CN118011977A - Self-adaptive adjustment method, system, equipment and storage medium for intelligent factory - Google Patents
Self-adaptive adjustment method, system, equipment and storage medium for intelligent factory Download PDFInfo
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Abstract
The application relates to a self-adaptive adjustment method, a system, equipment and a storage medium of an intelligent factory, wherein the method comprises the steps of obtaining production state data and corresponding process control data of each process node, carrying out data integration on the production state data and the process control data, obtaining business event data of a current product, carrying out business imbalance characteristic analysis on the business event data and the integrated production process related data, obtaining business imbalance characteristic data, obtaining product production data of a current production line, constructing a self-adaptive model of equipment, inputting equipment part aging data in the current production manufacturing process into the self-adaptive model of the equipment, optimizing the equipment, and carrying out production rhythm self-adaptive adjustment on the current imbalance process node and a subsequent node to obtain the self-adaptive control data of the current product production. The application has the effects of improving the self-adaptive adjustment capability in the product manufacturing process and improving the manufacturing intelligence of factories.
Description
Technical Field
The invention relates to the technical field of intelligent factories, in particular to a self-adaptive adjustment method, a self-adaptive adjustment system, self-adaptive adjustment equipment and a storage medium of an intelligent factory.
Background
Currently, with the deep combination of manufacturing industry and information technology, the replacement of manual manufacturing control management by information technology has become an important development direction of current factories relative to traditional manufacturing industry.
The existing factory production system structure is used for manufacturing products through combination of multiple working sections, and is used for dividing working procedure beats of the products through experienced operators, so that production procedures of each product are formed, when a large-scale production line is controlled, hysteresis is easily caused between working procedure speed difference of different products and actual working procedure switching of the whole factory, at the moment, the existing factory adjustment mode is used for dispatching corresponding experience personnel to conduct field analysis and carrying out working procedure adjustment through combination of abundant practical operation experiences of the workers, so that subsequent working procedures can be normally carried out, but the process sequence, personnel movement and material distribution in the production process of the products are all round, and particularly when the mass production is carried out simultaneously, a great amount of human resources are consumed by the mode of manually adjusting working procedure imbalance, normal operation of other related working procedures is influenced, and further optimization space exists for working procedure imbalance adjustment in the factory production and manufacturing process.
Disclosure of Invention
In order to improve the self-adaptive adjustment capability in the product manufacturing process and improve the manufacturing intelligence of a factory, the application provides a self-adaptive adjustment method, a self-adaptive adjustment system, self-adaptive adjustment equipment and a storage medium of an intelligent factory.
In a first aspect, the above object of the present application is achieved by the following technical solutions:
An adaptive adjustment method for an intelligent factory, comprising:
Acquiring production state data and corresponding process control data of each process node in the production and manufacturing process of the product, and carrying out data integration processing on the production state data and the process control data which are related to each other;
Acquiring business event data of a current production product, and carrying out business unbalance characteristic analysis on the business event data and integrated production procedure related data to obtain business unbalance characteristic data;
obtaining product production data of a current production line, constructing a device self-adaptive model, inputting ageing data of device parts in the current production and manufacturing process into the device self-adaptive model, and optimizing performance parameters of the current production device;
And carrying out production rhythm self-adaptive adjustment on the current unbalanced procedure node and the subsequent node according to the optimized production equipment optimization parameters and the service unbalanced characteristic data to obtain self-adaptive control data of current product production.
By adopting the technical scheme, the production state data and the process control data which are related to each other in the production and manufacturing process of the product are integrated, so that the joint analysis of the actual control data of each process node is facilitated, the service unbalance characteristic analysis is performed on the production process related data by combining the product service event data, the service unbalance characteristic is found out, the targeted adjustment of the service unbalance characteristic is facilitated, the model construction is performed by combining the product production data of the whole production line, the equipment performance parameter optimization is performed by combining the equipment part aging data, the parameter optimization is performed on equipment with the part aging, the influence of the part aging on the whole production progress is reduced, the production rhythm self-adaptive adjustment is performed on the current unbalance process node and the subsequent node by combining the production equipment optimization parameter and the service unbalance characteristic data, and the preparation compensation is performed on the unbalance process node by other process nodes, so that the whole production of the production line can be kept stable, the self-adaptive adjustment capability in the production process is improved, and the manufacturing intelligence of a factory is further improved.
The present application may be further configured in a preferred example to: the step of obtaining the business event data of the current production product, and carrying out business unbalance characteristic analysis on the business event data and the integrated production procedure associated data to obtain business unbalance characteristic data, which specifically comprises the following steps:
Acquiring service production time, material allocation rate and personnel movement rate of the current production product, and carrying out data connection according to the service type to obtain associated service event data;
respectively calculating adjacent yield ratios of adjacent product lines and preparation yield ratios of adjacent preparation periods, and jointly analyzing the current service production quality according to the adjacent yield ratios and the preparation yield ratios;
And carrying out combination comparison on the current service production quality and corresponding production procedure associated data, and carrying out service unbalance characteristic analysis according to the comparison result to obtain service unbalance characteristic data.
The present application may be further configured in a preferred example to: the method comprises the steps of obtaining the service production time, the material configuration rate and the personnel mobilization rate of the current production product, carrying out data connection according to the service type to obtain the associated service event data, and further comprising:
The data association relationship between the business event data is represented by a formula (1):
wherein D Production of represents a service event parameter, n represents a total number of process nodes of a service, t Production of represents a production time of a product corresponding to the service, V Article (B) represents a material conveying rate of each process node, V Human body represents a manual calling rate of each process node, μ n represents a production efficiency coefficient of each process node, and Δt (n-m) represents a transit time of an adjacent process node.
By adopting the technical scheme, the data connection is carried out by combining the service relevance among the service production time, the materials and the personnel configuration rate of the current production line, the service event of the current production product is integrally controlled, the joint analysis of the current service production quality is carried out by combining the adjacent yield ratio and the periodical preparation yield ratio, the analysis accuracy of the service production quality is improved by the multi-dimensional parameter joint analysis, the production quality and the related production procedure related data are combined and compared, the service unbalance characteristic data in the whole production process of the product is selected, and the analysis accuracy of the service unbalance characteristic data is improved.
The present application may be further configured in a preferred example to: the method comprises the steps of obtaining product production data of a current production line, constructing a device self-adaptive model, inputting ageing data of device parts in the current production and manufacturing process into the device self-adaptive model, and optimizing performance parameters of the current production device, and specifically comprises the following steps:
Acquiring product production data and corresponding machine use data of a current production line, performing data training according to the product production data and the machine use data, and constructing a device self-adaptive model of the current production line;
In the current production and manufacturing process, calculating the operation efficiency change of each machine equipment, and carrying out equipment part aging analysis according to the operation efficiency change to obtain equipment part aging data;
The aging data of the equipment parts of the current production line in the process of preparing products are input into the equipment self-adaptive model, and the aging data self-adaptive analysis is carried out;
and (3) independently adjusting the aged equipment control data according to the analysis result, and optimizing the performance parameters of the current production equipment according to the equipment adjustment result.
By adopting the technical scheme, the equipment self-adaptive model is constructed according to the product production data of the current production line and the data training result of the machine use data, visual control is conveniently carried out on equipment of the current production line, ageing analysis is carried out on ageing data of equipment parts through the equipment self-adaptive model, the production data with ageing conditions are compared with historical production data through the model, the accuracy of ageing analysis is improved, the equipment control parameters with ageing of the parts are independently regulated according to the analysis result, the performance parameters of the current production equipment are optimized according to the regulation result, the influence of ageing of the parts on the production efficiency is compensated, and the stability of product preparation is improved.
The present application may be further configured in a preferred example to: and carrying out production rhythm self-adaptive adjustment on the current unbalanced procedure node and the subsequent node according to the optimized production equipment optimization parameters and the service unbalance characteristic data to obtain self-adaptive control data of the current product production, wherein the self-adaptive control data comprises the following specific steps:
according to the optimized production equipment optimization parameters and the service unbalance characteristic data, unbalance positioning is carried out on unbalance procedure nodes of the current production line;
Preparing unbalance data of the positioned unbalance process nodes and production influence data of subsequent nodes are called, and preparing deviation of the unbalance process nodes is analyzed according to the preparing unbalance data and the production influence data;
Adjusting the equipment parameters of the current production line according to the preparation deviation analysis result and the production equipment optimization parameters, and controlling the production rhythms of the current unbalanced process node and the subsequent nodes according to the equipment parameter adjustment result;
And acquiring the real-time production efficiency after the adjustment of the production rhythm, comparing the real-time production efficiency with the adjacent production efficiency of the adjacent production line, and carrying out feedback adjustment on the current unbalanced process node according to the comparison result to obtain self-adaptive control data.
By adopting the technical scheme, the unbalance process nodes are positioned through the production equipment optimization parameters and the service unbalance characteristic parameters, the unbalance data prepared by the unbalance process nodes after the positioning and the production influence data of the subsequent nodes are adjusted, so that the direct influence and the indirect influence caused by the preparation unbalance are subjected to collaborative analysis, the preparation deviation analysis accuracy of the unbalance process nodes is improved, the equipment parameters of the current production line are adjusted according to the preparation deviation analysis result and the production equipment optimization parameters, the production rhythm of the current unbalance process nodes and the subsequent nodes is controlled, the preparation unbalance error is compensated through the production rhythm adjustment, the real-time production efficiency after the production rhythm adjustment and the adjacent production efficiency of the adjacent production line are compared, whether the adjustment of the unbalance process nodes reaches the preset aim is judged according to the comparison result, the current unbalance process nodes are adjusted according to the comparison result feedback, and the self-adaptive control capacity of the unbalance process nodes is improved through the feedback adjustment.
In a second aspect, the above object of the present application is achieved by the following technical solutions:
an adaptive adjustment system for an intelligent plant, comprising:
the data acquisition module is used for acquiring production state data of each process node and corresponding process control data in the production and manufacturing process of the product, and carrying out data integration processing on the production state data and the process control data which are related to each other;
The data analysis module is used for acquiring service event data of the current production product, and carrying out service unbalance characteristic analysis on the service event data and the integrated production procedure associated data to obtain service unbalance characteristic data;
The data optimization module is used for acquiring product production data of the current production line, constructing a device self-adaptive model, inputting ageing data of the device parts in the current production and manufacturing process into the device self-adaptive model, and optimizing performance parameters of the current production device;
And the data adjusting module is used for carrying out production rhythm self-adaptive adjustment on the current unbalance procedure node and the subsequent node according to the optimized production equipment optimization parameters and the service unbalance characteristic data to obtain self-adaptive control data of the current product production.
By adopting the technical scheme, the production state data and the process control data which are related to each other in the production and manufacturing process of the product are integrated, so that the joint analysis of the actual control data of each process node is facilitated, the service unbalance characteristic analysis is performed on the production process related data by combining the product service event data, the service unbalance characteristic is found out, the targeted adjustment of the service unbalance characteristic is facilitated, the model construction is performed by combining the product production data of the whole production line, the equipment performance parameter optimization is performed by combining the equipment part aging data, the parameter optimization is performed on equipment with the part aging, the influence of the part aging on the whole production progress is reduced, the production rhythm self-adaptive adjustment is performed on the current unbalance process node and the subsequent node by combining the production equipment optimization parameter and the service unbalance characteristic data, and the preparation compensation is performed on the unbalance process node by other process nodes, so that the whole production of the production line can be kept stable, the self-adaptive adjustment capability in the production process is improved, and the manufacturing intelligence of a factory is further improved.
In a third aspect, the above object of the present application is achieved by the following technical solutions:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above-mentioned method for adaptive adjustment of a smart factory when the computer program is executed.
In a fourth aspect, the above object of the present application is achieved by the following technical solutions:
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the intelligent factory adaptive tuning method described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method comprises the steps of integrating production state data and process control data which are related to each other in the production and manufacturing process of a product, conveniently carrying out joint analysis on actual control data of each process node, carrying out business unbalance characteristic analysis on the production process related data by combining product business event data, finding out business unbalance process characteristics from the business unbalance process characteristics, conveniently carrying out targeted adjustment on the business unbalance process characteristics, carrying out model construction by combining product production data of the whole production line, carrying out equipment performance parameter optimization by combining equipment part aging data, carrying out parameter optimization on equipment with part aging, reducing the influence of part aging on the whole production progress, carrying out production rhythm self-adaptive adjustment on the current unbalance process node and the subsequent unbalance process node by combining production equipment optimization parameters and the business unbalance characteristic data, and carrying out preparation compensation on the unbalance process node by other process nodes, so that the whole production rhythm of the production line can be kept stable, thereby improving the self-adaptive adjustment capability in the production and the manufacturing intelligence of a factory;
2. The method comprises the steps of carrying out data connection by combining service relativity among service production time, materials and personnel configuration rate of a current production line, carrying out overall control on service events of the current production product, carrying out joint analysis on current service production quality by combining adjacent yield ratio and periodical preparation yield ratio, improving analysis accuracy of the service production quality by multi-dimensional parameter joint analysis, carrying out joint comparison on the production quality and associated production procedure associated data, selecting service unbalance characteristic data in the whole production process of the product, and improving analysis accuracy of the service unbalance characteristic data;
3. The device self-adaptive model is constructed according to the product production data of the current production line and the data training result of the machine use data, visual control is conveniently carried out on the device of the current production line, the aging analysis is carried out on the aging data of the device parts through the device self-adaptive model, the production data with the current aging condition are compared with the historical production data through the model, the accuracy of the aging analysis is improved, the device control parameters with the aging of the parts are independently regulated according to the analysis result, the performance parameters of the current production device are optimized according to the regulation result, the influence of the aging of the parts on the production efficiency is compensated, and the stability of the product preparation is improved.
Drawings
Fig. 1 is a flowchart of an implementation of an adaptive adjustment method of an intelligent factory according to the present embodiment.
Fig. 2 is a flowchart showing an implementation of step S20 of the adaptive adjustment method of the smart factory according to the present embodiment.
Fig. 3 is a flowchart showing an implementation of step S30 of the adaptive adjustment method of the smart factory according to the present embodiment.
Fig. 4 is a flowchart showing an implementation of step S40 of the adaptive adjustment method of the smart factory according to the present embodiment.
Fig. 5 is a block diagram of an adaptive adjustment system for an intelligent factory according to the present embodiment.
Fig. 6 is a schematic diagram of the internal structure of a computer device for implementing the adaptive adjustment method of the smart factory.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
In one embodiment, as shown in fig. 1, the application discloses an adaptive adjustment method for an intelligent factory, which specifically comprises the following steps:
S10: and acquiring production state data and corresponding process control data of each process node in the production and manufacturing process of the product, and carrying out data integration processing on the production state data and the process control data which are related to each other.
Specifically, the process node distribution is performed according to the sequence of product preparation and assembly, and the product preparation or product assembly state of each process node is obtained, for example, the preparation is completed or the preparation is not completed, the assembly is completed or the assembly is not completed, so that the production state data of each process node is obtained, the equipment control data of each process node is called through the master console of the equipment, and after the data are subjected to the operations of data format conversion, data cleaning and the like, the production state data and the process control data of each process node are subjected to data integration by taking the process node as the relevant point, so that the mutually-relevant integration data are obtained.
S20: and acquiring service event data of the current production product, and carrying out service unbalance characteristic analysis on the service event data and the integrated production procedure related data to obtain service unbalance characteristic data.
Specifically, as shown in fig. 2, step S20 specifically includes:
S201: and acquiring the service production time, the material allocation rate and the personnel allocation rate of the current production product, and carrying out data connection according to the service type to obtain the associated service event data.
Specifically, the time from material adjustment to part molding and assembly of the current production product to the time when the finished product is sent out of the production line is service production time, the material allocation rate and personnel allocation rate are the rates of preparing materials and allocating personnel to corresponding production positions respectively, the service production time, the material allocation rate and the personnel allocation rate of the production product are subjected to data connection according to the service type, and the data association relationship between service event data is represented by the formula (1):
wherein D Production of represents a service event parameter, n represents a total number of process nodes of a service, t Production of represents a production time of a product corresponding to the service, V Article (B) represents a material conveying rate of each process node, V Human body represents a manual calling rate of each process node, μ n represents a production efficiency coefficient of each process node, and Δt (n-m) represents a transit time of an adjacent process node.
S202: and respectively calculating the adjacent yield ratio of the adjacent product lines and the preparation yield ratio of the adjacent preparation period, and jointly analyzing the current service production quality according to the adjacent yield ratio and the preparation yield ratio.
Specifically, a plurality of product lines for preparing the same product are compared with each other, under the condition of supplying the same materials, when the product preparation of each product line in unit time is collected, the time ratio of the preparation of a single product by the adjacent product line is taken as the adjacent output ratio, the same reason is that the adjacent preparation period is two adjacent days or two adjacent weeks, and the like, the period is set according to actual needs, the preparation quantity in the adjacent preparation period of the current production line is counted, the ratio of the preparation quantity of the adjacent preparation period is the preparation output ratio, and when the error between the adjacent output ratios or the preparation output ratio error of the adjacent preparation period exceeds 0.5, the deviation of the production quality of the current business is judged, and the production rhythm adjustment is needed to be carried out on the production line with less production quantity by combining the production line with more production quantity.
S203: and carrying out combination comparison on the current service production quality and corresponding production procedure associated data, and carrying out service unbalance characteristic analysis according to the comparison result to obtain service unbalance characteristic data.
Specifically, the current business production quality is combined with production procedure related data corresponding to a production line, the production business completion degree which is responsible for each production procedure node is independently analyzed, so that a procedure node with a slower production rhythm is found out, the reason of the slower production rhythm is analyzed by comparing the preparation data of the procedure node to perform business unbalance characteristic analysis, the business unbalance characteristic data of the current procedure node are obtained, and the business unbalance characteristic comprises various conditions that equipment of the procedure node is aged, personnel configuration is unsuitable, or personnel assembly rate cannot keep up with raw material conveying rate and the like.
S30: and obtaining product production data of the current production line, constructing a device self-adaptive model, inputting ageing data of the device parts in the current production and manufacturing process into the device self-adaptive model, and optimizing the performance parameters of the current production device.
Specifically, as shown in fig. 3, S30 specifically includes:
S301: and acquiring product production data and corresponding machine use data of the current production line, performing data training according to the product production data and the machine use data, and constructing a device self-adaptive model of the current production line.
Specifically, product production data of a current production line are obtained according to the process steps of product production, such as that the current production line is used for molding products or assembling components, machine use data of a corresponding production line are obtained according to the preparation sequence, such as whether a machine is in a working state, the working time length of the machine, the heat radiation capacity change and the like, the product production sequence is used as a correlation point, machine learning training is carried out on the product production data and the machine use data, and a device self-adaptive model of the current production line is constructed according to training results.
S302: in the current production and manufacturing process, the operation efficiency change of each machine device is calculated, and the aging analysis of the device parts is carried out according to the operation efficiency change, so that the aging data of the device parts are obtained.
Specifically, in the current production and manufacturing process, the operation efficiency change of each machine device is calculated in a unit time, for example, a day is taken as a time unit, the single God's will-row efficiency of each machine device on the production line is calculated, when the adjacent operation efficiency change value exists, the condition that the equipment is aged or failed in the current production line is indicated, the operation efficiency in the embodiment is calculated by calculating the daily product production quantity, when the operation efficiency change exceeds a preset threshold value, the condition that the equipment parts are aged or failed and the like is indicated, normal production of the current production line is affected, and the process nodes with larger preparation efficiency deviation are compared through the process nodes with the same preparation function, and the part working data of the machine device corresponding to the process nodes are used as part aging data.
S303: and (3) inputting the aging data of the equipment parts of the current production line in the process of preparing the product into the equipment self-adaptive model, and carrying out self-adaptive analysis on the aging data.
Specifically, ageing data of equipment parts in the process of preparing products in the current production line are input into an equipment self-adaptive model, the ageing data of the equipment parts are compared with the running data of parts normally produced in the adjacent production line through the equipment self-adaptive model, and an adaptive analysis result of ageing of the current parts is obtained according to an analysis result and is used for providing equipment control parameter control adjustment suggestions for the aged parts.
S304: and (3) independently adjusting the aged equipment control data according to the analysis result, and optimizing the performance parameters of the current production equipment according to the equipment adjustment result.
Specifically, the equipment control data corresponding to the aging parts are independently adjusted according to the analysis result, for example, the power of the equipment is quickened to quicken the operation of the equipment, or the standby equipment is controlled to replace the aging equipment, so that the performance parameters of the current production equipment can always meet the production requirements.
S40: and carrying out production rhythm self-adaptive adjustment on the current unbalanced procedure node and the subsequent node according to the optimized production equipment optimization parameters and the service unbalanced characteristic data to obtain self-adaptive control data of current product production.
Specifically, as shown in fig. 4, S40 specifically includes:
s401: and carrying out unbalance positioning on unbalance procedure nodes of the current production line according to the optimized production equipment optimization parameters and the service unbalance characteristic data.
Specifically, data fitting is performed on production equipment optimization parameters and service unbalance characteristic data, equipment control data corresponding to service unbalance characteristics are found through data comparison of the same type of production lines, and optimization index comparison is performed between the equipment control data and the production equipment optimization parameters, so that unbalance procedure node positioning of the current production line is obtained.
S402: and calling the preparation unbalance data of the positioned unbalance process node and the production influence data of the subsequent node, and analyzing the preparation deviation of the unbalance process node according to the preparation unbalance data and the production influence data.
Specifically, the preparation unbalance data of the positioned unbalance process node is called, wherein the preparation unbalance data comprise preparation speed deviation, preparation quality deviation, preparation qualification rate deviation and the like, and after the unbalance state of the current process node occurs, the difference between the production preparation data of the subsequent process node and the preparation data in the normal state is used for classifying all the deviation data of the current process node and the subsequent node, so that the direct preparation deviation data and the indirect preparation deviation data of the unbalance process node are obtained.
S403: and adjusting the equipment parameters of the current production line according to the preparation deviation analysis result and the production equipment optimization parameters, and controlling the production rhythms of the current unbalanced process node and the subsequent nodes according to the equipment parameter adjustment result.
Specifically, according to the preparation deviation analysis result and the production equipment optimization parameters, the equipment parameters of the current production line such as the running power, the running speed or the start-stop time and the like are regulated, and the production rhythms of the current unbalanced process node and the subsequent nodes are regulated and controlled in a faster or slower mode according to the regulated equipment control parameters.
S404: and acquiring the real-time production efficiency after the adjustment of the production rhythm, comparing the real-time production efficiency with the adjacent production efficiency of the adjacent production line, and carrying out feedback adjustment on the current unbalanced process node according to the comparison result to obtain self-adaptive control data.
Specifically, after the production rhythm is adjusted, the real-time production efficiency after the production rhythm is adjusted is obtained, the real-time production efficiency is compared with the adjacent production efficiency of the adjacent production line, the method comprises the steps of carrying out feedback adjustment on the current unbalanced process node according to the comparison result, namely, the product preparation qualification rate or the product preparation quantity in the same unit time, until the production efficiency of the current production line and the production efficiency of the adjacent production line tend to be the same, and the self-adaptive control data of the current production line is obtained.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
In one embodiment, an adaptive adjustment system of a smart factory is provided, which corresponds to the adaptive adjustment method of the smart factory in the above embodiment one by one. As shown in fig. 5, 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 functional modules are described in detail as follows:
the data acquisition module is used for acquiring the production state data of each process node and the corresponding process control data in the production and manufacturing process of the product and carrying out data integration processing on the production state data and the process control data which are related to each other.
The data analysis module is used for acquiring the business event data of the current production product, and carrying out business unbalance characteristic analysis on the business event data and the integrated production procedure related data to obtain business unbalance characteristic data.
The data optimization module is used for acquiring the product production data of the current production line, constructing the equipment self-adaptive model, inputting the equipment part aging data in the current production and manufacturing process into the equipment self-adaptive model, and optimizing the performance parameters of the current production equipment.
And the data adjusting module is used for carrying out self-adaptive adjustment on the production rhythm of the current unbalance procedure node and the subsequent node according to the optimized production equipment optimization parameters and the service unbalance characteristic data to obtain self-adaptive control data of the current product production.
Preferably, the data analysis module specifically includes:
and the data association sub-module is used for acquiring the service production time, the material configuration rate and the personnel movement rate of the current production product, and carrying out data connection according to the service type to obtain the associated service event data.
And the data calculation sub-module is used for respectively calculating the adjacent yield ratio of the adjacent product lines and the preparation yield ratio of the adjacent preparation period, and jointly analyzing the current service production quality according to the adjacent yield ratio and the preparation yield ratio.
And the characteristic analysis sub-module is used for carrying out combination comparison on the current service production quality and the corresponding production procedure associated data, and carrying out service unbalance characteristic analysis according to the comparison result to obtain service unbalance characteristic data.
Preferably, the data association sub-module further comprises:
The data association relationship between the business event data is represented by a formula (1):
wherein D Production of represents a service event parameter, n represents a total number of process nodes of a service, t Production of represents a production time of a product corresponding to the service, V Article (B) represents a material conveying rate of each process node, V Human body represents a manual calling rate of each process node, μ n represents a production efficiency coefficient of each process node, and Δt (n-m) represents a transit time of an adjacent process node.
Preferably, the data optimization module specifically includes:
The model construction sub-module is used for acquiring the product production data and the corresponding machine use data of the current production line, carrying out data training according to the product production data and the machine use data, and constructing the equipment self-adaptive model of the current production line.
And the aging analysis submodule is used for calculating the operation efficiency change of each machine equipment in the current production and manufacturing process, and carrying out equipment part aging analysis according to the operation efficiency change to obtain equipment part aging data.
And the self-adaptive analysis sub-module is used for inputting the aging data of the equipment parts of the current production line in the process of preparing the product into the equipment self-adaptive model and carrying out self-adaptive analysis on the aging data.
And the parameter optimization sub-module is used for independently adjusting the aged equipment control data according to the analysis result and optimizing the performance parameters of the current production equipment according to the equipment adjustment result.
Preferably, the data adjustment module specifically includes:
And the node positioning sub-module is used for carrying out unbalance positioning on the unbalance procedure nodes of the current production line according to the optimized production equipment optimization parameters and the service unbalance characteristic data.
And the deviation analysis sub-module is used for retrieving the preparation unbalance data of the positioned unbalance process node and the production influence data of the subsequent node, and analyzing the preparation deviation of the unbalance process node according to the preparation unbalance data and the production influence data.
And the rhythm adjusting sub-module is used for adjusting the equipment parameters of the current production line according to the preparation deviation analysis result and the production equipment optimization parameters, and controlling the production rhythm of the current unbalanced process node and the subsequent node according to the equipment parameter adjustment result.
And the feedback regulation sub-module is used for acquiring the real-time production efficiency after the production rhythm regulation, comparing the real-time production efficiency with the adjacent production efficiency of the adjacent production line, and carrying out feedback regulation on the current unbalance process node according to the comparison result to obtain self-adaptive control data.
For specific limitations of the adaptive adjustment system of the smart factory, reference may be made to the above limitation of the adaptive adjustment method of the smart factory, and no further description is given here. The various modules in the adaptive adjustment system of the intelligent plant described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing production line self-adaptive regulation data of the factory in the digital transformation process. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a method for adaptive adjustment of an intelligent factory.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, implements the steps of the above method for adaptive adjustment of a smart factory.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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. By way of illustration and not limitation, RAM is available in a variety of 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), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the system is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (8)
1. An adaptive adjustment method for an intelligent factory, comprising:
Acquiring production state data and corresponding process control data of each process node in the production and manufacturing process of the product, and carrying out data integration processing on the production state data and the process control data which are related to each other;
Acquiring business event data of a current production product, and carrying out business unbalance characteristic analysis on the business event data and integrated production procedure related data to obtain business unbalance characteristic data;
obtaining product production data of a current production line, constructing a device self-adaptive model, inputting ageing data of device parts in the current production and manufacturing process into the device self-adaptive model, and optimizing performance parameters of the current production device;
And carrying out production rhythm self-adaptive adjustment on the current unbalanced procedure node and the subsequent node according to the optimized production equipment optimization parameters and the service unbalanced characteristic data to obtain self-adaptive control data of current product production.
2. The method for adaptively adjusting an intelligent factory according to claim 1, wherein the acquiring business event data of a currently produced product, and performing business imbalance feature analysis on the business event data and integrated production process related data to obtain business imbalance feature data, specifically comprises:
Acquiring service production time, material allocation rate and personnel movement rate of the current production product, and carrying out data connection according to the service type to obtain associated service event data;
respectively calculating adjacent yield ratios of adjacent product lines and preparation yield ratios of adjacent preparation periods, and jointly analyzing the current service production quality according to the adjacent yield ratios and the preparation yield ratios;
And carrying out combination comparison on the current service production quality and corresponding production procedure associated data, and carrying out service unbalance characteristic analysis according to the comparison result to obtain service unbalance characteristic data.
3. The method for adaptively adjusting an intelligent factory according to claim 2, wherein the steps of obtaining the service production time, the material allocation rate and the personnel allocation rate of the current production product, and performing data connection according to the service type to obtain the associated service event data, further comprise:
The data association relationship between the business event data is represented by a formula (1):
wherein D Production of represents a service event parameter, n represents a total number of process nodes of a service, t Production of represents a production time of a product corresponding to the service, V Article (B) represents a material conveying rate of each process node, V Human body represents a manual calling rate of each process node, μ n represents a production efficiency coefficient of each process node, and Δt (n-m) represents a transit time of an adjacent process node.
4. The method for adaptively adjusting an intelligent factory according to claim 1, wherein the steps of obtaining product production data of a current production line, constructing an equipment adaptive model, inputting equipment part aging data in the current production and manufacturing process into the equipment adaptive model, and optimizing the performance parameters of the current production equipment comprise the following steps:
Acquiring product production data and corresponding machine use data of a current production line, performing data training according to the product production data and the machine use data, and constructing a device self-adaptive model of the current production line;
In the current production and manufacturing process, calculating the operation efficiency change of each machine equipment, and carrying out equipment part aging analysis according to the operation efficiency change to obtain equipment part aging data;
The aging data of the equipment parts of the current production line in the process of preparing products are input into the equipment self-adaptive model, and the aging data self-adaptive analysis is carried out;
and (3) independently adjusting the aged equipment control data according to the analysis result, and optimizing the performance parameters of the current production equipment according to the equipment adjustment result.
5. The adaptive adjustment method of the intelligent factory according to claim 1, wherein the adaptive adjustment of the production rhythm is performed on the current unbalanced process node and the subsequent node according to the optimized production equipment optimization parameter and the service unbalance characteristic data to obtain the adaptive control data of the current product production, specifically comprising:
according to the optimized production equipment optimization parameters and the service unbalance characteristic data, unbalance positioning is carried out on unbalance procedure nodes of the current production line;
Preparing unbalance data of the positioned unbalance process nodes and production influence data of subsequent nodes are called, and preparing deviation of the unbalance process nodes is analyzed according to the preparing unbalance data and the production influence data;
Adjusting the equipment parameters of the current production line according to the preparation deviation analysis result and the production equipment optimization parameters, and controlling the production rhythms of the current unbalanced process node and the subsequent nodes according to the equipment parameter adjustment result;
And acquiring the real-time production efficiency after the adjustment of the production rhythm, comparing the real-time production efficiency with the adjacent production efficiency of the adjacent production line, and carrying out feedback adjustment on the current unbalanced process node according to the comparison result to obtain self-adaptive control data.
6. An adaptive regulation system for an intelligent plant, comprising:
the data acquisition module is used for acquiring production state data of each process node and corresponding process control data in the production and manufacturing process of the product, and carrying out data integration processing on the production state data and the process control data which are related to each other;
The data analysis module is used for acquiring service event data of the current production product, and carrying out service unbalance characteristic analysis on the service event data and the integrated production procedure associated data to obtain service unbalance characteristic data;
The data optimization module is used for acquiring product production data of the current production line, constructing a device self-adaptive model, inputting ageing data of the device parts in the current production and manufacturing process into the device self-adaptive model, and optimizing performance parameters of the current production device;
And the data adjusting module is used for carrying out production rhythm self-adaptive adjustment on the current unbalance procedure node and the subsequent node according to the optimized production equipment optimization parameters and the service unbalance characteristic data to obtain self-adaptive control data of the current product production.
7. Computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method for adaptive adjustment of a smart plant according to any one of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the adaptive adjustment method of a smart factory according to any one of claims 1 to 5.
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