White spirit brewing process scheduling optimization method and equipment based on linear programming and medium

Abstract

The invention discloses a Baijiu brewing process scheduling optimization method based on linear programming, equipment and a medium, and belongs to the field of Baijiu brewing control, the method comprises the following steps: step 1, data acquisition and parameter setting: acquiring operation state data of each key equipment of a Baijiu brewing production line through a sensor, and setting priority and time parameters of each task in an initial production scheduling scheme; 2, constructing a scheduling model: constructing a linear programming model as the scheduling model in combination with a linear programming algorithm; 3, optimizing a task sequence: solving an optimal production scheduling scheme through linear programming, sending the optimal production scheduling scheme to an automatic control system, and executing a step 5; 4, dynamic adjustment: when changes occur, regenerating a new production scheduling scheme by the scheduling model, sending the new production scheduling scheme to the automatic control system, and executing the step 5; and step 5, controlling the white spirit brewing production line to execute each task by the automatic control system according to the latest production scheduling scheme.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of liquor brewing control, and particularly relates to a liquor brewing process scheduling optimization method based on linear programming. BACKGROUND

[0002] At present, there are many tasks manually operated by workers in the liquor brewing production using the liquor brewing production line, such as selection of fermentation tanks for out-pool and in-pool, and cleaning and lifting treatment of retort pots. Meanwhile, there are many tasks in the liquor brewing production line processed by the equipment according to the first readiness, such as selection of RGV trolley for spreading and airing or for discarding of grains. The sequence and distribution of these tasks have a direct impact on production efficiency. The existing production scheduling scheme mainly depends on worker experience, manual judgment and automatic equipment scheduling according to the readiness sequence, which may achieve certain local optimization effect, but has some obvious limitations. Since the liquor brewing process is complex and includes multiple steps such as fermentation tank out-pool, retort pot heating, spreading and airing, and grain discarding, and the time required for each step is difficult to accurately predict, it is difficult for workers to comprehensively and dynamically coordinate the arrangement between each process, and the automatic equipment scheduling according to the readiness sequence is not necessarily a reasonable scheduling method, which is easy to cause problems such as production line bottleneck and long retort pot time. These problems may affect the stability of the liquor brewing production line, increase the production time, and reduce the output rate and quality stability of the liquor.

[0003] In view of this, the present application is proposed. SUMMARY

[0004] The purpose of the present application is to provide a liquor brewing process scheduling optimization method, device and medium based on linear programming, which can realize optimal scheduling of the liquor brewing process, ensure the stability of the liquor brewing production line, shorten the production time, and improve the output rate and quality stability of the liquor, thereby solving the above technical problems in the prior art.

[0005] The purpose of the present application is achieved by the following technical solutions: A liquor brewing process scheduling optimization method based on linear programming, comprising: Step 1, data acquisition and parameter setting: acquiring the operation state data of each key device of the liquor brewing production line through a sensor, and setting the priority and time parameters of each task in the production scheduling scheme of the liquor brewing production line; Step 2, constructing a scheduling model: combining a linear programming algorithm, converting the total production task of each step in the liquor brewing process, such as the fermentation tank in and out of the tank, the RGV trolley transportation in the middle, the elevator transportation, the retort processing and the waste, into variables, defining the constraint conditions according to the real-time state of the liquor brewing production line, and constructing a linear programming model as a scheduling model with the minimum total working time and retort length as the objective function; whether the state of the liquor brewing production line changes or the working efficiency of the equipment is affected by the change of the production environment temperature and humidity, if yes, step 4 is executed, if not, step 3 is executed, Step 3, task sequence optimization: through linear programming solution, according to the device time parameter and the device working efficiency, the optimal scheduling scheme is generated as a new scheduling scheme from the scheduling model, and after the new scheduling scheme is transmitted to the automatic control system of the liquor brewing production line, step 5 is executed; Step 4, dynamic adjustment: the new scheduling model is automatically fine-tuned from the scheduling model, the new task priority is calculated from the new scheduling model, and the new scheduling scheme is regenerated, and after the new scheduling scheme is transmitted to the automatic control system of the liquor brewing production line, step 5 is executed; Step 5, the automatic control system controls the liquor brewing production line to execute each task according to the task sequence of the new scheduling scheme.

[0006] A processing device comprises: At least one memory for storing one or more programs; At least one processor capable of executing one or more programs stored in the memory, so that the processor can implement the method described in the application when the one or more programs are executed by the processor.

[0007] A readable storage medium stores a computer program, which can implement the method described in the application when the computer program is executed by the processor.

[0008] Compared with the prior art, the liquor brewing process scheduling optimization method, device and medium based on linear programming provided by the application have the following beneficial effects: According to the liquor brewing production line, a scheduling model is constructed based on the liquor brewing process, which considers steps such as fermentation tank in and out of the tank, retort lifting and waste disposal, generates the optimal process arrangement sequence by analyzing the time consumption of different scheduling schemes and the retort length, and dynamically adjusts the task sequence according to the real-time state of the production line and the task priority to ensure the efficiency and stability of the production line operation. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description only constitute some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0010] Figure 1 The flow chart of the liquor brewing process scheduling optimization method based on linear programming provided by the embodiments of the present application.

[0011] Figure 2 The overall flow chart of the liquor brewing process scheduling optimization method based on linear programming provided by the embodiments of the present application.

[0012] Figure 3 The production line flow chart of the liquor brewing process scheduling optimization method based on linear programming provided by the embodiments of the present application.

[0013] Figure 4 The flow example chart of the liquor brewing process scheduling optimization method based on linear programming provided by the embodiments of the present application. DETAILED DESCRIPTION

[0014] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the specific contents of the present application; obviously, the described embodiments only constitute some of the embodiments of the present application, but not all the embodiments, which does not constitute a limitation to the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0015] Firstly, the terms possibly used in the present text are explained as follows: The term "and / or" means either of the two or both can be realized, for example, X and / or Y means three cases including "X" or "Y" or "X and Y".

[0016] The terms "include", "contain", "have", "possess" or other similar semantic descriptions should be interpreted as non-exclusive inclusion. For example: including certain technical feature elements (such as raw materials, components, ingredients, carriers, dosage forms, materials, sizes, parts, components, mechanisms, devices, steps, processes, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products or articles, etc.) should be interpreted as not only including the explicitly listed certain technical feature elements, but also including other technical feature elements not explicitly listed but known in the art.

[0017] The term "consisting of" denotes an exclusion of any element, not specifically named. If the term is used in the claims, the term will make the claims closed, and will not include any element, not specifically named. If the term is only used in a clause of the claims, it only limits the clause, and other clauses of the claim are not limited to the specifically named elements.

[0018] Unless specifically stated otherwise, the terms "mounting", "connected", "connecting", "fixed", "fixedly connected", and the like, should be construed broadly and can be understood as intended to connote a relationship of physical or logical connection, electrical or mechanical connection, or the like, for example, it can be a fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through an intermediate medium, or internal connection of two elements. For those skilled in the art, the specific meaning of the above terms in this text can be understood according to the specific circumstances.

[0019] When the concentration, temperature, pressure, size or other parameters are expressed in the form of a numerical range, the numerical range should be understood as specifically disclosing all ranges formed by any pair of upper limit value, lower limit value, preferred value in the numerical range, regardless of whether the range is explicitly recorded; for example, if the numerical range "2-8" is recorded, the numerical range should be interpreted as including "2-7", "2-6", "5-7", "3-4 and 6-7", "3-5 and 7", "2 and 5-7", etc. Unless otherwise stated, the numerical range recorded in this text includes all integers and fractions within the numerical range, including the end values.

[0020] The terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of description and simplification of description, and do not explicitly or implicitly indicate that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting herein.

[0021] As shown in Figure 1 , Figure 2 The embodiment of the present application provides a linear programming-based scheduling optimization method for liquor brewing process, which comprises the following steps: Step 1, data acquisition and parameter setting: collecting the operation state data of each key equipment of the liquor brewing production line through a sensor, and setting the priority and time parameters of each task in the scheduling scheme of the liquor brewing production line; Step 2, constructing a scheduling model: combining a linear programming algorithm, converting the fermentation tank in and out of the tank, the RGV car in the middle, the elevator transportation, the retort processing and the discarded waste of the liquor brewing process and the total production task of one day into variables, defining the constraint conditions according to the real-time state of the liquor brewing production line, and constructing a linear programming model as a scheduling model with the objective function of minimizing the total working time and the retort pot length; whether the state of the liquor brewing production line changes or the working efficiency of the equipment is affected by the change of the production environment temperature and humidity, if yes, step 4 is executed, if not, step 3 is executed, Step 3, task sequence optimization: through linear programming solution, according to the device time parameter and the device working efficiency, the optimal scheduling scheme is generated from the scheduling model as a new scheduling scheme, and after the new scheduling scheme is transmitted to the automatic control system of the liquor brewing production line, step 5 is executed; Step 4, dynamic adjustment: automatically fine-tuning a new scheduling model from the scheduling model, calculating a new task priority from the new scheduling model and regenerating a new scheduling scheme, and after the new scheduling scheme is transmitted to the automatic control system of the liquor brewing production line, step 5 is executed; Step 5, controlling the liquor brewing production line to execute each task according to the task sequence of the new scheduling scheme by the automatic control system.

[0022] Referring to Figure 3 , preferably, in step 2 of the above method, the fermentation tank in and out of the tank, the RGV car in the middle, the elevator transportation, the retort processing and the discarded waste of the liquor brewing process and the total production task of one day are converted into variables in the following manner, which includes: (1) Set the following decision parameters: (11) Set the total number of tasks as J; (12) Set the number of links in the complete process as N, including the fermentation tank in and out of the tank, the RGV car in the middle, the elevator transportation, the retort processing and the discarded waste; (13) According to the operation state data of each key device collected in step 1, set the time required for the task to continuously occupy the device in the nth link as Convert the time of each step of the fermentation tank in and out of the tank, the RGV car in the middle, the elevator transportation, the retort processing and the discarded waste of the liquor brewing process into a constant; (14) According to the operation state data of each key device collected in step 1, set the number of devices in parallel processing in the nth link as Convert the number of production devices of each step of the fermentation tank in and out of the tank, the RGV car in the middle, the elevator transportation, the retort processing and the discarded waste of the liquor brewing process into a constant; (15) Set a large constant M as 10000; (2) Set the following decision variables: (21) Switching variable of task j in link n selecting the kth parallel device for processing , if the switching variable is 1 for selection and 0 for non-selection, the selection of parallel devices in each step of the fermentation tank in and out of the tank, the RGV trolley transportation in the middle, the elevator transportation, the retort processing and the discarded grains in the liquor-making process is converted into a variable; (22) The start time of task j in link n on the kth parallel device for processing is The start time of processing of a certain task in each step of the fermentation tank in and out of the tank, the RGV trolley transportation in the middle, the elevator transportation, the retort processing and the discarded grains in the liquor-making process is converted into a variable; (23) The completion time of task j in link n is , , which is the total task completion time, the time after the task is completed in each step of the fermentation tank in and out of the tank, the RGV trolley transportation in the middle, the elevator transportation, the retort processing and the discarded grains in the liquor-making process is converted into a variable; (24) The steaming time of task j in the retort is .

[0023] Referring to Figure 3 , preferably, in step 2 of the above method, the constraint conditions defined according to the real-time state of the liquor-making production line include task sequence, device processing time, device exclusivity, device parallelism and task completion time, specifically: (31) The constraint condition of task sequence and device processing time is: ; Wherein, is the time required for task j to use the k'th parallel device for processing in the n+1th link; indicates the switching variable of whether task j uses the k'th parallel device for processing in the n+1th link; k is the number of any one of the parallel devices in link n, and k' is the number of any one of the parallel devices in link n+1; (32) The constraint condition of device parallelism is that each task in link n can select only one device ; (33) The constraint condition of device exclusivity is that the time of using the device cannot overlap for tasks selecting the same device in the same link ; Wherein, is the time required for task j' to use the kth parallel device for processing in the nth link; is the switching variable of whether task j' uses the kth parallel device for processing in the nth link; (34) the duration of the steaming The constraint condition is that the duration of the steaming is calculated from the time when the retort pot starts processing in the link a to the time when the task enters the next link a+1 ; Wherein, is the time required for task j to process in the a+1th link using the k'th parallel device; is the on-off variable of whether task j uses the k'th parallel device to process in the a+1th link; is the time required for task j to process in the a'th link using the k'th parallel device; is the on-off variable of whether task j uses the k'th parallel device to process in the a'th link; (35) the total task completion time The constraint condition is: , is the total production time.

[0024] Referring to Figure 3 , preferably, in step 2 of the above method, a linear programming model is constructed as a scheduling model according to the transformed variables, the real-time state of the baijiu brewing production line and the defined constraint conditions, with the objective function of minimizing the total working time and the duration of the steaming, including: The objective function established to minimize the total production time and the duration of the steaming is to combine the two objectives of minimizing the total production time and the duration of the steaming into the objective function by weighted summation: ; Wherein, are weight parameters for balancing the importance of the two objectives of minimizing the total production time and the duration of the steaming ; represents the serial number of the task; represents the total number of tasks; The linear programming model composed of the above objective function and constraint conditions is the scheduling model.

[0025] Preferably, in step 1 of the above method, the operation state data of each key device of the baijiu brewing production line is collected by sensors, including the operation state data of each key device of the fermentation tank crane, RGV trolley, elevator, doser, mixer, retort pot robot, retort pot, spreading and airing machine, and waste discharge port.

[0026] Preferably, in the above method, the baijiu brewing production line of step 1 is composed of a transfer hopper, a rail shuttle car, a crane, an elevator, a doser, a mixer, a fermentation tank, a retort pot, a spreading and airing machine, multiple RGV trolleys, and an automatic control system. For example Figure 2As shown, the complete production process of the white spirit brewing production line is as follows: the crane transports raw materials from the cellar to the weigher; then the weighing is completed, and then the RGV trolley is used to transport to the elevator; the elevator is lifted to the second floor, and then the RGV trolley is used to transport to the mixer; the RGV trolley is transported to the mixing elevator, and the lifting to the third floor is completed, and then the RGV trolley is used to transport to the pot loading robot; the pot loading robot adds raw materials to the pot, and after the production is completed, the pot pours the raw materials into the RGV trolley, and after the mixing is completed, the RGV trolley is used to transport to the spreading and airing elevator to be lifted to the spreading and airing machine; after the spreading and airing machine is spread and aired, the RGV trolley of the weigher is used to transport to the crane, and the crane is used to return to the cellar.

[0027] In the whole process, except that the RGV trolley after weighing and the RGV trolley after spreading and airing are shared, the other RGV trolleys are dedicated to the respective processes.

[0028] The embodiment of the present application also provides a processing device, comprising: at least one memory for storing one or more programs; at least one processor capable of executing one or more programs stored in the memory, so that the processor can implement the above method when the one or more programs are executed by the processor.

[0029] The embodiment of the present application further provides a readable storage medium storing a computer program, which can implement the above method when the computer program is executed by the processor.

[0030] As can be seen from the above, the scheduling optimization method of the embodiment of the present application can determine the best operation time of each link of white spirit brewing through the dynamic optimization of the scheduling model, thereby improving the efficiency of the whole white spirit brewing production line.

[0031] In order to more clearly show the technical solutions provided by the present application and the technical effects generated, the schemes provided by the embodiments of the present application are described in detail below with specific examples.

[0032] Example 1 As Figure 1 shown, the embodiment of the present application provides a white spirit brewing process scheduling optimization method based on linear programming, which combines the automatic control system of the white spirit brewing production line to construct a scheduling model of the white spirit brewing process. The scheduling model considers the steps of out-of-pit and into-pit of the fermentation pit, pot cleaning and lifting, and waste distiller's grains, generates the optimal process arrangement sequence by analyzing the time consumption and equipment utilization of different production scheduling schemes. At the same time, the scheduling model dynamically adjusts the task sequence according to the real-time state and task priority of the production line, to ensure the efficiency and stability of the production line operation. The method introduces process timing prediction, so that the automatic control system can actively arrange the cleaning and lifting or cross-zone out-of-pit, thereby improving the efficiency of the whole white spirit brewing production line.

[0033] As Figure 1 shown in the figure, the Baijiu brewing process scheduling optimization method is based on linear programming, and by constructing a scheduling model containing the time, sequence and equipment restrictions of each process, the production line process is optimized, which specifically includes the following steps: Step 1, data acquisition and parameter setting: through sensors to collect the operation state data of key equipment such as fermentation tank traveling crane, RGV trolley, elevator, doser, mixer, pot loading robot, pot, spreading and airing machine, and waste disposal port, and set the priority and time parameters of each task in production scheduling.

[0034] Step 2, model building: combining linear programming algorithm, converting the steps of fermentation tank in and out of the tank, RGV trolley transportation in the middle, elevator transportation, pot treatment and waste disposal in the Baijiu brewing process and the total production task of one day into variables, and defining the constraint conditions according to the real-time state of the Baijiu brewing production line, including: task sequence, equipment processing time, equipment exclusivity, equipment parallelism and task completion time, etc.

[0035] Step 3, task sequence optimization: through linear programming solution, according to the equipment time parameters and equipment working efficiency, the optimal production scheduling scheme is generated from the scheduling model as a new production scheduling scheme, and after the new production scheduling scheme is transmitted to the automatic control system of the Baijiu brewing production line, step 5 is executed; Step 4, dynamic adjustment: when the Baijiu brewing production line state or the equipment working efficiency affected by the change of environmental temperature and humidity changes, the new scheduling model is automatically fine-tuned by the scheduling model, the new task priority is calculated by the new scheduling model, and the new production scheduling scheme is regenerated, the dynamic optimization of task sequence is realized, and after the new production scheduling scheme is transmitted to the automatic control system of the Baijiu brewing production line, step 5 is executed; Step 5, the automatic control system controls the Baijiu brewing production line to execute each task according to the task sequence of the new production scheduling scheme.

[0036] The linear programming used in the present application is a mathematical optimization method, which can maximize or minimize a linear objective function by determining the optimal value of a number of decision variables while satisfying certain constraint conditions. In linear programming, the relationship between variables and objective function is a linear expression. The present application uses linear programming to optimize the process scheduling in Baijiu brewing process.

[0037] As Figure 4 shown in the figure, the number of tasks in one day is set to 20, and each task needs to go through doser processing, RGV trolley transportation, elevator transportation, pot treatment and spreading and airing machine processing, among which there are 14 parallel devices in the pot step and 2 parallel devices in the spreading and airing machine step. According to the method in the embodiment, each step and production task is converted into a variable, including: (1) Set the following decision parameters: (11) Set the total number of tasks as 20; (12) Set the number of links in the complete process to be processed by the equipment as 5, including the five steps of the feeder processing, RGV trolley transportation, elevator transportation, retort processing, and spreading machine processing; (13) According to the operation state data of each key equipment collected in step 1, set the time that the task needs to continuously occupy the equipment at the nth link as , and convert the time of the five steps of the feeder processing, RGV trolley transportation, elevator transportation, retort processing, and spreading machine processing in the liquor brewing process into constants ; (14) According to the operation state data of each key equipment collected in step 1, set the number of devices in parallel processing at the nth link as , and convert the number of production equipment of the five steps of the feeder processing, RGV trolley transportation, elevator transportation, retort processing, and spreading machine processing in the liquor brewing process into constants; (15) Set the large constant M as 10000; (2) Set the following decision variables: (21) The on-off variable of task j selecting the kth parallel equipment for processing at link n , if the on-off variable is 1, it is selected, and 0 is not selected, and the selection of parallel equipment of the five steps of the feeder processing, RGV trolley transportation, elevator transportation, retort processing, and spreading machine processing in the liquor brewing process is converted into a variable; (22) The start time of the kth parallel equipment for processing of task j at link n is , and the start time of the processing of a certain task in the five steps of the feeder processing, RGV trolley transportation, elevator transportation, retort processing, and spreading machine processing in the liquor brewing process is converted into a variable; (23) The completion time of task j at link n is , , which is the total task completion time, and the time after the task is completed in the five steps of the feeder processing, RGV trolley transportation, elevator transportation, retort processing, and spreading machine processing in the liquor brewing process is converted into a variable; (24) The retort soaking time of task j in the retort is .

[0038] Referring to Figure 4 , preferably, in step 2 of the above method, the constraint conditions defined according to the real-time state of the liquor brewing production line include task order, equipment processing time, equipment exclusivity, equipment parallelism, and task completion time, specifically: (31) The constraint conditions of task order and equipment processing time are: ; where, is the time needed for task j to be processed in the n+1th stage using the k'th parallel device; is the switch variable indicating whether task j is processed in the n+1th stage using the k'th parallel device; k is the number of any one of the parallel devices in stage n, and k' is the number of any one of the parallel devices in stage n+1; (32) The constraint condition of device parallelism is that each task can only select one device in stage n ; (33) The constraint condition of device exclusivity is that the time of using the same device cannot overlap for tasks selecting the same device in the same stage ; where, is the time needed for task j' to be processed in the nth stage using the kth parallel device; is the switch variable indicating whether task j' is processed in the nth stage using the kth parallel device; (34) The constraint condition of the pot-soaking time is that the pot-soaking time is calculated from the time when the pot starts to be processed in stage a to the time when the task enters the next stage a+1 ; where, is the time needed for task j to be processed in the a+1th stage using the k'th parallel device; is the switch variable indicating whether task j is processed in the a+1th stage using the k'th parallel device; is the time needed for task j to be processed in the ath stage using the kth parallel device; is the switch variable indicating whether task j is processed in the ath stage using the kth parallel device; (35) The constraint condition of the total task completion time is: , is the total production time. The objective function established by minimizing the total production time and the pot-soaking time is to combine the two goals of minimizing the total production time and the pot-soaking time into the objective function by weighted summation:

[0039] ; where, and are weight parameters for balancing the importance of the two goals of minimizing the total production time and the pot-soaking time ; represents the serial number of the task; the total number of tasks is 20; The linear programming model constituted by the above determined constraint conditions and the established objective function is a scheduling model. The scheduling model can optimize the efficiency of the production process on the basis of comprehensively considering the constraint conditions, and ensure that the time is minimized while meeting the process requirements.

[0040] Through the above technical solution, the white wine brewing process scheduling optimization method optimizes the following four aspects: (1) Fermenter out-of-pool and into-pool optimization: In the set out-of-pool and into-pool time, the optimal out-of-pool and into-pool sequence is selected. Through linear programming, it is ensured that the rightmost cross out-of-pool will not affect the into-pool operation of the RGV car, and the cleaning and lifting task is reasonably arranged at the rightmost cross out-of-pool.

[0041] (2) Retort and elevator scheduling: The use sequence and cleaning and lifting time of the retort are predicted, and the operation sequence of the elevator and the retort is arranged based on the model to reduce the idle time of the retort and improve the overall processing rate.

[0042] (3) Task arrangement of waste distiller's grains insertion: The waste distiller's grains and the spreading machine process are independent of each other, and by inserting the cleaning and lifting task into the production process, the pressure of the spreading machine is reduced, and the production line load is optimized.

[0043] (4) Task execution and feedback: The final scheduling result is applied to the automatic control system, and the real-time state of the production line is fed back through the sensor. The system realizes dynamic adjustment of the task sequence through a closed-loop feedback mechanism.

[0044] In order to illustrate the advantages of the method of the present application, the following Figure 4 example is used as an example. This example describes the process of the wine lees being transported by the RGV car to the elevator after being discharged from the metering device, then entering one of the seven retorts, and finally being spread. Through the optimization of the scheduling model, the best operation time of each link can be determined, thereby improving the efficiency of the entire production line.

[0045] In Figure 4 the example, the following parameters (in units of time) are set: Metering device time : 2; RGV car transportation time : 1; Elevator time : 3; Retort time : 40; Spreading time : 30; By solving the corresponding linear programming equation, the start time of each task using a certain parallel device at each link can be obtained, and the devices are arranged for processing according to these time points.

[0046] For example, the No. 1 fermented grains complete each link at the following time points: : Time 0, enter the No. 1 quantifier; : Time 2, start transporting by the No. 1 RGV trolley; : Time 3, start transporting by the No. 1 elevator; : Time 6, start entering the No. 1 retort pot processing; : Time 46, start spreading by the No. 1 spreading machine; Next, the No. 1 fermented grains are also recorded according to the same logic. Through these records, we can draw the following conclusions: the total time required for the 20 pots of fermented grains to complete the entire process is 646 units of time. Since the model calculates the optimal time arrangement for each link, the maximum efficiency of the production process is achieved.

[0047] The method of the present application is suitable for an automated Baijiu brewing production line, which is realized by mechanical equipment and an automatic control system. The production line includes various types of mechanical equipment, such as an elevator, a spreading machine, an RGV trolley, etc. In the present application, the efficient connection of Baijiu brewing processes includes: a transfer hopper, an RGV trolley, a travelling crane, an elevator, a doser, a mixer, a fermentation tank, a retort, a stewing pot, a spreading machine, etc. The transfer hopper is a container for carrying and transferring materials. The RGV trolley is a rail-guided vehicle, which is an automatic transportation device commonly used in industrial production to transport materials back and forth on a fixed track. In the present application, the RGV trolley is used to transport the hopper between processing equipment, and is divided into single-station and double-station according to the number of transfer hoppers. The travelling crane is a heavy material transfer device used in the Baijiu brewing process, which moves the hopper or material from one production location to another, and is used for material transportation between processing equipment and fermentation tanks. A workshop includes 3 spans of travelling cranes. The elevator is responsible for vertically lifting the transfer hopper from one location to a designated device or area in the Baijiu brewing process, and functions as an elevator. The doser is a device for accurately measuring and dispensing materials, and is a process in the processing. The mixer is a device for mixing or stirring materials, which can achieve uniform mixing of different ingredients. The mixing process requires the addition of grain, and is also a process in the processing. The fermentation tank refers to the fermentation tank in the Baijiu fermentation process. Baijiu fermentation involves a multi-stage process, and the location and order of material in and out of the tank can affect the efficiency of the production line. A workshop contains 3 spans of fermentation tanks, corresponding to 3 spans of travelling cranes. The out-of-tank process refers to the process of removing the fermented material (such as distiller's grains) in the fermentation tank after fermentation is complete for subsequent processing. The in-tank process refers to the process of placing the raw material after spreading and adding koji by the spreading machine into the fermentation tank to start fermentation. The retort is used in the cooking process of Baijiu brewing. After the raw material is cooked in the retort, it forms distiller's grains, and the occupancy of the retort can affect the speed of the production line. The stewing time of the retort affects the quality of the fermented grains. The stewing pot is used for raw materials that need to be put into the tank again after the retort is processed. The phenomenon of raw materials that have been processed in the retort and are waiting to be spread is called stewing pot. Long-term stewing pot can affect the quality of the raw materials, and also affect the speed of the production line. The spreading machine is a device used in Baijiu brewing to spread and cool the distiller's grains, so that they can adapt to the subsequent addition of koji and in-tank process. The spreading speed of the spreading machine is affected by the environment, and has an important influence on the speed of the entire process. Koji addition refers to the addition of specific koji (fermentation agent rich in microorganisms) to the fermentation raw material during the spreading process to promote the fermentation process. The clear lifting is the bottom layer of the fermentation tank, which is directly discarded after the retort is cooked and the liquor is taken, and does not enter the tank for fermentation. Since the clear lifting process does not involve spreading treatment, the process arrangement can be optimized to reduce the burden of the spreading machine. The residue is the material in the fermentation tank except for the clear lifting, which needs to be spread and added with koji after being cooked in the retort, and then fermented again in the tank.Since the cobblestone process involves spreading and airing treatment, it is necessary to reasonably arrange the schedule.

[0048] In combination with the above example, it can be concluded that the liquor brewing process scheduling optimization method based on linear programming proposed in the present application has the following advantages and positive effects: 1) Improve production efficiency: Through the precise optimization of the linear programming model for the steps of pool out, retort pot lifting and discard, the coordination of the production process is improved, the idle time of the equipment is reduced, and the efficiency of the production line is significantly improved.

[0049] 2) Improve production stability: Due to the dynamic adjustment of the task sequence by the system, the bottleneck problem in the production process is effectively alleviated, and the retort pot time is better controlled, ensuring the stability of the liquor brewing process.

[0050] 3) Reduce human dependence and error rate: The system automates the scheduling decision-making process, effectively reducing the problems caused by human judgment errors and making the operation more standardized.

[0051] 4) Deal with fluctuations in processing time: For fluctuations in processing time in each link, the system continuously reads data from the real production line through simulation, so that the mathematical model can provide real-time optimization schemes to ensure dynamic adjustment of the production process.

[0052] 5) Simulate the impact of added equipment: The system can simulate the change in production efficiency after adding a spreading and airing machine or other equipment, providing valuable guidance for future equipment planning and construction in the park.

[0053] 6) Deal with equipment failure: In the case of temporary equipment failure (such as damage), the system can use simulation and mathematical modeling to find the optimal production scheme under the existing available equipment conditions, ensuring maximum production efficiency.

[0054] Figure 2 The correlation diagram of each device and process is shown, Figure 2 The relative layout of different cross-regional fermentation tanks, retort pots, spreading and airing equipment, and RGV trolleys is shown. For a pot of material, it first needs to be transported from the fermentation tank to the doser by the crane, then transported to the elevator by the RGV trolley after dosing, and then goes through the steps of mixer, elevator, retort robot, retort pot, etc. If it is lifted, it goes to the discard, otherwise it goes to the spreading and airing machine, and finally it is transported back to the fermentation tank by the crane.

[0055] The method of this embodiment is suitable for production line scheduling optimization in liquor brewing enterprises based on automatic control, especially for enterprises equipped with sensors and real-time feedback systems. This method not only improves the production efficiency of the production line, but also reduces the waiting time of the equipment and manual intervention, and is suitable for automatic brewing enterprises with multiple fermentation tanks, retort pots and spreading and airing equipment.

[0056] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium. When the programs are executed, the processes of the above-mentioned embodiments of the methods can be included. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0057] The above describes only the preferred embodiments of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims. The information disclosed in the background section of the present application is only intended to deepen the understanding of the general background of the present application, and should not be regarded as acknowledging or implying in any form that the information constitutes the prior art known by those skilled in the art.

Claims

1. A method for scheduling and optimizing the brewing process of Baijiu (Chinese liquor) based on linear programming, characterized in that, include: Step 1, Data Acquisition and Parameter Setting: Collect the operating status data of each key piece of equipment in the liquor brewing production line through sensors, and set the priority and time parameters of each task in the production scheduling plan of the liquor brewing production line. Step 2, Construct the scheduling model: Combining linear programming algorithm, the steps in the baijiu brewing process—fermentation tank inlet and outlet, RGV trolley transportation, elevator transportation, still processing, and lees disposal—as well as the total daily production task, are transformed into variables. Constraints are defined based on the real-time status of the baijiu brewing production line. A linear programming model is constructed as the scheduling model, with the objective function being to minimize the total working time and the stilling time. The scheduling model determines whether the status of the baijiu brewing production line has changed or whether the equipment efficiency has changed due to variations in the production environment's temperature and humidity. If yes, proceed to step 4; otherwise, proceed to step 3. Step 3, Task sequence optimization: By solving linear programming, the optimal production scheduling scheme is generated by the scheduling model based on the equipment time parameters and equipment working efficiency as the new production scheduling scheme. After the new production scheduling scheme is transmitted to the automated control system of the liquor brewing production line, step 5 is executed. Step 4, Dynamic Adjustment: The scheduling model is automatically fine-tuned to generate a new scheduling model. The new scheduling model calculates the new task priorities and regenerates a new production schedule. After the new production schedule is transmitted to the automated control system of the liquor brewing production line, Step 5 is executed. Step 5: The automated control system controls the baijiu brewing production line to execute each task according to the task sequence of the new production schedule.

2. The method for scheduling and optimizing the liquor brewing process based on linear programming according to claim 1, characterized in that, In step 2, the following variables are used to transform the fermentation tank inlet and outlet, RGV trolley transportation, elevator transportation, still processing, and lees disposal steps in the Baijiu brewing process, as well as the total daily production task: (1) Set the following decision parameters: (11) Let the total number of tasks be J; (12) Let N be the number of equipment processing steps required for the complete process, including fermentation tank entry and exit, RGV trolley transportation, elevator transportation, still processing and slag disposal. (13) Based on the operational status data of each key device collected in step 1, set the time that the device needs to continuously occupy in the nth stage of the task as follows: The time for each step in the Baijiu brewing process, including fermentation tank entry and exit, RGV trolley transportation, elevator transportation, still processing, and lees disposal, is converted into constants. (14) Based on the operational status data of each key device collected in step 1, set the nth step as having... The number of production equipment in each step of the liquor brewing process—fermentation tank inlet and outlet, RGV trolley transportation, elevator transportation, still treatment, and lees disposal—is converted into a constant. (15) Let the large constant be M, and take the value of 10000; (2) Define the following decision variables: (21) The switching variable for task j to select the kth parallel device for processing in stage n. If the switch variable A value of 1 indicates selection, and a value of 0 indicates no selection. The selection of parallel equipment in each step of the Baijiu brewing process, such as fermentation tank inlet and outlet, RGV trolley transportation, elevator transportation, still treatment, and lees disposal, is transformed into variables. (22) The start time for task j to be processed on the k-th parallel device in stage n is... The processing start time of a certain task in the Baijiu brewing process is converted into a variable, which includes the entry and exit of the fermentation tank, the transportation by RGV trolley, the transportation by elevator, the treatment of the still and the disposal of the lees. (23) The completion time of task j in stage N is: , This refers to the total task completion time, which converts the time taken to complete each step in the Baijiu brewing process—from fermentation tank entry and exit, RGV trolley transport, elevator transport, still processing, and lees disposal—into a variable. (24) The simmering time of task j in the steamer is... .

3. The method for optimizing the scheduling of Baijiu brewing process based on linear programming according to claim 2, characterized in that, In step 2, the constraints defined based on the real-time status of the liquor brewing production line include: task sequence, equipment processing time, equipment exclusivity, equipment parallelism, and task completion time. Specifically: (31) The constraints on task sequence and equipment processing time are: ; in, Let k' be the time required for task j to be processed using the k'-th parallel device in the (n+1)th stage. This variable represents a switch variable indicating whether task j uses the k'-th parallel device for processing in the (n+1)-th stage; k is the number of any parallel device in stage n, and k' is the number of any parallel device in stage n+1. (32) The constraint on device parallelism is that each task can only select one device in stage n. ; (33) The constraint of equipment exclusivity is that for tasks that select the same equipment in the same stage, the time for using the equipment cannot overlap. ; in, Let j' be the time required to process task j' in the nth stage using the kth parallel device; This is a switch variable that determines whether task j' uses the k-th parallel device for processing in the nth stage. (34) Simmering time The constraint is that the cooking time is calculated from the time from the start of processing in stage a (the steamer) to the time when the task enters the next stage a+1. ; in, Let be the time required for task j to be processed using the k'th parallel device in the (a+1)th stage; Let be the switch variable for whether task j uses the k'th parallel device for processing in the (a+1)th stage; Let be the time required for task j to be processed using the k-th parallel device in the a-th stage; Let $\mathbf{a}$ be the switch variable for whether task $j$ uses the $k$-th parallel device for processing in the $a$-th stage. (35) Total task completion time The constraints are: , This represents the total production time.

4. The method for scheduling and optimizing the liquor brewing process based on linear programming according to claim 3, characterized in that, In step 2, a linear programming model is constructed as the scheduling model based on the transformed variables, the real-time status of the liquor brewing production line, and the defined constraints, with the objective function of minimizing the total working time and the simmering time. This includes: The objective function established to minimize the total production time and the cooking time is: by combining the two objectives of minimizing the total production time and the cooking time into a single objective function through a weighted summation: ; in, These are used to balance and minimize total production time. and simmering time The weighting parameters for the importance of these two objectives; Indicates the task number; Indicates the total number of tasks; The linear programming model consisting of the objective function and constraints described above is the scheduling model.

5. The method for scheduling and optimizing the liquor brewing process based on linear programming according to any one of claims 1-4, characterized in that, In step 4, the scheduling model is automatically fine-tuned to generate a new scheduling model in the following manner: In cases where equipment failure affects the number of parallel devices, the scheduling model adjusts the number of parallel devices to match the number of available devices by modifying the corresponding parameter values, thereby changing the constraints and fine-tuning to generate a new scheduling model. When changes in the temperature and humidity of the production environment affect the working efficiency of equipment, the scheduling model is fine-tuned by modifying the equipment time parameters of the corresponding equipment to change the constraints and generate a new scheduling model.

6. The method for scheduling and optimizing the liquor brewing process based on linear programming according to any one of claims 1-4, characterized in that, In step 1, the operating status data of each key piece of equipment in the liquor brewing production line are collected by sensors, including: Operational status data of key equipment including fermentation tank crane, RGV trolley, elevator, metering device, mixer, steaming robot, steaming pot, spreading and cooling machine, and slag discharge port.

7. The method for optimizing the scheduling of a liquor brewing process based on linear programming according to any one of claims 1-4, characterized in that, The liquor brewing production line in step 1 consists of a transfer hopper, a rail shuttle, a traveling car, an elevator, a metering device, a mixer, a fermentation tank, a stewing pot, a spreading and cooling machine, multiple RGV trolleys, and an automated control system.

8. A processing apparatus, comprising: At least one memory for storing one or more programs; At least one processor is capable of executing one or more programs stored in the memory, such that when the one or more programs are executed by the processor, the processor can perform the method according to any one of claims 1-7.

9. A readable storage medium storing a computer program that, when executed by a processor, enables the implementation of the method according to any one of claims 1-7.

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