A management method and system for a digital twin intelligent factory

Abstract

The present invention relates to the field of intelligent factory management, and discloses a management method and system for a digital twin intelligent factory. By adopting the digital twin method, the digitization of the factory's actual situation is realized to evaluate and schedule the management of factory equipment, production, and operating personnel in the cloud. Specifically, here, through the periodic evaluation of operating personnel, based on the yield rate of the products produced by the operating personnel in different time periods of the cycle, the operating input status of the operating personnel in different time periods is judged. Furthermore, operating personnel with similar operating inertia can be grouped together, and the production habits of the factory's production equipment can be correspondingly adjusted to optimize the product yield rate to the greatest extent and reduce the periodic reduction of the yield rate caused by the change of the operating status of the operating personnel.

Description

Technical Field

[0001] The present invention relates to the field of intelligent factory management, and specifically to a management method and system for a digital twin intelligent factory. Background Art

[0002] During the factory production process, even in a modern intelligent factory, manual participation is required in some processes, including different processes such as assembly and processing. However, due to the working status at different time periods, the backlog of long-term work fatigue, and the required working rate, etc., the yield rate of qualified products will change to varying degrees due to changes in the operation status.

[0003] Most factories currently adopt a simple mode of matching assembly line workers without special human-machine connection. After the basic output efficiency is allocated, the production line has a fixed output, and the workers adapt to the production line. Therefore, the problem of uneven yield rates will occur at different stages. Moreover, in long-term operations, the decline in the status of the operators will even lead to a significant reduction in the yield rate, ultimately affecting the product and factory benefits. Summary of the Invention

[0004] The purpose of the present invention is to provide a management method and system for a digital twin intelligent factory to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A management method for a digital twin intelligent factory includes:

[0007] Obtain the real-time monitoring data of the sensor group, and establish a digital twin factory mirroring the real world based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product inspection data;

[0008] Set the operator as an associated object to trace and associate the product and production equipment, so as to obtain a product information chain corresponding to the product. The product inspection data is used to represent the deviation data between the product and the target qualified product;

[0009] Divide a number of equal-length evaluation periods within the operation cycle of the operator, count the output and yield rate of the operator in each evaluation period, and obtain the production capacity record data corresponding to the operator;

[0010] Calculate the change trend of the yield rate within the evaluation period based on the historical production capacity record data of the array, and obtain the peak period and valley period of the yield rate of the operator within the operation cycle. The change trend is used to represent the change in the production concentration of the operator;

[0011] Match the peak periods and valley periods of multiple workers to schedule and group the workers, and establish a pipeline equipment control signal based on the peak and valley periods corresponding to the scheduling group;

[0012] Respond to the pipeline control signal to control the output rhythm of the equipment at different times, and correspondingly generate a dispatch signal to dispatch the workers in the scheduling group to the pipeline with the corresponding output rhythm.

[0013] As a further solution of the present invention: it further includes an output classification and scheduling judgment step:

[0014] Calculate the average output of workers per unit evaluation period within the operation cycle, and divide several workers into different efficiency groups based on the average output. When performing scheduling grouping, the workers are within the same efficiency group;

[0015] Calculate the trend of the historical good product rate of workers in multiple evaluation periods within the operation cycle. If the trend of the good product rate of the workers shows a downward trend in more than a threshold proportion of evaluation periods, trigger a scheduling request;

[0016] Respond to the scheduling request, perform a downward scheduling of the efficiency group of the workers, and further monitor the operation of the workers.

[0017] As a further solution of the present invention: it further includes an equipment risk judgment step, specifically including:

[0018] When a scheduling request is triggered, calculate the trend of the historical good product rate of multiple workers on the current pipeline in multiple evaluation periods within the operation cycle to obtain an array of operation trend data;

[0019] Statistically analyze the operation trend data of multiple workers on the current pipeline. If, without change in the average output of the pipeline, multiple pieces of the operation trend data all show a downward trend, generate an equipment risk notification, reduce the output priority of adjusting the current pipeline, and perform re-scheduling of workers based on the adjusted output priority. The output priority corresponds to the efficiency group and is respectively used to define the equipment side and the personnel side.

[0020] As a further solution of the present invention: it further includes a scheduling control step based on personnel movements, specifically including:

[0021] Real-time obtain the monitoring data of workers to judge the corresponding movement status. If the movement status of the workers is characterized as leaving the pipeline, trigger a collaborative scheduling instruction;

[0022] Continuously monitor the operator. If the continuous operation duration of the operator reaches the preset buffer duration and the output efficiency of the operator decreases, a collaborative scheduling instruction is triggered;

[0023] Respond to the collaborative scheduling instruction to control the output beat of the pipeline equipment corresponding to the operator.

[0024] As a further scheme of the present invention: it further includes a deviation evaluation step, specifically including:

[0025] Obtain corresponding deviation data based on the historical product detection data, and statistically analyze the historical deviation data of the operator to obtain the deviation preference corresponding to the operator. The deviation preference is used to characterize the operation habit characteristics of the operator;

[0026] Real-time record the product detection data, and statistically analyze several product detection data within the current operation cycle to obtain the cycle deviation preference of the operator within the current operation cycle. According to the matching judgment between the cycle deviation preference and the deviation preference, if the judgment result shows that the matching difference exceeds the rated threshold, an equipment risk notification is generated and fed back.

[0027] The embodiment of the present invention aims to provide a management system for a digital twin intelligent factory, including:

[0028] A data synchronization module, which is used to obtain the real-time monitoring data of the sensing group, and establish a digital twin factory mirroring the reality based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product detection data;

[0029] A data association module, which is used to set the operator as the associated object to trace and associate the product and production equipment, so as to obtain the product information chain corresponding to the product. The product detection data is used to characterize the deviation data between the product and the target good product;

[0030] An operation statistics module, which is used to split several equal-duration evaluation periods within the operation cycle of the operator, and statistically analyze the output quantity and good product rate of the operator in each evaluation period to obtain the production capacity record data corresponding to the operator;

[0031] A focus evaluation module, which is used to calculate the change trend of the good product rate within the evaluation period based on the array of historical production capacity record data, and obtain the peak period and trough period of the good product rate of the operator within the operation cycle. The change trend is used to characterize the change of the production focus of the operator;

[0032] A matching and grouping module, which is used to match the peak periods and valley periods of multiple workers, so as to schedule and group the workers, and establish a pipeline equipment control signal based on the peak and valley periods corresponding to the scheduling group;

[0033] A scheduling management module, which is used to respond to the pipeline control signal to control the output beat of the equipment at different times, and correspondingly generate a dispatching signal to dispatch the workers in the scheduling group to the pipeline with the corresponding output beat.

[0034] As a further solution of the present invention: It further includes an output hierarchical scheduling module, including:

[0035] An efficiency division unit, which is used to calculate the average output volume of workers in a unit evaluation period within the operation cycle, divide several workers into different efficiency groups based on the average output volume, and when performing scheduling grouping, the workers are within the same efficiency group;

[0036] A trend evaluation unit, which is used to calculate the trend of the historical good product rate of workers in multiple evaluation periods within the operation cycle. If the good product rate trend of the workers shows a downward trend in several evaluation periods exceeding the threshold ratio, a scheduling request is triggered;

[0037] A scheduling allocation unit, which is used to respond to the scheduling request, perform a downward scheduling of the efficiency group of the workers, and further monitor the operation of the workers.

[0038] As a further solution of the present invention: It further includes an equipment risk judgment module, specifically including:

[0039] A risk judgment trigger unit, which is used to calculate the trend of the historical good product rate of multiple workers on the current pipeline in multiple evaluation periods within the operation cycle when a scheduling request is triggered, and obtain an array of operation trend data;

[0040] A risk judgment management unit, which is used to statistically analyze the operation trend data of multiple workers on the current pipeline. If, without the average output volume of the pipeline changing, multiple pieces of the operation trend data all show a downward trend, an equipment risk notification is generated, and the output priority of the current pipeline is reduced, and a rescheduling of the workers is performed based on the adjusted output priority. The output priority corresponds to the efficiency group and is used to define the equipment side and the personnel side respectively.

[0041] As a further solution of the present invention: It further includes a personnel collaborative scheduling module, specifically including:

[0042] A movement evaluation unit is used to obtain the monitoring data of the operator in real time to determine the corresponding movement status. If the movement status of the operator is characterized as leaving the assembly line, a coordinated scheduling instruction is triggered;

[0043] A trend evaluation unit is used to monitor the continuous operation of the operator, and if the continuous operation time of the operator reaches a preset buffer time and the output efficiency of the operator decreases, a collaborative scheduling instruction is triggered;

[0044] The equipment control unit is used to respond to the collaborative scheduling instructions to control the output rhythm of the corresponding assembly line equipment by the operators.

[0045] As a further solution of the present invention: it also includes a deviation assessment module, specifically including:

[0046] a deviation characterization unit, configured to obtain corresponding deviation data based on the historical product inspection data, and to perform statistics on the historical deviation data of the operator to obtain deviation preferences corresponding to the operator, wherein the deviation preferences are used to characterize the operating habit characteristics of the operator;

[0047] The deviation auxiliary judgment unit is used to record product inspection data in real time, and to make statistics based on several product inspection data in the current operation cycle, to obtain the cycle deviation preference of the operator in the current operation cycle, and to make a matching judgment based on the cycle deviation preference and the deviation preference. If the judgment result is characterized as a matching difference exceeding the rated threshold, an equipment risk notification and feedback is generated.

[0048] Compared with the prior art, the beneficial effect of the present invention is that the digitalization of the actual factory situation is realized by adopting the digital twin method, so as to evaluate and schedule the factory equipment, production and operators in the cloud. Specifically, here, by periodically evaluating the operators, judging based on the yield rate of the operators' operations in different time periods of the cycle, and understanding the operating status of the operators in different time periods, the operators with similar operating inertia can be grouped together, and the production habits of the factory production equipment can be adjusted accordingly, so as to optimize the product yield rate to the greatest extent and reduce the periodic yield rate reduction caused by changes in the operating status of the operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a flowchart of a management method for a digital twin smart factory.

[0050] Figure 2 The present invention is a flowchart of the output grading and scheduling judgment steps in a management method of a digital twin smart factory.

[0051] Figure 3It is a block diagram of the composition of a management system for a digital twin intelligent factory.

[0052] Figure 4 It is a block diagram of the composition of an equipment risk judgment module in a management system for a digital twin intelligent factory. Specific implementation manners

[0053] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] The following describes in detail the specific implementation manners of the present invention with reference to specific embodiments.

[0055] As Figure 1 described, a management method for a digital twin intelligent factory provided by an embodiment of the present invention includes the following steps:

[0056] S10. Obtain real-time monitoring data of a sensing group, and establish a digital twin factory mirroring the real world based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product inspection data;

[0057] S20. Set an operator as an associated object to perform traceability association on products and production equipment, so as to obtain a product information chain corresponding to the product. The product inspection data is used to represent the deviation data between the product and the target good product;

[0058] S30. Split a plurality of equal-duration evaluation periods within the operation cycle of the operator, count the output quantity and good product rate of the operator in each evaluation period, and obtain production capacity record data corresponding to the operator;

[0059] S40. Calculate the change trend of the good product rate within the evaluation period based on the production capacity record data of the array history, and obtain the peak period and valley period of the good product rate of the operator within the operation cycle. The change trend is used to represent the change of the production concentration of the operator;

[0060] S50. Match the peak periods and valley periods of multiple operators to perform scheduling grouping on the operators, and establish a pipeline equipment control signal based on the peak and valley periods corresponding to the scheduling grouping;

[0061] S60. Respond to the pipeline control signal to control the output beat of the equipment at different times, and correspondingly generate a dispatch signal to dispatch the operators in the scheduling grouping to the pipeline with the corresponding output beat.

[0062] In this embodiment, a management method for a digital twin intelligent factory is presented. By adopting the digital twin method, the digitization of the factory's actual situation is realized to evaluate and schedule the factory equipment, production, and operating personnel in the cloud. Specifically, here, through the periodic evaluation of the operating personnel, based on the qualified product rate of the operating personnel during different time periods of the cycle, the operating input status of the operating personnel in different time periods is judged. Furthermore, the operating personnel with similar operating inertia can be grouped together, and the production habits of the factory production equipment can be adjusted accordingly to optimize the product qualified rate to the greatest extent and reduce the phased reduction of the qualified product rate caused by the change of the operating status of the operating personnel. In the prior art, during the factory production process, even in a modern intelligent factory, some processes may involve manual participation in assembly and other supply and demand. Limited by the working status in different time periods, the backlog of long-term work fatigue, and the required working rate, etc., all will cause problems with the qualified product rate of the produced products to varying degrees. And most of the factories currently adopted are also simple assembly line worker matching models without special human-machine association. Therefore, the problem of uneven qualified product rates will occur in different stages. The solution feature of this embodiment to solve this technical problem lies in: monitoring the factory in a twin manner, associating and binding the products, equipment, operating personnel, and related detection data during the production process. Therefore, when the quality inspection-related equipment program obtains the quality inspection data, it can judge the participating equipment and operating personnel. Based on this, through continuous monitoring for one cycle, the operating input status of the operating personnel in different time periods can be judged by the change of the qualified product rate of the products processed by the operating personnel in different time periods. For example, the operating status of operator A is poor in the first hour in the morning, and the qualified product rate is low, but then the qualified product rate increases. However, after continuous operation for 5 hours until after 3 pm, the qualified product rate begins to decrease again (i.e., the peak or trough period of the corresponding qualified product rate), which may indicate that the accumulated work fatigue has reached a certain value and begins to affect the operation efficiency and quality. Therefore, users with basically overlapping same-state time periods can be grouped together to work together. In this way, the equipment on the production line can be scheduled according to this feature. Appropriately increase the production rhythm of the products during the period with a high qualified product rate to ensure that the qualified product rate will not decrease, while during the period with a low qualified product rate, reduce the production rhythm of the products, which can relieve the work pressure of the operating personnel to a certain extent, enable them to take a light rest to relieve fatigue and achieve the purpose of recovery. Through such a scheduling management method, while ensuring the output as much as possible, the overall qualified product rate of the products can be greatly optimized, and the output structure can be optimized.

[0063] As Figure 2 shown, as another preferred embodiment of the present invention, it further includes an output staging and scheduling judgment step:

[0064] S71. Calculate the average output of an operator within a unit evaluation period during the operation cycle. Based on the average output, several operators are divided into different efficiency groups. When performing scheduling grouping, the operators are within the same efficiency group.

[0065] S72. Calculate the trend of the historical qualified product rate of an operator in multiple evaluation periods during the operation cycle. If the trend of the qualified product rate shows a decline in more than a threshold proportion of evaluation periods, trigger a scheduling request.

[0066] S73. Respond to the scheduling request, perform a downward scheduling of the efficiency group of the operator, and further monitor the operation of the operator.

[0067] In this embodiment, the output management structure is further optimized by the method of output grading. In the previous embodiment, grouping was performed according to the change period of the qualified product rate. However, for different operators, their basic operation efficiencies are different. Therefore, they cannot be simply mixed in the same group. For example, for Operator K and Operator L, their average time outputs are 50 and 30 respectively. Obviously, if Operator L and Operator K are in the same group, Operator L will surely not be able to keep up with the output, which will lead to a significant reduction in the qualified product rate. Therefore, here, operators are grouped according to the average output of the operators, and judged by the overall change trend of the qualified product rate of the operators within the cycle. If an operator can no longer keep up with the production of the group, scheduling is performed to re-group and allocate them.

[0068] As another preferred embodiment of the present invention, it further includes a device risk judgment step, specifically including:

[0069] When a scheduling request is triggered, calculate the trend of the historical qualified product rate of multiple operators on the current production line in multiple evaluation periods during the operation cycle to obtain an array of operation trend data.

[0070] Statistically analyze the operation trend data of multiple operators on the current production line. If, when the average output of the production line remains unchanged, multiple pieces of the operation trend data all show a downward trend, generate a device risk notification, and reduce and adjust the output priority of the current production line. Based on the adjusted output priority, perform re-scheduling of the operators. The output priority corresponds to the efficiency group and is respectively used to define the device side and the personnel side.

[0071] In this embodiment, based on the management method of the previous embodiment, taking the pipeline equipment as the associated object, when the output efficiency of the same pipeline equipment remains unchanged and the yield rates of multiple operators on the same production line all decrease synchronously, it indicates that there may be certain problems with the pipeline equipment, resulting in certain quality problems with the produced products. At this time, what needs to be adjusted is the equipment side rather than the personnel side. At this time, in order to reduce the output of defective products, it is necessary to reduce the output priority of the current production line and reduce the output volume.

[0072] As another preferred embodiment of the present invention, it further includes a scheduling control step based on the movement trend of personnel, specifically including:

[0073] Real-time obtain the monitoring data of the operator to judge the corresponding movement status. If the movement status of the operator is characterized by leaving the production line, a collaborative scheduling instruction is triggered;

[0074] Conduct continuous operation monitoring on the operator. If the continuous operation duration of the operator reaches the preset buffer duration and the output efficiency of the operator decreases, a collaborative scheduling instruction is triggered;

[0075] Respond to the collaborative scheduling instruction to control the output beat of the pipeline equipment corresponding to the operator.

[0076] In this embodiment, a device scheduling scheme based on the movement trend of personnel is supplemented. In daily production, it is inevitable that operators need to go to the toilet, drink water, etc. to meet physiological needs, so there will be situations where they leave the production line. In the prior art, in most cases, the personnel in the same work station need to supplement for them, which will cause the output of the personnel in the same work station to increase forcedly, and may lead to a decrease in the yield rate. Therefore, it is not an optimal solution. For factories that pay more attention to the yield rate, the collaborative scheduling scheme in this embodiment can be adopted to dynamically adjust the output beat of the production line according to the movement trend of personnel and ensure the yield rate of the output.

[0077] As another preferred embodiment of the present invention, it further includes a deviation evaluation step, specifically including:

[0078] Obtain the corresponding deviation data based on the historical product detection data, and count the historical deviation data of the operator to obtain the deviation preference corresponding to the operator. The deviation preference is used to characterize the operation habit characteristics of the operator;

[0079] Real-time record the product detection data, and count based on several product detection data within the current operation cycle to obtain the cycle deviation preference of the operator within the current operation cycle. According to the matching judgment between the cycle deviation preference and the deviation preference, if the judgment result indicates that the matching difference exceeds the rated threshold, a device risk notification is generated and fed back.

[0080] In this embodiment, the judgment process for the deviation of product detection results is supplemented. For the same operator, their work habits are basically consistent within a certain period of time. Therefore, under normal conditions, when they produce production assembly errors, the overall characteristics are also similar. For example, in assembly, if over-assembly is set as a negative value and under-assembly is set as a positive value, most of the data of the same operator's bias should fluctuate around a certain positive or negative value. Therefore, when the detected error of the product significantly deviates from this characteristic value, it indicates that there may be an error in the equipment and further detection and judgment are required. By this means, risks can be discovered and eliminated as early as possible.

[0081] As Figure 3 shown, the present invention also provides a management system for a digital twin smart factory, which includes:

[0082] A data synchronization module 100, configured to obtain real-time monitoring data of the sensing group, and establish a digital twin factory mirroring the real world based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product detection data;

[0083] A data association module 200, configured to set the operator as an associated object to perform traceability association on the product and production equipment, so as to obtain a product information chain corresponding to the product. The product detection data is used to represent the deviation data between the product and the target good product;

[0084] An operation statistics module 300, configured to split a plurality of equal-duration evaluation periods within the operation cycle of the operator, and count the output and good product rate of the operator in each evaluation period, so as to obtain production capacity record data corresponding to the operator;

[0085] A focus evaluation module 400, configured to calculate the change trend of the good product rate within the evaluation period based on the array history of the production capacity record data, and obtain the peak period and trough period of the good product rate of the operator within the operation cycle. The change trend is used to represent the change of the operator's production focus;

[0086] A matching grouping module 500, configured to match the peak periods and trough periods of multiple operators, so as to schedule and group the operators, and establish a pipeline equipment control signal based on the peak and trough periods corresponding to the scheduling grouping;

[0087] A scheduling management module 600, configured to respond to the pipeline control signal to control the output beat of the equipment at different times, and correspondingly generate a dispatching signal to dispatch the operators in the scheduling grouping to the pipeline with the corresponding output beat.

[0088] As another preferred embodiment of the present invention, it further includes an output hierarchical scheduling module, including:

[0089] An efficiency division unit is used to calculate the average output quantity of an operator within a unit evaluation period during an operation cycle, and divide several operators into different efficiency groups based on the average output quantity. When performing scheduling grouping, the operators are within the same efficiency group;

[0090] A trend evaluation unit is used to calculate the trend of the historical good product rate of an operator in multiple evaluation periods during an operation cycle. If the good product rate trend of the operator shows a downward trend in several evaluation periods exceeding the threshold ratio, a scheduling request is triggered;

[0091] A scheduling allocation unit is used to respond to the scheduling request, perform a downward scheduling of the efficiency group of the operator, and further monitor the operation of the operator.

[0092] As Figure 4 shown, as another preferred embodiment of the present invention, it further includes an equipment risk judgment module, specifically including:

[0093] A risk judgment trigger unit 810 is used to, when a scheduling request is triggered, calculate the trend of the historical good product rate of multiple operators on the current production line in multiple evaluation periods during an operation cycle to obtain an array of operation trend data;

[0094] A risk determination management unit 820 is used to statistically analyze the operation trend data of multiple operators on the current production line. If, under the condition that the average output quantity of the production line remains unchanged, multiple pieces of the operation trend data all show a downward trend, an equipment risk notification is generated, and the output priority of the current production line is reduced, and re-scheduling of the operators is performed based on the adjusted output priority. The output priority corresponds to the efficiency group and is respectively used to define the equipment side and the personnel side.

[0095] As another preferred embodiment of the present invention, it further includes a personnel collaborative scheduling module, specifically including:

[0096] A movement evaluation unit is used to obtain the monitoring data of an operator in real time to judge the corresponding movement state. If the movement state of the operator is characterized by leaving the production line, a collaborative scheduling instruction is triggered;

[0097] A tendency evaluation unit is used to continuously monitor the operation of the operator. If the continuous operation duration of the operator reaches a preset buffer duration and the output efficiency of the operator decreases, a collaborative scheduling instruction is triggered;

[0098] An equipment control unit is used to respond to the collaborative scheduling instruction to control the output beat of the production line equipment corresponding to the operator.

[0099] As another preferred embodiment of the present invention, it further includes a deviation evaluation module, specifically including:

[0100] A deviation characterization unit, configured to obtain corresponding deviation data based on the historical product detection data, and statistically analyze the historical deviation data of the operator to obtain the deviation preference corresponding to the operator, where the deviation preference is used to characterize the operation habit characteristics of the operator;

[0101] A deviation assistance determination unit, configured to record product detection data in real time, and statistically analyze several product detection data within the current operation cycle to obtain the cycle deviation preference of the operator within the current operation cycle, and perform a matching judgment based on the cycle deviation preference and the deviation preference. If the judgment result indicates that the matching difference exceeds the rated threshold, a device risk notification is generated and fed back.

[0102] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0103] After considering the specification and the disclosure of the embodiments, those skilled in the art will readily conceive of other embodiments of the present disclosure. The present application is intended to cover any variations, uses, or adaptations of the present disclosure, which follow the general principles of the present disclosure and include well-known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0104] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A management method for a digital twin intelligent factory, characterized in that, Including: Obtain the real-time monitoring data of the sensing group, and establish a digital twin factory corresponding to the real mirror based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product inspection data; Set the operator as an associated object to trace and associate the product and production equipment, so as to obtain a product information chain corresponding to the product. The product inspection data is used to characterize the deviation data between the product and the target good product; Split several equal-length evaluation cycles within the operator's operation cycle, count the output quantity and good product rate of the operator in each evaluation cycle, and obtain the production capacity record data corresponding to the operator; Calculate the change trend of the good product rate within the evaluation cycle based on the production capacity record data of the array history, and obtain the peak period and trough period of the good product rate of the operator within the operation cycle. The change trend is used to characterize the change of the operator's production concentration; Match the peak periods and trough periods of multiple operators to schedule and group the operators, and establish a pipeline equipment control signal based on the peak and trough periods corresponding to the scheduling group; Respond to the pipeline equipment control signal to control the output beat of the equipment at different times, and correspondingly generate a dispatch signal to dispatch the operators in the scheduling group to the pipeline with the corresponding output beat; It also includes output grading and scheduling judgment steps: Calculate the average output quantity of the operator per unit evaluation cycle within the operation cycle, and divide several operators into different efficiency groups based on the average output quantity. When performing scheduling grouping, the operators are within the same efficiency group; Calculate the trend of the historical good product rate of the operator in multiple evaluation cycles within the operation cycle. If the good product rate trend of the operator shows a downward trend in several evaluation cycles exceeding the threshold ratio, trigger a scheduling request; Respond to the scheduling request, perform a downward scheduling of the efficiency group of the operator, and further monitor the operation of the operator; It also includes an equipment risk judgment step, specifically including: When a scheduling request is triggered, calculate the trend of the historical good product rate of multiple operators on the current pipeline in multiple evaluation cycles within the operation cycle to obtain an array of operation trend data; Statistically analyze the operation trend data of multiple operators on the current pipeline. If, without a change in the average output quantity of the pipeline, multiple operation trend data show a downward trend, generate an equipment risk notification, reduce the output priority of the current pipeline, and perform operator rescheduling based on the adjusted output priority. The output priority corresponds to the efficiency group and is used to define the equipment side and the personnel side respectively.

2. The management method of a digital twin intelligent factory according to claim 1, characterized in that It also includes a scheduling control step based on personnel movement, specifically including: Obtain the monitoring data of the operator in real time to judge the corresponding movement status. If the movement status of the operator indicates leaving the pipeline, trigger a collaborative scheduling instruction; Continuously monitor the operation of the operator. If the continuous operation time of the operator reaches a preset buffer time and the output efficiency of the operator decreases, trigger a collaborative scheduling instruction; In response to the collaborative scheduling instruction, control the output rhythm of the pipeline equipment corresponding to the operator.

3. The management method of a digital twin intelligent factory according to claim 2, wherein, It also includes a deviation evaluation step, specifically including: Obtain corresponding deviation data based on the historical product detection data, and statistically analyze the historical deviation data of the operator to obtain the deviation preference corresponding to the operator, and the deviation preference is used to characterize the operation habit characteristics of the operator; Record the product detection data in real time, and statistically analyze several product detection data within the current operation cycle to obtain the cycle deviation preference of the operator within the current operation cycle. According to the matching judgment between the cycle deviation preference and the deviation preference, if the judgment result indicates that the matching difference exceeds the rated threshold, generate and feedback an equipment risk notice.

4. A management system for a digital twin intelligent factory, characterized in that, Includes: A data synchronization module, which is used to obtain the real-time monitoring data of the sensing group, and establish a digital twin factory mirroring the reality based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product detection data; A data association module, which is used to set the operator as the associated object to trace and associate the product and production equipment, so as to obtain the product information chain corresponding to the product, and the product detection data is used to characterize the deviation data between the product and the target good product; An operation statistics module, which is used to split several equal-duration evaluation cycles within the operation cycle of the operator, and statistically analyze the output quantity and the good product rate of the operator in each evaluation cycle to obtain the production capacity record data corresponding to the operator; A focus evaluation module, which is used to calculate the change trend of the good product rate within the evaluation cycle based on the array of historical production capacity record data, and obtain the peak period and valley period of the good product rate of the operator within the operation cycle. The change trend is used to characterize the change of the production focus of the operator; A matching grouping module, which is used to match the peak period and valley period of multiple operators to schedule and group the operators, and establish a pipeline equipment control signal based on the peak and valley periods corresponding to the scheduling group; A scheduling management module, which is used to respond to the pipeline equipment control signal to control the output rhythm of the equipment at different times, and correspondingly generate a dispatch signal to dispatch the operators in the scheduling group to the pipeline with the corresponding output rhythm; It also includes an output hierarchical scheduling module, including: An efficiency division unit, which is used to calculate the average output quantity of the operator per unit evaluation cycle within the operation cycle, and divide several operators into different efficiency groups based on the average output quantity. When performing scheduling grouping, the operators are within the same efficiency group; A trend evaluation unit, which is used to calculate the trend of the historical good product rate of the operator in multiple evaluation cycles within the operation cycle. If the good product rate trend of the operator shows a downward trend in more than the threshold proportion of evaluation cycles, trigger a scheduling request; A scheduling allocation unit, which is used to respond to the scheduling request, perform a downward scheduling of the efficiency group of the operator, and further monitor the operation of the operator; It also includes an equipment risk judgment module, specifically including: A risk judgment trigger unit, which is used to calculate the trend of the historical good product rates of multiple evaluation cycles within the operation cycle of multiple operators on the current production line when a scheduling request is triggered, and obtain array operation trend data; A risk determination management unit, which is used to statistically analyze the operation trend data of multiple operators on the current production line. If, under the condition that the average output of the production line remains unchanged, all the operation trend data show a downward trend, it generates a device risk notification, reduces and adjusts the output priority of the current production line, and performs operator rescheduling based on the adjusted output priority. The output priority corresponds to the efficiency group and is used to define the device side and the personnel side respectively.

5. The management system of a digital twin smart factory according to claim 4, characterized in that, It further includes a personnel collaborative scheduling module, specifically including: A movement evaluation unit, which is used to obtain the monitoring data of the operator in real time to judge the corresponding movement state. If the movement state of the operator is characterized as leaving the production line, it triggers a collaborative scheduling instruction; A tendency evaluation unit, which is used to continuously monitor the operation of the operator. If the continuous operation duration of the operator reaches a preset buffer duration and the output efficiency of the operator decreases, it triggers a collaborative scheduling instruction; A device control unit, which is used to respond to the collaborative scheduling instruction to control the output beat of the production line equipment corresponding to the operator.

6. The management system of a digital twin intelligent factory according to claim 5, wherein, It further includes a deviation evaluation module, specifically including: A deviation characterization unit, which is used to obtain the corresponding deviation data based on the historical product detection data, statistically analyze the historical deviation data of the operator, and obtain the deviation preference corresponding to the operator. The deviation preference is used to characterize the operation habit characteristics of the operator; A deviation auxiliary determination unit, which is used to record the product detection data in real time, statistically analyze several product detection data within the current operation cycle, obtain the cycle deviation preference of the operator within the current operation cycle, and perform a matching judgment based on the cycle deviation preference and the deviation preference. If the judgment result shows that the matching difference exceeds the rated threshold, it generates a device risk notification and gives feedback.

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