Intelligent management and control system for quick-frozen package point production workshop based on digital twinning

Abstract

The invention relates to the technical field of quick-freezing package point production, and discloses a quick-freezing package point production workshop intelligent management and control system based on digital twinning, which comprises a data acquisition system, a database system, a modeling simulation system and a dynamic management and control system, and is characterized in that the data acquisition system is used for acquiring multi-source heterogeneous data of each link in the quick-freezing package point production process; the database system is used for managing real-time data and historical data of key manufacturing elements such as people, machines, materials, methods and rings, the modeling simulation system is used for constructing a multi-level and executable simulation model close to actual production services, and the dynamic management and control system is used for achieving the functions of intelligent production scheduling, production monitoring, quality management, exception handling and the like. The digital twinning-based intelligent management and control system for the quick-freezing package point production workshop has the advantages that 1, the transparency of the workshop is increased, and the labor intensity of related management and control personnel of the workshop is reduced; and 2, the utilization rate of equipment is improved through global optimization, the material waste is reduced, and the working intensity of workers is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of quick-frozen bun production, and specifically to an intelligent management and control system for a quick-frozen bun production workshop based on digital twins. Background Art

[0002] Quick-frozen buns are a successful example of the industrialization of traditional food in my country, with a large market space and development potential. Faced with more intense market competition and rapidly changing personalized needs, quick-frozen buns present typical characteristics of multi-variety and small-batch production. However, the production process of quick-frozen buns is changeable and the equipment types are diverse. The production process must not only ensure product quality and food safety, but also promptly discover and resolve production anomalies and promote the smooth implementation of production plans, which puts forward new requirements for the dynamic control of the workshop.

[0003] With the development of new-generation information technologies such as industrial Internet of Things and big data, various manufacturing elements in the production workshop have achieved state perception and data collection, but there are still problems such as being out of touch with actual production operations and inaccurate control indicators, and a lack of effective fine-grained, multi-level workshop control methods. At the same time, the traditional MES system lacks attention to food technology and quality safety, cannot solve the problem of data islands between business systems, and cannot meet the control needs of dynamic production of multiple varieties and small batches of quick-frozen buns. Digital twin technology collects real-time data from the production workshop, builds a state and behavior model that reflects each manufacturing entity in the production workshop in the virtual space, and uses real-time mapping and dynamic interaction between physical space and virtual space to achieve intelligent control of remote visual monitoring, analysis and optimization of the production workshop.

[0004] First, according to the comparison of patent number 202310738524.X, the comparative patent has the following disadvantages:

[0005] 1. The patented control system is only for nut processing workshops and is not suitable for the control needs of quick-frozen bun production workshops.

[0006] 2. Compared with the patented management and control system, it mainly focuses on data collection and transmission, and lacks a management and control method for the interaction between various manufacturing elements in the entire production process.

[0007] And according to the comparison with patent number 202210283404.0, the comparative patent has the following disadvantages:

[0008] 1. The proposed control system is not suitable for the dynamic control needs of the interaction between multiple manufacturing factors such as people, machines, materials, methods, and environment in the actual production process.

[0009] 2. In the whole process of information management of the proposed flexible manufacturing system, there is a lack of methods to deal with production anomalies.

[0010] In summary, the present invention provides an intelligent management and control system for a quick-frozen bun production workshop based on digital twins. Summary of the invention

[0011] 1. Technical issues to be solved

[0012] In view of the shortcomings of the prior art, the present invention provides an intelligent management and control system for a quick-frozen bun production workshop based on digital twins, which solves the problems raised in the above background technology.

[0013] (II) Technical solution

[0014] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent management and control system for a quick-frozen bun production workshop based on digital twins, comprising a data acquisition system, a database system, a modeling and simulation system, and a dynamic management and control system, characterized in that: the data acquisition system is used to obtain multi-source heterogeneous data from various links in the production process of quick-frozen buns, the database system is used to manage real-time and historical data of key manufacturing elements such as people, machines, materials, methods, and environment, the modeling and simulation system is used to build a multi-level, executable simulation model close to actual production business, and the dynamic management and control system is used to realize intelligent scheduling, production monitoring, quality management, exception handling and other functions.

[0015] Preferably, the data collection system includes a bun process data collection module, a bun quality data collection module, and a bun production data collection module, and constructs a triple data collection system from the dimensions of food process, quality, and production. Specifically, in the ingredients and kneading process, workers record data such as water temperature, additives, kneading time, and dough temperature through industrial PADs. In the molding process, static data such as the location, model, and production capacity of the molding machine and real-time status data are collected, and RFID tags that adapt to high temperature, high pressure, and humid environments are installed on the steaming cart to save the current steaming cart number, the type and quantity of loaded buns. In the proofing, cooking, pre-cooling and other processes, temperature and humidity sensors are deployed to collect temperature and humidity data in the proofing room and pre-cooling room, read the steam pressure data of the cooking equipment, and deploy RFID readers and writers on the inlet and outlet channels of each process to obtain the location data of the steaming cart in real time. In the quick freezing and inner packaging process, the real-time status of the quick freezing machine, the temperature change curve and other data are obtained, and counting sensors are installed on the conveyor belt to obtain logistics data. During the metal inspection, outer packaging, and warehousing stages, obtain the operating status data of metal inspection machines, packing robots, palletizing robots and other equipment, and set up sensors to count the number of finished products entering the warehouse and the real-time warehouse temperature data.

[0016] Preferably, the database system includes data models of various manufacturing elements such as product formula, processing technology, production equipment, production order, production plan, worker team, etc., which are used to store data acquired by the data acquisition system and relevant data of intelligent management and control of the production workshop, and realize data exchange with external systems such as ERP and MES through data interface. At the same time, the database system also provides data support for the modeling and simulation system and the dynamic management and control system. Specifically, the product formula data model includes fields such as flour brand, water temperature, additives, ratio, and process. The processing technology data model includes fields such as skin material type, filling material type, skin filling ratio, molding equipment, proofing time, cooking time, precooling time, and quick freezing time. The production equipment data model includes fields such as manufacturer, equipment name, equipment number, equipment function, equipment location, production capacity, maintenance record, and current status. The production plan data model includes fields such as product name, product number, production quantity, filling material quantity, label quantity, start time, production equipment, end time, and equipped team. The worker team data model includes fields such as team number, worker number, worker name, department, skill level, per capita labor efficiency, production performance, personnel attendance, task assignment, etc.

[0017] Preferably, the modeling and simulation system includes multi-level models such as three-dimensional models, behavioral models, rule models, and logical models, which are used to characterize production operations, accumulate industry knowledge, and provide decision support for dynamic management and control systems in combination with production data. Specifically, the three-dimensional model is used to display the overall layout and operating status of the production workshop and realize remote visual monitoring. The behavioral model is used to describe the dynamic switching mechanism between various equipment in the production workshop in different operating states. By collecting real-time status data of the equipment and combining the monitoring algorithm developed with the key indicators of the equipment, the status information in the equipment behavior model is updated in an event-driven manner. The rule model is used to construct management and control rules from the aspects of production, process, and quality, such as scheduling constraints, process operation sequence, and package defect types. The logical model is used to combine the production process of quick-frozen buns to describe the business processes and workshop operation modes of the interaction between the main production factors, and perform dynamic simulation of the production process through the simulation engine.

[0018] Preferably, the dynamic management and control system includes modules such as automatic scheduling, remote monitoring, production tracking, and exception handling, and workshop managers and team members use handheld PADs, large workshop screens, web pages, and other interactive methods to achieve intelligent management and control of production workshops. Specifically, the automatic scheduling module is used to convert production orders into production tasks and distribute them to specific equipment and team members. The remote monitoring module is used to count and update key management and control indicators in real time from the level of each production section and the entire workshop level, including current production capacity, equipment status, personnel attendance, energy consumption, product qualification rate, etc. The production tracking module is used to display the completion status of the current production task, including multiple states such as completed, unfinished, in progress, and abnormal. The exception handling module is used to coordinate various manufacturing elements in the production process and transmit execution instructions, effectively handle various abnormal events such as insertion orders, material shortages, machine failures, and order cancellations, and ensure the orderly completion of the production plan.

[0019] Preferably, the operation steps of the dynamic control system are:

[0020] S1. The workshop planner uses the automatic production scheduling module to generate the workshop production plan one day before production, and presents it in the form of Gantt chart, task list, etc. After confirming that it is reasonable, the production task can be notified to the relevant departments and relevant personnel on their mobile phones;

[0021] S2. The warehouse department confirms the packaging material supply information, the filling workshop confirms the filling supply information, and the quick-frozen bun workshop confirms the production personnel and equipment information;

[0022] S3. Summarize the production task information fed back by each department and personnel, and the workshop planners operate the automatic production scheduling module through the web page interaction to adjust the production tasks;

[0023] S4. The adjusted production task list is sent back to the mobile phones of relevant personnel, and production is organized according to the production tasks on the next day;

[0024] S5. During the production process, the responsible personnel of each section use handheld PAD to update the status of completed, in progress, abnormal, etc. of production tasks. For different types of abnormal status of production tasks, corresponding notifications are automatically generated and sent to relevant department personnel;

[0025] S6. Workshop planners use the production tracking module to handle abnormal tasks in the production task list, execute corresponding processing methods for different types of abnormalities such as equipment failure, raw material shortage, order insertion, order cancellation, etc., and notify the corresponding personnel;

[0026] S7. The real-time data of each work section in the production process is transmitted to the large screen in the workshop through the remote monitoring module. The control indicators at the entire workshop level are transmitted to the large screen in the workshop office for visual display, and can also be accessed through mobile phones and web pages.

[0027] Preferably, the steps of the above automatic production scheduling module are:

[0028] S1. Receive production orders and confirm whether production can be arranged;

[0029] S2. Clarify the parameters related to the production scheduling algorithm, including optimization objectives, initial parameters, constraints, etc.;

[0030] S3, interact with the database system, obtain relevant real-time data, and update the modeling and simulation system;

[0031] S4, run the modeling simulation system, and obtain the production task list through the production scheduling algorithm;

[0032] S5. Update the production task list and reschedule the remaining production tasks manually or automatically based on the processing results of the abnormal tasks.

[0033] Preferably, the above automatic production scheduling algorithm is used to split and reorganize the multi-variety, small-batch orders of the quick-frozen workshop, generate production tasks and allocate them to production equipment and worker teams, meet the constraints of delivery time, shelf life, etc., and achieve production goals such as optimal equipment utilization and minimum production cycle. Specifically, assume that the existing production order set is O and the quick-frozen product set is P. Among them, the i-th production order O i The production sub-order of quick-frozen products numbered j is O ij , the production quantity is n ij , the delivery date is dt i , shelf life is ft j , then the quick-frozen product production sub-order is expressed as:

[0034] O ij = {P j , n ij , dt i , ft j}

[0035] The equipment such as bun making machines and steamed bread making machines in the quick-frozen production workshop form a set M. The kth equipment M k The number is l k , the set of quick-frozen product types that can be produced is {P k}, daily production capacity is C k , the defect rate is w k , then the equipment M in the quick freezing workshop k It is expressed as:

[0036] M k = {l k , {P k}, C k , w k}.

[0037] Preferably, the steps of the automatic production scheduling algorithm are:

[0038] S1, all production sub-orders are aggregated to form a set S = {O ij};

[0039] S2. According to the types of quick-frozen products that each device can produce, the set S is divided into k subsets, S = {s1, s2, ..., s k}, where s k represents the set of production sub-orders that can be completed by the k-th equipment;

[0040] S3, use integer encoding to encode each subset s in S k Chromosome encoding is performed, and k chromosome segments are spliced ​​to form one chromosome;

[0041] S4, change the random number seed and generate N chromosomes to form an initial population;

[0042] S5. Input each chromosome in the initial population into the modeling simulation system and calculate each order o i The completion time of equipment utilization and delivery date satisfaction rate are taken as individual fitness values ​​in the population;

[0043] S6, performing selection, crossover, and mutation operations on the chromosome population, and updating the chromosome population;

[0044] S7. After several rounds of iterations, stop the chromosome population evolution, decode the chromosome of the optimal individual, and generate each subset s on the kth device. k The optimal sub-order execution order of

[0045] S8. According to each subset s k The optimal sub-order execution order is determined, these sub-orders are assigned to daily production, and worker team information is integrated to form daily production tasks;

[0046] The modeling and simulation system calculates the equipment utilization rate and delivery date satisfaction rate of each chromosome, and the algorithm steps are as follows:

[0047] S1. Considering the number of different types of equipment in the workshop and the production defect rate, the actual production capacity RC of the kth equipment is calculated as

[0048] S2, the kth device encodes the subset s according to the chromosome k Each sub-order in the production is executed in turn. The production switching of different quick-frozen products takes 0.5 hours. Each subset s on the k-th device is updated. k Production sub-order o ij Completion timeij ;

[0049] S3. Calculate order o i Completion time i , st i =max j {st ij}, that is, production sub-order O ij The maximum value of

[0050] S4. Calculate the utilization rate of each type of equipment in turn according to the allocation of production sub-orders, and take the average value as the overall utilization rate of workshop equipment;

[0051] S5. Compare each order O i Completion time i and delivery time dt i , calculate the delivery time fulfillment rate of all orders.

[0052] Preferably, the data acquisition system uses the workshop wireless communication network as a medium, and connects the equipment controller, host computer, sensor, industrial computer, handheld PAD, workshop large screen, and server's hardware and software interfaces through network devices such as hubs, switches, industrial routers, and optical fiber cables. The database system, modeling and simulation system, and dynamic management and control system run on the workshop server, allowing remote access through computers, mobile phones, workshop large screens, and other devices.

[0053] (III) Beneficial effects

[0054] Compared with the prior art, the present invention provides an intelligent management and control system for quick-frozen bun production workshop based on digital twin, which has the following beneficial effects:

[0055] The intelligent management and control system of the quick-frozen bun production workshop based on digital twin,

[0056] 1. Increase the transparency of the workshop and reduce the labor intensity of relevant workshop management and control personnel.

[0057] 2. Global optimization improves equipment utilization, reduces material waste, and reduces workers' workload.

[0058] 3. It helps to accumulate data assets, form production big data, and promote continuous improvement of the production process.

[0059] 4. Improved the management and control level of quick-frozen bun production workshops, which helps to improve food production efficiency and ensure food safety.

[0060] 5. Through digital twin technology, intelligent management and control of the production process of the quick-frozen bun workshop can be realized, including automatic scheduling, production tracking, remote monitoring, exception handling and other major business processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a structural composition diagram of an intelligent management and control system for a quick-frozen bun production workshop based on digital twins proposed by the present invention;

[0062] Figure 2 This is a working step diagram of an intelligent management and control system for a quick-frozen bun production workshop based on digital twins proposed by the present invention;

[0063] Figure 3 A data model diagram of various manufacturing elements in the database system proposed by the present invention;

[0064] Figure 4 This is a multi-level model composition diagram of a modeling and simulation system in an intelligent management and control system of a quick-frozen bun production workshop based on digital twins proposed by the present invention;

[0065] Figure 5 This is an operation flow chart of a dynamic control system in an intelligent control system for a quick-frozen bun production workshop based on digital twins proposed by the present invention;

[0066] Figure 6 This is an operation flow chart of the automatic production scheduling module in the intelligent management and control system of the quick-frozen bun production workshop based on digital twin proposed by the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] See also Figure 1-6The present invention provides a technical solution: including a data acquisition system, a database system, a modeling and simulation system and a dynamic control system, characterized in that: the data acquisition system is used to obtain multi-source heterogeneous data of each link in the production process of quick-frozen buns, the database system is used to manage the real-time data and historical data of key manufacturing elements such as people, machines, materials, methods, and environments, the modeling and simulation system is used to build a multi-level, executable simulation model close to the actual production business, and the dynamic control system is used to realize intelligent production scheduling, production monitoring, quality management, exception handling and other functions. The data acquisition system includes a bun process data acquisition module, a bun quality data acquisition module, and a bun production data acquisition module, respectively constructing a triple data acquisition system from the dimensions of food process, quality, and production. Specifically, in the ingredients and kneading links, workers record data such as water temperature, additives, kneading time, and dough temperature through industrial PADs. In the molding link, static data such as the location, model, and production capacity of the molding machine and real-time status data are collected, and RFID tags that adapt to high temperature, high pressure, and humid environments are installed on the steaming cart to save the current steaming cart number, the type and quantity of the loaded buns. In the process of proofing, cooking, pre-cooling, etc., temperature and humidity sensors are deployed to collect temperature and humidity data in the proofing room and pre-cooling room, read the steam pressure data of the cooking equipment, and deploy RFID readers on the entry and exit channels of each link to obtain the position data of the steamer in real time. In the process of quick freezing and inner packaging, the real-time status of the quick freezing machine, temperature change curve and other data are obtained, and counting sensors are installed on the conveyor belt to obtain logistics data. In the process of metal inspection, outer packaging, and warehousing, the operating status data of equipment such as metal inspection machines, packing robots, and stacking robots are obtained, and sensors are set to count the number of finished products in the warehouse and the real-time warehouse temperature data. In the database system, data models of various manufacturing elements such as product formula, processing technology, production equipment, production orders, production plans, and worker teams are included, which are used to store data obtained by the data acquisition system and related data of intelligent management and control of the production workshop, and realize data exchange with external systems such as ERP and MES through data interfaces. At the same time, the database system also provides data support for modeling and simulation systems and dynamic management and control systems. Specifically, the product formula data model includes fields such as flour brand, water temperature, additives, ratio, and process. The processing technology data model includes fields such as skin material type, filling material type, skin-filling ratio, molding equipment, proofing time, steaming time, pre-cooling time, quick freezing time, etc. The production equipment data model includes fields such as manufacturer, equipment name, equipment number, equipment function, equipment location, production capacity, maintenance record, current status, etc. The production plan data model includes fields such as product name, product number, production quantity, filling quantity, label quantity, start time, production equipment, end time, and staffing team.The worker team data model includes fields such as team number, worker number, worker name, department, skill level, per capita labor efficiency, production performance, personnel attendance, task allocation, etc. In the modeling and simulation system, it includes multi-level models such as three-dimensional models, behavioral models, rule models, and logical models, which are used to characterize production operations, accumulate industry knowledge, and provide decision support for dynamic management and control systems in combination with production data. Specifically, the three-dimensional model is used to display the overall layout and operating status of the production workshop and realize remote visual monitoring. The behavioral model is used to describe the dynamic switching mechanism between various equipment in the production workshop in different operating states. By collecting real-time status data of the equipment and combining the monitoring algorithm developed with the key indicators of the equipment, the status information in the equipment behavior model is updated in an event-driven manner. The rule model is used to construct management and control rules from the aspects of production, process, and quality, such as scheduling constraints, process operation sequence, and package point defect types. The logical model is used to combine the production process of quick-frozen buns, describe the business process and workshop operation mode of the interaction of each major production factor, and perform dynamic simulation of the production process through the simulation engine. The dynamic control system includes automatic scheduling, remote monitoring, production tracking, exception handling and other modules. The workshop managers and team members use handheld PAD, workshop large screen, web page and other interactive methods to realize intelligent control of the production workshop. Specifically, the automatic scheduling module is used to convert production orders into production tasks and distribute them to specific equipment and team members. The remote monitoring module is used to count and update key control indicators in real time from the level of each production section and the entire workshop level, including current production capacity, equipment status, personnel attendance, energy consumption, product qualification rate, etc. The production tracking module is used to display the completion status of the current production task, including multiple states such as completed, unfinished, in progress, and abnormal. The exception handling module is used to coordinate the various manufacturing elements in the production process and transmit execution instructions, effectively handle various abnormal events such as insertion orders, lack of materials, machine failures, and cancellation of orders, and ensure the orderly completion of the production plan.

[0069] Working steps: The operating steps of the dynamic control system are:

[0070] S1. The workshop planner uses the automatic production scheduling module to generate the workshop production plan one day before production, and presents it in the form of Gantt chart, task list, etc. After confirming that it is reasonable, the production task can be notified to the relevant departments and relevant personnel on their mobile phones;

[0071] S2. The warehouse department confirms the packaging material supply information, the filling workshop confirms the filling supply information, and the quick-frozen bun workshop confirms the production personnel and equipment information;

[0072] S3. Summarize the production task information fed back by each department and personnel, and the workshop planners operate the automatic production scheduling module through the web page interaction to adjust the production tasks;

[0073] S4. The adjusted production task list is sent back to the mobile phones of relevant personnel, and production is organized according to the production tasks on the next day;

[0074] S5. During the production process, the responsible personnel of each section use handheld PAD to update the status of completed, in progress, abnormal, etc. of production tasks. For different types of abnormal status of production tasks, corresponding notifications are automatically generated and sent to relevant department personnel;

[0075] S6. Workshop planners use the production tracking module to handle abnormal tasks in the production task list, execute corresponding processing methods for different types of abnormalities such as equipment failure, raw material shortage, order insertion, order cancellation, etc., and notify the corresponding personnel;

[0076] S7. The real-time data of each work section in the production process is transmitted to the large screen in the workshop through the remote monitoring module. The control indicators at the entire workshop level are transmitted to the large screen in the workshop office for visual display, and can also be accessed through mobile phones and web pages.

[0077] The steps of the above automatic production scheduling module are:

[0078] S1. Receive production orders and confirm whether production can be arranged;

[0079] S2. Clarify the parameters related to the production scheduling algorithm, including optimization objectives, initial parameters, constraints, etc.;

[0080] S3, interact with the database system, obtain relevant real-time data, and update the modeling and simulation system;

[0081] S4, run the modeling simulation system, and obtain the production task list through the production scheduling algorithm;

[0082] S5. Update the production task list and reschedule the remaining production tasks manually or automatically based on the processing results of the abnormal tasks.

[0083] The steps of the automatic production scheduling algorithm are:

[0084] S1, all production sub-orders are aggregated to form a set S = {O ij};

[0085] S2. According to the types of quick-frozen products that each device can produce, the set S is divided into k subsets, S = {s1, s2, ..., s k}, where s k represents the set of production sub-orders that can be completed by the k-th equipment;

[0086] S3, use integer encoding to encode each subset s in S k Chromosome encoding is performed, and k chromosome segments are spliced ​​to form one chromosome;

[0087] S4, change the random number seed and generate N chromosomes to form an initial population;

[0088] S5. Input each chromosome in the initial population into the modeling simulation system and calculate each order o i The completion time of equipment utilization and delivery date satisfaction rate are taken as individual fitness values ​​in the population;

[0089] S6, performing selection, crossover, and mutation operations on the chromosome population, and updating the chromosome population;

[0090] S7. After several rounds of iterations, stop the chromosome population evolution, decode the chromosome of the optimal individual, and generate each subset s on the kth device. k The optimal sub-order execution order of

[0091] S8. According to each subset s k The optimal sub-order execution order is determined, these sub-orders are assigned to daily production, and worker team information is integrated to form daily production tasks;

[0092] The modeling and simulation system calculates the equipment utilization rate and delivery date satisfaction rate of each chromosome, and the algorithm steps are as follows:

[0093] S1. Considering the number of different types of equipment in the workshop and the production defect rate, the actual production capacity RC of the kth equipment is calculated as

[0094] S2, the kth device encodes the subset s according to the chromosome k Each sub-order in the production is executed in turn. The production switching of different quick-frozen products takes 0.5 hours. Each subset s on the k-th device is updated. k Production sub-order o ij Completion time ij ;

[0095] S3. Calculate order O i Completion time i , st i =max j {st ij}, that is, production sub-order O ij The maximum value of

[0096] S4. Calculate the utilization rate of each type of equipment in turn according to the allocation of production sub-orders, and take the average value as the overall utilization rate of workshop equipment;

[0097] S5. Compare each order o i Completion time i and delivery time dt i, calculate the delivery time fulfillment rate of all orders.

[0098] The data acquisition system uses the workshop wireless communication network as a medium, and connects the equipment controller, host computer, sensor, industrial computer, handheld PAD, workshop large screen, and server's hardware and software interfaces through network devices such as hubs, switches, industrial routers, and optical fiber cables. The database system, modeling and simulation system, and dynamic management and control system run on the workshop server, allowing remote access through computers, mobile phones, workshop large screens, and other devices.

[0099] In summary, the intelligent management and control system of the quick-frozen bun production workshop based on digital twins 1. Increases the transparency of the workshop and reduces the labor intensity of relevant workshop management personnel. 2. Improves equipment utilization, reduces material waste, and reduces the work intensity of workers through global optimization. 3. Helps accumulate data assets, form production big data, and promote continuous improvement of the production process. 4. Improves the management and control level of the quick-frozen bun production workshop, helps to improve food production efficiency and ensure food safety. 5. Through digital twin technology, realizes intelligent management and control of the production process of the quick-frozen bun workshop, including automatic scheduling, production tracking, remote monitoring, exception handling and other major business processes.

[0100] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent management and control system for a quick-frozen bun production workshop based on digital twins, including a data acquisition system, a database system, a modeling and simulation system, and a dynamic management and control system, characterized in that: The data acquisition system is used to obtain multi-source heterogeneous data from each link in the production process of quick-frozen buns. The database system is used to manage the real-time and historical data of key manufacturing elements such as people, machines, materials, methods, and environment. The modeling and simulation system is used to build a multi-level, executable simulation model close to the actual production business. The dynamic management and control system is used to realize intelligent scheduling, production monitoring, quality management, exception handling and other functions.

2. According to claim 1, a digital twin-based intelligent management and control system for a quick-frozen bun production workshop is characterized in that: The data collection system includes a bun process data collection module, a bun quality data collection module, and a bun production data collection module, which respectively construct a triple data collection system from the dimensions of food process, quality, and production. Specifically, in the ingredients and kneading process, workers use industrial PADs to record data such as water temperature, additives, kneading time, and dough temperature. In the molding process, static data such as the location, model, and production capacity of the molding machine and real-time status data are collected, and RFID tags that adapt to high temperature, high pressure, and humid environments are installed on the steaming cart to save the current steaming cart number, the type and quantity of loaded buns. In the proofing, cooking, pre-cooling and other processes, temperature and humidity sensors are deployed to collect temperature and humidity data in the proofing room and pre-cooling room, read the steam pressure data of the cooking equipment, and deploy RFID readers and writers on the inlet and outlet channels of each process to obtain the location data of the steaming cart in real time. In the quick freezing and inner packaging process, the real-time status of the quick freezing machine, the temperature change curve and other data are obtained, and counting sensors are installed on the conveyor belt to obtain logistics data. During the metal inspection, outer packaging, and warehousing stages, obtain the operating status data of metal inspection machines, packing robots, palletizing robots and other equipment, and set up sensors to count the number of finished products entering the warehouse and the real-time warehouse temperature data.

3. According to claim 1, a digital twin-based intelligent management and control system for a quick-frozen bun production workshop is characterized in that: The database system includes data models of various manufacturing elements such as product formula, processing technology, production equipment, production order, production plan, worker team, etc., which are used to store data obtained by the data acquisition system and relevant data of intelligent management and control of the production workshop, and realize data exchange with external systems such as ERP and MES through data interface. At the same time, the database system also provides data support for modeling and simulation system and dynamic management and control system. Specifically, the product formula data model includes fields such as flour brand, water temperature, additives, ratio, process, etc. The processing technology data model includes fields such as skin material type, filling material type, skin filling ratio, molding equipment, proofing time, cooking time, pre-cooling time, quick freezing time, etc. The production equipment data model includes fields such as manufacturer, equipment name, equipment number, equipment function, equipment location, production capacity, maintenance record, current status, etc. The production plan data model includes fields such as product name, product number, production quantity, filling material quantity, label quantity, start time, production equipment, end time, and equipped team. The worker team data model includes fields such as team number, worker number, worker name, department, skill level, per capita labor efficiency, production performance, personnel attendance, task assignment, etc.

4. According to claim 1, the intelligent management and control system for quick-frozen bun production workshop based on digital twin is characterized in that: The modeling and simulation system includes multi-level models such as three-dimensional models, behavioral models, rule models, and logical models, which are used to characterize production operations, accumulate industry knowledge, and provide decision support for dynamic management and control systems in combination with production data. Specifically, the three-dimensional model is used to display the overall layout and operating status of the production workshop and realize remote visual monitoring. The behavioral model is used to describe the dynamic switching mechanism between various equipment in the production workshop in different operating states. By collecting real-time status data of the equipment and combining the monitoring algorithm developed with the key indicators of the equipment, the status information in the equipment behavior model is updated in an event-driven manner. The rule model is used to construct management and control rules from the aspects of production, process, and quality, such as scheduling constraints, process operation sequence, and package defect types. The logical model is used to combine the production process of quick-frozen buns to describe the business processes and workshop operation methods of the interaction between the main production factors, and perform dynamic simulation of the production process through the simulation engine.

5. According to claim 1, the intelligent management and control system for quick-frozen bun production workshop based on digital twin is characterized in that: The dynamic management and control system includes modules such as automatic scheduling, remote monitoring, production tracking, and exception handling. The workshop managers and team members use handheld PADs, large screens in the workshop, web pages and other interactive methods to realize intelligent management and control of the production workshop. Specifically, the automatic scheduling module is used to convert production orders into production tasks and distribute them to specific equipment and team members. The remote monitoring module is used to count and update key management and control indicators in real time from the level of each production section and the entire workshop level, including current production capacity, equipment status, personnel attendance, energy consumption, product qualification rate, etc. The production tracking module is used to display the completion status of the current production task, including completed, unfinished, in progress, and abnormal conditions. The exception handling module is used to coordinate the various manufacturing elements in the production process and transmit execution instructions, effectively handle various abnormal events such as insertion orders, material shortages, machine failures, and order cancellations, and ensure the orderly completion of the production plan.

6. According to claim 1, the intelligent management and control system for quick-frozen bun production workshop based on digital twin is characterized by: The operation steps of the dynamic control system are as follows: S1. The workshop planner uses the automatic production scheduling module to generate the workshop production plan one day before production, and presents it in the form of Gantt chart, task list, etc. After confirming that it is reasonable, the production task can be notified to the relevant departments and relevant personnel on their mobile phones; S2. The warehouse department confirms the packaging material supply information, the filling workshop confirms the filling supply information, and the quick-frozen bun workshop confirms the production personnel and equipment information; S3. Summarize the production task information fed back by each department and personnel, and the workshop planners operate the automatic production scheduling module through the web page interaction to adjust the production tasks; S4. The adjusted production task list is sent back to the mobile phones of relevant personnel, and production is organized according to the production tasks on the next day; S5. During the production process, the responsible personnel of each section use handheld PAD to update the status of completed, in progress, abnormal, etc. of production tasks. For different types of abnormal status of production tasks, corresponding notifications are automatically generated and sent to relevant department personnel; S6. Workshop planners use the production tracking module to handle abnormal tasks in the production task list, implement corresponding processing methods for different types of abnormalities such as equipment failure, raw material shortage, order insertion, order cancellation, etc., and notify the corresponding personnel; S7. The real-time data of each work section in the production process is transmitted to the large screen in the workshop through the remote monitoring module. The control indicators at the entire workshop level are transmitted to the large screen in the workshop office for visual display, and can also be accessed through mobile phones and web pages.

7. According to claim 1, the intelligent management and control system for quick-frozen bun production workshop based on digital twin is characterized by: The steps of the above automatic production scheduling module are: S1. Receive production orders and confirm whether production can be arranged; S2. Clarify the parameters related to the production scheduling algorithm, including optimization objectives, initial parameters, constraints, etc.; S3, interact with the database system, obtain relevant real-time data, and update the modeling and simulation system; S4, run the modeling simulation system, and obtain the production task list through the production scheduling algorithm; S5. Update the production task list and reschedule the remaining production tasks manually or automatically based on the processing results of the abnormal tasks.

8. According to claim 1, a digital twin-based intelligent management and control system for a quick-frozen bun production workshop is characterized in that: The above automatic production scheduling algorithm is used to split and reorganize the multi-variety and small-batch orders of the quick-frozen workshop, generate production tasks and assign them to production equipment and worker teams, meet the constraints such as delivery date and shelf life, and achieve production goals such as optimal equipment utilization and minimum production cycle. Specifically, assume that the existing production order set is O and the quick-frozen product set is P. Among them, the i-th production order O i The production sub-order of quick-frozen products numbered j is O ij , the production quantity is n ij , the delivery date is dt i , shelf life is ft j , then the quick-frozen product production sub-order is expressed as: o ij ={P j ,n ij ,dt i ,ft j } The equipment such as bun making machines and steamed bread making machines in the quick-frozen production workshop form a set M. The kth equipment M k The number is l k , the set of quick-frozen product types that can be produced is {P k }, daily production capacity is C k The defect rate is w k , then the equipment M in the quick freezing workshop k It is expressed as: M k ={l k ,{P k },C k ,w k }。 9. The intelligent management and control system for quick-frozen bun production workshop based on digital twin according to claim 1, characterized in that: The steps of the automatic production scheduling algorithm are: S1, all production sub-orders are aggregated to form a set S = {O ij }; S2. According to the types of quick-frozen products that each device can produce, the set S is divided into k subsets, S = {s1, s2, ..., s k }, where s k represents the set of production sub-orders that can be completed by the k-th equipment; S3, use integer encoding to encode each subset s in s k Chromosome encoding is performed, and k chromosome segments are spliced ​​to form one chromosome; S4, change the random number seed and generate N chromosomes to form an initial population; S5. Input each chromosome in the initial population into the modeling simulation system and calculate each order O i The completion time of equipment utilization and delivery date satisfaction rate are taken as individual fitness values ​​in the population; S6, performing selection, crossover, and mutation operations on the chromosome population, and updating the chromosome population; S7. After several rounds of iterations, stop the chromosome population evolution, decode the chromosome of the optimal individual, and generate each subset s on the kth device. k The optimal sub-order execution order of S8. According to each subset s k The optimal sub-order execution order is determined, these sub-orders are assigned to daily production, and worker team information is integrated to form daily production tasks; The modeling and simulation system calculates the equipment utilization rate and delivery date satisfaction rate of each chromosome, and the algorithm steps are as follows: S1. Considering the number of different types of equipment in the workshop and the production defect rate, the actual production capacity RC of the kth equipment is calculated as RC = C k *i k *w k ; S2, the kth device encodes the subset s according to the chromosome k Each sub-order in the production is executed in turn. The production switching of different quick-frozen products takes 0.5 hours. Each subset s on the k-th device is updated. k Production sub-order O ij Completion time ij ; S3. Calculate order O i Completion time i , st i =max j {st ij }, that is, production sub-order O ij The maximum value of S4. Calculate the utilization rate of each type of equipment in turn according to the allocation of production sub-orders, and take the average value as the overall utilization rate of workshop equipment; S5. Compare each order O i Completion time i and delivery time dt i , calculate the delivery time fulfillment rate of all orders.

10. The intelligent management and control system for quick-frozen bun production workshop based on digital twin according to claim 1, characterized in that: The data acquisition system uses the workshop wireless communication network as the medium, and connects the equipment controller, host computer, sensor, industrial computer, handheld PAD, workshop large screen, and server's hardware and software interfaces through network devices such as hubs, switches, industrial routers, and optical fiber cables. The database system, modeling and simulation system, and dynamic management and control system run on the workshop server, allowing remote access through computers, mobile phones, workshop large screens and other devices.

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