Factory composed of modular design medical intermediate production workshops capable of being freely combined

Abstract

The invention provides a factory composed of medical intermediate workshops which can be freely combined and are modularly designed, and the factory is composed of the medical intermediate workshops which can be freely combined and are modularly designed. Workshops such as a hydrogenation workshop, an anti-corrosion reaction workshop, a high-temperature and high-pressure reaction workshop, a low-temperature reaction workshop, a common enamel reaction workshop, a continuous flow workshop, a rectification workshop, a drying workshop and a public engineering workshop are standardized and independent, each workshop is provided with a multi-direction interface, rapid connection and switching are supported, flexible combination is achieved, and the production efficiency is improved. Therefore, the production requirements of different medical intermediates are met. All the workshops are connected through intelligent pipelines and an automatic dispatching system, efficient conveying of liquid or gas or solid materials is completed through quick connectors, multi-way valves and the dynamic switching technology, meanwhile, real-time path planning and parameter adjustment are achieved in combination with the advanced control technology, and the stability and quality consistency of the production process are guaranteed. According to the technology, flexible production of multiple medical intermediates can be supported, the switching time and the equipment investment cost are remarkably reduced, the workshop utilization rate is increased, and an efficient, intelligent and extensible solution is provided for production of the medical intermediates.

Description

Technical Field

[0001] The invention relates to the field of pharmaceutical production equipment and manufacturing technology thereof, and in particular to a factory composed of pharmaceutical intermediate production workshops with modular design that can be freely combined. Background Art

[0002] Traditional chemical pharmaceutical intermediate factories design production lines based on the specific process routes of pharmaceutical intermediates. The equipment layout is basically based on the process route layout of specific varieties (such as penicillin intermediates, amoxicillin intermediates or cephalosporin intermediates). If there are no orders for the variety, when the product needs to be replaced, it will be limited by the design of the original process route equipment layout. It will cost a lot to change the process route layout for the replaced product, which increases the production cost. Moreover, even if the equipment process of the original process route layout is changed, it may not be able to fully match the replaced product, resulting in limited equipment process matching. Traditional pharmaceutical intermediate production workshops use fixed processes and rigid equipment layouts, which make it difficult to respond quickly to changes in market demand. Especially in the production of pharmaceutical intermediates, frequent changes in formulas and processes have put forward higher requirements for production flexibility.

[0003] For example, Chinese patent CN116383948A discloses a method for designing a workshop for pharmaceutical intermediates. According to the equipment reaction types of the process equipment in each process unit, at least one process workshop corresponding to different equipment reaction types is designed. However, the intermediates have the defects of poor mutual coordination and insufficient self-adaptation.

[0004] In addition, the present embodiment also has the following defects:

[0005] 1. Traditional equipment maintenance is mainly based on "regular maintenance" and lacks real-time monitoring of equipment status. Unplanned equipment downtime will affect production continuity.

[0006] 2. Fixed production lines usually have idle equipment or insufficient capacity, and are unable to adjust the utilization of equipment resources according to real-time demand.

[0007] 3. Fixed process flow is difficult to dynamically adapt to raw material fluctuations or environmental changes, affecting product qualification rate.

[0008] The present invention is made to solve the common problems in the field such as low efficiency of equipment management and maintenance, poor adaptive adjustment capability, lack of process monitoring and evaluation, low intelligence and weak dynamic regulation capability. Summary of the invention

[0009] The purpose of the present invention is to address the current deficiencies and propose a factory consisting of freely combinable modular design pharmaceutical intermediate production workshops.

[0010] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions:

[0011] A factory composed of freely combinable modular design pharmaceutical intermediate production workshops, the pharmaceutical intermediate workshop factory is composed of at least one independent workshop, each workshop only completes a certain type or similar type of physical or chemical reaction, when a pharmaceutical intermediate has two or more reaction types, two or more workshops are required to complete them in collaboration; one of the workshops is only used for the same type or similar type of physical processing technology or chemical processing technology; the workshops at least include: hydrogenation workshop, corrosion resistance reaction workshop, high temperature and high pressure reaction workshop, low temperature and ultra-low temperature reaction workshop, ordinary enamel reaction workshop, continuous flow hazardous chemical reaction workshop, distillation workshop, drying workshop and public engineering workshop, etc.

[0012] At least one of the independent workshops is connected by a detachable connecting device to realize the transportation of pharmaceutical intermediates. The intelligent control system performs real-time monitoring, production scheduling, data analysis and equipment management on at least one independent workshop to achieve precise control of the workshop, ensuring that the drug synthesis process of the pharmaceutical intermediates generated in the workshop meets the process requirements and achieves quality control.

[0013] Optionally, the intelligent control system includes a monitoring management module, a production scheduling module, an equipment management module and a data analysis module, wherein the monitoring management module collects operating data of at least one independent workshop in real time; the production scheduling module evaluates each independent workshop based on the collected operating data to form an evaluation result, and schedules and combines each independent workshop based on the evaluation result to complete the production of pharmaceutical intermediates; the data analysis module analyzes the production demand and the real-time operating data of the scheduled workshop to form an analysis result, and the equipment management module determines whether it is necessary to trigger maintenance of at least one independent workshop involved in the scheduling based on the analysis result.

[0014] Optionally, the monitoring and management module includes a data acquisition unit and a data transmission unit, the data acquisition unit collects the operating data of at least one independent workshop participating in the scheduling, and the data transmission unit transmits the collected operating data of at least one independent workshop participating in the scheduling to the production scheduling module.

[0015] Optionally, the production scheduling module includes a data receiving unit, a unit performance evaluation unit, a scheduling decision unit, and a scheduling execution unit. The data receiving unit receives the operation data from the monitoring management module. The unit performance evaluation unit performs a comprehensive evaluation on the current status, production tasks, transfer tasks and equipment operation tasks of each workshop based on the received data to generate a quantitative evaluation result. The scheduling decision unit formulates an optimized scheduling plan according to the evaluation results. The scheduling execution unit will execute the scheduling according to the scheduling plan and trigger a scheduling prompt to the manager.

[0016] Optionally, the data analysis module obtains the real-time data of the workshops to be scheduled and forms a data matrix X:

[0017]

[0018] In the formula, x i,j is the jth running data value at the i-th time point;

[0019] The volatility index σ of the j-th running data is calculated according to the following formula: j :

[0020]

[0021] In the formula, is the average value of the jth operating data, and N is the total number of time points in the set monitoring period;

[0022] The data analysis module constructs an optimization objective function f(X) through the volatility index and production demand to determine the optimized operation data, wherein the optimization objective function f(X) is:

[0023]

[0024] Where Y 当前 is the actual output of the pharmaceutical intermediate currently produced, Y 目标 is the target output of pharmaceutical intermediates specified by production demand, and λ is the weight coefficient.

[0025] Optionally, the equipment management module includes a data analysis unit, a health status assessment unit and a maintenance trigger judgment unit. The data analysis unit receives the analysis results from the data analysis module and parses the equipment status information. The health status assessment unit evaluates the current operating data of the equipment to determine whether a threshold for triggering maintenance is reached. The maintenance trigger judgment unit determines whether maintenance is required for at least one independent workshop involved in the scheduling based on the health status assessment results.

[0026] Optionally, determining the optimized operating data requires satisfying constraints of process requirements.

[0027] The beneficial effects achieved by the present invention are:

[0028] 1. Through modular and intelligent design, the hydrogenation workshop, anti-corrosion reaction workshop, high temperature and high pressure reaction workshop, low temperature reaction workshop, ordinary enamel reaction workshop, continuous flow workshop, distillation workshop, drying workshop and public engineering workshop are standardized and independent. Each unit is equipped with a multi-directional interface to support fast connection and switching, and realize flexible combination to meet the production needs of different pharmaceutical intermediates. Intelligent pipeline connection and automatic scheduling system are used between each workshop. Through quick connectors, multi-way valves and dynamic switching technology, efficient transmission of liquid, gas or solid materials is completed. At the same time, advanced control technology is combined to realize real-time path planning and parameter adjustment to ensure the stability and quality consistency of the production process. This technology can not only support the flexible production of a variety of pharmaceutical intermediates, but also significantly reduce the switching time and equipment investment costs, improve the utilization rate of the workshop, and provide an efficient, intelligent and scalable solution for the production of pharmaceutical intermediates.

[0029] 2. Through the cooperation of the production scheduling module and the data analysis module, the system can dynamically adjust the combination of workshops according to production needs, improve production efficiency and quality, and ensure that the entire system has the advantages of intelligence and flexibility.

[0030] 3. Through the cooperation of the data analysis module and the equipment management module, the system can detect potential equipment problems in real time and perform maintenance, reduce equipment downtime, and ensure the stability and reliability of the entire system.

[0031] 4. Through the cooperation of the equipment management module and the monitoring management module, the system can quickly respond to maintenance needs and accurately perform maintenance tasks, ensuring that the entire system has the advantages of high accuracy and maintenance efficiency.

[0032] 5. Through the cooperation of the monitoring management module and the production scheduling module, the system can dynamically adjust the production process to ensure the real-time and high efficiency of the production process, and ensure that the entire system has the advantages of rapid response and high productivity. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0034] Figure 1 It is a block diagram of the pharmaceutical intermediate production workshop and workshop of the present invention.

[0035] Figure 2 It is a block diagram of the intelligent control system of the present invention.

[0036] Figure 3 It is a block diagram of the monitoring management module and the production scheduling module of the present invention.

[0037] Figure 4 It is a block diagram of the device management module and the data analysis module of the present invention.

[0038] Figure 5 The figure is a schematic diagram showing the effect of the present invention compared with the prior art. DETAILED DESCRIPTION

[0039] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0040] Embodiment 1: According to Figure 1 , Figure 2 , Figure 3 , Figure 4 ,as well as Figure 5 As shown, this embodiment provides a freely combinable modular design pharmaceutical intermediate production workshop, the pharmaceutical intermediate workshop is composed of at least one independent workshop, at least one independent workshop is connected by a detachable connection device to realize the transportation of process products or pharmaceutical intermediates, each workshop only completes a certain type or similar type of physical or chemical reaction, when a pharmaceutical intermediate has two or more reaction types, two or more workshops are required to complete them collaboratively; one of the workshops is only used for the same type or similar type of physical processing technology or chemical processing technology; the workshop includes at least: hydrogenation unit, corrosion-resistant reaction unit, high temperature and high pressure reaction unit, low temperature reaction unit, ordinary enamel reaction unit, continuous flow unit, distillation unit, drying unit and public engineering unit, etc.

[0041] For example: For example: In an intermediate process route, the process between intermediate product ① and intermediate product ② is a hydrogenation reaction, which is carried out in a hydrogenation workshop; the process between intermediate product ② and intermediate product ③ is a common reaction, which is carried out in a common enamel reaction workshop; the process between intermediate product ③ and intermediate product ④ is a high temperature and high pressure catalysis, which is carried out in a high temperature and high pressure workshop; the process between intermediate product ④ and intermediate product ⑤ is a common reaction, which is carried out in a common enamel reaction workshop; the process between intermediate product ⑤ and intermediate product ⑥ is a drying reaction, which is carried out in a drying workshop; so to obtain pharmaceutical intermediate ⑥, only four workshops are required to participate in the production. According to this method, almost most pharmaceutical intermediates can be produced in the intermediate production workshop of the pharmaceutical intermediates of this application. In other words, there is no need to build new workshops or factories for the production of more intermediates, as long as the existing multiple workshops are intelligently combined and scheduled.

[0042] The pharmaceutical intermediate production workshop includes an intelligent control system, which performs real-time monitoring, production scheduling, data analysis and equipment management on at least one independent workshop to achieve precise control of the workshop, ensure that the drug synthesis process of the pharmaceutical intermediate generated in the workshop meets the process requirements and achieves quality control. Optionally, the intelligent control system includes a monitoring management module, a production scheduling module, an equipment management module and a data analysis module, the monitoring management module collects the operation data of at least one independent workshop in real time; the production scheduling module evaluates each independent workshop according to the operation data collected to form an evaluation result, and schedules and combines each independent workshop according to the evaluation result to complete the production of the pharmaceutical intermediate; the data analysis module analyzes the production demand and the real-time operation data of the scheduled workshop to form an analysis result, and the equipment management module determines whether it is necessary to trigger maintenance of at least one independent workshop participating in the scheduling according to the analysis result.

[0043] The intelligent control system also includes a central processing unit, which is respectively connected to the monitoring management module, the production scheduling module, the equipment management module and the data analysis module, and centrally controls the monitoring management module, the production scheduling module, the equipment management module and the data analysis module based on the central processing unit to improve the accuracy of the operation of the entire intelligent control system and ensure the efficiency and accuracy of the operation of the pharmaceutical intermediates by the entire system.

[0044] The intelligent control system also includes a server, which is connected to the monitoring management module, the production scheduling module, the equipment management module and the data analysis module respectively, and stores the intermediate data and process data of the monitoring management module, the production scheduling module, the equipment management module and the data analysis module in the database of the server for query and call.

[0045] In this embodiment, the at least one independent workshop includes a basic workshop, an auxiliary workshop, a material transfer unit, a special processing unit and a waste processing unit;

[0046] The basic workshop includes a mixing unit, a reaction unit, a separation unit, a drying unit, and a filtering unit. The mixing unit mixes different raw materials to ensure uniform distribution. The reaction unit completes a specific chemical reaction, such as neutralization, oxidation or reduction reaction. The separation unit separates different components in the mixture. The drying unit removes water or other solvents from the product or raw materials, and the filtering unit removes solid impurities or separates solid-liquid mixtures.

[0047] The auxiliary workshop specifically includes a feeding unit, a temperature control unit, a pressure regulating unit, and a cleaning unit. The feeding unit provides a stable supply of raw materials for the production process. The temperature control unit regulates and maintains the temperature of the workshop. The pressure regulating unit provides support for workshops that require specific pressure, such as booster pumps and pressure reducing valves. The cleaning unit cleans the equipment and production modules.

[0048] The material transfer unit specifically includes: a liquid conveying unit and a solid conveying unit, wherein the liquid conveying unit is used to transfer liquid raw materials or products between various associated units, and the solid conveying unit is used to transfer powder or granular materials between associated units.

[0049] The special treatment unit includes a crystallization unit and a surface treatment unit, wherein the crystallization unit controls the formation and size of crystals, and the surface treatment unit performs surface coating or modification on the product.

[0050] The waste treatment unit includes a waste gas treatment unit, a waste liquid treatment unit and a solid waste treatment unit.

[0051] The waste gas treatment unit removes or reduces harmful gas emissions. The waste liquid treatment unit treats waste water or waste liquid generated during production. The solid waste treatment unit compresses or decomposes solid waste.

[0052] The at least one independent workshop mentioned above is a unit and instrument commonly used in a pharmaceutical intermediate production workshop, which is a technical means well known to technicians in this field, so it will not be described one by one in this embodiment.

[0053] Optionally, the monitoring and management module includes a data acquisition unit and a data transmission unit, the data acquisition unit collects the operating data of at least one independent workshop participating in the scheduling, and the data transmission unit transmits the collected operating data of at least one independent workshop participating in the scheduling to the production scheduling module.

[0054] In this embodiment, the operation data includes process parameters, environmental parameters, and material information;

[0055] The data collection unit includes an activator and a data collection component. The data collection components are respectively arranged in at least one independent workshop. The activator activates the corresponding data collection component so that the data collection component in at least one independent workshop participating in the scheduling can be collected.

[0056] When collecting data of a workshop, the data collection component set in the workshop is activated, and the activated data collection component can be collected and transmitted to the production scheduling module through the data transmission unit.

[0057] The data acquisition component includes a process parameter sensor group, an environmental parameter sensor group and a material information sensor group. The process parameter sensor group is used to collect the process parameters of the workshop, the environmental parameter sensor collects the environmental data of the workshop, and the material information sensor collects the material information data of the workshop.

[0058] The process parameter sensor group includes but is not limited to the following: temperature sensor, pressure sensor, flow sensor, rotation speed sensor, viscometer, humidity sensor.

[0059] That is, the process parameters include temperature, pressure, flow rate, rotation speed, viscosity and humidity.

[0060] The environmental parameter sensor group includes but is not limited to the following: temperature and humidity sensors, air pressure sensors, vibration sensors, and noise sensors.

[0061] The material information sensor includes but is not limited to the following: a spectrometer, a laser particle size analyzer, a weighing sensor, and a refractometer.

[0062] The data transmission unit includes a data interface, a transmission protocol, and a transmitter. The data interface is for connection to an activated sensor component. The transmission protocol is built into the data interface and the transmitter. The transmitter collects operating data of at least one independent workshop participating in the scheduling and transmits it to the production scheduling module.

[0063] Optionally, the production scheduling module includes a data receiving unit, a unit performance evaluation unit, a scheduling decision unit, and a scheduling execution unit. The data receiving unit receives the operation data from the monitoring management module. The unit performance evaluation unit performs a comprehensive evaluation on the current status of each workshop and the production tasks, transfer tasks, and equipment operation tasks based on the received data to generate a quantitative evaluation result. The scheduling decision unit formulates an optimized scheduling plan based on the evaluation results. The scheduling execution unit will execute the scheduling according to the scheduling plan and trigger a scheduling prompt to the manager.

[0064] The data receiving unit includes a data receiver and a data storage. The data receiver receives the operating data transmitted by the data transmission unit, and the data storage stores the operating data received by the data receiver.

[0065] The data receiver may receive data in the following ways:

[0066] The unit performance evaluation unit obtains the operation data and calculates the stability index S according to the following formula: j :

[0067]

[0068] In the formula, x i,j is the ith operation data value of the jth workshop before, x i,j 目标 is the reference value of the ith operating data under normal process conditions, including the target value of the process parameter, the central value of the environmental parameter or the expected value of the material information, and M is the number of types of operating data currently collected by the workshop;

[0069] The unit performance evaluation unit obtains the operation data and calculates the task completion index T according to the following formula: j :

[0070]

[0071] In the formula, the completed task volume is the completed output or processed volume of a single workshop in the current task, and the total task volume is the total processed volume or total output of a single workshop in this task according to the current production plan or target setting.

[0072] The unit performance evaluation unit obtains the operating data and calculates the health status index H according to the following formula: j :

[0073]

[0074] In the formula, the number of abnormal operation data is the number of operation data that exceeds the target range, and the total number of operation data is the number of all monitored operation data.

[0075] The unit performance evaluation unit comprehensive stability index S j , Task Completion Index T j , Health Status Index H j , comprehensive evaluation index E j :

[0076]

[0077] In the formula, κ is the volatility penalty coefficient, whose value is set by the system, △S j is the fluctuation amplitude of the current state index, defined as ΔS j =max(S j )-min(S j );

[0078] In this embodiment, the volatility penalty coefficient κ ranges from [0, 10], and the value trend of κ varies with the scenario requirements: 1) When the volatility impact is small (such as raw material mixing), κ is set to a smaller value; 3) When the volatility impact is significant (such as precision reaction), κ is set to a larger value; 2) In scenarios with stability priority (such as products with high quality requirements), κ is set to a larger value.

[0079] At the same time, in this embodiment, an example of the value of the fluctuation penalty coefficient κ is provided: 1) in the scenario of a common mixing unit (the mixing unit mixes multiple raw materials, the goal is to ensure uniformity, but the tolerance for fluctuations is high), the fluctuation penalty coefficient κ=0.5;

[0080] 2) In the scenario of high-precision reaction units (complex chemical reactions are carried out in the reaction units, and temperature and pressure fluctuations may cause significant degradation of product quality), the volatility penalty coefficient κ = 5;

[0081] 3) In the drying unit scenario (the goal of the drying unit is to reduce the moisture content of the material to a specific level, and temperature fluctuations may affect the drying efficiency), the volatility penalty coefficient κ = 2;

[0082] 4) Ultra-high precision drug preparation unit (

[0083] In the preparation unit, the reaction or separation of drug intermediates is carried out, and any fluctuation may significantly affect the purity of the final product), then the volatility penalty coefficient κ = 10;

[0084] The scheduling decision unit is based on the comprehensive evaluation index E j Sort tasks from large to small, and give priority to assigning tasks to workshops with high comprehensive evaluations;

[0085] Among them, if the comprehensive evaluation index E of a workshop j If the load drops significantly or the task times out, dynamic adjustment is triggered, which includes transferring the task to other available units and reducing the current task load.

[0086] The scheduling execution unit includes an instruction generator and an interactive prompter. The instruction generator generates corresponding instructions according to the scheduling decision unit, and the interactive prompter triggers a scheduling prompt to the manager.

[0087] The instructions include but are not limited to the following: start / stop task instructions, adjustment of operating data (such as temperature, pressure, speed), and switching instructions after the task is completed.

[0088] After the instruction is generated, the instruction is transmitted to the central processing unit, and the central processing unit is used to combine and schedule the workshops involved in the scheduling to produce the pharmaceutical intermediates.

[0089] Optionally, the data analysis module obtains the real-time data of the workshops to be scheduled and forms a data matrix X:

[0090]

[0091] In the formula, x i,j is the jth running data value at the i-th time point;

[0092] The volatility index σ of the j-th running data is calculated according to the following formula: j :

[0093]

[0094] In the formula, is the average value of the jth operating data, and N is the total number of time points in the set monitoring period;

[0095] The data analysis module constructs an optimization objective function f(X) through the volatility index and production demand to determine the optimized operation data, wherein the optimization objective function f(X) is:

[0096]

[0097] Where Y 当前 is the actual output of the pharmaceutical intermediate currently produced, Y 目标 is the target output of pharmaceutical intermediates specified by production demand, and λ is the weight coefficient.

[0098] In this embodiment, the weight coefficient λ is calculated according to the following formula:

[0099]

[0100] In the formula, △Y is the increase in output, and △σ is the increase in volatility index.

[0101] Among them, when the increase in output is greater than the increase in volatility, λ is reduced, otherwise it is increased.

[0102] The optimized operating data X is obtained by the optimization objective function * =[x1 * , x2 * , …, xM * ];

[0103] Among them, it is determined that the optimized operating data needs to meet the constraints of the process requirements. In this embodiment, the set constraints are:

[0104] 1) Satisfy the operating data range, specifically, L j ≤x j ≤U j (j=1,2,…,M), where L j and U j For running data x j The lower and upper limits are determined by the specific pharmaceutical intermediates produced.

[0105] 2) Meet production demand: current output is greater than or equal to target output;

[0106] The current quality is greater than or equal to the target quality.

[0107] In addition, the above objective function is solved by using an optimization algorithm (gradient descent method), combining the objective function and iteratively adjusting the running data X * , under the premise of satisfying the physical constraints of the operating data and the production conditions, gradually approach the operating data combination that maximizes the objective function value. The largest operating data combination is the optimized operating data X * =[x1 * , x2 * , …, x M * ].

[0108] The process of solving the objective function includes the following steps:

[0109] S100, initialization operation data:

[0110] Randomly generate or use experience to set the initial operation data X0 = [x1 0 , x2 0 , …, x M 0 ];

[0111] Set the learning rate η and volatility penalty coefficient λ;

[0112] Define the iteration stop condition:

[0113] 1) The change in the objective function value is less than the threshold ∈;

[0114] 2) Reach the maximum number of iterations T max .

[0115] S101. Calculate the objective function value:

[0116] According to the current running data X t ,calculate:

[0117] Current output Y 当前 =g(X t );

[0118] Current volatility σ j , for each j∈[1,M] calculate:

[0119]

[0120] Substitute it into the objective function and calculate f(X t ) value;

[0121] S102, calculate gradient

[0122] Calculate the yield partial gradient:

[0123] in, Obtained through experiments or simulations.

[0124] Calculate the partial gradient of the volatility index:

[0125] S103, update operation data:

[0126] According to the gradient descent update formula:

[0127]

[0128] S104, check convergence:

[0129] If |f(X t+1 )-f(X t )|<ò, or t≥Tmax, then stop the iteration;

[0130] Otherwise, set t=t+1, return to step S101, and continue to execute until convergence is satisfied.

[0131] The X obtained in the last iteration t Optimize operation data X*;

[0132] After convergence, it is necessary to further verify whether the physical constraints and production conditions are met and output the final optimization results. The specific steps include:

[0133] S200, extract convergence operation data X * :

[0134] The optimization algorithm records the last updated running data X t :

[0135]

[0136] The operating data is a combination of operating data that maximizes the objective function f(X);

[0137] S201. Verify physical constraints:

[0138] Check X * Whether the physical upper and lower limits of each operating data are met:

[0139] If a certain running data x * j If it exceeds the range, adjust according to the following method:

[0140] If x * j Less than x min j , let x * j =x min j ;

[0141] If x * j Greater than x max j , let x * j =x max j ;

[0142] S202. Verify production conditions

[0143] Check X * Whether production conditions are met, such as output constraints: Y 当前 (X * )≥Y 目标 ;

[0144] If Y 当前 (X * ) is less than the target output Y target, further adjustment X is needed * , the specific steps are:

[0145] In x j Gradually search for local adjustments within the range of 当前 (X * ) approaches or reaches Y 目标 .

[0146] S203, output the final optimization result:

[0147] Determine the running data X * After physical constraints and production conditions are met, it is used as the final optimized operation data X *=[x1 * , x2 * , …, x M * ].

[0148] Through the cooperation of the production scheduling module and the data analysis module, the system can dynamically adjust the combination of workshops according to production needs, improve production efficiency and quality, and ensure that the entire system has the advantages of intelligence and flexibility.

[0149] Through the cooperation of the data analysis module and the equipment management module, the system can detect potential equipment problems in real time and perform maintenance, reduce equipment downtime, and ensure the stability and reliability of the entire system.

[0150] Optionally, the equipment management module includes a data analysis unit, a health status assessment unit and a maintenance trigger judgment unit. The data analysis unit receives the analysis results from the data analysis module and parses the equipment status information. The health status assessment unit evaluates the current operating data of the equipment to determine whether a threshold for triggering maintenance is reached. The maintenance trigger judgment unit determines whether maintenance is required for at least one independent workshop involved in the scheduling based on the health status assessment results.

[0151] The data parsing unit performs data parsing according to the following steps to determine the status information of the device:

[0152] S1. Receive data: receive the analysis results sent by the data analysis module;

[0153] S2, extract key equipment status information and standardize it into a unified structure;

[0154] The device status information includes device ID and device operation data.

[0155] S3. Output parsing results: pass the converted standardized data to the health status assessment unit.

[0156] The monitoring status evaluation unit receives the device status data and calculates the health status indicator Health according to the following formula:

[0157]

[0158] Where M is the total number of operating data, that is, the total number of key operating data monitored in the current operating data of the workshop participating in the scheduling, P j is the current value of the jth operating data, which is obtained in real time from the sensor or data acquisition module.

[0159] P opt jis the target value of the jth operating data, which is set according to the equipment operation manual or process requirements. range j is the range of the jth operating data, and its value is determined according to the equipment design and operating specifications, η j is the standard deviation of the j-th running data, R f is the failure risk value.

[0160] Among them, the standard deviation η of the j-th running data j , calculated according to the following formula:

[0161]

[0162] Where P i,j is the jth running data value of the i-th sampling, is the average value of the jth running data in the time window, and N is the number of sampling times.

[0163] The failure risk value R f Calculate according to the following formula:

[0164]

[0165] Where M is the total number of operating data, that is, the total number of key operating data monitored in the current operating data of the workshop participating in the scheduling, P j is the current value of the jth operating data, which is obtained in real time from the sensor or data acquisition module.

[0166] P opt j is the target value of the jth operating data, which is set according to the equipment operation manual or process requirements. range j is the range of the jth operating data, and its value is determined according to the equipment design and operating specifications.

[0167] The maintenance trigger judgment unit receives the health status indicator Health and compares it with the set monitoring threshold H 阈值 Compare:

[0168] If Health>H 阈值 This indicates that the workshops involved in the scheduling are in good condition and continue to operate without maintenance;

[0169] When Health≤H 阈值 , indicating that the health status of the equipment is below the standard, triggering a maintenance decision.

[0170] In addition, the monitoring threshold H set in this embodiment is 阈值The system sets the threshold according to the actual situation. This is a technical means known to the technicians in the prior art, so it will not be described in detail in this embodiment. For example, in a certain scenario, when performing a key task, the threshold is increased (such as the set monitoring threshold H 阈值 =0.8); when executing low-priority tasks, lower the threshold (such as the set monitoring threshold H 阈值 =0.6).

[0171] The maintenance decision includes:

[0172] 1) Maintenance task list: list of equipment that needs maintenance;

[0173] 2) Maintenance priority: sort by task importance and health status value;

[0174] 3) Maintenance Scheduling: Insert maintenance tasks into the scheduling plan.

[0175] The specific implementation code of the maintenance trigger judgment unit is:

[0176]

[0177] After the maintenance trigger judgment unit determines the corresponding decision, it prompts the manager or the control center with the maintenance priority and execution time.

[0178] Through the cooperation of the equipment management module and the monitoring management module, the system can quickly respond to maintenance needs and accurately perform maintenance tasks, ensuring that the entire system has the advantages of high accuracy and maintenance efficiency.

[0179] By collecting the operation data of the workshop in real time through the monitoring management module and evaluating and optimizing the collected data through the production scheduling module, the system can dynamically adjust the production process to ensure the real-time and efficiency of the production process, and ensure that the entire system has the advantages of rapid response and high productivity.

[0180] Embodiment 2: This embodiment should be understood to include all the features of any of the above embodiments, and further improve upon them. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown, the intelligent control system also includes a scheduling and control module, which analyzes the current task load of a single workshop to form an analysis result, and dynamically controls other workshops of the same type to start according to the analysis result to ensure that the production task is not affected.

[0181] The scheduling and control module includes a scheduling analysis unit and a scheduling control unit. The scheduling analysis unit analyzes the current task load of a single workshop to form an analysis result, and the scheduling control unit starts workshops of the same type according to the analysis result.

[0182] In this embodiment, each workshop is equipped with at least two workshops of the same type to improve the redundancy capability of the system.

[0183] The scheduling analysis unit obtains the task load of each workshop and calculates the real-time health index H of each workshop according to the following formula: 单元 :

[0184]

[0185] Where P i is the current value of the i-th operation data of the workshop, P i opt is the target value of the running data, P i range It is the allowed range of operating data, and its value is set by the system according to the type of pharmaceutical intermediate to be produced.

[0186] The scheduling control unit is based on the health index H 单元 , dynamically allocate production tasks to other units in better health.

[0187] At the same time, if the health index H 单元 <0.5: transfer some tasks to health index H 单元 Spare workshop of the same type with a value of ≥0.8.

[0188] Specifically, the scheduling control unit is based on the health index H of a single workshop. 单元 and the current task load L 当前 , where for the current workshop j and the set of standby workshops of the same type S, the task volume L assigned by the scheduling control unit is k satisfy:

[0189]

[0190] Where, L k The task volume assigned to the spare workshop k, H k is the health index of spare workshop k, L 转移 The amount of tasks that need to be transferred:

[0191] L 转移 =L 当前 -(L 当前 ·H 单元 );

[0192] Where, L 当前is the total task load of the current process, H 单元 It is the health index of the current process.

[0193] In summary, the scheduling formula of the scheduling control unit is:

[0194]

[0195] In the formula, S is a set of spare workshops that meet the conditions and are used to receive tasks transferred from the current workshop. ΔL represents the remaining amount of task allocation. The workshops in the set usually have equipment with the same or similar functions as the current workshop.

[0196] Among them, the task load margin: L k +L 转移 ≤L 最大 , so that the workshop k must have enough remaining processing capacity to receive the transferred tasks. k +L 转移 ≤L 最大 The task will be assigned to workshop k only when the condition is met. If the condition is not met, it is necessary to: adjust the task allocation plan, assign it to other backup workshops, and issue a warning of insufficient resources.

[0197] Among them, the task load margin refers to the additional amount of tasks that a workshop can undertake under the current task load.

[0198] Through the cooperation of the scheduling analysis unit and the scheduling control unit, the entire system can dynamically analyze the task load and quickly start the backup unit, ensuring that the entire system has the advantages of high reliability, strong redundancy, dynamic optimization, rapid response and low failure rate.

[0199] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A factory composed of freely combinable modular pharmaceutical intermediate workshops. Each pharmaceutical intermediate workshop only completes a certain type or similar type of chemical reaction. When a pharmaceutical intermediate has more than two reaction types, more than two workshops are required to complete them in coordination. There are convenient devices between workshops to realize the transportation of intermediate products; It is characterized in that Each workshop is only used to complete a certain type or similar type of physical or chemical processing technology. When a pharmaceutical intermediate has two or more reaction types, two or more workshops are required to complete them in coordination; The pharmaceutical intermediate production workshop factory includes an intelligent control system, which performs real-time monitoring, production scheduling, data analysis and equipment management on each independent workshop to achieve precise control of the workshop, ensuring that the drug synthesis process of the pharmaceutical intermediates generated in the workshop meets the process requirements and achieves quality control.

2. The factory composed of freely combinable modular pharmaceutical intermediate workshops according to claim 1, characterized in that: The intelligent control system includes a monitoring management module, a production scheduling module, an equipment management module and a data analysis module. The monitoring management module collects the operating data of at least one independent workshop in real time; the production scheduling module evaluates each independent workshop according to the collected operating data to form an evaluation result, and schedules and combines each independent workshop according to the evaluation result to complete the production of pharmaceutical intermediates; the data analysis module analyzes according to production demand and the real-time operating data of the scheduled workshop to form an analysis result, and the equipment management module determines whether it is necessary to trigger maintenance of at least one independent workshop involved in the scheduling according to the analysis result.

3. The factory composed of freely combinable modular pharmaceutical intermediate workshops according to claim 2, characterized in that: The monitoring and management module includes a data acquisition unit and a data transmission unit. The data acquisition unit collects the operating data of at least one independent workshop participating in the scheduling, and the data transmission unit transmits the collected operating data of at least one independent workshop participating in the scheduling to the production scheduling module.

4. The factory composed of freely combinable modular pharmaceutical intermediate workshops according to claim 3, characterized in that: The production scheduling module includes a data receiving unit, a unit performance evaluation unit, a scheduling decision unit, and a scheduling execution unit. The data receiving unit receives the operation data from the monitoring management module. The unit performance evaluation unit performs a comprehensive evaluation on the current status, production tasks, transfer tasks and equipment operation tasks of each workshop based on the received data to generate a quantitative evaluation result. The scheduling decision unit formulates an optimized scheduling plan according to the evaluation results. The scheduling execution unit will execute the scheduling according to the scheduling plan and trigger a scheduling prompt to the manager.

5. The factory composed of freely combinable modular pharmaceutical intermediate workshops according to claim 4, characterized in that: The data analysis module obtains the real-time data of the workshops that need to be scheduled and forms a data matrix X: In the formula, x i,j is the jth running data value at the i-th time point; The volatility index σ of the j-th running data is calculated according to the following formula: j : In the formula, is the average value of the jth operating data, and N is the total number of time points in the set monitoring period; The data analysis module constructs an optimization objective function f(X) through the volatility index and production demand to determine the optimized operation data, wherein the optimization objective function f(X) is: Where Y 当前 is the actual output of the pharmaceutical intermediate currently produced, Y 目标 is the target output of pharmaceutical intermediates specified by production demand, and λ is the weight coefficient.

6. The factory composed of freely combinable modular pharmaceutical intermediate workshops according to claim 5, characterized in that: The equipment management module includes a data analysis unit, a health status assessment unit and a maintenance trigger judgment unit. The data analysis unit receives the analysis results from the data analysis module and analyzes the equipment status information. The health status assessment unit evaluates the current operating data of the equipment to determine whether the threshold for triggering maintenance is reached. The maintenance trigger judgment unit determines whether maintenance is required for at least one independent workshop involved in the scheduling based on the health status assessment results.

7. The factory composed of freely combinable modular pharmaceutical intermediate workshops according to claim 6, characterized in that: Determine the optimized operating data required to meet the constraints of process requirements.

8. The factory composed of freely combinable modular pharmaceutical intermediate workshops according to claim 7, characterized in that: A production workshop is only used for the same or similar types of physical processing or chemical processing. The workshops in the entire factory area include at least: hydrogenation workshop, anti-corrosion reaction workshop, high temperature and high pressure reaction workshop, low temperature and ultra-low temperature reaction workshop, ordinary enamel reaction workshop, continuous flow hazardous chemical reaction workshop, distillation workshop, drying workshop and public engineering workshop.

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