Management method and system for digital twin smart factory

Abstract

The invention relates to the related field of smart factory management, discloses a management method and system for a digital twin smart factory, and aims to realize the digitalization of the actual condition of the factory by adopting a digital twin mode so as to carry out the evaluation and scheduling management of factory equipment, production and operating personnel at a cloud end. According to the method, the operators are periodically evaluated, judgment is carried out based on the yield of the operators in different time periods of the period, and the operation input states of the operators in different time periods are known, so that the operators with similar operation inertia can be jointly grouped; and the production habits of the factory production equipment are correspondingly scheduled and adjusted, so that the product yield is optimized to the maximum extent, and the periodical yield reduction caused by the change of the operation state of the operator is reduced.

Description

Technical Field

[0001] The present invention relates to fields related to smart factory management, and specifically to a management method and system for a digital twin smart factory. Background Art

[0002] In the factory production process, even modern smart factories will involve manual work in some processes, including different processes such as assembly and processing. Manual work is limited by working conditions in different time periods, accumulated stress from long-term work, and required working speed, etc., which will lead to different degrees of changes in output yield due to changes in working conditions.

[0003] Most factories now use a simple assembly line worker matching model without special human-machine interaction. After basic output efficiency allocation, the production line has a fixed output and is manually adapted to the production line. This will lead to uneven yield rates at different stages, and in long-term operations, the decline in the status of operators may even lead to a significant reduction in yield rates, ultimately affecting 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 smart factory to solve the problems raised in the above-mentioned background technology.

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

[0006] A management method for a digital twin smart factory, comprising:

[0007] Acquire real-time monitoring data of the sensor group, and establish a digital twin factory that is a mirror image of reality based on the real-time monitoring data, wherein the real-time monitoring data includes equipment operation data, operator status data, and product inspection data;

[0008] The operator is set as an associated object to trace the product and the production equipment to obtain the product information chain corresponding to the product, and the product test data is used to characterize the deviation data between the product and the target good product;

[0009] The operation cycle of the operator is divided into several evaluation cycles of equal length, and the output and yield rate of the operator in each evaluation cycle are counted to 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 production capacity record data of the array history, obtain the peak period and trough period of the yield rate of the operator within the operation period, and the change trend is used to characterize the change of the production concentration of the operator;

[0011] Match the peak and trough periods of multiple operators to schedule the operators in groups, and establish assembly line equipment control signals based on the peak and trough periods corresponding to the scheduling groups;

[0012] Respond to the pipeline control signal to control the output rhythm of the equipment in different time periods, and generate a dispatch signal accordingly to dispatch the operators in the scheduling group to the pipeline with the corresponding output rhythm.

[0013] As a further solution of the present invention, it also includes output grading and scheduling judgment steps:

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

[0015] Perform trend calculation on the historical yield rate of the operator in multiple evaluation cycles within the operation cycle. If the yield rate trend of the operator in several evaluation cycles exceeding the threshold percentage all shows a downward trend, a scheduling request is triggered;

[0016] In response to the scheduling request, the operating personnel are scheduled downward according to their efficiency group, and the operation of the operating personnel is further monitored.

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

[0018] When a scheduling request is triggered, the trend calculation of the historical yield rate of multiple evaluation cycles within the operation cycle of multiple operators of the current assembly line is performed to obtain the array operation trend data;

[0019] Statistics are collected on the operation trend data of multiple operators on the current assembly line. If the average output of the assembly line does not change, but multiple operation trend data show a downward trend, an equipment risk notification is generated, and the output priority of the current assembly line is adjusted to be lowered. Operators are rescheduled 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.

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

[0021] Acquire 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, trigger the coordinated scheduling instruction;

[0022] Continuous operation monitoring is performed on the operator. If the operator's continuous operation time reaches a preset buffer time and the operator's output efficiency decreases, a collaborative scheduling instruction is triggered;

[0023] Respond to the collaborative scheduling instructions to control the output rhythm of the corresponding assembly line equipment by the operators.

[0024] As a further solution of the present invention: it also includes a deviation assessment step, specifically including:

[0025] Acquire corresponding deviation data based on the historical product inspection data, and 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;

[0026] Record product inspection data in real time, and make statistics based on several product inspection data in the current operation cycle, obtain the operator's cycle deviation preference in the current operation cycle, and make a matching judgment based on the cycle deviation preference and the deviation preference. If the judgment result is characterized by the matching difference exceeding the rated threshold, generate equipment risk notification and feedback.

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

[0028] A data synchronization module is used to obtain real-time monitoring data of the sensor group and establish a digital twin factory that is a mirror image of reality based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product inspection data.

[0029] A data association module is used to set the operator as an association object to trace the product and the production equipment to obtain the product information chain corresponding to the product, and the product inspection data is used to characterize the deviation data between the product and the target good product;

[0030] The operation statistics module is used to divide the operation cycle of the operator into several evaluation cycles of equal length, count the output and yield rate of the operator in each evaluation cycle, and obtain the production capacity record data corresponding to the operator;

[0031] A focus evaluation module, used to calculate the change trend of the yield rate within the evaluation period based on the production capacity record data of the array history, and obtain the peak period and trough period of the yield rate of the operator within the operation period, and the change trend is used to characterize the change of the production focus of the operator;

[0032] A matching grouping module is used to match the peak time periods and trough time periods of multiple operators to schedule the operators and establish a control signal for the assembly line equipment based on the peak and trough time periods corresponding to the scheduling groups;

[0033] The scheduling management module is used to respond to the pipeline control signal to control the output rhythm of the equipment in different time periods, and to generate a dispatch signal to dispatch the operators in the scheduling group to the pipeline with the corresponding output rhythm.

[0034] As a further solution of the present invention: it also includes an output stage scheduling module, including:

[0035] An efficiency division unit is used to calculate the average output of operators per unit evaluation cycle within the operation cycle, and divide several operators into different efficiency groups based on the average output. When performing scheduling grouping, the operators are in the same efficiency group;

[0036] A trend evaluation unit is used to calculate the trend of the historical yield rate of the operator in multiple evaluation cycles within the operation cycle, and if the yield rate trend of the operator in several evaluation cycles exceeding the threshold percentage all shows a downward trend, a scheduling request is triggered;

[0037] The scheduling allocation unit is used to respond to the scheduling request, perform downward scheduling of the efficiency group of the operating personnel, and further perform operation monitoring on the operating personnel.

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

[0039] The risk judgment trigger unit is used to calculate the trend of the historical yield rate of multiple evaluation cycles within the operation cycle of multiple operators of the current assembly line when a scheduling request is triggered, and obtain the operation trend data of the array;

[0040] The risk assessment management unit is used to collect statistics on the operation trend data of multiple operators on the current assembly line. If the average output of the assembly line does not change, but multiple operation trend data show a downward trend, an equipment risk notification is generated, and the output priority of the current assembly line is lowered and adjusted. Operators are rescheduled 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 also 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 3This is a block diagram of the management system of a digital twin smart factory.

[0052] Figure 4 This is a block diagram of the composition of the equipment risk judgment module in the management system of a digital twin smart factory. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0054] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0055] like Figure 1 A management method for a digital twin smart factory provided in one embodiment of the present invention includes the following steps:

[0056] S10, acquiring real-time monitoring data of the sensor group, and establishing a digital twin factory that is a mirror image of reality based on the real-time monitoring data, wherein the real-time monitoring data includes equipment operation data, operator status data, and product inspection data;

[0057] S20, setting the operator as an associated object to trace the product and the production equipment to obtain a product information chain corresponding to the product, wherein the product inspection data is used to characterize the deviation data between the product and the target good product;

[0058] S30, dividing the operation cycle of the operator into several evaluation cycles of equal duration, counting the output and yield rate of the operator in each evaluation cycle, and obtaining production capacity record data corresponding to the operator;

[0059] S40, calculating the change trend of the yield rate within the evaluation period based on the production capacity record data of the array history, obtaining the peak period and the trough period of the yield rate of the operator within the operation period, and the change trend is used to characterize the change of the production concentration of the operator;

[0060] S50, matching the peak time periods and the trough time periods of a plurality of operators to schedule the operators into groups, and establishing a control signal for the assembly line equipment based on the peak time periods and the trough time periods corresponding to the scheduling groups;

[0061] S60, responding to the pipeline control signal to control the output rhythm of the equipment in different time periods, and correspondingly generating a dispatch signal to dispatch the operators in the scheduling group to the pipeline with the corresponding output rhythm.

[0062] In this embodiment, a management method for a digital twin smart factory is provided. The digital twin method is used to realize the digitization of the actual factory situation, so as to evaluate and schedule the factory equipment, production and operators in the cloud. Specifically, the operators are evaluated periodically, and the yield rate of the operators in different time periods of the cycle is judged to understand the operation input status of the operators in different time periods. Then, the operators with similar operation inertia can be grouped together, and the production habits of the factory production equipment can be adjusted accordingly to optimize the product yield rate to the greatest extent and reduce the impact caused by the change of the operation status of the operators. The yield rate is reduced periodically; in the prior art, in the factory production process, even a modern smart factory may involve manual participation in assembly and other supply and demand in some processes, and manual labor is limited by working conditions in different time periods, long-term work pressure, and required working speed, which will lead to different degrees of yield rate problems in the output products. Most of the factories now use a simple assembly line worker matching mode without special human-machine association, which will lead to uneven yield rates at different stages; the characteristic point of the solution to this technical problem in this embodiment is: twin monitoring of the factory, comparing the products and equipment in the production process, The operators and related inspection data are associated and bound, so when the quality inspection-related equipment program obtains the quality inspection data, it can determine the participating equipment and operators. Based on this, through a cycle of continuous monitoring, the operator's work input status in different time periods can be determined by the changes in the yield rate of the products processed by the operators in different time periods. For example, operator A's work status is poor in the first hour in the morning, and the yield rate is low, but then the yield rate increases. However, after working continuously for 5 hours until 3 pm, the yield rate begins to decrease again (that is, the corresponding peak period or trough period of the yield rate), which may indicate that his work fatigue has accumulated to When the time interval reaches a certain value, it begins to affect the work efficiency and quality. Therefore, some users with basically overlapping time periods in the same status can be grouped to work together. In this way, the equipment of the production line can be scheduled according to this feature. In the time period with higher yield rate, the output rhythm of the product can be appropriately increased to ensure that the yield rate will not decrease. In the time period with lower yield rate, the output rhythm of the product can be reduced. This can reduce the work pressure of the operators to a certain extent, allowing them to take a light rest to relieve fatigue and achieve the purpose of recovery. Through such scheduling management method, while ensuring the output as much as possible, the overall yield rate of the product can be greatly optimized and the output structure can be optimized.

[0063] like Figure 2 As shown, as another preferred embodiment of the present invention, it also includes output grading and scheduling judgment steps:

[0064] S71, calculating the average output of the operators per evaluation period within the operation cycle, and dividing the operators into different efficiency groups based on the average output. When performing scheduling grouping, the operators are in the same efficiency group;

[0065] S72, performing trend calculation on the historical yield rate of the operator in multiple evaluation cycles within the operation cycle, and triggering a scheduling request if the yield rate trend of the operator in several evaluation cycles exceeding the threshold percentage all shows a downward trend;

[0066] S73, in response to the scheduling request, performing downward scheduling of the operating personnel to an efficiency group, and further performing operation monitoring on the operating personnel.

[0067] In this embodiment, the output management structure is further optimized by means of output stratification. In the previous embodiment, groups are made according to the time period of yield rate change. However, different operators have different basic operating efficiencies and therefore cannot be simply mixed in the same group. For example, the average time outputs of personnel K and personnel L are 50 and 30 respectively. Obviously, if personnel L and personnel K are in the same group, they will not be able to keep up with the output, which will lead to a significant reduction in the yield rate. Therefore, the operators are grouped here according to their output mean and judged by the overall change trend of the yield rate of the operators within the cycle. If the operators can no longer keep up with the production of the group, they will be scheduled and regrouped.

[0068] As another preferred embodiment of the present invention, it also includes an equipment risk judgment step, specifically including:

[0069] When a scheduling request is triggered, the trend calculation of the historical yield rate of multiple evaluation cycles within the operation cycle of multiple operators of the current assembly line is performed to obtain the array operation trend data;

[0070] Statistics are collected on the operation trend data of multiple operators on the current assembly line. If the average output of the assembly line does not change, but multiple operation trend data show a downward trend, an equipment risk notification is generated, and the output priority of the current assembly line is adjusted to be lowered. Operators are rescheduled 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.

[0071] In this embodiment, based on the management method of the previous embodiment, the assembly line equipment is taken as the associated object. When the output efficiency of the same assembly line equipment remains unchanged, the yield rates of multiple operators on the same assembly line are simultaneously reduced, which means that there may be certain problems with the assembly line equipment, resulting in certain quality problems in the output products. At this time, it is the equipment side rather than the personnel side that needs to be adjusted. At this time, in order to reduce the output of defective products, the output priority of the current assembly line should be lowered to reduce the output.

[0072] As another preferred embodiment of the present invention, a scheduling control step based on personnel movement is also included, specifically including:

[0073] Acquire 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, trigger the coordinated scheduling instruction;

[0074] Continuous operation monitoring is performed on the operator. If the operator's continuous operation time reaches a preset buffer time and the operator's output efficiency decreases, a collaborative scheduling instruction is triggered;

[0075] Respond to the collaborative scheduling instructions to control the output rhythm of the corresponding assembly line equipment by the operators.

[0076] In this embodiment, an equipment scheduling scheme based on personnel movements is supplemented. In daily production, workers will inevitably need to go to the toilet, drink water, etc. to meet physiological needs, so they will leave the production line. In the prior art, this situation mostly requires personnel at the same workstation to supplement them, which will force the output of workers at the same workstation to increase, which may lead to a decrease in yield rate. Therefore, it is not an excellent solution. For factories that pay more attention to yield rate, the collaborative scheduling scheme in this embodiment can be adopted to dynamically adjust the output rhythm of the production line according to personnel movements to ensure the output yield rate.

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

[0078] Acquire corresponding deviation data based on the historical product inspection data, and 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;

[0079] Record product inspection data in real time, and make statistics based on several product inspection data in the current operation cycle, obtain the operator's cycle deviation preference in the current operation cycle, and make a matching judgment based on the cycle deviation preference and the deviation preference. If the judgment result is characterized by the matching difference exceeding the rated threshold, generate equipment risk notification and feedback.

[0080] In this embodiment, the judgment process of the deviation of the product inspection results is supplemented. For the same operator, his working habits are basically the same within a certain period of time. Therefore, under normal circumstances, when he produces production assembly errors, the overall characteristics are also similar. For example, in assembly, if over-assembly is set to a negative value and under-assembly is set to a positive value, most of the biased data of the same operator should fluctuate around a certain positive or negative value. Therefore, when the error of the detected product obviously deviates from this characteristic value, it indicates that there may be an error in the equipment and further detection and judgment are needed. In this way, risks can be discovered and eliminated as early as possible.

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

[0082] The data synchronization module 100 is used to obtain the real-time monitoring data of the sensor group and establish a digital twin factory that is a mirror image of reality based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data and product inspection data.

[0083] The data association module 200 is used to set the operator as an association object to perform traceability association on the product and the production equipment to obtain the product information chain corresponding to the product, and the product inspection data is used to characterize the deviation data between the product and the target good product;

[0084] The operation statistics module 300 is used to divide the operation cycle of the operator into several evaluation cycles of equal length, count the output and yield rate of the operator in each evaluation cycle, and obtain the production capacity record data corresponding to the operator;

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

[0086] A matching grouping module 500 is used to match the peak time periods and trough time periods of multiple operators to schedule the operators in groups, and to establish a control signal for the assembly line equipment based on the peak and trough time periods corresponding to the scheduling groups;

[0087] The scheduling management module 600 is used to respond to the pipeline control signal to control the output rhythm of the equipment at different time periods, and to generate a dispatch signal to dispatch the operators in the scheduling group to the pipeline with the corresponding output rhythm.

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

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

[0090] A trend evaluation unit is used to calculate the trend of the historical yield rate of the operator in multiple evaluation cycles within the operation cycle, and if the yield rate trend of the operator in several evaluation cycles exceeding the threshold percentage all shows a downward trend, a scheduling request is triggered;

[0091] The scheduling allocation unit is used to respond to the scheduling request, perform downward scheduling of the efficiency group of the operating personnel, and further perform operation monitoring on the operating personnel.

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

[0093] The risk judgment trigger unit 810 is used to calculate the trend of the historical yield rate of multiple evaluation cycles within the operation cycle of multiple operators of the current assembly line when a scheduling request is triggered, and obtain the operation trend data of the array;

[0094] The risk assessment management unit 820 is used to collect statistics on the operation trend data of multiple operators on the current assembly line. If the average output of the assembly line does not change, but multiple operation trend data show a downward trend, an equipment risk notification is generated, and the output priority of the current assembly line is adjusted to be lowered. Operators are rescheduled 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.

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

[0096] 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;

[0097] 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;

[0098] 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.

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

[0100] 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;

[0101] 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.

[0102] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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 embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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).

[0103] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This 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 common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

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

Claims

1. A management method for a digital twin smart factory, characterized in that: Include: Acquire real-time monitoring data of the sensor group, and establish a digital twin factory that is a mirror image of reality based on the real-time monitoring data, wherein the real-time monitoring data includes equipment operation data, operator status data, and product inspection data; The operator is set as an associated object to trace the product and the production equipment to obtain the product information chain corresponding to the product, and the product test data is used to characterize the deviation data between the product and the target good product; The operation cycle of the operator is divided into several evaluation cycles of equal length, and the output and yield rate of the operator in each evaluation cycle are counted to obtain the production capacity record data corresponding to the operator; Calculate the change trend of the yield rate within the evaluation period based on the production capacity record data of the array history, obtain the peak period and trough period of the yield rate of the operator within the operation period, and the change trend is used to characterize the change of the production concentration of the operator; Match the peak and trough periods of multiple operators to schedule the operators in groups, and establish assembly line equipment control signals based on the peak and trough periods corresponding to the scheduling groups; Respond to the pipeline control signal to control the output rhythm of the equipment in different time periods, and generate a dispatch signal accordingly to dispatch the operators in the scheduling group to the pipeline with the corresponding output rhythm.

2. According to a management method of a digital twin smart factory according to claim 1, it is characterized in that: It also includes output grading and scheduling judgment steps: Calculate the average output of operators per evaluation period within the operation cycle, and divide several operators into different efficiency groups based on the average output. When scheduling groups, the operators are in the same efficiency group; Calculate the trend of the historical yield rate of the operator in multiple evaluation cycles within the operation cycle. If the yield rate trend of the operator in several evaluation cycles exceeding the threshold percentage all shows a downward trend, trigger a scheduling request; In response to the scheduling request, the operating personnel are scheduled downward according to their efficiency group, and the operation of the operating personnel is further monitored.

3. A management method for a digital twin smart factory according to claim 2, characterized in that: It also includes equipment risk assessment steps, including: When a scheduling request is triggered, the trend calculation of the historical yield rate of multiple evaluation cycles within the operation cycle of multiple operators of the current assembly line is performed to obtain the array operation trend data; Statistics are collected on the operation trend data of multiple operators on the current assembly line. If the average output of the assembly line does not change, but multiple operation trend data show a downward trend, an equipment risk notification is generated, and the output priority of the current assembly line is adjusted to be lowered. Operators are rescheduled 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.

4. According to claim 1, a digital twin smart factory management method is characterized in that: It also includes dispatch control steps based on personnel movements, including: Acquire 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, trigger the coordinated scheduling instruction; Continuous operation monitoring is performed on the operator. If the operator's continuous operation time reaches a preset buffer time and the operator's output efficiency decreases, a collaborative scheduling instruction is triggered; Respond to the collaborative scheduling instructions to control the output rhythm of the corresponding assembly line equipment by the operators.

5. A management method for a digital twin smart factory according to claim 4, characterized in that: It also includes a deviation assessment step, which includes: Acquire corresponding deviation data based on the historical product inspection data, and 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; Record product inspection data in real time, and make statistics based on several product inspection data in the current operation cycle, obtain the operator's cycle deviation preference in the current operation cycle, and make a matching judgment based on the cycle deviation preference and the deviation preference. If the judgment result is characterized by the matching difference exceeding the rated threshold, generate equipment risk notification and feedback.

6. A management system for a digital twin smart factory, characterized in that: Include: A data synchronization module is used to obtain real-time monitoring data of the sensor group and establish a digital twin factory that is a mirror image of reality based on the real-time monitoring data. The real-time monitoring data includes equipment operation data, operator status data, and product inspection data. A data association module is used to set the operator as an association object to trace the product and the production equipment to obtain the product information chain corresponding to the product, and the product inspection data is used to characterize the deviation data between the product and the target good product; The operation statistics module is used to divide the operation cycle of the operator into several evaluation cycles of equal length, count the output and yield rate of the operator in each evaluation cycle, and obtain the production capacity record data corresponding to the operator; A focus evaluation module, used to calculate the change trend of the yield rate within the evaluation period based on the production capacity record data of the array history, and obtain the peak period and trough period of the yield rate of the operator within the operation period, and the change trend is used to characterize the change of the production focus of the operator; A matching grouping module is used to match the peak time periods and trough time periods of multiple operators to schedule the operators and establish a control signal for the assembly line equipment based on the peak and trough time periods corresponding to the scheduling groups; The scheduling management module is used to respond to the pipeline control signal to control the output rhythm of the equipment in different time periods, and to generate a dispatch signal to dispatch the operators in the scheduling group to the pipeline with the corresponding output rhythm.

7. A digital twin smart factory management system according to claim 6, characterized in that: It also includes output stage scheduling module, including: An efficiency division unit is used to calculate the average output of operators per unit evaluation cycle within the operation cycle, and divide several operators into different efficiency groups based on the average output. When performing scheduling grouping, the operators are in the same efficiency group; A trend evaluation unit is used to calculate the trend of the historical yield rate of the operator in multiple evaluation cycles within the operation cycle, and if the yield rate trend of the operator in several evaluation cycles exceeding the threshold percentage all shows a downward trend, a scheduling request is triggered; The scheduling allocation unit is used to respond to the scheduling request, perform downward scheduling of the efficiency group of the operating personnel, and further perform operation monitoring on the operating personnel.

8. A digital twin smart factory management system according to claim 7, characterized in that: It also includes an equipment risk judgment module, which specifically includes: The risk judgment trigger unit is used to calculate the trend of the historical yield rate of multiple evaluation cycles within the operation cycle of multiple operators of the current assembly line when a scheduling request is triggered, and obtain the operation trend data of the array; The risk assessment management unit is used to collect statistics on the operation trend data of multiple operators on the current assembly line. If the average output of the assembly line does not change, but multiple operation trend data show a downward trend, an equipment risk notification is generated, and the output priority of the current assembly line is lowered and adjusted. Operators are rescheduled 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.

9. A digital twin smart factory management system according to claim 6, characterized in that: It also includes a personnel collaborative scheduling module, including: 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; 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; 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.

10. A digital twin smart factory management system according to claim 9, characterized in that: It also includes a deviation assessment module, which includes: 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; 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.

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