Scheduling scheme generation method, system and device based on digital twinborn model

Abstract

The invention provides a production scheduling scheme generation method, system and device based on a digital twinning model, and relates to the technical field of digital twinning, and the scheme comprises the steps: determining a process route according to order information; the process route comprises a plurality of procedures and a flow sequence among the procedures; the order information at least comprises delivery time; according to the order information, the process route and preset parameters, performing station matching calculation on each process in the process route to obtain a station matching number; inputting the distribution number and the order information into a pre-constructed digital twinborn model, and performing simulation calculation by the digital twinborn model to obtain a simulation result; and determining a production scheduling scheme according to the simulation result and the order information. According to the scheme, through the digital twinning technology, the production demand is accurately predicted, the production scheduling scheme is optimized, the production process is monitored in real time, the digital twinning model is constructed, intelligent and flexible production management is achieved, efficiency, delivery accuracy and emergency handling capacity are effectively improved, and industry intelligent transformation is assisted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of digital twinning, in particular to a production scheduling scheme generation method, system and device based on a digital twinning model. BACKGROUND

[0002] With the advent of the intelligent manufacturing era, traditional manufacturing is accelerating its transformation towards digitization and intelligentization. As a new information technology, digital twinning has gradually become an important means to promote the intelligent development of manufacturing. Digital twinning builds a digital mapping of physical objects, accurately replicates the information of entities such as equipment, production processes and products in the virtual space, and realizes the deep integration of physical and virtual spaces. Digital twinning not only can comprehensively integrate data information of the whole production process, but also can monitor the running state of physical objects in real time, and predict future production conditions through simulation, helping enterprises optimize production processes and improve resource allocation efficiency.

[0003] Currently, the production plan of traditional manufacturing plants relies on manual experience, and the order delivery date calculation is challenging. The opacity of order data in the production process increases the difficulty of control. In addition, it is difficult to achieve flexible production when encountering unexpected situations. Although some enterprises have adopted digital systems such as Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) for production management, due to their limitations in data visualization, prediction ability and flexible manufacturing, they are difficult to fully meet the needs of the industry's intelligent transformation.

[0004] Therefore, it is urgent to develop an intelligent production management system that can integrate data information of the whole production process, accurately simulate the actual production situation, and realize the rational scheduling of production resources, which is the key to intelligent management. SUMMARY

[0005] The present application relates to the technical field of digital twinning, in particular to a production scheduling scheme generation method, system and device based on a digital twinning model.

[0006] In a first aspect, to solve the above technical problems, the present application provides a production scheduling scheme generation method based on a digital twin model, which comprises: determining a process route according to order information; the process route comprises a plurality of working procedures and a flow sequence between the plurality of working procedures; the order information at least comprises a delivery time; performing table allocation calculation on each working procedure in the process route according to the order information, the process route and preset parameters to obtain a table allocation quantity; performing simulation calculation on the table allocation quantity and the order information through a pre-constructed digital twin model to obtain a simulation result; and determining a production scheduling scheme according to the simulation result and the order information.

[0007] Optionally, the process route is determined according to the order information, which comprises: the order information further comprises product information; a key working procedure is determined according to the product information; and the process route is determined from a preset process route library according to the key working procedure.

[0008] Optionally, the order information further comprises an order quantity; the preset parameters at least comprise an effective operation time proportion of each working procedure, a generation efficiency of each working procedure and a working time length of each working procedure; the table allocation quantity is obtained by performing table allocation calculation on each working procedure in the process route according to the order information, the process route and the preset parameters, which comprises: a working procedure set to be allocated is determined according to the process route; an order workload and an order working time length of any working procedure are determined according to the order quantity, the delivery time and the preset parameters for any working procedure in the working procedure set to be allocated; and the table allocation quantity is determined according to the order workload and the order working time length.

[0009] Optionally, the order workload and the order working time length of any working procedure are determined according to the order quantity, the preset effective operation time proportion and the preset production efficiency, which comprises: the order workload of any working procedure is calculated according to the order quantity, the preset effective operation time proportion and the preset production efficiency; and the order working time length of any working procedure is calculated according to the delivery time and a unit working time.

[0010] Optionally, the preset digital twin model is pre-constructed in the following manner: a geometric physical model, a behavior model and a data model of each working procedure are constructed according to working procedure characteristics of each working procedure; and the digital twin model is constructed according to the geometric physical model, the behavior model and the data model.

[0011] Optionally, the working procedure characteristics at least comprise physical characteristics, dynamic characteristics and data characteristics; the geometric physical model, the behavior model and the data model of each working procedure are constructed according to the working procedure characteristics of each working procedure, which comprises: the working procedure characteristics comprise the physical characteristics, the dynamic characteristics and the data characteristics; the physical characteristics of each entity object in each working procedure are acquired; the geometric physical model is constructed according to the physical characteristics; the dynamic characteristics of each working procedure are acquired; the dynamic characteristics at least comprise behavior rules, process flows and operation sequences; the behavior model is constructed according to the dynamic characteristics; the data characteristics of each working procedure are acquired; and the data model is constructed according to the data characteristics.

[0012] Optionally, the simulation result at least comprises: a start time of any process and an end time of any process; and the production scheduling scheme is determined according to the simulation result and the order information, comprising: determining an order completion time corresponding to the order information according to the end time of any process; and generating the production scheduling scheme according to the simulation result when the order completion time meets the delivery time.

[0013] Optionally, the production scheduling scheme is determined according to the simulation result and the order information, further comprising: adjusting the preset parameter when the order completion time does not meet the delivery time, re-performing the table allocation calculation, and generating the production scheduling scheme according to the simulation result after the order completion time meets the delivery time.

[0014] In a second aspect, based on the same inventive concept, the application further provides a production scheduling scheme generation system based on a digital twin model, comprising: a process determination module configured to determine a process route according to order information; the process route comprising a plurality of processes and a flow sequence between the plurality of processes; the order information at least comprising a delivery time; a table allocation calculation module configured to perform table allocation calculation on each process in the process route according to the order information, the process route and a preset parameter, to obtain a table allocation quantity; a model simulation module configured to input the table allocation quantity and the order information into a pre-constructed digital twin model, to perform simulation calculation by the digital twin model, to obtain a simulation result; and a scheme generation module configured to determine a production scheduling scheme according to the simulation result and the order information.

[0015] In a third aspect, based on the same inventive concept, the application further provides a production scheduling scheme generation device based on a digital twin model, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of any of the embodiments.

[0016] By using the above technical solutions, the application has the following beneficial effects: a production management system is constructed by using digital twin technology, and traditional production methods are comprehensively optimized. Based on the order pre-operation function of digital twin, the production process can be accurately simulated, the production cycle and resource demand can be predicted, and human experience interference and resource waste can be reduced. At the same time, the system intelligently generates an optimal production scheduling scheme, including a process flow, equipment allocation and a predicted production time, to improve production efficiency and delivery accuracy. Real-time monitoring of the production process is achieved by integrating dynamic data to accurately grasp resources and progress, and to quickly respond to emergencies. The construction of the digital twin model ensures the deep integration of digitalization and virtualization of the production process, realizes efficient, intelligent and flexible production management, and provides strong support for the intelligent transformation of the industry.

[0017] It is to be understood that the description of the background art is not an acknowledgement or admission that the embodiments of the present disclosure correspond to the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the specific embodiments or prior art, the drawings needed in the description of the specific embodiments or prior art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 A flowchart of a production scheduling scheme generation method based on a digital twin model is provided for embodiments of the present disclosure. Figure 2 A flowchart of determining the number of tables for a production scheduling scheme generation method based on a digital twin model is provided for embodiments of the present disclosure. Figure 3 A flowchart of determining the number of tables for a production scheduling scheme generation method based on a digital twin model is provided for embodiments of the present disclosure. Figure 4 A structural diagram of a production scheduling scheme generation system based on a digital twin model is provided for embodiments of the present disclosure. Figure 5 A structural diagram of a production scheduling scheme generation device based on a digital twin model is provided for embodiments of the present disclosure. DETAILED DESCRIPTION

[0020] The technical solutions of the present disclosure will be described in detail below with reference to the drawings. Obviously, the described embodiments are some embodiments of the present disclosure, not all embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0021] In the description of the present disclosure, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" 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 describing the present disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore it cannot be understood as a limitation of the present disclosure. In addition, the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0022] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0023] In the related art, with the advent of the intelligent manufacturing era, traditional manufacturing is accelerating the transformation towards digitization and intelligentization. Digital twin technology, as an emerging information technology, has gradually become an important means to promote the intelligent development of manufacturing. Digital twin builds a digital mapping of physical objects, accurately replicates the information of entities such as equipment, production processes, and products in the virtual space, and realizes the deep integration of physical and virtual spaces. Digital twin can not only comprehensively integrate data information of the whole production process, but also monitor the running state of physical objects in real time, and predict future production conditions through simulation, helping enterprises optimize production processes and improve resource allocation efficiency. Currently, the production plan of traditional manufacturing plants relies on manual experience, and the order delivery calculation is facing challenges. The opacity of order data in the production process increases the difficulty of control, and it is difficult to achieve flexible production when encountering unexpected situations. Although some enterprises have adopted digital systems such as Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES) for production management, due to their limitations in data visualization, prediction ability, and flexible manufacturing, it is difficult to fully meet the needs of industry intelligent transformation.

[0024] The present disclosure proposes a production scheduling scheme generation method based on a digital twin model to at least partially solve one or more of the above problems and other potential problems. The method can build a production management system through digital twin technology and comprehensively optimize traditional production methods. Based on the order pre-run function of digital twin, the production process can be accurately simulated, the production cycle and resource demand can be predicted, and human experience interference and resource waste can be reduced. At the same time, the system intelligently generates an optimal production scheduling scheme, including process flow, equipment allocation, and estimated production time, to improve production efficiency and delivery accuracy. Real-time monitoring of the production process integrates dynamic data to accurately grasp resources and progress, and realizes rapid response to unexpected events. The construction of the digital twin model ensures the deep integration of digitalization and virtualization of the production process, realizes efficient, intelligent, and flexible production management, and provides strong support for the intelligent transformation of the industry.

[0025] The application will be further explained in conjunction with specific embodiments.

[0026] Embodiment one: The embodiment of the present disclosure provides a production scheduling scheme generation method based on a digital twin model, Figure 1 is a flowchart of a production scheduling scheme generation method based on a digital twin model according to an embodiment of the present disclosure. The production scheduling scheme generation method based on a digital twin model can be applied to a production scheduling scheme generation device based on a digital twin model. The production scheduling scheme generation device based on a digital twin model is located in an electronic device. The electronic device includes but is not limited to a fixed device and / or a mobile device. For example, the fixed device includes but is not limited to a server, which can be a cloud server or a general server. For example, the mobile device includes but is not limited to a mobile phone, a tablet computer, and a vehicle-mounted terminal. In some possible implementation manners, the production scheduling scheme generation method based on a digital twin model can also be realized by calling computer readable instructions stored in a memory by a processor. As shown in the figure, the production scheduling scheme generation method based on a digital twin model includes the following steps. Figure 1 Step S101, determining a process route according to order information; the process route includes a plurality of processes and a process sequence between the plurality of processes; the order information at least includes a delivery time.

[0027] Step S102, performing table allocation calculation on each process in the process route according to the order information, the process route and a preset parameter, to obtain a table allocation quantity.

[0028] Step S103, performing simulation calculation on the table allocation quantity and the order information through a pre-constructed digital twin model, to obtain a simulation result.

[0029] Step S104, determining a production scheduling scheme according to the simulation result and the order information.

[0030] The digital twin model is a model that integrates actual factory production data, process parameters and virtual simulation technology, and can reflect the state and behavior of the actual production system in real time, and help optimize production planning and resource allocation.

[0031] The production scheduling scheme refers to a production plan formulated according to order demand, resource capacity and the like. Specifically, the production scheduling scheme can include production process arrangement, task allocation, time node and resource use rules.

[0032] The order information refers to data related to a production task. In the embodiment of the present disclosure, the order information can include a delivery time, product types, quantity, quality requirements and the like.

[0033] ​The process route refers to the production and processing path of a product from raw materials to finished products, including a series of processes that need to be performed and the order of these processes.

[0034] Each process corresponds to a specific processing task or operation.

[0035] In the embodiments of the present disclosure, product type information can be obtained from order information first, and then the production process route of the product can be determined by analyzing the design and process requirements of the product, and a plurality of processes in the process route and their order can be determined. Exemplarily, the process route can include multiple stages, such as raw material preparation, preliminary processing, finishing, quality inspection, and packaging. In particular, the rationality of the specific processes in the process route and the order of these processes can also be verified to ensure the accuracy of the obtained process route. By extracting order information to determine the production target, the operation content of each process is defined in combination with the technical specifications of the product, and finally a production path that meets the order demand is formed, providing basic data for subsequent production scheduling scheme formulation. The above is only an exemplary description and does not limit all possible cases of determining the process route, but here we do not make an exhaustive enumeration.

[0036] The preset parameters refer to fixed values or rules used in the production scheduling process. In the embodiments of the present disclosure, the preset parameters can include equipment processing capacity, shift arrangement, number of workers, material supply speed, etc. These parameters are important basis for calculating the resources required for each process in the process route.

[0037] The number of allocated tables is the optimal number of resource allocations calculated for each process, such as the number of equipment tables or operating personnel, to ensure that the process can be completed on time and meet the order delivery requirements.

[0038] In the embodiments of the present disclosure, the processing time and resource requirements of each process can be determined according to the order information and the requirements of each process in the process route. Then, based on preset parameters such as equipment efficiency and worker efficiency, each process is calculated to obtain the number of equipment and job positions required to complete each process. Finally, considering the resource limitations of equipment and personnel and the order delivery time requirements, the number of allocated tables for each process is calculated to achieve optimal allocation of resources. Exemplarily, the number of allocated tables can be calculated using optimization algorithms or mathematical models to analyze the resource allocation required for each process, such as the number of equipment or personnel, to ensure that each process can be completed within the specified time and meet the order delivery time requirements, to achieve optimal allocation of resources and maximize process efficiency. The above is only an exemplary description and does not limit all possible cases of obtaining the number of allocated tables, but here we do not make an exhaustive enumeration.

[0039] The simulation result refers to production performance prediction data output by the digital twin model by simulating the actual operation of the process route. In the embodiments of the present disclosure, these data can include production cycle, equipment processing load, order completion time, resource utilization, etc.

[0040] In the embodiments of the present disclosure, the number of matching tables and order information can be first input into the pre-constructed digital twin model, and the model will construct a virtual production environment according to the input data. Then, a simulation program is run in the digital twin model to simulate the execution process of the process route and the process in actual production, and to predict the actual production condition. Secondly, data generated in the simulation process, such as the completion time of each process, resource utilization, production bottleneck, etc., can also be collected and stored. Exemplarily, these simulation data can be used to evaluate the feasibility of the current production planning scheme and help identify potential bottlenecks and risks in the production process. It can also be used to analyze whether the equipment utilization and personnel allocation are reasonable and whether the material supply and demand match, further assisting in optimizing resource allocation and adjusting the production planning scheme. Finally, the simulation result is output, and according to the simulation result, it is judged whether the current scheme achieves the target. If the simulation shows that the delivery cannot be made on time or the resources are insufficient, the scheme needs to be adjusted and simulated again. The above is only an exemplary description and is not a limitation on all possible cases of obtaining simulation results. Here, we do not make an exhaustive enumeration.

[0041] In the embodiments of the present disclosure, the production bottlenecks, resource utilization, etc. can be first analyzed according to the simulation result to optimize the production planning scheme. Exemplarily, if the production is delayed due to insufficient process equipment, the production planning scheme can be optimized by considering increasing equipment or adjusting the process sequence. Then, based on the order information and the simulation result, a comprehensive consideration is made to ensure that the production planning scheme meets the delivery time requirement while minimizing resource waste, and the final production planning scheme is determined. Exemplarily, the production planning scheme can include specific process time arrangement, equipment use plan, personnel allocation, etc. The above is only an exemplary description and is not a limitation on all possible cases of determining the production planning scheme. Here, we do not make an exhaustive enumeration.

[0042] The technical scheme of the embodiments of the present disclosure comprehensively integrates order information, process route, resource allocation and digital twin technology, realizes accurate formulation and optimization of production plan, discovers production bottlenecks and risks in advance, improves resource utilization and production efficiency, and ensures timely delivery of orders. At the same time, the application of simulation technology makes the production planning scheme more scientific and feasible, reduces the uncertainty in actual production, reduces the cost, and enhances the flexibility and reliability of the production process, providing strong support for enterprises to improve competitiveness.

[0043] In some embodiments, according to the order information, the process route is determined, including: the order information further includes product information; according to the product information, a key process is determined; and according to the key process, a process route is determined from a pre-set process route library.

[0044] wherein the product information refers to detailed data and characteristics related to a specific product. In the embodiments of the present disclosure, the product information can include product name, specification model, technical requirements, quality standards, processing technology, material composition, etc. The product information is the basic data for determining the production process, which directly affects the selection of process route and the arrangement of each process.

[0045] wherein the key process refers to a core link or technical step in the product processing flow that has important influence. These processes usually determine the main performance or quality of the product.

[0046] In the embodiments of the present disclosure, order information can be collected first to ensure that the basic data of the product has been included. For example, the order information can include product name, specification, quantity, delivery time, etc. Then, according to the order requirements, product information is further extracted. For example, the extracted product information can include detailed processing requirements, technical parameters and quality standards, etc. Finally, the product information can be entered into the system to provide a complete and accurate data basis for the subsequent selection of process route. The above is only an exemplary description, and is not intended to limit all possible cases of obtaining product information, but here is not an exhaustive enumeration.

[0047] In the embodiments of the present disclosure, the processing characteristics and quality requirements of the product can be determined by analyzing the product information first, and then the key processing links of the product processing are identified as key processes. For example, the process of identifying key processes can use predefined rules or expert systems to automatically or semi-automatically identify the key processing links of the corresponding product. The above is only an exemplary description, and is not intended to limit all possible cases of determining key processes, but here is not an exhaustive enumeration.

[0048] wherein the process route library is a pre-established database or knowledge base for storing standardized process route information related to product production. In the embodiments of the present disclosure, each process route in the process route library defines a complete processing flow of a certain type of product from raw materials to finished products, including all processes to be passed through, the order of processes, equipment, process parameters and possible constraint conditions.

[0049] In the embodiments of the present disclosure, the process of determining the process route can first match the identified key process in the preset process route library. Exemplarily, the matching process can filter and match the key process in the process route library through a predefined set of rules or conditions. The similarity calculation method can also be used to match the key process with the process in the process route, and the most similar route is selected. In particular, the process route library is a database containing various product processing flows, wherein each route defines specific processes and sequences. Then, the selected process route is checked and analyzed to ensure that the process route can cover all key processes and meet the product production requirements, and the process route is selected as the selected process route. The above is only an exemplary description and does not limit all possible cases of determining the process route. Here, an exhaustive enumeration is not made.

[0050] In this way, by combining complete order information and product characteristics, accurately extracting key processes and matching process route library, the planning and implementation of the production process are optimized. This method not only ensures a high match between product processing flow and quality requirements, but also improves the scientificity and rationality of process route selection, effectively reduces the trial-and-error cost and resource waste in the production process, and ensures the stability of production efficiency and product quality.

[0051] In some embodiments, the order information further includes an order quantity; the preset parameters at least include an effective operation time proportion of each process, a production efficiency of each process, and a single-day work time of each process; and the process route is calculated according to the order information, the process route, and the preset parameters to obtain a quantity of tables, including: determining a set of processes to be tabled according to the process route; determining an order workload and an order working time length of any process according to the order quantity, the delivery time, and the preset parameters; and determining the quantity of tables according to the order workload and the order working time length.

[0052] The order quantity refers to the number of products that need to be produced in the current order, which is the basic input data of the production plan and directly affects the scale and time arrangement of the production task. In the embodiments of the present disclosure, for the production of a single order, the order quantity can be the number of the same type of products that need to be produced at present. For the production of multiple orders at the same time, the order quantity can be the number of different types of products that need to be produced at present.

[0053] The effective operation time proportion refers to the proportion of time actually used for production tasks in the total working time, excluding non-production time such as equipment failure, tool change, and worker rest.

[0054] The production efficiency refers to the output capacity of each process per unit of time. Exemplarily, the production efficiency can be expressed in terms of product quantity per hour or processing speed.

[0055] wherein the single-day working hours refer to the daily working time of the production equipment or workers, reflecting the time limit of the equipment available for processing tasks per day.

[0056] wherein the set of to-be-assigned processes refers to all processes in the current process route that need to be assigned to the equipment. In the embodiments of the present disclosure, the target of the process assignment is to assign appropriate equipment and other production resources to each process to ensure that the production tasks are completed on time while the resource utilization rate is the highest.

[0057] In the embodiments of the present disclosure, the set of to-be-assigned processes can be first constructed by obtaining all processes that need equipment support in the product production process from the selected process route. Exemplarily, the process of constructing the set of to-be-assigned processes can first traverse each process from the selected process route one by one. Then, for each process, it is judged whether the process needs equipment support, and the process that does not need to be assigned is removed. Finally, the process that needs equipment support is added to the set of to-be-assigned processes. The above is only an exemplary description, and is not limited as the only possible case of determining the set of to-be-assigned processes, and here is not exhaustive.

[0058] wherein the order workload refers to the total processing amount required for completing the order for a process, reflecting the total scale of tasks that need to be processed in the current order in this process.

[0059] wherein the order working time length refers to the total time required for completing the order workload of a process, reflecting the time requirement of the current process.

[0060] In the embodiments of the present disclosure, the order quantity information and the delivery time can be first obtained from the order information. Then, the effective working time proportion of any process, the production efficiency of the process, and the working time length of the process are extracted from the preset parameters. Finally, the order workload of the process can be calculated according to the order quantity, the effective working time proportion of any process, and the production efficiency of the process. Exemplarily, the order workload of any process can be obtained by directly multiplying the order quantity, the effective working time proportion of any process, and the production efficiency of the process. Similarly, the order working time length of any process can also be calculated according to the delivery time and the single-day working hours of any process. Exemplarily, the order working time length of any process can be obtained by directly multiplying the delivery time and the single-day working hours of any process. The above is only an exemplary description, and is not limited as the only possible case of determining the order workload and the order working time length of any process, and here is not exhaustive.

[0061] In the embodiments of the present disclosure, the number of tables for any process can be determined according to the order workload of any process and the order working time length of any process. For example, the number of tables required for any process can be calculated by dividing the order workload of any process by the order working time length of any process. The above is only an example and is not limited as the only way to determine the number of tables. Here, the number of tables is not limited.

[0062] In this way, by comprehensively considering the order quantity, delivery time, production efficiency and equipment capacity, accurate table calculation is performed for each process in the process route, so as to ensure reasonable allocation and efficient utilization of production resources. It not only can avoid the delay of orders due to insufficient resources, but also can effectively reduce the idle rate of equipment, improve the production efficiency and delivery capacity, realize the scientific optimization of resource allocation and production plan, and provide reliable support for enterprises to save cost and improve competitiveness.

[0063] In some embodiments, according to the order quantity, delivery time and preset parameters, the order workload and order working time length of any process are determined, including: according to the order quantity, the preset effective working time proportion and the preset production efficiency, the order workload of any process is calculated; according to the delivery time and the daily working hours, the order working time length of any process is calculated.

[0064] The order workload refers to the total task quantity required for completing an order for a process, which reflects the total processing quantity that needs to be processed by the process.

[0065] In the embodiments of the present disclosure, the order quantity can be obtained from the order information, and the order workload of any process can be calculated based on the effective working time proportion and the production efficiency of the equipment of any process. For example, the order workload of any process can be obtained by multiplying the order quantity, the effective working time proportion and the production efficiency of the equipment of any process. The above is only an example and is not limited as the only way to calculate the order workload of any process. Here, the order workload of any process is not limited.

[0066] The order working time length refers to the total time required for completing the order workload of a process. In the embodiments of the present disclosure, the order working time length reflects the time requirement of the process within the order delivery time range, which is calculated based on the order workload and the unit working hours.

[0067] In the embodiments of the present disclosure, the delivery time can be obtained from the order information, and the order working time length of any process can be calculated based on the daily working hours in the preset parameters. For example, the order working time length of any process can be obtained by directly multiplying the delivery time and the daily working hours of any process. The above is only an example and is not limited as the only way to calculate the order working time length of any process. Here, the order working time length of any process is not limited.

[0068] Thus, by calculating the order workload and order work duration respectively, combined with key factors such as order quantity, delivery time, production efficiency, effective working time proportion, and single-day working hours, the method can accurately determine the process processing task quantity and the time required for completion. This systematic calculation method can effectively avoid the problem of over- or under-allocation of production resources, while ensuring timely delivery of orders, helping to improve production efficiency, optimize resource utilization, and reduce production costs, providing a scientific basis for the production planning and scheduling of enterprises In some embodiments, the digital twin model is pre-constructed by: constructing a geometric-physical model, a behavior model, and a data model for each process according to the process characteristics of each process; and constructing the digital twin model according to the geometric-physical model, the behavior model, and the data model.

[0069] The process characteristics refer to the specific attributes and characteristic information of a process, which describe the processing tasks, processing methods, equipment requirements, environmental constraints, etc. of the process. In the embodiments of the present disclosure, the process characteristics can include physical characteristics, dynamic characteristics, and data characteristics, such as the processing type of the process, the input and output requirements of the process, the time requirements of the process, the resource requirements, the process parameters, etc.

[0070] The geometric-physical model is a model that describes the physical environment and geometric characteristics of the process, which includes the spatial layout of the process equipment, the geometric shape of the processed material, the equipment size, the processing path, etc., reflecting the physical composition and spatial attributes of the process. In the embodiments of the present disclosure, the geometric-physical model can include the size of the processing equipment, the range of the processing area, the geometric shape of the processed parts, etc.

[0071] The behavior model is a dynamic model that describes how the process operates in the production process, which reflects the operation process, rules, and logic of the process. In the embodiments of the present disclosure, the behavior model can include the running state of the equipment, the steps and process path of the processing process, etc. The behavior model simulates the dynamic behavior of equipment operation, which can help predict the results and state variables of equipment operation.

[0072] The data model is a model that organizes and describes the static or dynamic data involved in the operation process of the process, which supports the monitoring, analysis, and optimization of the process. In the embodiments of the present disclosure, the data model can include parameter data related to the process, real-time data and historical records in the operation process, etc.

[0073] In the embodiments of the present disclosure, the geometric model of the equipment can be first established according to the equipment characteristics of the process, the geometric characteristic description of the processed material can be established according to the raw materials required by the process, the spatial layout information of the process can be taken, and the geometric relationship between the equipment and the material in the space is defined. Then, according to the constructed geometric model, the geometric characteristic description of the processed material, and the geometric relationship between the equipment and the material in the space, the geometric and physical model of any process is constructed. Further, the process of constructing the behavior model can be that the dynamic running process of the process is first simulated, the operation logic and action sequence of the equipment are defined, the behavior rules of the equipment are established based on the process flow, and the process behavior is simulated using a simulation tool. Then, based on the operation logic and action sequence of the equipment, the behavior rules of the equipment, and the simulated process behavior, the behavior model of any process is constructed. Further, the static parameter data is defined according to the monitoring requirements of the process, the dynamic data type is defined in combination with the real-time monitoring system, these data are standardized and organized through a database or a data architecture, and finally the data model of any process is obtained. The above is only an exemplary description, and is not limited as the complete possible situation of constructing the geometric and physical model, the behavior model, and the data model of each process. Here, the exhaustive enumeration is not made.

[0074] In the embodiments of the present disclosure, the spatial and physical information of the geometric and physical model can be first extracted, and a virtual three-dimensional model is constructed. The geometric information of the equipment, raw materials, process path, and the like is uniformly integrated into the virtual environment. Then, the dynamic behavior model is embedded into the geometric and physical model, so that the virtual model can dynamically simulate the processing process. Next, the static parameter data and real-time dynamic data are accessed into the digital twin model. The sensor and the communication interface can be used to collect the equipment running data in real time, and the data stream is bound with the geometric and behavior models. Using the data-driven method, the digital twin model can reflect the equipment running state and data changes in real time. Finally, a complete digital twin model is obtained, which includes geometric and physical characteristics, behavior simulation process, and real-time data support. The above is only an exemplary description, and is not limited as the complete possible situation of constructing the digital twin model. Here, the exhaustive enumeration is not made.

[0075] In this way, by pre-construction of the geometric and physical model, the behavior model, and the data model based on the process characteristics, and integration of these models into the digital twin model, comprehensive virtual simulation and real-time monitoring of each process can be realized. This method can effectively improve the visualization degree of the production process, optimize the equipment running state, reduce production failures, and at the same time support dynamic data acquisition and analysis, which provides strong technical support for intelligent manufacturing and process optimization.

[0076] In some embodiments, the process characteristics at least include: physical characteristics, dynamic characteristics and data characteristics; according to the process characteristics of each process, a geometric physical model, a behavior model and a data model of each process are constructed, including: obtaining the physical characteristics of each entity object in each process; constructing a geometric physical model according to the physical characteristics; obtaining the dynamic characteristics of each process; the dynamic characteristics at least include: behavior rules, process flow and operation sequence; constructing a behavior model according to the dynamic characteristics; obtaining the data characteristics of each process; constructing a data model according to the data characteristics.

[0077] The physical characteristics refer to static attributes of entity objects in the process, such as geometric shape, size, material, position relationship, etc. They are the basic information of physical existence in the process, such as equipment, parts and processing tasks.

[0078] The dynamic characteristics refer to the characteristics of actions, operation logic and process changes occurring in the execution process of the process. The dynamic characteristics include dynamic information such as behavior rules, process flow and operation sequence in the process.

[0079] The data characteristics refer to static parameters and dynamic data in the process operation, including setting values, real-time monitoring data and historical records of equipment operation, etc.

[0080] The entity objects are specific objects with physical existence or representing processing tasks in the process. These objects are the carriers of physical characteristics, dynamic characteristics and data characteristics. The entity objects can include equipment, tools, raw materials, semi-finished products, workpieces, etc.

[0081] In the embodiments of the present disclosure, the characteristics of the process can be divided into three information categories of physical, dynamic and data according to the attributes and requirements of the process. The role and demand of each information category are clarified, which lays a foundation for subsequent model construction. The above is only an exemplary description, and is not limited as the acquisition of physical characteristics, dynamic characteristics and data characteristics. Exhaustive enumeration is not made here.

[0082] In the embodiments of the present disclosure, the entity objects in the process can be determined first, including equipment, raw materials, tools, etc. Then the geometric parameters of these entity objects are collected, including size, shape, etc. The material properties of the entity objects are collected, including type, strength, etc. and the positional relationship of the entity objects. Finally, the physical property data of the entity objects is extracted. Exemplarily, the process of extracting the physical property data of the entity objects can use drawing software, measuring tools or other methods. Exemplarily, the physical characteristics of the entity objects can be obtained by directly measuring the entity objects using measuring tools to obtain their geometric information such as size, shape, etc. Further, the physical characteristics can also be obtained by querying the technical manual of the equipment or tool to obtain the size, weight, processing capacity, rated power, etc. of the equipment. Still further, the physical characteristics can also be obtained by observing the production site to record the arrangement mode, spatial positional relationship, external shape characteristics, etc. of the entity objects. The above are only exemplary descriptions and are not intended to limit all possible cases of obtaining the physical characteristics of each entity object in each process, but here we do not make an exhaustive enumeration.

[0083] In the embodiments of the present disclosure, the physical characteristics of the entity objects can be used to establish a geometric-physical model to reflect the spatial layout and physical structure of the process. The collected physical characteristics can be first converted into a geometric model. Exemplarily, the three-dimensional model of the equipment and parts can be drawn by drawing software to convert the collected physical characteristics into a geometric model. Then, the spatial relationship between the entity objects is defined, the geometric information of each entity object is integrated, and a complete geometric-physical model is constructed. The above are only exemplary descriptions and are not intended to limit all possible cases of constructing a geometric-physical model, but here we do not make an exhaustive enumeration.

[0084] Among them, the behavior rule is the operation specification and conditional logic in the process, which indicates the operation mode and behavior constraint of the process.

[0085] Among them, the process flow refers to the processing path and operation steps of the entire process, including how the parts flow from one processing stage to the next stage.

[0086] Among them, the operation sequence refers to the specific sequence and time arrangement of each operation step in the process.

[0087] In the embodiments of the present disclosure, the dynamic characteristics are core information reflecting the running process of the process, and the process of obtaining any dynamic characteristic of the process can adopt recording the operation steps and action changes in the process running, obtaining the control rules and behavior logic, and analyzing the action sequence and time arrangement, and further refining the dynamic characteristics into behavior rules, process flow and operation sequence. For example, the process of obtaining the dynamic characteristics can adopt reading the behavior description of the process from the manufacturing process file or the equipment operation manual, including the running mode, operation condition and parameter range of the equipment. Further, the dynamic behavior of the equipment running and the operation details of the process can also be recorded through observation and video recording of the process on the spot in the workshop. Further, motion sensors, vibration sensors, pressure sensors and the like can also be installed to monitor the equipment in real time to capture the dynamic changes in the process running. The above is only an exemplary description and is not limited as the total possible conditions for obtaining the dynamic characteristics of each process.

[0088] In the embodiments of the present disclosure, the process of constructing the behavior model according to the dynamic characteristics can first analyze the obtained dynamic characteristics such as behavior rules, process flow and operation sequence. The operation logic, condition constraint and state change of the process running are determined. Then, these dynamic characteristics are converted into mathematical models or logic models describing the behavior of the process. For example, the state machine model can be used to represent different states of the equipment running and the switching conditions thereof, or the process flow and operation sequence can be simulated by using the flowchart and timing chart. Then, the behavior model is digitally implemented in combination with the simulation software, and the dynamic relationship and time characteristics of the equipment are further defined. Finally, the accuracy and rationality of the behavior model are verified by running the simulation to ensure that it can truly reflect the dynamic running characteristics of the process and provide reliable support for subsequent optimization. The above is only an exemplary description and is not limited as the total possible conditions for constructing the behavior model, but here is not exhaustive.

[0089] In the embodiments of the present disclosure, the data characteristics are the parameters and running data of the process, and the data model is the organization and description of these data. The process of obtaining the data characteristics of each process can adopt obtaining the basic setting parameters of the equipment and process from the process file, or collecting the process running data in real time through the sensors or monitoring system on the equipment. The historical data can also be extracted from the production management system or the running log of the equipment to analyze the data such as the failure frequency, processing efficiency and energy consumption of the equipment. Further, the historical data and analysis result data can also be extracted from the database and cloud platform. The above is only an exemplary description and is not limited as the total possible conditions for obtaining the data characteristics of each process, but here is not exhaustive.

[0090] In the embodiments of the present disclosure, the process of constructing a data model according to data characteristics can first analyze the data characteristics of the process, including static parameters such as equipment specifications, process setting values, dynamic data such as real-time monitored temperature, pressure, vibration, and historical data such as fault records, processing efficiency. Then, according to the type and purpose of the data, a suitable data structure is designed. Exemplarily, a relational database can be used to store static parameters, a time series database can be used to record dynamic data, or a big data platform can be used to store and analyze historical data. Next, the key attributes and association relationships of the data model are defined. Exemplarily, the device running state can be associated with real-time data, and the fault record can be associated with historical data to construct a comprehensive data expression. Subsequently, data acquisition tools, data processing techniques, and visualization tools are integrated to enable real-time updating and interactive analysis of the data model. Finally, the accuracy and completeness of the data model are verified to ensure that it can support process monitoring, prediction, and optimization, providing strong data support for the production process. The above is only an exemplary description and does not limit all possible situations for constructing a data model. Here, we do not make an exhaustive enumeration.

[0091] In this way, by obtaining the physical characteristics, dynamic characteristics and data characteristics of the process, and constructing geometric-physical models, behavior models and data models respectively, a comprehensive expression and systematic description of the process can be formed. This method not only accurately reflects the static and dynamic characteristics of the process, but also enables simulation, optimization and real-time monitoring of the process operation, providing a solid foundation for the digitalization and intelligentization of the production process.

[0092] In some embodiments, the simulation result at least includes: a start time of any process and an end time of any process; determining a production scheduling scheme according to the simulation result and the order information, including: determining an order completion time corresponding to the order information according to the end time of any process; when the order completion time meets the delivery time, generating a production scheduling scheme according to the simulation result.

[0093] In the embodiments of the present disclosure, based on the digital twin model, the simulation model is run to calculate the start time and end time of each process, and the time result is output. The simulation result can include the start time and end time of any process. By simulating the production process, the running of each process is simulated and its time characteristics are obtained. The process of determining the order completion time can first obtain the order demand, and determine the product composition of the order and its corresponding process flow path. Then, combined with the end time of each process in the simulation result, according to the process flow of the order, the end time of the last process is regarded as the order completion time. Then, the order completion time is compared with the delivery time requirement of the order to judge whether it meets the delivery demand. The above is only an exemplary description and does not limit all possible situations for determining the order completion time corresponding to the order information. Here, we do not make an exhaustive enumeration.

[0094] In the embodiments of the present disclosure, the production scheduling scheme can be generated based on the simulation results and the order completion time, ensuring that the production plan meets the delivery requirements. The process of generating the production scheduling scheme can first determine whether the order completion time is earlier than or equal to the delivery time of the order. If it is satisfied, the production scheduling scheme is generated. Then, according to the start time and end time of each process in the simulation results, the specific production scheduling time of each process is determined, and a complete production plan is generated. Finally, the production scheduling scheme is output in the form of a table, a time sequence diagram or a flowchart, which clearly shows the time arrangement and resource allocation of the process, and generates the final production scheduling scheme. The above is only an exemplary description and does not limit all possible cases of generating the production scheduling scheme. Here, we do not make an exhaustive enumeration.

[0095] In this way, by determining the production scheduling scheme based on the simulation results and the order information, the accuracy and efficiency of the production plan can be achieved. The simulation results provide the start time and end time of each process, ensuring that the production path is clear and visible. Combined with the order information, the order completion time is further calculated to ensure that the delivery time requirement is met. The final production scheduling scheme optimizes the process arrangement and resource allocation, avoids production scheduling conflicts and delayed delivery problems, improves production efficiency and customer satisfaction, and provides reliable technical support for intelligent manufacturing and digital factory.

[0096] In some embodiments, according to the simulation results and the order information, determining the production scheduling scheme further includes: when the order completion time does not meet the delivery time, adjusting the preset parameters, re-performing the table allocation calculation, and generating the production scheduling scheme based on the simulation results after the order completion time meets the delivery time.

[0097] In the embodiments of the present disclosure, the order completion time can be first determined according to the simulation results whether it meets the delivery time requirement. If it does not meet, the simulation model is analyzed to identify the bottleneck process or resource limitation that causes the delay, and based on this, the preset parameters are adjusted, the equipment configuration is optimized, the process sequence is adjusted, the processing efficiency is improved or the task resources are redistributed. Subsequently, the simulation model is updated and the simulation calculation is re-run, and the start time, end time of the process and the order completion time are re-generated based on the optimized parameters. When the re-calculated order completion time meets the delivery time requirement, the production scheduling scheme is finally generated, which clearly shows the specific time arrangement and resource allocation of each process, and the scheme is output in the form of a table or a diagram and is issued to the relevant departments for implementation. Through continuous adjustment and verification, the scheme ensures the feasibility, efficiency and on-time delivery of the production plan. The above is only an exemplary description and does not limit all possible cases of generating the production scheduling scheme. Here, we do not make an exhaustive enumeration.

[0098] Thus, by adjusting the preset parameters and recalculating the table matching, the problem of order completion time being later than the delivery time can be effectively solved. By identifying the bottleneck process and optimizing resource allocation or process parameters, the production process is ensured to be more efficient; multiple iterations of simulation and verification ensure the accuracy and reliability of the simulation results; the final generated production scheduling scheme optimizes resource utilization, improves the flexibility and executability of the production plan, meets the customer delivery time requirements, improves production efficiency, reduces delay risks, and provides strong support for the realization of intelligent production and order management for enterprises.

[0099] In some embodiments, the process route of the production process includes n processes, n > 1, and n is a positive integer. Exemplarily, process i is taken as an example, where 1≤i≤n.

[0100] In some embodiments, the preset digital twin model is pre-constructed in the following manner: according to the process characteristics of each process, a geometric-physical model, a behavior model, and a data model of each process are constructed; and according to the geometric-physical model, the behavior model, and the data model, the digital twin model is constructed. Specifically, the geometric-physical model includes digital reproduction of physical characteristics such as geometric shape, size specification, and material attribute of entity objects such as machine equipment, intermediate products, and finished products. The behavior model includes digital representation of dynamic characteristics such as behavior rules, process flow, and operation sequence of each process in the production process. The data model includes various data generated in the production process, such as production progress, quality data, and equipment state data, and the data model is a digital representation of the organization, storage, and management method of these data.

[0101] Further, the geometric-physical model, the behavior model, and the data model of each production process in the production line are used to construct the digital twin model of any process. The digital twin model can be represented by the following formula: ; In the formula, represents the digital twin model of process i; represents the geometric-physical model of process i; represents the behavior model of process i; represents the data model of process i.

[0102] Exemplarily, the production scheduling scheme generation method based on the digital twin model can be applied to the field of knitted sweater production and manufacturing. The knitted sweater processing technology is complex, with many processes and a long processing flow. The knitted sweater production process can include computerized flat knitting, blank inspection, linking, collar setting, hand sewing, semi-inspection, milling, drying, sticking, final inspection, ironing, machine sewing, lamp inspection, quality inspection, and packaging. The overall processing flow is divided into several specific processing processes, so that each processing process will be reflected in the digital twin model of the knitted sweater production line, simulating the entire knitted sweater production and processing process.

[0103] In some embodiments, Figure 2 A flow chart of determining the number of tables based on the production scheduling scheme generation method based on the digital twin model is shown, as shown in Figure 2 As shown, the production efficiency of each process is taken as the production efficiency of each process, the order delivery time is taken as the delivery time, the daily working time is taken as the daily working time, and the effective operation time of the total number of processes is accumulated as the order workload of the process. The organization working production time is taken as the order working time. According to the order information, the process route and the preset parameters, the table calculation of each process in the process route is carried out, and the number of tables is obtained, including: determining the set of tables to be arranged according to the process route; for any process in the set of tables to be arranged, determining the order workload and order working time of any process according to the order quantity, delivery time and preset parameters; determining the number of tables according to the order workload and order working time.

[0104] Specifically, the process table method before simulation aims to determine the configuration of resources such as equipment, personnel and materials required in the production process, to ensure that the simulation model can accurately reflect the actual production environment. First, the input, output and mutual relationship of orders and processes are determined, then the process route is determined, the process table calculation is carried out, the equipment selection is completed, the idle equipment is selected and the specific equipment is determined, and finally the task quantity is allocated. The order product information input specifically includes: order number, product name, style, order quantity, delivery time. The style design determines the difference of the process route, which leads to the difference of the equipment used. The process route library is established in advance, and the process route closest to the style of the simulation order is selected during process table calculation. The order quantity and delivery time determine the number of equipment required for each process, and the table calculation of each process is carried out. Before table calculation, the order quantity, the effective operation time proportion of each process, the single-piece production efficiency of the equipment of each process, the order delivery time and the daily working time need to be input. Specifically, the number of tables of any process can be represented by the following formula:

[0105] In the formula, represents the number of tables of process i; represents the order quantity; represents the effective operation time ratio of the process i; represents the equipment production single-piece efficiency of the process i; represents the order delivery time; represents the daily working hours.

[0106] In some embodiments, Figure 3 A flowchart of a process for determining the equipment allocation of a production scheduling scheme based on a digital twin model is shown, as shown in Figure 3 The process comprises the following steps: S301, performing equipment allocation calculation on the process i to obtain the equipment allocation quantity of the process.

[0107] S302, screening the equipment meeting the process requirements in the equipment library, and determining whether the number of idle equipment is greater than or equal to the equipment allocation quantity. If the result is yes, step S303 is performed; if the result is no, step S304 is performed.

[0108] Exemplarily, the process of screening the equipment can be achieved by obtaining the production state of the equipment in the equipment library in real time, and a check box UI component with a state of idle can also be designed to connect the equipment library, which is used to screen the equipment in the idle state.

[0109] In particular, when screening the equipment meeting the process requirements in the equipment library, in addition to screening the equipment state, the equipment type meeting the process requirements also needs to be matched. Exemplarily, in the field of knitted sweater production and manufacturing, the corresponding equipment also needs to be selected according to different product types during the equipment allocation. Specifically, the fabric organization of the knitted sweater determines the type of the computerized flat knitting machine, such as the island computerized flat knitting machine for plain stitch, and the tianyuan flat knitting machine for intarsia stitch. Similarly, the yarn count structure of the knitted sweater determines the needle type of the computerized flat knitting machine, the type of the circular machine and the type of the collar machine, such as the 8G computerized flat knitting machine needle type, 12G loop machine and collar machine for yarn count less than 11nm (metric number).

[0110] S303, randomly selecting idle equipment meeting the equipment allocation quantity as the equipment allocation, and then performing step S305.

[0111] S304, adding future idle equipment together with the idle equipment as the equipment allocation.

[0112] Specifically, when the idle devices do not meet the quantity requirement of the matching, first, the remaining carrying capacity of all future idle devices is calculated, and all future idle devices are sorted in descending order of the remaining carrying capacity. Then, the current task is preferentially allocated to the idle devices, and the current remaining task is obtained. The current remaining task is split according to the time consumption, and the task with long time consumption is preferentially allocated to the future idle device with large remaining carrying capacity. Finally, the idle devices and the future idle devices allocated with the current task are combined as the matching devices of the current process.

[0113] S305, complete matching, and determine the camera position.

[0114] In some embodiments, according to the simulation result and the order information, a production scheduling scheme is determined, including: according to the end time of any process, the order completion time corresponding to the order information is determined; when the order completion time meets the delivery time, the production scheduling scheme is generated according to the simulation result. Specifically, a transfer piece number parameter condition can be designed for each process, that is, when the production of each process is produced to the transfer piece number, the next process is transferred. With the simulation process, the simulation data is updated and counted to the data board in real time. Exemplarily, a time slider function can also be designed, which binds the simulation time to the progress slider, and sets that when the simulation time reaches the time when the single piece is produced, the production piece number on the data board is automatically updated to production piece number plus 1. The slider is a user interface component, and the system calculates the current simulation time according to the value of the slider. The time update operation can be triggered by the event of the slider, and the slider controls the simulation time to advance / backward during the system simulation process, and displays the corresponding relationship between the simulation time and the actual time. In particular, the start time of any process can be represented by the following formula: ; In the formula, represents the start time of process i; represents the start time of process i-1; represents the transfer piece number of process i-1; represents the production efficiency of process i-1; represents the matching number of process i-1.

[0115] In particular, the end time of any process can be represented by the following formula: ; In the formula, represents the end time of process i; represents the end time of process i-1; represents the transfer piece number of process i-1; represents the production efficiency of process i; The number of tables representing the process i is shown. In some embodiments, the simulation result output is in the form of real-time simulation monitoring data and result analysis report. The real-time simulation monitoring data is used to observe the simulation process and realize data transparency of the simulation process. In the virtual scene, a 3 Dimensional User Interface (3DUI) data board is arranged at each workstation twin model and process, respectively. The data of real-time production simulation of each workstation is updated to the data panel, and the production simulation data of each process is calculated from the data of each workstation and displayed on the board at the corresponding process position. During the simulation, the real-time progress of the simulation is monitored by scene roaming. After the simulation is completed, the scheduling scheme of the simulation of the order is generated, including the process route, the type of equipment required for each process, the number of equipment, and the specific number of equipment, the estimated production time, and whether it can be completed within the delivery period required by the customer.

[0116] In some embodiments, a digital twin system can be built in Unreal Engine. Based on the construction of the digital twin model, the product production line twin model is modeled. The twin data is collected by data acquisition technology on physical workshop data and virtual workshop data. Data acquisition generally uses bar code, Radio Frequency Identification (RFID), sensor, Programmable Logic Controller (PLC), intelligent equipment and management application system. For example, MES collects product production information and work order information. The collected data establishes static database and real-time dynamic database to store and filter the data, and the processed data is mapped to the twin model in real time. The production management process simulation method function is written in the twin system by using blueprint script and C++ language, and a user operation interface is made for function binding.

[0117] It should be understood that Figure 2 and Figure 3 The schematic diagram shown is only exemplary and not limiting, and it is extensible, and those skilled in the art can make various obvious changes and / or replacements based on the examples of Figure 2 and Figure 3 The resulting technical solutions still belong to the disclosure range of the embodiments of the present disclosure.

[0118] Embodiment two: The embodiments of the present disclosure provide a scheduling scheme generation system based on a digital twin model, as shown in Figure 4As shown, the system can comprise: a process determination module 401 configured to determine a process route according to order information; the process route comprises a plurality of processes and a flow sequence between the plurality of processes; the order information at least comprises: a delivery time; a table allocation module 402 configured to perform table allocation for each process in the process route according to the order information, the process route and preset parameters to obtain a table allocation quantity; a model simulation module 403 configured to input the table allocation quantity and the order information into a pre-constructed digital twin model to perform simulation calculation by the digital twin model to obtain a simulation result; and a scheme generation module 404 configured to determine a production scheduling scheme according to the simulation result and the order information.

[0119] In some embodiments, the process determination module 401 comprises: a product information submodule configured to obtain product information according to the order information; a key process submodule configured to determine key processes according to the product information; and a process route submodule configured to determine a process route from a preset process route library according to the key processes.

[0120] In some embodiments, the table allocation module 402 comprises: a process set submodule configured to determine a set of processes to be allocated according to the process route; a process order submodule configured to determine, for any process in the set of processes to be allocated, an order workload and an order working duration of the any process according to the order quantity, the delivery time and preset parameters; and a table allocation quantity submodule configured to determine the table allocation quantity according to the order workload and the order working duration.

[0121] In some embodiments, the process order submodule is configured to calculate the order workload of any process according to the order quantity, a preset effective working time proportion and a preset production efficiency; and calculate the order working duration of any process according to the delivery time and a daily working hour.

[0122] In some embodiments, the production scheduling scheme generation system based on the digital twin model further comprises a model construction module (not shown in the figure) comprising: a process modeling submodule configured to construct a geometric-physical model, a behavior model and a data model of each process according to process characteristics of each process; and a model integration submodule configured to construct a digital twin model according to the geometric-physical model, the behavior model and the data model. Figure 4

[0123] In some embodiments, the process modeling submodule is configured to: the process characteristics comprise physical characteristics, dynamic characteristics and data characteristics; obtain physical characteristics of each entity object in each process; construct a geometric-physical model according to the physical characteristics; obtain dynamic characteristics of each process; the dynamic characteristics at least comprise: behavior rules, process flow and operation sequence; construct a behavior model according to the dynamic characteristics; obtain data characteristics of each process; and construct a data model according to the data characteristics.

[0124] ​In some embodiments, the scheme generation module 404 includes an order completion submodule configured to determine an order completion time corresponding to the order information according to an end time of any process. The scheme integration submodule is configured to generate a production scheduling scheme according to the simulation result when the order completion time meets the delivery time.

[0125] In some embodiments, the scheme generation module 404 further includes a scheme optimization submodule configured to adjust the preset parameters, re-perform the table allocation calculation, and generate a production scheduling scheme according to the simulation result when the order completion time meets the delivery time.

[0126] The specific functions and examples of the modules and submodules of the system of the embodiments of the present disclosure are described above in the related description of the corresponding steps in the method embodiments, and will not be described here.

[0127] The production scheduling scheme generation system based on the digital twin model in the embodiments of the present disclosure can comprehensively integrate order information, process routes, resource allocation, and digital twin technology, accurately formulate and optimize production plans, discover production bottlenecks and risks in advance, improve resource utilization and production efficiency, and ensure timely delivery of orders. At the same time, the application of simulation technology makes the production scheduling scheme more scientific and feasible, reduces the uncertainty in actual production, reduces costs, and enhances the flexibility and reliability of the production process, providing strong support for enterprises to improve competitiveness.

[0128] Embodiment Three According to the embodiments of the present disclosure, the present disclosure further provides a production scheduling scheme generation device based on a digital twin model.

[0129] Figure 5 A schematic block diagram of a device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present disclosure described and / or claimed in this document.

[0130] As Figure 5As shown, the device 500 includes a computing unit 501 that can perform various appropriate actions and processes in accordance with a computer program stored in a Read-Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. Various programs and data required for the operation of the device 500 can also be stored in the Random Access Memory 503. The computing unit 501, the Read-Only Memory 502, and the Random Access Memory 503 are connected to each other through a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0131] Various components in the device 500 are connected to the I / O interface 505, including an input unit 506 such as a keyboard, a mouse, etc., an output unit 507 such as various types of displays, speakers, etc., a storage unit 508 such as a magnetic disk, an optical disk, etc., and a communication unit 509 such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0132] The computing unit 501 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various special-purpose Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs various methods and processes described above, such as the digital twin model based production scheduling scheme generation method. For example, in some embodiments, the multi-label feature selection method can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 500 via the Read-Only Memory 502 and / or the communication unit 509. When the computer program is loaded into the Random Access Memory 503 and executed by the computing unit 501, one or more steps of the multi-label feature selection method described above can be performed. Alternatively, in other embodiments, the computing unit 501 can be configured to perform the digital twin model based production scheduling scheme generation method by any other appropriate means, e.g., by means of firmware.

[0133] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0134] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium would include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory, read-only memory, erasable programmable read-only memory (EPROM), flash memory, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0135] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a Cathode Ray Tube (CRT) or Liquid Crystal Display (LCD) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0136] It should be understood that the various forms of flow shown above can be used to reorder, add, or delete steps. For example, the steps described in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, which are not limited herein.

[0137] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for generating a production scheduling scheme based on a digital twin model, characterized in that, The method comprises the following steps: determining a process route according to order information; the process route comprises a plurality of processes and a flow sequence between the plurality of processes; the order information at least comprises a delivery time; performing table allocation calculation on each process in the process route according to the order information, the process route and preset parameters to obtain a table allocation quantity; performing simulation calculation on the table allocation quantity and the order information through a pre-constructed digital twin model to obtain a simulation result; determining a production scheduling scheme according to the simulation result and the order information.

2. The method of claim 1, wherein, The method of determining a process route according to order information comprises the following steps: the order information further comprises product information; determining a key process according to the product information; determining the process route from a preset process route library according to the key process.

3. The method of claim 1, wherein, The order information further comprises an order quantity; and the preset parameters at least comprise an effective operation time proportion of each process, a production efficiency of each process and a single-day working hour of each process. The method of performing table allocation calculation on each process in the process route according to the order information, the process route and preset parameters to obtain a table allocation quantity comprises the following steps: determining a set of processes to be allocated according to the process route; for any process in the set of processes to be allocated, determining an order workload and an order working time length of the process according to the order quantity, the delivery time and the preset parameters; determining the table allocation quantity according to the order workload and the order working time length.

4. The method of claim 3, wherein, The method of determining an order workload and an order working time length of any process according to the order quantity, the delivery time and the preset parameters comprises the following steps: calculating the order workload of any process according to the order quantity, the preset effective operation time proportion and the preset production efficiency; calculating the order working time length of any process according to the delivery time and the single-day working hour.

5. The method of claim 1, wherein, The digital twin model is pre-constructed in the following manner: constructing a geometric-physical model, a behavior model and a data model of each process according to process characteristics of each process; constructing the digital twin model according to the geometric-physical model, the behavior model and the data model.

6. The method of claim 5, wherein, The process characteristics at least comprise physical characteristics, dynamic characteristics and data characteristics. The method of constructing a geometric-physical model, a behavior model and a data model of each process according to process characteristics of each process comprises the following steps: obtaining the physical characteristics of each entity object in each process; constructing the geometric-physical model according to the physical characteristics; obtaining the dynamic characteristics of each process; the dynamic characteristics at least comprise behavior rules, process flows and operation sequences; constructing the behavior model according to the dynamic characteristics; obtaining the data characteristics of each process; constructing the data model according to the data characteristics.

7. The method of claim 1, wherein, The simulation result at least comprises a start time of any process and an end time of any process; The method of determining a production scheduling scheme according to the simulation result and the order information comprises the following steps: determining an order completion time corresponding to the order information according to the end time of any process. When the order completion time meets the delivery time, the production scheduling scheme is generated according to the simulation result.

8. The method of claim 1, wherein, The method further includes: When the order completion time does not meet the delivery time, the preset parameters are adjusted, the table allocation calculation is performed again, and the production scheduling scheme is generated according to the simulation result after the order completion time meets the delivery time. 9.A digital-twin model based scheduling scheme generation system, characterized in that, The method further includes: The process determination module is configured to determine a process route according to the order information. The process route includes a plurality of processes and a flow sequence between the plurality of processes. The order information at least includes a delivery time. The table allocation calculation module is configured to perform table allocation calculation on each process in the process route according to the order information, the process route and preset parameters, and obtain a table allocation quantity. The model simulation module is configured to input the table allocation quantity and the order information into a pre-constructed digital twin model, and perform simulation calculation by the digital twin model to obtain a simulation result. The scheme generation module is configured to determine a production scheduling scheme according to the simulation result and the order information.

10. A production scheduling scheme generation device based on a digital twin model, characterized in that, The method further includes: At least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

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