A method, system, device and storage medium for adaptive adjustment of smart factories

Abstract

The present application relates to an adaptive adjustment method, system, device and storage medium of a smart factory, the method comprising obtaining the production status data and corresponding process control data of each process node, integrating the production status data and process control data, obtaining the business event data of the current production product, analyzing the business imbalance characteristics of the business event data and the integrated production process related data, obtaining the business imbalance characteristic data, obtaining the product production data of the current production line to build an equipment adaptive model, and inputting the aging data of the equipment parts in the current production process into the equipment adaptive model for optimization, and then adaptively adjusting the production rhythm of the current imbalance process node and subsequent nodes to obtain the adaptive control data of the current product production. The present application has the effect of improving the adaptive adjustment capability in the product manufacturing process and improving the manufacturing intelligence of the factory.

Description

Technical Field

[0001] The present invention relates to the technical field of smart factories, and in particular to an adaptive adjustment method, system, device and storage medium for smart factories. Background Art

[0002] At present, with the in-depth integration of manufacturing and information technology, compared with traditional manufacturing, replacing manual manufacturing control management with information technology has become an important development direction for current factories.

[0003] The existing factory production system structure often manufactures products through the combination of multiple process segments, and divides the product process rhythm by experienced operators to form the production process of each product. However, when controlling large-scale production lines, the difference in process speed of different products can easily cause a lag between materials, manual adjustments and actual process switching, resulting in an imbalance in the manufacturing process rhythm of the entire factory. At this time, the existing factory adjustment method is often to send corresponding experienced personnel to conduct on-site analysis, and adjust the process in combination with the workers' rich practical experience, so that subsequent processes can proceed normally. However, the process sequence, personnel transfer and material allocation in the product production process are all closely linked. Especially when facing the simultaneous manufacturing of large quantities of products, the manual adjustment of process imbalance will consume a lot of human resources and affect the normal progress of other related processes. There is further room for optimization in the adjustment of process imbalance in the factory production process. Summary of the invention

[0004] In order to improve the adaptive adjustment capability in the product manufacturing process and enhance the manufacturing intelligence of the factory, the present application provides an adaptive adjustment method, system, device and storage medium for a smart factory.

[0005] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0006] An adaptive adjustment method for a smart factory, comprising:

[0007] Acquire the production status data and corresponding process control data of each process node in the product manufacturing process, and perform data integration processing on the production status data and process control data that are associated with each other;

[0008] Acquire business event data of the current production product, and perform business imbalance feature analysis on the business event data and the integrated production process related data to obtain business imbalance feature data;

[0009] Acquire the product production data of the current production line to build an equipment adaptive model, and input the aging data of equipment parts in the current production process into the equipment adaptive model to optimize the performance parameters of the current production equipment;

[0010] According to the optimized production equipment optimization parameters and the business imbalance characteristic data, the production rhythm of the current imbalance process node and subsequent nodes is adaptively adjusted to obtain adaptive control data for the current product production.

[0011] By adopting the above technical solution, the production status data and process control data that are related to each other in the product manufacturing process are integrated, which is convenient for joint analysis of the actual control data of each process node, and combined with the product business event data, the business imbalance feature analysis of the production process related data is carried out to find out the business imbalance process characteristics, which is convenient for targeted adjustment of the business imbalance process characteristics, and the product production data of the entire production line are combined to build a model, and the equipment performance parameters are optimized in combination with the aging data of equipment parts, and the parameters of the equipment with aging parts are optimized to reduce the impact of the aging of parts on the overall production progress. In addition, the production equipment optimization parameters and business imbalance feature data are combined to adaptively adjust the production rhythm of the current imbalance process node and subsequent nodes, and the imbalance process node is prepared and compensated through other process nodes, so that the overall production rhythm of the production line can be kept stable, thereby improving the adaptive adjustment capability in the product manufacturing process and further improving the manufacturing intelligence of the factory.

[0012] In a preferred example, the present application may be further configured as follows: the business event data of the currently produced product is obtained, and the business imbalance feature analysis is performed on the business event data and the integrated production process related data to obtain the business imbalance feature data, specifically including:

[0013] Obtain the business production time, material configuration rate, and personnel transfer rate of the current production products, connect the data according to the business type, and obtain the associated business event data;

[0014] Calculate the adjacent output ratios of adjacent product lines and the preparation output ratios of adjacent preparation cycles respectively, and jointly analyze the current business production quality based on the adjacent output ratios and the preparation output ratios;

[0015] The current business production quality is jointly compared with the corresponding production process related data, and the business imbalance characteristics are analyzed based on the comparison results to obtain business imbalance characteristic data.

[0016] In a preferred example, the present application can be further configured as follows: the business production time, material configuration rate and personnel mobilization rate of the current production product are obtained, and data is connected according to the business type to obtain the associated business event data, and further includes:

[0017] The data association relationship between business event data is expressed by formula (1):

[0018]

[0019] Among them, D 产 represents the business event parameter, n represents the total number of process nodes of the business, t 产 Indicates the production time of the product corresponding to the business, V 物 Represents the material delivery rate of each process node, V 人 represents the manual call rate of each process node, μ n Represents the production efficiency coefficient of each process node, Δt (n-m) Indicates the transit time between adjacent process nodes.

[0020] By adopting the above technical solution, data connection is carried out in combination with the business correlation between the business production time, materials and personnel allocation rate of the current production line, and the business events of the current production products are controlled as a whole. In combination with the adjacent output ratio and the periodic preparation output ratio, a joint analysis of the current business production quality is carried out. The analysis accuracy of the business production quality is improved through multi-dimensional parameter joint analysis, and the production quality is jointly compared with the related production process related data. The business imbalance feature data of the entire product production process is selected to improve the analysis accuracy of the business imbalance feature data.

[0021] In a preferred example, the present application can be further configured as follows: the product production data of the current production line is obtained to build an equipment adaptive model, and the aging data of equipment parts in the current production process is input into the equipment adaptive model to optimize the performance parameters of the current production equipment, specifically including:

[0022] Acquire product production data and corresponding machine usage data of the current production line, perform data training based on the product production data and the machine usage data, and build an equipment adaptive model for the current production line;

[0023] In the current manufacturing process, the operating efficiency change of each machine equipment is calculated, and the aging analysis of equipment parts is performed according to the operating efficiency change to obtain the aging data of equipment parts;

[0024] Inputting the aging data of equipment parts in the current production line when preparing products into the equipment adaptive model to perform adaptive analysis of the aging data;

[0025] The control data of the aged equipment is independently adjusted according to the analysis results, and the performance parameters of the current production equipment are optimized according to the equipment adjustment results.

[0026] By adopting the above technical solution, an equipment adaptive model is constructed according to the data training results of the product production data and machine usage data of the current production line, so as to facilitate intuitive control of the equipment of the current production line, and perform aging analysis on the aging data of equipment parts through the equipment adaptive model. The current production data with aging conditions is compared and analyzed with the historical production data through the model to improve the accuracy of the aging analysis, and the control parameters of the equipment with aging parts are independently adjusted according to the analysis results. The performance parameters of the current production equipment are optimized according to the adjustment results to compensate for the impact of part aging on production efficiency and improve the stability of product preparation.

[0027] In a preferred example, the present application can be further configured as follows: the production rhythm of the current imbalanced process node and subsequent nodes is adaptively adjusted according to the optimized production equipment optimization parameters and the business imbalance characteristic data to obtain adaptive control data for the current product production, specifically including:

[0028] According to the optimized production equipment optimization parameters and the business imbalance characteristic data, imbalance positioning is performed on the imbalance process nodes of the current production line;

[0029] Retrieving the preparation imbalance data of the located imbalance process node and the production impact data on the subsequent nodes, and analyzing the preparation deviation of the imbalance process node according to the preparation imbalance data and the production impact data;

[0030] Adjust the equipment parameters of the current production line according to the preparation deviation analysis results and the production equipment optimization parameters, and control the production rhythm of the current unbalanced process node and subsequent nodes according to the equipment parameter adjustment results;

[0031] The real-time production efficiency after the production rhythm is adjusted is obtained, and the real-time production efficiency is compared with the adjacent production efficiency of the adjacent production line. According to the comparison result, the current unbalanced process node is feedback-adjusted to obtain adaptive control data.

[0032] By adopting the above technical scheme, the imbalanced process nodes are located through the production equipment optimization parameters and the business imbalance characteristic parameters, and the excess preparation imbalance data of the located imbalanced process nodes and the production impact data of the subsequent nodes are retrieved, so as to collaboratively analyze the direct and indirect impacts caused by the preparation imbalance, improve the accuracy of the preparation deviation analysis of the imbalanced process nodes, and adjust the equipment parameters of the current production line according to the preparation deviation analysis results and the production equipment optimization parameters, so as to control the production rhythm of the current imbalanced process node and the subsequent nodes, compensate for the preparation imbalance error through production rhythm adjustment, and compare the real-time production efficiency after the production rhythm adjustment with the adjacent production efficiency of the adjacent production lines, so as to judge whether the adjustment of the imbalanced process node has achieved the preset purpose according to the comparison results, and adjust the current imbalanced process node according to the comparison results, so as to improve the adaptive control capability of the imbalanced process node through feedback adjustment.

[0033] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0034] An adaptive adjustment system for a smart factory, comprising:

[0035] A data acquisition module is used to acquire the production status data and corresponding process control data of each process node in the product manufacturing process, and perform data integration processing on the production status data and process control data that are associated with each other;

[0036] A data analysis module is used to obtain business event data of the current production product, and to perform business imbalance feature analysis on the business event data and the integrated production process related data to obtain business imbalance feature data;

[0037] A data optimization module is used to obtain the product production data of the current production line to build an equipment adaptive model, and input the aging data of equipment parts in the current production process into the equipment adaptive model to optimize the performance parameters of the current production equipment;

[0038] The data adjustment module is used to adaptively adjust the production rhythm of the current imbalance process node and subsequent nodes according to the optimized production equipment optimization parameters and the business imbalance characteristic data, so as to obtain adaptive control data for the current product production.

[0039] By adopting the above technical solution, the production status data and process control data that are related to each other in the product manufacturing process are integrated, which is convenient for joint analysis of the actual control data of each process node, and combined with the product business event data, the business imbalance feature analysis of the production process related data is carried out to find out the business imbalance process characteristics, which is convenient for targeted adjustment of the business imbalance process characteristics, and the product production data of the entire production line are combined to build a model, and the equipment performance parameters are optimized in combination with the aging data of equipment parts, and the parameters of the equipment with aging parts are optimized to reduce the impact of the aging of parts on the overall production progress. In addition, the production equipment optimization parameters and business imbalance feature data are combined to adaptively adjust the production rhythm of the current imbalance process node and subsequent nodes, and the imbalance process node is prepared and compensated through other process nodes, so that the overall production rhythm of the production line can be kept stable, thereby improving the adaptive adjustment capability in the product manufacturing process and further improving the manufacturing intelligence of the factory.

[0040] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0041] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned adaptive adjustment method of the smart factory when executing the computer program.

[0042] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned adaptive adjustment method of the smart factory.

[0044] In summary, the present application includes at least one of the following beneficial technical effects:

[0045] 1. Integrate the production status data and process control data that are related to each other in the product manufacturing process to facilitate the joint analysis of the actual control data of each process node, and combine the product business event data to analyze the business imbalance characteristics of the production process related data, find out the business imbalance process characteristics, and facilitate targeted adjustment of the business imbalance process characteristics. Combine the product production data of the entire production line to build a model, combine the equipment parts aging data to optimize the equipment performance parameters, optimize the parameters of the equipment with aging parts, and reduce the impact of parts aging on the overall production progress. Combine the production equipment optimization parameters and business imbalance feature data to adaptively adjust the production rhythm of the current imbalance process node and subsequent nodes, and prepare compensation for the imbalance process node through other process nodes, so that the overall production rhythm of the production line can be kept stable, thereby improving the adaptive adjustment ability in the product manufacturing process and further improving the manufacturing intelligence of the factory;

[0046] 2. Combine the business correlation between the business production time, materials and personnel allocation rate of the current production line to connect data, conduct overall control of the business events of the current production products, and conduct joint analysis of the current business production quality in combination with the adjacent output ratio and the periodic preparation output ratio. Improve the analysis accuracy of business production quality through multi-dimensional parameter joint analysis, conduct joint comparison between production quality and related production process related data, and select the business imbalance feature data of the entire product production process to improve the analysis accuracy of business imbalance feature data;

[0047] 3. Construct an equipment adaptive model based on the data training results of the product production data and machine usage data of the current production line to facilitate intuitive control of the equipment of the current production line, and perform aging analysis on the aging data of equipment parts through the equipment adaptive model. The current production data with aging conditions is compared and analyzed with the historical production data through the model to improve the accuracy of aging analysis, and the control parameters of the equipment with aging parts are independently adjusted according to the analysis results. The performance parameters of the current production equipment are optimized according to the adjustment results to compensate for the impact of component aging on production efficiency and improve the stability of product preparation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart for implementing an adaptive adjustment method for a smart factory in this embodiment.

[0049] Figure 2 This is a flowchart for implementing step S20 of the adaptive adjustment method for a smart factory in this embodiment.

[0050] Figure 3 This is a flowchart for implementing step S30 of the adaptive adjustment method for a smart factory in this embodiment.

[0051] Figure 4 This is a flowchart for implementing step S40 of the adaptive adjustment method for a smart factory in this embodiment.

[0052] Figure 5 This is a structural block diagram of an adaptive adjustment system for a smart factory in this embodiment.

[0053] Figure 6 It is a schematic diagram of the internal structure of a computer device used to implement the adaptive adjustment method of a smart factory. DETAILED DESCRIPTION

[0054] The present application is further described in detail below in conjunction with the accompanying drawings.

[0055] In one embodiment, if Figure 1 As shown, the present application discloses an adaptive adjustment method for a smart factory, which specifically includes the following steps:

[0056] S10: Acquire the production status data and corresponding process control data of each process node in the product manufacturing process, and perform data integration processing on the production status data and process control data that are related to each other.

[0057] Specifically, process nodes are allocated according to the order of product preparation and assembly, and the product preparation or product assembly status of each process node is obtained, such as preparation completed or incomplete, assembly completed or incomplete, etc., so as to obtain the production status data of each process node, and the equipment control data of each process node is retrieved through the equipment's main control console, and the data is subjected to data format conversion, data cleaning and other operations. Then, with the process node as the connection point, the production status data and process control data of each process node are integrated to obtain integrated data that are related to each other.

[0058] S20: Acquire business event data of the current production product, perform business imbalance feature analysis on the business event data and the integrated production process related data, and obtain business imbalance feature data.

[0059] Specifically, Figure 2 As shown, step S20 specifically includes:

[0060] S201: Obtain the business production time, material configuration rate and personnel mobilization rate of the current production product, connect the data according to the business type, and obtain the associated business event data.

[0061] Specifically, the time taken for the current production of products from material call to component molding, assembly, and delivery of finished products to the production line is the business production time. The material configuration rate and the personnel mobilization rate are the rates at which the preparation materials and assembly personnel are deployed to the corresponding production locations. The business production time, material configuration rate, and personnel mobilization rate of the production products are connected according to the business type. The data association relationship between business event data is expressed by formula (1):

[0062]

[0063] Among them, D 产 represents the business event parameter, n represents the total number of process nodes of the business, t 产 Indicates the production time of the product corresponding to the business, V 物 Represents the material delivery rate of each process node, V 人 represents the manual call rate of each process node, μ n Represents the production efficiency coefficient of each process node, Δt (n-m) Indicates the transit time between adjacent process nodes.

[0064] S202: Calculate adjacent output ratios of adjacent product lines and preparation output ratios of adjacent preparation cycles respectively, and jointly analyze the current business production quality based on the adjacent output ratios and the preparation output ratios.

[0065] Specifically, multiple product lines that prepare the same product are compared with each other. When the same materials are supplied, the product preparation time per unit time of each product line is collected, and the ratio of the time taken by adjacent product lines to prepare a single product is taken as the adjacent output ratio. Similarly, adjacent preparation cycles are two adjacent days or two adjacent weeks, etc. The cycle is set according to actual needs, and the preparation quantities within adjacent preparation cycles of the current production line are counted. The ratio of the preparation quantities of adjacent preparation cycles is the preparation-output ratio. When the error between adjacent output ratios or the error of the preparation-output ratios of adjacent preparation cycles exceeds 0.5, it is determined that there is a deviation in the current business production quality, and it is necessary to adjust the production rhythm of the production line with a smaller production quantity in combination with the production line with a larger production quantity.

[0066] S203: jointly compare the current business production quality with the corresponding production process related data, and perform business imbalance feature analysis based on the comparison result to obtain business imbalance feature data.

[0067] Specifically, the current business production quality is combined with the production process related data corresponding to the production line, and the production business completion degree of each production process node is independently analyzed to find out the process nodes with a slower production rhythm, and the preparation data of the process nodes are compared to analyze the reasons for the slow production rhythm to perform business imbalance feature analysis, so as to obtain the business imbalance feature data of the current process node. The business imbalance features include equipment aging at the process node, inappropriate personnel allocation, or the personnel assembly rate cannot keep up with the raw material delivery rate, and many other situations.

[0068] S30: Obtain product production data of the current production line to build an equipment adaptive model, and input aging data of equipment parts in the current production process into the equipment adaptive model to optimize the performance parameters of the current production equipment.

[0069] Specifically, Figure 3 As shown, S30 specifically includes:

[0070] S301: Obtain product production data and corresponding machine usage data of the current production line, perform data training based on the product production data and machine usage data, and build an equipment adaptive model for the current production line.

[0071] Specifically, the product production data of the current production line is obtained according to the process steps of product production. For example, whether the current production line is used for plastic film or component assembly of product parts, and the machine usage data of the corresponding production line is obtained according to the preparation order, such as whether the machine is in working condition, the working time of the machine and the change of heat dissipation capacity, etc. With the order of product production as the connection point, machine learning training is performed on the product production data and machine usage data, and an equipment adaptation model of the current production line is constructed according to the training results.

[0072] S302: In the current production process, the operating efficiency change of each machine equipment is calculated, and the aging analysis of equipment parts is performed according to the operating efficiency change to obtain the aging data of equipment parts.

[0073] Specifically, in the current production and manufacturing process, the change in the operating efficiency of each machine equipment is calculated within a unit of time. For example, the single-day operating efficiency of each machine equipment on the production line is calculated using days as the time unit. When there is a change in adjacent operating efficiencies, it means that the current production line has equipment aging or failure. The operating efficiency in this embodiment is calculated by calculating the daily product production quantity. When the operating efficiency change exceeds a preset threshold, it means that the equipment parts are aging or failing, which affects the normal production of the current production line. The process nodes with the same preparation function are compared to select the process nodes with larger preparation efficiency deviations, and the working data of the parts of the machine equipment corresponding to the process node are used as the parts aging data.

[0074] S303: Inputting the aging data of equipment parts in the current production line when preparing products into the equipment adaptive model to perform adaptive analysis of the aging data.

[0075] Specifically, the aging data of equipment parts and components when the current production line is preparing products is input into the equipment adaptive model. The aging data of equipment parts and components are compared with the operating data of parts and components in normal production of adjacent production lines through the equipment adaptive model. Based on the analysis results, an adaptive analysis result of the current part aging is obtained, which is used to make suggestions for adjusting the equipment control parameters for the aged parts.

[0076] S304: independently adjusting the aged equipment control data according to the analysis results, and optimizing the performance parameters of the current production equipment according to the equipment adjustment results.

[0077] Specifically, the equipment control data corresponding to the aging parts are independently adjusted according to the analysis results, such as increasing the power of the equipment to speed up the operation of the equipment, or controlling the spare equipment to replace the aging equipment, so that the performance parameters of the current production equipment can always meet the production needs.

[0078] S40: According to the optimized production equipment optimization parameters and business imbalance characteristic data, the production rhythm of the current imbalance process node and subsequent nodes is adaptively adjusted to obtain adaptive control data for the current product production.

[0079] Specifically, Figure 4 As shown, S40 specifically includes:

[0080] S401: Imbalance positioning of imbalanced process nodes of the current production line is performed according to optimized production equipment optimization parameters and business imbalance characteristic data.

[0081] Specifically, data fitting is performed on the production equipment optimization parameters and the business imbalance characteristic data, and by comparing the data of the same type of production lines, the equipment control data corresponding to the business imbalance characteristics are found, and the optimization indicators are compared between the equipment control data and the production equipment optimization parameters, so as to obtain the imbalance process node positioning of the current production line.

[0082] S402: Retrieve the preparation imbalance data of the located imbalance process node and the production impact data on the subsequent nodes, and analyze the preparation deviation of the imbalance process node according to the preparation imbalance data and the production impact data.

[0083] Specifically, the preparation imbalance data of the located imbalanced process node is called, including the preparation speed deviation, preparation quality deviation, preparation qualification rate deviation, etc., as well as the gap between the production preparation data of the subsequent process nodes and the preparation data under normal conditions after the imbalanced state occurs at the current process node. All deviation data of the current process node and the subsequent nodes are classified to obtain the direct and indirect preparation deviation data of the imbalanced process node.

[0084] S403: adjusting the equipment parameters of the current production line according to the preparation deviation analysis results and the production equipment optimization parameters, and controlling the production rhythm of the current unbalanced process node and subsequent nodes according to the equipment parameter adjustment results.

[0085] Specifically, according to the preparation deviation analysis results and the production equipment optimization parameters, the equipment parameters of the current production line, such as operating power, operating speed, or start and stop timing and other control parameters are adjusted, and the production rhythm of the current unbalanced process node and subsequent nodes is accelerated or slowed down according to the adjusted equipment control parameters.

[0086] S404: Obtain the real-time production efficiency after the production rhythm is adjusted, compare the real-time production efficiency with the adjacent production efficiency of the adjacent production line, and perform feedback adjustment on the current unbalanced process node according to the comparison result to obtain adaptive control data.

[0087] Specifically, after the production rhythm is adjusted, the real-time production efficiency after the adjustment is obtained, and the real-time production efficiency is compared with the adjacent production efficiency of the adjacent production line, including the product preparation qualification rate or product preparation quantity in the same unit time, etc., and the current unbalanced process node is feedback-adjusted according to the comparison result until the production efficiency of the current production line and the adjacent production line tends to be the same, so as to obtain the adaptive control data of the current production line.

[0088] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0089] In one embodiment, an adaptive adjustment system for a smart factory is provided, and the adaptive adjustment system for the smart factory corresponds one-to-one to the adaptive adjustment method for the smart factory in the above embodiment. Figure 5 As shown, the adaptive adjustment system of the smart factory includes a data acquisition module, a data analysis module, a data optimization module and a data adjustment module. The detailed description of each functional module is as follows:

[0090] The data acquisition module is used to obtain the production status data and corresponding process control data of each process node in the product manufacturing process, and to perform data integration processing on the production status data and process control data that are related to each other.

[0091] The data analysis module is used to obtain the business event data of the current production products, and to perform business imbalance feature analysis on the business event data and the integrated production process related data to obtain business imbalance feature data.

[0092] The data optimization module is used to obtain the product production data of the current production line to build an equipment adaptive model, and input the aging data of equipment parts in the current production process into the equipment adaptive model to optimize the performance parameters of the current production equipment.

[0093] The data adjustment module is used to adaptively adjust the production rhythm of the current imbalance process node and subsequent nodes according to the optimized production equipment optimization parameters and business imbalance characteristic data, so as to obtain adaptive control data for the current product production.

[0094] Preferably, the data analysis module specifically includes:

[0095] The data association submodule is used to obtain the business production time, material configuration rate and personnel mobilization rate of the current production products, connect the data according to the business type, and obtain the associated business event data.

[0096] The data calculation submodule is used to calculate the adjacent output ratios of adjacent product lines and the preparation output ratios of adjacent preparation cycles respectively, and jointly analyze the current business production quality based on the adjacent output ratios and the preparation output ratios.

[0097] The feature analysis submodule is used to jointly compare the current business production quality with the corresponding production process related data, and perform business imbalance feature analysis based on the comparison results to obtain business imbalance feature data.

[0098] Preferably, the data association submodule further includes:

[0099] The data association relationship between business event data is expressed by formula (1):

[0100]

[0101] Among them, D 产 represents the business event parameter, n represents the total number of process nodes of the business, t 产 Indicates the production time of the product corresponding to the business, V 物 Represents the material delivery rate of each process node, V 人 represents the manual call rate of each process node, μ nRepresents the production efficiency coefficient of each process node, Δt (n-m) Indicates the transit time between adjacent process nodes.

[0102] Preferably, the data optimization module specifically includes:

[0103] The model building submodule is used to obtain the product production data of the current production line and the corresponding machine usage data, perform data training based on the product production data and machine usage data, and build an equipment adaptive model for the current production line.

[0104] The aging analysis submodule is used to calculate the changes in the operating efficiency of each machine equipment in the current production and manufacturing process, perform aging analysis on equipment parts based on the changes in operating efficiency, and obtain aging data on equipment parts.

[0105] The adaptive analysis submodule is used to input the aging data of equipment parts in the current production line when preparing products into the equipment adaptive model to perform adaptive analysis of the aging data.

[0106] The parameter optimization submodule is used to independently adjust the control data of the aged equipment according to the analysis results, and optimize the performance parameters of the current production equipment according to the equipment adjustment results.

[0107] Preferably, the data adjustment module specifically includes:

[0108] The node positioning submodule is used to locate the imbalance process nodes of the current production line according to the optimized production equipment optimization parameters and business imbalance characteristic data.

[0109] The deviation analysis submodule is used to retrieve the preparation imbalance data of the imbalance process node after positioning and the production impact data on the subsequent nodes, and analyze the preparation deviation of the imbalance process node based on the preparation imbalance data and production impact data.

[0110] The rhythm adjustment submodule is used to adjust the equipment parameters of the current production line according to the preparation deviation analysis results and the production equipment optimization parameters, and control the production rhythm of the current unbalanced process node and subsequent nodes according to the equipment parameter adjustment results.

[0111] The feedback regulation submodule is used to obtain the real-time production efficiency after the production rhythm is adjusted, compare the real-time production efficiency with the adjacent production efficiency of the adjacent production line, and perform feedback regulation on the current unbalanced process node according to the comparison result to obtain adaptive control data.

[0112] For the specific definition of the adaptive adjustment system of the smart factory, please refer to the definition of the adaptive adjustment method of the smart factory above, which will not be repeated here. Each module in the adaptive adjustment system of the above-mentioned smart factory can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0113] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 6 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the production line adaptive adjustment data of the factory during the digital transformation process. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for adaptive adjustment of a smart factory is implemented.

[0114] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned adaptive adjustment method of a smart factory are implemented.

[0115] 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 computer program can be stored in a non-volatile computer-readable storage medium. When the computer 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 can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can 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).

[0116] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0117] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An adaptive adjustment method for a smart factory, characterized in that: include: Acquire the production status data and corresponding process control data of each process node in the product manufacturing process, and perform data integration processing on the production status data and process control data that are associated with each other; Acquire business event data of the current production product, and perform business imbalance feature analysis on the business event data and the integrated production process related data to obtain business imbalance feature data; Acquire the product production data of the current production line to build an equipment adaptive model, and input the aging data of equipment parts in the current production process into the equipment adaptive model to optimize the performance parameters of the current production equipment; According to the optimized production equipment optimization parameters and the business imbalance characteristic data, the production rhythm of the current imbalance process node and subsequent nodes is adaptively adjusted to obtain adaptive control data for the current product production; The acquiring of the business event data of the current production product and the analysis of the business imbalance characteristics of the business event data and the integrated production process related data to obtain the business imbalance characteristic data specifically include: Obtain the business production time, material configuration rate, and personnel transfer rate of the current production products, connect the data according to the business type, and obtain the associated business event data; Calculate the adjacent output ratios of adjacent product lines and the preparation output ratios of adjacent preparation cycles respectively, and jointly analyze the current business production quality based on the adjacent output ratios and the preparation output ratios; The current business production quality is jointly compared with the corresponding production process related data, and the business imbalance characteristics are analyzed according to the comparison results to obtain business imbalance characteristic data; The process of obtaining the business production time, material configuration rate and personnel mobilization rate of the current production product, performing data connection according to the business type, and obtaining the associated business event data also includes: The data association relationship between business event data is expressed by formula (1): Among them, D 产 represents the business event parameter, n represents the total number of process nodes of the business, t 产 Indicates the production time of the product corresponding to the business, V 物 Represents the material delivery rate of each process node, V 人 represents the manual call rate of each process node, μ n Represents the production efficiency coefficient of each process node, Δt (n-m) Indicates the transit time between adjacent process nodes; The process of acquiring the product production data of the current production line to construct an equipment adaptive model, and inputting the aging data of equipment parts in the current production process into the equipment adaptive model to optimize the performance parameters of the current production equipment, specifically includes: Acquire product production data and corresponding machine usage data of the current production line, perform data training based on the product production data and the machine usage data, and build an equipment adaptive model for the current production line; In the current manufacturing process, the operating efficiency change of each machine equipment is calculated, and the aging analysis of equipment parts is performed according to the operating efficiency change to obtain the aging data of equipment parts; Inputting the aging data of equipment parts in the current production line when preparing products into the equipment adaptive model to perform adaptive analysis of the aging data; Independently adjust the control data of the aged equipment according to the analysis results, and optimize the performance parameters of the current production equipment according to the equipment adjustment results; Among them, the production rhythm of the current imbalance process node and subsequent nodes is adaptively adjusted according to the optimized production equipment optimization parameters and the business imbalance characteristic data to obtain the adaptive control data of the current product production, specifically including: According to the optimized production equipment optimization parameters and the business imbalance characteristic data, imbalance positioning is performed on the imbalance process nodes of the current production line; Retrieving the preparation imbalance data of the located imbalance process node and the production impact data on the subsequent nodes, and analyzing the preparation deviation of the imbalance process node according to the preparation imbalance data and the production impact data; Adjust the equipment parameters of the current production line according to the preparation deviation analysis results and the production equipment optimization parameters, and control the production rhythm of the current unbalanced process node and subsequent nodes according to the equipment parameter adjustment results; The real-time production efficiency after the production rhythm is adjusted is obtained, and the real-time production efficiency is compared with the adjacent production efficiency of the adjacent production line. According to the comparison result, the current unbalanced process node is feedback-adjusted to obtain adaptive control data.

2. An adaptive adjustment system for a smart factory, characterized in that: include: A data acquisition module is used to acquire the production status data and corresponding process control data of each process node in the product manufacturing process, and perform data integration processing on the production status data and process control data that are associated with each other; A data analysis module is used to obtain business event data of the current production product, and to perform business imbalance feature analysis on the business event data and the integrated production process related data to obtain business imbalance feature data; A data optimization module is used to obtain the product production data of the current production line to build an equipment adaptive model, and input the aging data of equipment parts in the current production process into the equipment adaptive model to optimize the performance parameters of the current production equipment; A data adjustment module is used to adaptively adjust the production rhythm of the current imbalance process node and subsequent nodes according to the optimized production equipment optimization parameters and the business imbalance characteristic data, so as to obtain adaptive control data for the current product production; Wherein, the data analysis module specifically includes: The data association submodule is used to obtain the business production time, material configuration rate and personnel transfer rate of the current production products, connect the data according to the business type, and obtain the associated business event data; A data calculation submodule, used to calculate the adjacent output ratios of adjacent product lines and the preparation output ratios of adjacent preparation cycles, and jointly analyze the current business production quality based on the adjacent output ratios and the preparation output ratios; The feature analysis submodule is used to jointly compare the current business production quality with the corresponding production process related data, and perform business imbalance feature analysis based on the comparison results to obtain business imbalance feature data; Wherein, the data association submodule further includes: The data association relationship between business event data is expressed by formula (1): Among them, D 产 represents the business event parameter, n represents the total number of process nodes of the business, t 产 Indicates the production time of the product corresponding to the business, V 物 Represents the material delivery rate of each process node, V 人 represents the manual call rate of each process node, μ n Represents the production efficiency coefficient of each process node, Δt (n-m) Indicates the transit time between adjacent process nodes; Wherein, the data optimization module specifically includes: A model building submodule is used to obtain product production data of the current production line and corresponding machine usage data, perform data training based on the product production data and the machine usage data, and build an equipment adaptive model for the current production line; The aging analysis submodule is used to calculate the change in the operating efficiency of each machine equipment in the current production and manufacturing process, and perform aging analysis on equipment parts according to the change in the operating efficiency to obtain aging data of equipment parts; The adaptive analysis submodule is used to input the aging data of equipment parts in the current production line when preparing products into the equipment adaptive model to perform adaptive analysis of the aging data; The parameter optimization submodule is used to independently adjust the control data of the aged equipment according to the analysis results, and optimize the performance parameters of the current production equipment according to the equipment adjustment results; Wherein, the data adjustment module specifically includes: A node positioning submodule, used to perform imbalance positioning on the imbalance process node of the current production line according to the optimized production equipment optimization parameters and the business imbalance characteristic data; The deviation analysis submodule is used to retrieve the preparation imbalance data of the imbalance process node after positioning and the production impact data on the subsequent nodes, and analyze the preparation deviation of the imbalance process node according to the preparation imbalance data and the production impact data; The rhythm adjustment submodule is used to adjust the equipment parameters of the current production line according to the preparation deviation analysis results and the production equipment optimization parameters, and control the production rhythm of the current unbalanced process node and subsequent nodes according to the equipment parameter adjustment results; The feedback regulation submodule is used to obtain the real-time production efficiency after the production rhythm is adjusted, compare the real-time production efficiency with the adjacent production efficiency of the adjacent production line, and perform feedback regulation on the current unbalanced process node according to the comparison result to obtain adaptive control data.

3. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the adaptive adjustment method of the smart factory as claimed in claim 1 are implemented.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the adaptive adjustment method of the smart factory as claimed in claim 1 are implemented.

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