Production process collaborative management and control method, management and control system and computer program product

Abstract

The embodiment of the invention provides a production process collaborative management and control method, a management and control system and a computer program product, and belongs to the field of automatic control. Comprising the following steps: according to order information and production process information of a plurality of orders, determining resource demand information and a predicted production cycle of each order and a predicted production sequence priority among the plurality of orders; according to the above information, the equipment capacity, the production material information and the inventory information, adjusting the production sequence among the plurality of orders to obtain the actual production sequence among the plurality of orders; obtaining actual production data corresponding to the current production order according to the actual production sequence among the plurality of orders, the equipment operation state in the actual production process and the material consumption data; and adjusting the current production order strategy in real time according to the actual production data corresponding to the current production order and the material inventory data and inventory of the current production order product. According to the invention, cooperative control of production links can be realized, so that the efficiency of the production links is improved.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and specifically to a method for collaborative control of production processes, a collaborative control system for production processes, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] For the entire production chain of steel enterprises, from the steelmaking stage to the delivery of finished rolled steel products, process efficiency is the key to ensuring the smooth operation of the entire production and manufacturing process. In the context of contemporary enterprise production, the Manufacturing Execution System (MES) serves as a bridge, seamlessly connecting the Enterprise Resource Planning (ERP) system and the Production Process Control (PCS) system, playing a core role in connecting the upper and lower levels. However, the MES systems of most steel enterprises currently operate in a steel-rolling separation mode. The common problem of this mode is that the operation execution status of each production plant has not been effectively integrated, resulting in a lack of coordination. At the same time, equipment usage details and material flow information have not been fully integrated. In view of this, it is particularly urgent and important to develop a set of methods that can strengthen collaboration and control between processes to promote efficient coordinated adjustment of steelmaking and rolling links. Summary of the invention

[0003] The purpose of the embodiments of the present invention is to provide a method for collaborative control of production processes, a collaborative control system for production processes, an electronic device, a computer-readable storage medium, and a computer program product. The method can partially or completely solve the technical problem that the prior art cannot collaboratively control various links of the production process, thereby resulting in low efficiency of the production links.

[0004] In order to achieve the above-mentioned purpose, an embodiment of the present invention provides a method for collaborative management and control of production processes, the method comprising: determining the resource requirement information, estimated production cycle and estimated production sequence priority among multiple orders of each order based on order information and production process information of multiple orders; dynamically adjusting the production sequence among multiple orders based on the resource requirement information, estimated production cycle, estimated production sequence priority among multiple orders and equipment capacity, production material information, and inventory information of each order to obtain the actual production sequence among multiple orders; obtaining the actual production data corresponding to the current production order based on the actual production sequence among the multiple orders, the equipment operating status of the actual production process, and the material consumption data; and adjusting the current production order strategy in real time based on the actual production data corresponding to the current production order, the material inventory data and inventory quantity of the current production order products.

[0005] Optionally, the order information includes order ID, order urgency, and customer importance, and the production process information includes production complexity and resource allocation corresponding to each order. Determining the estimated production sequence priority among multiple orders based on the order information and production process information of multiple orders includes: establishing a correlation between the order ID and the order urgency, customer importance, production complexity, and resource allocation corresponding to the order ID; taking a weighted sum of the order urgency, customer importance, production complexity, and resource allocation to obtain the estimated production sequence priority corresponding to the order ID; and obtaining the estimated production sequence priority among multiple orders based on the correlation and the estimated production sequence priority corresponding to the order ID.

[0006] Optionally, determining the resource requirement information and estimated production cycle of each order based on the order information and production process information of multiple orders includes: constructing a resource requirement prediction model and a production cycle prediction model corresponding to the historical orders based on the order information and production process information of multiple historical orders; and inputting the order information and production process information of the multiple orders into the resource requirement prediction model and the production cycle prediction model, respectively, to determine the resource requirement information and the estimated production cycle of each order.

[0007] Optionally, the method also includes: using a Gantt chart to display in real time the production progress, equipment operating status and material flow status of the current production order; and when the equipment operating status is abnormal and / or the material flow status is abnormal, adjusting the production of the next priority product accordingly.

[0008] Optionally, the method also includes: constructing a health prediction model based on historical equipment operating status and historical material flow status, and determining the health status of the equipment of the current production order based on the equipment operating status and material flow status of the current production order and the health prediction model.

[0009] Optionally, the method also includes: the method also includes: performing random inspection on the products corresponding to the current production order to obtain multiple random inspection samples; comparing the element indicators of the multiple sampling samples with preset element indicator thresholds to obtain the quality fluctuation trend of the products of the current production order.

[0010] On the other hand, the present invention also provides a management and control system for coordinated production processes, the management and control system comprising: an order processing module, which is used to determine the resource demand information, estimated production cycle and estimated production sequence priority among multiple orders of each order based on the order information and production process information of multiple orders; a dynamic production plan optimization module, which is used to dynamically adjust the production sequence among multiple orders based on the resource demand information, estimated production cycle, estimated production sequence priority among multiple orders and equipment capacity, production material information and inventory information of each order to obtain the actual production sequence among multiple orders; an operation performance intelligent analysis module, which is used to obtain the actual production data corresponding to the current production order based on the actual production sequence among the multiple orders, the equipment operation status of the actual production process and the material consumption data; and an inventory management module, which is used to adjust the current production order strategy in real time based on the actual production data corresponding to the current production order, the material inventory data and inventory quantity of the current production order products.

[0011] On the other hand, the present invention also provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method of collaborative management and control of production processes as described.

[0012] On the other hand, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method for collaborative management and control of production processes as described above.

[0013] On the other hand, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the method for collaborative management and control of production processes as described above.

[0014] Through the above technical scheme, the resource requirement information, estimated production cycle and estimated production sequence priority among multiple orders of each order are determined according to the order information and production process information of multiple orders; the production sequence among multiple orders is dynamically adjusted according to the resource requirement information, estimated production cycle, estimated production sequence priority among multiple orders, equipment capacity, production material information and inventory information of each order to obtain the actual production sequence among multiple orders; the actual production data corresponding to the current production order is obtained according to the actual production sequence among the multiple orders, the equipment operation status of the actual production process and the material consumption data; and the current production order strategy is adjusted in real time according to the actual production data corresponding to the current production order, the material inventory data and the inventory quantity of the current production order products.

[0015] The technical solution of the present invention determines the resource demand information required for each order, the expected production cycle, and the priority of the expected production sequence between each order according to the order information and the production process information, that is, the time and resources required for each order and which order is produced first are determined in general. Since there may be differences between the actual production and the expected production sequence, this mainly considers the actual production material information and inventory information in the production. Therefore, it is necessary to dynamically adjust the actual production sequence of each order according to the actual situation, realize real-time dynamic adjustment of the production strategy, so as to realize the collaborative work between each production link and improve the production efficiency. Then, after determining the actual production sequence, production is started, and the actual production data of the current order is obtained according to the equipment operation status and material consumption of the actual production process, so that the current production order strategy can be adjusted in real time according to these data and the inventory data and inventory of the material. The method of collaborative control of the production process provided by the present invention realizes the collaborative control of the production link, thereby improving the efficiency of the production link.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0018] Figure 1 It is a flow chart of the production process collaborative control method provided by the first embodiment of the present invention;

[0019] Figure 2 It is a structural diagram of a management and control system for collaborative production processes provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0020] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0021] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, some existing solutions in the industry such as certain software, components, and models may be mentioned, which should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use the solution.

[0022] like Figure 1The first embodiment of the present invention provides a method flow chart of a collaborative control method for a production process. A method for collaborative control of a production process, the method comprising the following steps S10-S13.

[0023] In step S10, resource requirement information, an estimated production cycle, and an estimated production sequence priority of each order are determined based on order information and production process information of the multiple orders.

[0024] For example, the current steel rolling system adopts a mode in which steelmaking and steel rolling subsystems operate independently. Each system is further subdivided into functional modules to independently manage the plan details, operation performance, equipment operation status, material flow records and other information of its responsible area. The embodiment of the present invention aims to achieve coordinated control of all links from planning to production. The embodiment of the present invention is particularly suitable for the production process of steelmaking and steel rolling.

[0025] Furthermore, the order information includes order ID, order urgency, and customer importance, and the production process information includes production complexity and resource allocation corresponding to each order.

[0026] Determining the estimated production sequence priority among the multiple orders according to the order information and production process information of the multiple orders includes the following steps S101-S105.

[0027] In step S101, an association relationship is established between the order ID and the order urgency, customer importance, production complexity and resource allocation status corresponding to the order ID.

[0028] For example, in the actual production process, in order to ensure production efficiency, multiple orders are processed sequentially, but the production order of each order is determined by considering the order urgency, customer importance, production complexity and resource allocation of each order. For example, there are three orders, namely A, B and C, and the expected production of these three orders is sorted. Among them, A, B and C may be product names, product specifications or customer company names, etc., which are used as order IDs (unique codes of orders), and then the order IDs of each order are associated with the order urgency, customer importance, production complexity and the resource allocation, so as to determine the expected production order priority of the production order.

[0029] In step S103, a weighted sum is taken of the order urgency, customer importance, production complexity and resource allocation to obtain an estimated production sequence priority corresponding to the order ID.

[0030] For example, the order can be divided into very urgent, relatively urgent, generally urgent, and not urgent according to the urgency of the order. Different values ​​can be set for the order urgency to distinguish, for example, the value corresponding to the very urgent is 10, the value corresponding to the relatively urgent is 8, the value corresponding to the generally urgent is 6, the value corresponding to the not urgent is 2, etc., and different values ​​can be set according to actual needs. Similarly, different values ​​can be set for the user importance (very important, relatively important, generally important), the production complexity (extremely complex, very complex, generally complex), and the resource allocation situation (for example, sufficient resources, insufficient resources, etc.) according to the setting method of the order urgency. Finally, the order urgency, customer importance, production complexity and the resource allocation situation are weighted and summed to obtain the final numerical sum of orders A, B, and C, and then sorted according to the numerical size, corresponding to the expected production order priority between multiple orders. The weight can be allocated as needed, and the embodiment of the present invention does not make specific restrictions.

[0031] For example, if the final value of order C is the largest, order A is the second, and order B is the smallest, then the estimated production order priority of each order is order C, order A, and order B. When two orders have the same score, you can select one of them to be sorted.

[0032] In step S105, the estimated production sequence priorities among the multiple orders are obtained according to the association relationship and the estimated production sequence priorities corresponding to the order ID.

[0033] For example, the estimated production order priority may be finally sorted according to the order ID number.

[0034] Determining the resource requirement information and the estimated production cycle of each order based on the order information and production process information of multiple orders includes the following steps S102-S104.

[0035] In step S102, a resource demand prediction model and a production cycle prediction model corresponding to the historical orders are constructed based on the order information of a plurality of historical orders and the historical production process information.

[0036] Exemplarily, in order to obtain the production cycle of each order, the embodiment of the present invention can use multiple historical order information and historical production process information, which includes data related to resource demand (such as the amount of materials required for production, material types, etc.) and production cycle data, and then use these data to construct a resource demand prediction model and a production cycle prediction model corresponding to the historical orders through data fitting or existing big data models. The specific construction process can adopt existing technical means, and the embodiment of the present invention will not be repeated here.

[0037] In step S104, the order information and production process information of the multiple orders are respectively input into the resource demand prediction model and the production cycle prediction model to determine the resource demand information and the estimated production cycle of each order.

[0038] Exemplarily, the order information of the actual order and the information in the production process are input into the resource demand prediction model and the production cycle prediction model obtained in the above steps, and the resource demand and the estimated production cycle of each order can be obtained accordingly.

[0039] Step S10 of the embodiment of the present invention implements intelligent order processing: it subtly integrates multiple perspectives of information such as order urgency, customer importance, production complexity, and current production resource allocation status, and uses advanced intelligent algorithms to achieve automatic and accurate sorting of order priorities. This mechanism ensures that critical orders can be immediately identified and prioritized, greatly accelerating the order response process and improving production efficiency. In addition, based on this priority assessment result, a preliminary production demand plan can be automatically generated, clearly outlining the resource requirements, expected production cycle and priority order of each order, laying a solid foundation for the detailed preparation of subsequent production plans, and significantly enhancing the overall response agility and flexibility of the production system.

[0040] In step S11, the production sequence between multiple orders is dynamically adjusted according to the resource demand information of each order, the estimated production cycle, the estimated production sequence priority between multiple orders, and the equipment capacity, production material information, and inventory information to obtain the actual production sequence between multiple orders.

[0041] Exemplarily, the production process of each order further includes multiple production links. The specific production processes corresponding to different orders are different, and the equipment capacity, production material information (such as the stability of material supply), inventory information, etc. must be fully considered during the production process. Therefore, the estimated production order priority of each order determined in the above step S10 does not necessarily meet the actual production order. This process can be achieved through production simulation and optimization algorithms, which can fully consider the equipment capacity, material supply stability and inventory status real-time, so as to generate the most optimized production plan in real time. For example, assuming that the production order according to the estimated production order priority is order C, order A, and order B, but there is insufficient material supply in the process of producing order C, and reverse deduction that order C cannot be carried out normally temporarily, then the production of order C is postponed, and the next priority level order A is produced in sequence, and the corresponding actual production order is adjusted to order A, order C, and order B, so as to achieve the continuity and stability of production.

[0042] Furthermore, the method also includes: using a Gantt chart to display in real time the production progress, equipment operation status and material flow status of the current production order; and when the equipment operation status is abnormal and / or the material flow status is abnormal, adjusting the production of the next priority product accordingly.

[0043] Exemplarily, in order to enhance the intuitiveness and flexibility of production scheduling and facilitate producers to intuitively observe production progress, equipment operating status and material flow status, the embodiment of the present invention also uses a Gantt chart visualization tool, which can not only intuitively display the production plan, but also dynamically reflect the real-time situation of production progress, equipment operating status and material flow.

[0044] In addition, when the equipment operation status is abnormal and / or the material flow status is abnormal, the emergency mechanism is activated. When faced with emergencies such as equipment failure and material shortage, the system can automatically trigger the emergency plan, quickly adjust the production plan, and adjust the production to the next priority product accordingly to ensure the continuity and stability of the production process, thereby comprehensively improving the level of intelligent production management.

[0045] In step S12, actual production data corresponding to the current production order is obtained according to the actual production sequence among the multiple orders, the equipment operation status of the actual production process and the material consumption data.

[0046] For example, when the actual production sequence of multiple orders, the equipment operation status of the actual production process, and the material consumption data are determined, the actual production data corresponding to the current production order can be obtained in real time. By integrating existing data processing and analysis technologies, the actual production data can be deeply compared with the preset plan to reveal production deviations and efficiency improvement potential. On this basis, a multi-dimensional, graphical operation performance report is automatically generated, which not only comprehensively presents production data comparison and statistical analysis, but also deeply explores production bottlenecks, efficiency losses, and resource optimization space, providing management with an intuitive and comprehensive overview of production performance. Its continuous learning and self-optimization characteristics continuously optimize the analysis model and prediction algorithm based on historical data to ensure that the report is accurate and timely, injecting strong impetus into the intelligent upgrade and continuous improvement of enterprise production management.

[0047] In step S13, the current production order strategy is adjusted in real time according to the actual production data corresponding to the current production order, the material inventory data and the inventory quantity of the current production order product.

[0048] For example, the actual production data corresponding to the current production order, the material inventory data and inventory quantity of the current production order products are monitored in real time, and every slight change in the raw materials, work-in-progress and finished product inventory is tracked comprehensively and accurately to ensure the absolute accuracy and immediacy of inventory information. Relying on the intelligent integration of deep data analysis, historical inventory data and market demand trends, the system can accurately predict inventory demand, and flexibly adjust the production plan according to the preset upper and lower limits of safety inventory, realize fine regulation of inventory levels, effectively avoid inventory backlog and out-of-stock risks, and significantly improve inventory turnover. We have innovatively incorporated an inventory early warning mechanism. Once the inventory approaches the preset threshold, the system will immediately trigger an early warning and quickly notify the procurement, production, sales and other departments through diversified channels. The early warning information not only displays the inventory status in detail, but also intelligently recommends response strategies, such as emergency procurement and production scheduling adjustments, which greatly enhances the flexibility and response efficiency of inventory management. In addition, the system has the ability to continuously learn and self-optimize. It can continuously optimize the inventory forecasting model and early warning strategy based on historical data and market dynamics to ensure accurate and forward-looking inventory management, help enterprises minimize inventory costs and maximize operational efficiency, and promote inventory management towards intelligence and efficiency.

[0049] Furthermore, the method also includes: performing random inspections on the products corresponding to the current production order to obtain multiple random inspection samples; comparing the elemental indicators of the multiple sampling samples with preset elemental indicator thresholds to obtain the quality fluctuation trend of the products of the current production order.

[0050] For example, the products corresponding to the current production order are further sampled to obtain multiple sampled samples, and then the elemental indicators of the sampled samples are analyzed. The elemental indicators can specifically be the content of various chemical elements in the product, etc. Then, the elemental indicators of the multiple sampled samples are compared with the preset elemental indicator thresholds to obtain the quality fluctuation trend of the products of the current production order, so as to timely warn of potential quality problems, and intelligently adjust the production plan according to the test results, effectively prevent quality defects, and significantly enhance product quality control.

[0051] When the indicator elements do not meet the preset indicator thresholds, you can choose to adjust the corresponding production parameters, or if they are seriously unqualified, stop the batch production and switch to the next priority production in order to continue to optimize the defective production process.

[0052] In addition, detailed quality reports are generated in real time, which not only comprehensively display the distribution and trend of problems, but also deeply analyze the essence of the problems and put forward targeted improvement suggestions, providing solid support for the continuous optimization of product quality. At the same time, we have innovatively developed a quality traceability and feedback mechanism to achieve tracking and tracing of the entire life cycle of products, quickly locate the source of problems, efficiently respond to customer feedback, and form a closed-loop improvement system in combination with internal data, continuously promoting product quality to a new high and ensuring that corporate quality management reaches an excellent level.

[0053] Furthermore, the method also includes: constructing a health prediction model based on historical equipment operating status and historical material flow status; determining the health status of the equipment of the current production order based on the equipment operating status and the material flow status of the current production order and the health prediction model.

[0054] For example, it fully receives all kinds of key data transmitted by the lower-level system, including equipment operation status, real-time production capacity and accurate fault warning information. With these data, the system can use the intelligent health prediction model to predict the health status of the equipment of the current production order.

[0055] Automatically adjust production plans, effectively avoid the risk of equipment overload or idleness, and achieve optimal allocation of resources. On this solid foundation, we have introduced advanced equipment health management and predictive maintenance functions, deeply mined equipment operation data, and used machine learning algorithms to build health prediction models to identify potential failure risks in advance, provide forward-looking guidance for equipment maintenance and overhaul, and automatically generate maintenance and overhaul plans to ensure that the equipment is always in the best operating state. Furthermore, we have innovatively developed equipment performance optimization and upgrade recommendation functions. Through equipment operation data analysis, we can accurately identify performance bottlenecks and room for improvement, and propose targeted optimization plans to help companies improve equipment performance and production efficiency. At the same time, the system supports the visual display of equipment information, and provides managers with an accurate and comprehensive overview of equipment status and production status with intuitive charts and interfaces, providing strong support for decision-making.

[0056] The technical solution of the present invention determines the resource demand information required for each order, the expected production cycle, and the priority of the expected production sequence between each order according to the order information and the production process information, that is, the time and resources required for each order and which order is produced first are determined in general. Since there may be differences between the actual production and the expected production sequence, this mainly considers the actual production material information and inventory information in the production. Therefore, it is necessary to dynamically adjust the actual production sequence of each order according to the actual situation, realize real-time dynamic adjustment of the production strategy, so as to realize the collaborative work between each production link and improve the production efficiency. Then, after determining the actual production sequence, production is started, and the actual production data of the current order is obtained according to the equipment operation status and material consumption of the actual production process, so that the current production order strategy can be adjusted in real time according to these data and the inventory data and inventory of the material. The method of collaborative control of the production process provided by the present invention realizes the collaborative control of the production link, thereby improving the efficiency of the production link.

[0057] like Figure 2 It is a structural diagram of a production process collaborative control system provided by the second embodiment of the present invention. A production process collaborative control system, the control system 20 includes: an order processing module 201, which is used to determine the resource demand information, expected production cycle and expected production sequence priority between multiple orders according to the order information and production process information of multiple orders; a dynamic production plan optimization module 202, which is used to dynamically adjust the production sequence between multiple orders according to the resource demand information, expected production cycle, expected production sequence priority between multiple orders and equipment capacity, production material information, and inventory information of each order to obtain the actual production sequence between multiple orders; an operation performance intelligent analysis module 203, which is used to obtain the actual production data corresponding to the current production order according to the actual production sequence between the multiple orders, the equipment operation status of the actual production process and the material consumption data; and an inventory management module 204, which is used to adjust the current production order strategy in real time according to the actual production data corresponding to the current production order, the material inventory data and inventory of the current production order product.

[0058] The production process collaborative control system provided in the second embodiment of the present invention implements the method for collaborative control of production processes as described and the technical effects achieved are the same as those of the first embodiment, which will not be repeated here.

[0059] The third embodiment of the present invention also provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method of collaborative management and control of production processes as described.

[0060] The method for implementing the collaborative control of production processes and the technical effects achieved by the electronic device provided in the third embodiment of the present invention are the same as those of the first embodiment and will not be described in detail herein.

[0061] The fourth embodiment of the present invention further provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method for collaborative management and control of production processes as described above.

[0062] The computer execution instructions of the fourth embodiment of the present invention are used to implement the method of collaborative control of production processes as described and the technical effects achieved when the instructions are executed by the processor are the same as those of the first embodiment, and will not be described in detail here.

[0063] The fifth embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for collaborative management and control of production processes as described above.

[0064] The fifth embodiment of the present invention implements the method for collaborative control of production processes as described and the technical effects achieved are the same as those of the first embodiment, and will not be described in detail here.

[0065] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0066] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0067] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0069] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0070] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0071] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0072] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0073] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for collaborative control of production processes, characterized in that: The method comprises: determining resource requirement information, an estimated production cycle, and an estimated production sequence priority of each order according to order information and production process information of a plurality of orders; According to the resource demand information of each order, the estimated production cycle, the estimated production sequence priority among multiple orders, and the equipment capacity, production material information, and inventory information, the production sequence among multiple orders is dynamically adjusted to obtain the actual production sequence among multiple orders; According to the actual production sequence between the multiple orders, the equipment operation status of the actual production process and the material consumption data, the actual production data corresponding to the current production order is obtained; and, The current production order strategy is adjusted in real time according to the actual production data corresponding to the current production order, the material inventory data and the inventory quantity of the current production order product.

2. The method according to claim 1, characterized in that The order information includes order ID, order urgency, and customer importance; the production process information includes production complexity and resource allocation for each order. According to the order information of multiple orders and the production process information, the estimated production sequence priority among the multiple orders is determined including: Establishing an association relationship between the order ID and the order urgency, customer importance, production complexity, and resource allocation status corresponding to the order ID; Performing a weighted summation of the order urgency, customer importance, production complexity, and resource allocation to obtain an estimated production sequence priority corresponding to the order ID; According to the association relationship and the estimated production sequence priority corresponding to the order ID, the estimated production sequence priority among multiple orders is obtained.

3. The method according to claim 2, characterized in that Based on the order information and production process information of multiple orders, the resource demand information and estimated production cycle of each order are determined, including: Constructing a resource demand prediction model and a production cycle prediction model corresponding to the historical orders based on the order information of multiple historical orders and the historical production process information; and, The order information and production process information of the multiple orders are respectively input into the resource demand prediction model and the production cycle prediction model to determine the resource demand information and the estimated production cycle of each order.

4. The method according to claim 1, characterized in that: The method further includes: using a Gantt chart to display in real time the production progress, equipment operation status, and material flow status of the current production order; and When the equipment operation status is abnormal and / or the material flow status is abnormal, the production is adjusted to the next priority product accordingly.

5. The method according to claim 4, characterized in that The method further includes: constructing a health prediction model based on historical equipment operation status and historical material flow status; The health status of the equipment of the current production order is determined according to the equipment operation status, the material flow status and the health prediction model of the current production order.

6. The method according to claim 1, characterized in that The method further comprises: Perform random inspection on the products corresponding to the current production order to obtain multiple random inspection samples; The element indexes of the multiple sampling samples are compared with the preset element index thresholds to obtain the quality fluctuation trend of the product of the current production order.

7. A production process coordination management and control system, characterized in that: The control system includes: An order processing module, used to determine the resource requirement information, estimated production cycle and estimated production sequence priority of each order according to the order information and production process information of multiple orders; Dynamic production plan optimization module, used to dynamically adjust the production sequence between multiple orders according to the resource demand information of each order, the estimated production cycle, the estimated production sequence priority between multiple orders, and the equipment capacity, production material information, and inventory information, so as to obtain the actual production sequence between multiple orders; an intelligent operation performance analysis module, for obtaining actual production data corresponding to the current production order according to the actual production sequence between the multiple orders, the equipment operation status of the actual production process, and the material consumption data; and The inventory management module is used to adjust the current production order strategy in real time according to the actual production data corresponding to the current production order, the material inventory data and inventory quantity of the current production order product.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method for collaborative management and control of production processes as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method for collaborative management and control of production processes according to any one of claims 1 to 6.

10. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the method for collaborative management and control of production processes as described in any one of claims 1 to 6.

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