speed time difference ΔVmix of the i-th injection by calculating through the following formula i ;

[0027] ΔVmix i = Vmix - Tv i ;

[0028] Among them, Tv i represents the preset mixing speed standard time value of the i-th injection;

[0029] The first comparison unit is used to preset the first time difference threshold X 1 and the second time difference threshold X 2 . When ΔTwait i > X 1 , it means that too long waiting time will affect the production efficiency of the i-th injection; when ΔVmix i > X 2 , it means that too slow mixing speed will cause the production progress to lag, triggering a production path switching instruction. If ΔTwait i ≤ X 1 and ΔVmix i ≤ X 2 , it means that the current initial production path is qualified, and the initial production path is maintained for production.

[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 production equipment energy consumption data when receiving a production path switching instruction. The production equipment energy consumption data includes: the additional working time Cswitch of the j-th production equipment during the switching process j and the additional energy consumption Eswitch j ;

[0032] The switching coefficient calculation unit is used to based on the dispensing waiting time difference ΔTwait of the i-th injection i , the mixing speed time difference ΔVmix i, the additional working time Cswitch of the j-th production equipment during the switching process j , the additional energy consumption Eswitch j and the time T required for the j-th production equipment to complete production from startup cycle，j , after dimensionless processing, the switching coefficient Qhxs is generated through 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, a 1 , a 2 , a 3 and a 4 represent weight coefficients, U equip represents the equipment utilization rate, which is calculated through the following formula:

[0035]

[0036] Among them, T effective，j is the actual production time of the j-th production equipment, T cycle，j represents the time required for the j-th production equipment to complete production from startup.

[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 < the switching energy consumption threshold Y, it means that the energy consumption during the switching process 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 equipment 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 ≥ the switching energy consumption threshold Y, it means that the energy consumption during the switching process is abnormal, no path switching is performed, and production is maintained on the initial production path.

[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 on the second optimal production path, the energy consumption ratio Nh of the j-th production equipment j and the production rate Scs of the j-th production equipment j, after dimensionless processing, the production path state coefficient Ljzt of the i-th injection is calculated through the following formula i :

[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, and Nh max represents the maximum energy consumption ratio of the j-th equipment among all production equipment, and 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, specifically, the shorter the time required to complete production, the better; represents the energy efficiency factor term, specifically, the lower the energy consumption of the production equipment, the better; represents the production efficiency factor term, specifically, the higher the production rate, the better, so this term is placed in the denominator;

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

[0045] If the production path state coefficient Ljzt of the i-th injection i exceeds the comprehensive threshold Z, indicating that the path has not reached the optimization goal, then trigger the second path switching instruction, and the second path switching instruction includes: increasing the stirring speed of the current mixing equipment by 6%-9%, adding 3-4 parallel standby equipment to reduce the serial waiting between processes, and increasing the raw material feeding rate by 6%-9%, and generating the third optimal production path and the second production path switching report;

[0046] If the production path state coefficient Ljzt of the i-th injection i does not exceed the comprehensive threshold Z, indicating that the path has reached the optimization goal, then continue production according to the second optimal production path.

[0047] The present invention provides an intelligent drug production line management system based on rapid switching of multiple dosage forms. It has the following beneficial effects:

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

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

[0050] (3) The intelligent drug production line management system based on rapid multi-dosage form switching can quickly respond to the production requirements and process changes of different types of injections, ensuring that the production line can flexibly handle 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 faced with new production tasks or the need to adjust the production process, the system can automatically adjust the production path to adapt to the new production goals, thus ensuring the stability and sustainability of drug production. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic flow chart of the intelligent drug production line management system based on rapid multi-dosage form switching of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0053] Embodiment 1

[0054] Please refer to Figure 1 , the present invention provides an intelligent drug production line management system based on rapid switching of multiple dosage forms, including a process modeling module, a dispensing time monitoring module, a rapid switching module, and a production path optimization module;

[0055] The process modeling module is used to pre-model the production processes of different injections. According to the ingredient processes of each injection, collect and model the dispensing waiting time of each raw material to form a dispensing waiting time model for different injection types; model the working cycles of the production equipment for each injection, collect and calculate the time required for the production equipment to start and complete production, and preliminarily select an initial production path;

[0056] The dispensing time monitoring module is used to record the dispensing waiting time Twait i and the mixing speed Vmix i of the i-th injection in real time, compare with the preset standard values, and calculate the dispensing waiting time difference ΔTwait i and the mixing speed time difference ΔVmix i of the i-th injection; and preset a first time difference threshold X 1 and a second time difference threshold X 2 . When ΔTwait i > X 1 or ΔVmix i > X 2 , trigger a production path switching instruction;

[0057] The rapid switching module is used to, when receiving a production path switching instruction, based on the dispensing waiting time difference ΔTwait i and the mixing speed time difference ΔVmix i of the i-th injection, collect the energy consumption data of the production equipment, and construct a switching coefficient Qhxs; preset a switching energy consumption threshold Y. If the switching coefficient Qhxs is lower than the switching energy consumption threshold Y, trigger a first path switching instruction and generate a second optimal production path;

[0058] The production path optimization module is used to, after executing the second optimal production path, collect the total predicted time Zsj required for the i-th injection from the second optimal production path to 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, to construct a production path state coefficient Ljzt i of the i-th injection, and preset a comprehensive threshold Z. If the production path state coefficient Ljzt i of the i-th injection exceeds the comprehensive threshold Z, trigger a second path switching instruction and generate a third optimal production path; if the production path state coefficient Ljzti If it does not exceed the comprehensive threshold Z, continuous production is carried out according to the second optimal production path.

[0059] In this embodiment, by real-time monitoring and adjusting the waiting time and mixing speed, the system can timely identify potential problems in the production process, such as production delays or low equipment efficiency, so as to achieve automatic adjustment. The monitoring of this real-time data not only reduces manual intervention but also reduces the waste of time and resources. Through process modeling and the dispensing time monitoring module, the system can formulate personalized production paths for different types of injections and achieve efficient and rapid switching according to the load of the production line and the production requirements of various drugs. This function not only improves production flexibility but also maximizes the utilization 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 economy and environmental protection of the production process. The production path optimization module can accurately adjust the production path based on data such as equipment energy consumption and production rate to ensure that each type of injection can be produced along the optimal path. During the production process, adjusting the production path according to real-time data helps to improve production efficiency and reduce production delays. Since the system can dynamically adjust the production path and switch paths at critical moments, it reduces the production delays and uncertainties commonly seen in traditional production caused by manual intervention or improper production path adjustment.

[0060] Embodiment 2

[0061] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the process modeling module includes a batching and dispensing modeling unit and a production equipment cycle modeling unit;

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

[0063] 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;

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

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

[0066] The equipment type includes: batching tank, mixing tank, vortex mixer, stirrer, sterilization kettle, lamp inspection machine, and capper.

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

[0068] Example chart of the raw material preparation time data set:

[0069]

[0070]

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

[0072] Collect and record the equipment type of the production equipment for each injection, the injection type, and the time T required for the jth production equipment to complete production from startup; cycle，j , and establish a working cycle data set; the time T required for the jth production equipment to complete production from startup 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 startup 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. The initial production path is: preferentially select the combination with the minimum value of the time T required for the jth production equipment to complete production from startup. cycle，j

[0076] Example representation of the initial production path diagram of the ith injection:

[0077]

[0078] In this embodiment, the system utilizes the duty cycle dataset and gives priority to the shortest duty cycle of the equipment when selecting the production path, thereby achieving the optimal allocation of equipment resources and the optimization of the production path. By preferentially selecting the equipment combination with the minimum time required for the production equipment to start and complete production, the system can reduce the waiting time and non-productive time during the production process, such as equipment debugging time, avoid delays caused by frequent equipment switching or excessive debugging time, and improve production efficiency and production flexibility. When selecting the initial production path, the system is based on the actual duty cycle data, reducing manual adjustment and unnecessary intervention, thereby enhancing 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 explanatory description carried out 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 of the i-th injection in real time i ; and obtain the deployment waiting time difference ΔTwait of the i-th injection through the following formula i :

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

[0083] Among them, 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 of the i-th injection in real time i , and obtain the mixing speed time difference ΔVmix of the i-th injection through the following formula i ;

[0085] ΔVmix i = Vmix - Tv i ;

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

[0087] The first comparison unit is used to preset the first time difference threshold X 1 and the second time difference threshold X 2 . When ΔTwait i > X 1, indicating that excessive waiting time will affect the production efficiency of the i-th injection; when ΔVmix i > X 2 it indicates that too slow mixing speed will lead to a lag in the production progress, triggering a production path switching instruction. If ΔTwait i ≤ X 1 and ΔVmix i ≤ X 2 it indicates 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 collection and calculation of the dispensing 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 dispensing 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 detects a deviation in the dispensing waiting time or the mixing speed (such as exceeding the preset threshold), the system can promptly trigger a production path switching instruction and automatically adjust the production path to optimize production efficiency. This ensures that the production process will not be delayed due to a lag in a certain link. By accurately calculating and comparing the actual time difference between the dispensing waiting time and the mixing speed, potential problems can be discovered in advance, avoiding efficiency losses caused by production lags. When the dispensing waiting time is too long or the mixing speed is too slow, the system can respond quickly and optimize the production path, reducing the time waste caused by equipment or process problems during production. The preset threshold set by the first comparison unit provides a clear basis for judgment, avoiding system instability caused by excessive adjustment or overly frequent path switching. The strategy of maintaining the initial production path can ensure stability during the production process, avoid unnecessary path switching, and improve the stability and consistency of the production line.

[0089] Embodiment 4

[0090] This embodiment is an explanatory description based on Embodiment 1. Please refer to Figure 1 , specifically, the quick switching module includes a production energy consumption collection unit, a switching coefficient calculation unit, and a second comparison unit;

[0091] The production energy consumption collection unit is used to collect production equipment energy consumption data when receiving a production path switching instruction. The production equipment energy consumption data includes: the additional working time Cswitch of the j-th production equipment during the switching process j and the additional energy consumption Eswitch i ;

[0092] The switching coefficient calculation unit is used to calculate based on the dispensing waiting time difference ΔTwait of the i-th injection i , the mixing speed time difference ΔVmix i, the additional working time Cswitch of the jth production equipment during the switching process j , the additional energy consumption Eswitch j and the time T required for the jth production equipment to complete production from startup cycle，j , after dimensionless processing, the switching coefficient Qhxs is generated through the following formula:

[0093]

[0094] 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, a 1 , a 2 , a 3 and a 4 represent weight coefficients, U equip represents the equipment utilization rate, which is calculated through the following formula:

[0095]

[0096] Among them, T effective，j is the actual production time of the jth production equipment, and T cycle，j represents the time required for the jth production equipment to complete production from startup.

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

[0098] If the switching coefficient Qhxs < the switching energy consumption threshold Y, it means that the energy consumption during the switching process is qualified, triggering the first path switching instruction. The first path switching instruction includes: increasing the stirring speed of the current mixing equipment by 3% - 5%, adding 1 - 2 parallel standby equipment to reduce the serial waiting between processes, and increasing the raw material feeding rate by 3% - 5%, and generating the second optimal production path and the first production path switching report;

[0099] If the switching coefficient Qhxs ≥ the switching energy consumption threshold Y, it means that the energy consumption during the switching process is abnormal, and no path switching is performed, and production is maintained on the initial production path.

[0100] In this embodiment, by collecting and evaluating the energy consumption, working time, and changes during the production process of production equipment in real time, the system can timely determine whether the production path needs to be optimized, avoiding production delays caused by excessive energy or time consumption of 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 indicates that the path switching is energy-efficient and qualified, and the system will trigger an optimized path instruction, thereby improving production efficiency. Through precise 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 conditions, the system further optimizes the production path by triggering the "First Path Switching Instruction" and taking measures such as adjusting the stirring speed, increasing standby equipment, and increasing the raw material feeding rate. This can not only improve the production speed but also effectively reduce the waiting time and improve the overall production efficiency. By generating the second optimal production path and the first production path switching report, the system can record and analyze the path switching in detail, providing data support for subsequent optimization.

[0101] Example representation of the second optimal production path diagram of the i-th injection:

[0102]

[0103]

[0104] Embodiment 5

[0105] This embodiment is an explanatory description carried out 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 required for the i-th injection from the second optimal production path to 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 j , after dimensionless processing, the production path status coefficient Ljzt of the i-th injection is calculated through 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, Scs maxrepresents 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, specifically, the shorter the time required to complete production, the better; represents the energy efficiency factor term, specifically, the lower the energy consumption of the production equipment, the better; represents the production efficiency factor term, specifically, the higher the production rate, the better, so this term is placed in the denominator;

[0109] The third comparison unit is used to preset the comprehensive threshold Z and compare the production path status coefficient Ljzt of the i-th injection i with the comprehensive threshold Z to obtain the third evaluation result, including:

[0110] If the production path status coefficient Ljzt of the i-th injection i exceeds the comprehensive threshold Z, indicating that the path has not reached the optimization goal, then 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 standby equipment to reduce the serial waiting between processes, and increasing the raw material feeding rate by 6% - 9%, and generating the third optimal production path and the second production path switching report;

[0111] If the production path status coefficient Ljzt of the i-th injection i does not exceed the comprehensive threshold Z, indicating that the path has reached the optimization goal, then continue production according to the second optimal production path.

[0112] In this embodiment, the prediction unit collects relevant data and calculates the production path status coefficient according to the execution situation of the second optimal production path. When the status coefficient does not reach the optimization goal, the system will automatically trigger the second path switching instruction. By changing the working parameters of the production equipment (such as increasing the stirring speed of the mixing equipment, adding standby equipment, increasing the raw material feeding rate, etc.), the production path is dynamically adjusted to meet the requirements of more efficient and low-energy consumption production. Through the comparison between the set comprehensive threshold Z and the production path status coefficient, the system can automatically judge whether the current production path has reached the optimization goal. If the goal is not reached, the system can timely trigger the optimization instruction to reduce the waiting and waste between processes and improve production continuity. This flexibility and self-adaptability enable the system to make effective adjustments under different production demands, equipment states, and production conditions to ensure the best production path and process efficiency.

[0113] Example of the second optimal production path diagram of the i-th injection:

[0114]

[0115]

[0116] In the initial production path, the ingredient tank takes 30 minutes. In the second optimal path, it is reduced to 28 minutes, and in the optimized path, it is further reduced to 26 minutes. The reduction in time for the optimized path is mainly due to the increased raw material feeding rate, which speeds up the ingredient process. In the initial path, the mixer takes 35 minutes, in the second optimal path it is 33 minutes, and in the optimized path it is reduced to 30 minutes. Increasing the mixing speed effectively improves production efficiency and reduces the time required for the mixing process. The initial path is 25 minutes, the second optimal path is reduced to 18 minutes, and the optimized path is further reduced to 16 minutes. Increasing the parallel standby equipment (from 1 - 2 units to 3 - 4 units) effectively reduces serial waiting and significantly shortens the canning time. The time for the lamp inspection stage remains at 20 minutes without optimization because the quality inspection process is usually fixed and depends on the inspection capabilities of the equipment. The time for the packaging stage remains unchanged at 10 minutes as the sealing process is already close to the shortest. By optimizing the production path, especially in key stages such as ingredient preparation, mixing, and canning, the overall production time is significantly reduced, from the initial 175 minutes to the optimized 155 minutes, saving 20 minutes. This reduction in time means increased production capacity and improved production efficiency. Optimization measures such as increasing parallel standby equipment and raising the mixing speed significantly improve the equipment utilization rate during the production process. The equipment idle time is reduced, the workload is more evenly distributed, and the overall efficiency of the production line is enhanced. Through intelligent path optimization and adjustment, the system can flexibly respond to different production requirements and changes. Optimization measures (such as increasing the raw material feeding rate and adding standby equipment) can be adjusted according to the actual production situation to maintain the high efficiency and stability of the production process. Despite the optimization of the production process, the quality inspection time for the lamp inspection and packaging stages remains unchanged, ensuring the stability and consistency of product quality.

[0117] The setting of the threshold value is for the convenience of comparison. Regarding the size of the threshold value, it depends on the amount of sample data and the base quantity set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0118] The above formulas are all obtained through software simulation by collecting a large amount of data and selecting a formula close to the true value. The coefficients in the formula are set by those skilled in the art according to the actual situation. As described above, this is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered within the protection scope of the present invention.

Claims

1. An intelligent drug production line management system based on rapid switching of multiple dosage forms, characterized in that: It includes process modeling module, deployment time monitoring module, fast switching module and production path optimization module; The process modeling module is used to pre-model the production processes of different injections. According to the batching process of each injection, the preparation waiting time of each raw material is collected and modeled to form a preparation 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-up to completion of production is collected and calculated, and the initial production path is preliminarily selected; The preparation time monitoring module is used to record the preparation waiting time Twait of the i-th injection in real time. i and mixing speed Vmix i , compared with the preset standard value, calculate the preparation waiting time difference ΔTwait of the i-th injection i and mixing speed time difference ΔVmix i ; And preset the first time difference threshold value X1 and the second time difference threshold value X2, when ΔTwait i >X1 or ΔVmix i >X2, trigger the production path switching instruction; The fast switching module is used to, when receiving the production path switching instruction, based on the preparation waiting time difference ΔTwait of the i-th injection i and mixing speed time difference ΔVmix i , collect energy consumption data of production equipment and construct a switching coefficient Qhxs; preset a switching energy consumption threshold Y, if the switching coefficient Qhxs is lower than the switching energy consumption threshold Y, trigger the first path switching instruction and generate the second optimal production path; The production path optimization module is used to collect the predicted total time Zsj required for the i-th injection to be completed in the second optimal production path and the energy consumption ratio Nh of the j-th production equipment after the second optimal production path is executed. j and the production rate Scs of the jth production equipment j , to construct the production path state coefficient Ljzt of the i-th injection i , and preset the comprehensive threshold Z, if the production path state coefficient Ljzt of the i-th injection i If the comprehensive threshold Z is exceeded, the second path switching instruction is triggered to generate the third optimal production path; if the production path state coefficient Ljzt of the i-th injection i If the comprehensive threshold Z is not exceeded, production will continue according to the second optimal production path.

2. According to claim 1, an intelligent drug production line management system based on rapid switching of multiple dosage forms is characterized in that: The process modeling module includes a batching and mixing modeling unit and a production equipment cycle modeling unit; The ingredient preparation modeling unit is used to collect the preparation waiting time and related process parameters of each raw material of different types of injections according to the production requirements of each injection, establish a raw material preparation time data set, and use neural network technology to pre-establish a preparation waiting time model for each injection based on the raw material preparation time data set; The raw material preparation time data set includes: injection ID, raw material label, raw material name, preparation waiting time, raw material physical properties, process conditions and equipment type; Equipment types include: batching tanks, mixing tanks, vortex mixers, blenders, autoclaves, light inspection machines and sealing machines.

3. The intelligent drug production line management system based on rapid switching of multiple dosage forms according to claim 2, characterized in that: The production equipment cycle modeling unit is used to establish a work cycle model for each injection production equipment, collect and calculate the time T required for the production equipment from start-up to completion of production cycle , and preliminarily select the initial production path; the specific steps are as follows: Collect and record the equipment type, injection type, and time T required for the jth production equipment to start and complete production for each injection production equipment. cycle,j , and establish a work cycle data set; the time T required for the jth production equipment to start up and complete production cycle,j Including the preheating time T of the i-th production equipment preheat,j , running time T opreation,j And disinfection cleaning time T cleaning,j ; The time T required for the jth production equipment to start up and complete production cycle,j Obtained by the following summation formula: T cycle,j =T preheat,j +T opreation,j +T cleaning,j ; Based on the work cycle data set, the initial production path is selected for each injection. The initial production path is: the time T required for the j-th production equipment from start-up to completion of production is preferentially selected. cycle,j Minimum values ​​are combined.

4. The intelligent drug production line management system based on rapid switching of multiple dosage forms according to claim 1, characterized in that: The mixing time monitoring module includes a mixing time monitoring unit, a mixing speed monitoring unit and a first comparison unit; The preparation time monitoring unit is used to record the preparation waiting time Twait of the i-th injection in real time. i ; and the preparation waiting time difference ΔTwait of the i-th injection is calculated by the following formula: i : ΔTwait i =Twait i -Tstandard i ; Among them, Tstandard i Represents the preset standard preparation time value of the i-th injection.

5. The intelligent drug production line management system based on rapid switching of multiple dosage forms according to claim 4, characterized in that: The mixing speed monitoring unit is used to obtain the mixing speed Vmix of the i-th injection in real time. i , and the mixing speed time difference ΔVmix of the i-th injection is calculated by the following formula: i ; ΔVmix i =Vmix-Tv i ; Among them, TV i Indicates the preset mixing speed standard time value of the i-th injection; The first comparison unit is used to preset a first time difference threshold value X1 and a second time difference threshold value X2. i >X1, indicating that too long waiting time will affect the production efficiency of the i-th injection; when ΔVmix i >X2, it means that the mixing speed is too slow, which will cause the production progress to lag behind, triggering the production path switching instruction. If ΔTwait i ≤X1 and ΔVmix i When ≤X2, it means that the current initial production path is qualified and production continues along the initial production path.

6. The intelligent drug production line management system based on rapid switching of multiple dosage forms according to claim 1, characterized in that: The fast 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 used to collect production equipment energy consumption data when receiving a production path switching instruction. 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 energy consumption Eswitch j ; The switching coefficient calculation unit is used to calculate the preparation waiting time difference ΔTwait based on the i-th injection. i , mixing speed time difference ΔVmix i , the extra working time of the jth production equipment during the switching process Cswitch j , Additional energy consumption Eswitch j and the time T required for the jth production equipment to start up and complete production cycle,j , after dimensionless processing, the switching coefficient Qhxs is generated by the following formula: Where, T total Indicates standard production time, E pridl represents the normal production energy consumption of production equipment per unit time, a1, a2, a3 and a4 represent weight coefficients, U equip Indicates the equipment utilization rate, which is calculated using the following formula: Among them, T effective,j is the actual production time of the jth production equipment, T cycle,j It represents the time required for the jth production equipment to start up and complete production.

7. The intelligent drug production line management system based on rapid switching of multiple dosage forms according to claim 6, characterized in that: The second comparison unit is used to preset a switching energy consumption threshold value Y, and compare the switching coefficient Qhxs with the switching energy consumption threshold value Y to obtain a second evaluation result, including: If the switching coefficient Qhxs is less than the switching energy consumption threshold Y, it means that the energy consumption of the switching process is qualified, and the 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 equipment to reduce serial waiting between processes, and increasing the raw material delivery rate by 3%-5%, and generating a second optimal production path and a first production path switching report; If the switching coefficient Qhxs ≥ the switching energy consumption threshold Y, it means that the energy consumption of the switching process is abnormal, and the path switching is not performed, and the initial production path is maintained for production.

8. The intelligent drug production line management system based on rapid switching of multiple dosage forms 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 required for the i-th injection to be completed in the second optimal production path and the energy consumption ratio Nh of the j-th production equipment after the second optimal production path is executed. j and the production rate Scs of the jth production equipment 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, and 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 equipment; α, β and δ are weight coefficients, and α+β+δ=1; It represents the time factor, specifically, the shorter the time required to complete production, the better; It represents the energy efficiency factor, specifically, the lower the energy consumption of production equipment, the better; It represents the production efficiency factor, specifically, the higher the production rate, the better, so this item is placed in the denominator; The third comparison unit is used to preset the comprehensive threshold Z and calculate the production path state coefficient Ljzt of the i-th injection. i Compare with the comprehensive threshold Z to obtain the third evaluation result, including: If the production path state coefficient Ljzt of the i-th injection i If the comprehensive threshold Z is exceeded, it means that the path has not reached the optimization target, and the 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 standby equipment to reduce serial waiting between processes, and increasing the raw material delivery rate by 6%-9%, and generating a switching report between the third optimal production path and the second production path; If the production path state coefficient Ljzt of the i-th injection i If the comprehensive 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.

Images