CN116307664A - Intelligent manufacturing flow management system based on big data - Google Patents

Intelligent manufacturing flow management system based on big data Download PDF

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CN116307664A
CN116307664A CN202310151877.XA CN202310151877A CN116307664A CN 116307664 A CN116307664 A CN 116307664A CN 202310151877 A CN202310151877 A CN 202310151877A CN 116307664 A CN116307664 A CN 116307664A
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赵乐乐
黄政杰
雷宏发
李艳洲
卢永康
刘双晴
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Guangdong Autonomous Intelligent Technology Co ltd
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Abstract

The invention discloses an intelligent manufacturing flow management system based on big data, which belongs to the technical field of production and manufacturing management and comprises a marking module, a scheme module, a recommending module and a server; the marking module is used for marking flow steps to be improved in the intelligent manufacturing flow; the scheme module is used for setting an improvement scheme corresponding to the step to be improved, obtaining corresponding step information to be improved, setting a corresponding retrieval formula based on the obtained step information to be improved, and carrying out corresponding retrieval in the Internet according to the obtained retrieval formula to obtain a to-be-selected improvement scheme; carrying out adaptability calculation on the obtained to-be-selected improved scheme to obtain a corresponding suitable value, and marking the to-be-selected improved scheme corresponding to the suitable value being greater than a threshold value X3 as a target improved scheme; the recommending module is used for recommending the target improvement scheme to a corresponding manager, acquiring the target improvement scheme, setting the corresponding manager and sending the acquired target improvement scheme to the corresponding manager.

Description

Intelligent manufacturing flow management system based on big data
Technical Field
The invention belongs to the technical field of production and manufacturing management, and particularly relates to an intelligent manufacturing flow management system based on big data.
Background
Along with the rapid development of the production and manufacturing technology, more and more production and manufacturing enterprises adopt an intelligent production technology, so that the automation and the intellectualization of production are improved, and the production efficiency of the enterprises is improved; however, with the rapid development of the related technology, the update of the production equipment brings new challenges to the management of the production and manufacturing process, such as when an enterprise manager discovers an efficient production equipment, but the actual situation of the enterprise production process is not comprehensively considered, so that the replaced production equipment does not play a predicted target, and the enterprise cost is increased; or because enterprise management personnel cannot timely know the real-time technical development information, enterprises cannot timely update production equipment, and production efficiency and the like cannot be improved correspondingly; therefore, in order to intelligently manage the manufacturing process, the invention provides an intelligent manufacturing process management system based on big data.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides an intelligent manufacturing flow management system based on big data.
The aim of the invention can be achieved by the following technical scheme:
the intelligent manufacturing flow management system based on big data comprises a marking module, a scheme module, a recommending module and a server;
the marking module is used for marking flow steps to be improved in the intelligent manufacturing flow;
the scheme module is used for setting an improvement scheme corresponding to the step to be improved, obtaining corresponding step information to be improved, setting a corresponding retrieval formula based on the obtained step information to be improved, and carrying out corresponding retrieval in the Internet according to the obtained retrieval formula to obtain a to-be-selected improvement scheme; carrying out adaptability calculation on the obtained to-be-selected improved scheme to obtain a corresponding suitable value, and marking the to-be-selected improved scheme corresponding to the suitable value being greater than a threshold value X3 as a target improved scheme;
the recommending module is used for recommending the target improvement scheme to a corresponding manager, acquiring the target improvement scheme, setting the corresponding manager and sending the acquired target improvement scheme to the corresponding manager.
Further, the working method of the marking module comprises the following steps:
acquiring an intelligent manufacturing process adopted at present, dividing the intelligent manufacturing process into a plurality of production steps according to production nodes, establishing a corresponding step data record table according to the acquired production steps, acquiring advice data of the corresponding production steps in real time, and inputting the acquired advice data into the step data record table; analyzing the data in the step data record table to obtain the steps to be improved, and carrying out corresponding marks in the intelligent manufacturing flow according to the obtained steps to be improved.
Further, the method for analyzing the data in the step data record table comprises the following steps:
identifying suggested data in the step data record table, classifying the identified suggested data to obtain a plurality of classified data, obtaining the number of suggested bars in the classified data, and marking the corresponding classified data as i according to the number value matched with the obtained number of suggested bars, wherein i=1, 2, … …, n is positive integerA number; marking the obtained quantity value as PLi, analyzing the suggested content corresponding to each classified data to obtain the corresponding content value, marking the obtained content value as NRi, and according to the formula
Figure BDA0004091104560000021
Calculating a corresponding suggested value, marking the corresponding production step as a step to be improved when the suggested value exceeds a threshold value X1, and marking the corresponding emphasis classification; when the recommended value is not greater than the threshold value X1, no operation is performed.
Further, the method for marking the corresponding emphasis classification comprises the following steps:
and calculating the emphasis value corresponding to each classified data according to the formula b1×PLi+b2×NRi, and listing the classified data with the emphasis value larger than the threshold value X2 as the emphasis classification, wherein when no emphasis value is larger than the threshold value X2, no emphasis classification exists.
Further, the method for adaptively calculating the obtained alternative improvement scheme comprises the following steps:
acquiring flow data of a current step to be improved, integrating the acquired flow data and a to-be-selected improvement scheme into comprehensive analysis data, establishing a comprehensive value analysis model, inputting the comprehensive analysis data into the comprehensive value analysis model, and obtaining corresponding implementation values, efficiency improvement values, flow engagement values and operation change values, which are respectively marked as SZ, GZ, LX and BZ, and calculating corresponding suitable values according to a suitability calculation formula SYZ=β1×SZ+β2×GZ+β3×LX+β4×BZ, wherein β1, β2, β3 and β4 are weight coefficients of the implementation values, the efficiency improvement values, the flow engagement values and the operation change values respectively.
Further, the integrated value analysis model is built based on a CNN network or a DNN network.
Compared with the prior art, the invention has the beneficial effects that:
through the arrangement of the marking module, the visual understanding of the production flow problems in the current enterprise is realized, the truest problem of the production flow and the corresponding modifiable direction are known from the production line, and the direction is indicated for the subsequent industrial upgrading; through the mutual cooperation among the marking module, the scheme module and the recommending module, the intelligent management of the production and manufacturing flow is realized, the scheme from problem discovery to problem solution is realized, the closed-loop management is realized, even if the discovered problem cannot be solved by the existing mode, the searching analysis is carried out by the scheme module on the basis of big data in real time, and the method can be timely recommended to management staff when the method has an improvement scheme suitable for enterprises.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the intelligent manufacturing process management system based on big data comprises a marking module, a scheme module, a recommending module and a server;
the marking module is used for marking flow steps to be improved in the intelligent manufacturing flow, and the specific method comprises the following steps:
the method comprises the steps of acquiring an intelligent manufacturing process adopted at present, dividing the intelligent manufacturing process into a plurality of production steps according to production nodes, and particularly, carrying out corresponding division according to common knowledge of the current production steps, such as washing, drying, grinding and the like, wherein the steps can be divided into a washing production step, a drying production step and a grinding production step; establishing a corresponding step data record table according to the obtained production steps, collecting the suggestion data of the corresponding production steps in real time, and inputting the obtained suggestion data into the step data record table;
analyzing the data in the step data record table to obtain the steps to be improved, and carrying out corresponding marks in the intelligent manufacturing flow according to the obtained steps to be improved.
The step data record table is used for recording relevant data such as problems, places needing improvement and the like in the corresponding production steps in the production and manufacturing process, and particularly, corresponding staff can carry out supplementary suggestion data according to own operators and the like in the daily production and manufacturing process.
In one embodiment, a method of collecting advice data corresponding to a production step in real time includes:
setting a corresponding suggestion recording unit, when a worker has suggestions, directly recording the suggestions through the suggestion recording unit, recording the suggestions in modes of manual input, document transmission, voice input and the like, and then inputting the corresponding suggestions into a corresponding step data record table through the butt joint suggestion recording unit when the suggestions are recorded by the suggestion recording unit.
In another embodiment, in the actual operation process, corresponding workers often cannot record suggestions even if related suggestions are provided due to fear of trouble and the like, which affects the data acquisition work, so in this embodiment, a manager is set, the corresponding suggestions can be known in work through the contact of the manager with the workers in the corresponding production steps in the working process, the manager performs summarization and then inputs the suggestions into the suggestion recording unit, and in order to further excite the enthusiasm of the corresponding workers to propose the suggestions, related rewarding measures can be set, and the device can be specifically set according to the actual production needs.
The method for analyzing the data in the step data record table comprises the following steps:
identifying the suggested data in the step data record table, classifying the identified suggested data to obtain a plurality of classified data, obtaining the suggested number in the classified data, matching the corresponding number value according to the obtained suggested number, and matching the corresponding number valueThe classification data is labeled i, where i=1, 2, … …, n is a positive integer; marking the obtained quantity value as PLi, analyzing the suggested content corresponding to each classified data to obtain the corresponding content value, marking the obtained content value as NRi, and according to the formula
Figure BDA0004091104560000051
Calculating a corresponding suggested value, marking the corresponding production step as a step to be improved when the suggested value exceeds a threshold value X1, and marking the corresponding emphasis classification; when the recommended value is not greater than the threshold value X1, no operation is performed.
According to the obtained number of advice strips matched with the corresponding number value, the data value is set according to the advice proposition person, the proposition times and the total proposition times, and because the proposition times of the same person and the proposition times of different persons are different, comprehensive analysis is needed, a corresponding number value analysis model can be specifically built based on a CNN network or a DNN network, a corresponding training set is set for training in a manual mode, and the corresponding number value is obtained through analysis of the number value analysis model after successful training.
The method comprises the steps of analyzing the suggested content corresponding to each classified data, mainly analyzing the suggested content, the proposed persons and the proposed quantity corresponding to the suggested content, setting different weight coefficients for different proposed persons according to the acceptance degree of historical proposed suggestions, carrying out corresponding analysis, specifically establishing a corresponding content value analysis model based on a CNN network or a DNN network, setting a corresponding training set in a manual mode for training, and analyzing through the content value analysis model after successful training to obtain a corresponding content value.
The method for marking the corresponding emphasis classification comprises the following steps:
and calculating the emphasis value corresponding to each classified data according to the formula b1×PLi+b2×NRi, and listing the classified data with the emphasis value larger than the threshold value X2 as the emphasis classification, wherein when no emphasis value is larger than the threshold value X2, no emphasis classification exists.
The scheme module is used for setting an improvement scheme corresponding to the step to be improved, and the specific method comprises the following steps:
acquiring corresponding step information to be improved, wherein the step information to be improved comprises an operation mode of the step to be improved and stress classification, and if the stress classification is not carried out, the stress classification is not carried out; setting a corresponding search formula based on the obtained step information to be improved, and carrying out corresponding search in the Internet according to the obtained search formula to obtain a to-be-selected improvement scheme;
and carrying out adaptability calculation on the obtained to-be-selected improvement schemes to obtain corresponding suitable values, and marking the to-be-selected improvement scheme corresponding to the suitable values larger than the threshold value X3 as a target improvement scheme.
The corresponding search formula is set based on the obtained step information to be improved, and the search formula of the corresponding target search data can be set by the existing search technology, so that detailed description is omitted.
According to the obtained search formula, corresponding search is carried out in the Internet to obtain a to-be-selected improved scheme, the to-be-selected improved scheme is an existing improved measure provided according to the current technology, because the technology is continuously developed and advanced, when a new technology or a certain technology is mature in application, different improved technologies aiming at the current production step always appear, and therefore, the corresponding to-be-selected improved scheme is obtained based on the set search formula by carrying out real-time search, and the corresponding realization can be carried out specifically through the existing search technology.
The method for adaptively calculating the obtained to-be-selected improvement scheme comprises the following steps:
the method comprises the steps of obtaining flow data of a current step to be improved, integrating the obtained flow data and a to-be-selected improvement scheme into comprehensive analysis data, establishing a comprehensive value analysis model, inputting the comprehensive analysis data into the comprehensive value analysis model, obtaining corresponding implementation values, efficiency improvement values, flow engagement values and operation change values, respectively marked as SZ, GZ, LX and BZ, and calculating corresponding suitable values according to a suitability calculation formula SYZ=β1×SZ+β2×GZ+β3×LX+β4×BZ, wherein β1, β2, β3 and β4 are weight coefficients of the implementation values, the efficiency improvement values, the flow engagement values and the operation change values respectively, and specifically, the method is discussed and set by an expert group.
The comprehensive value analysis model is built based on a CNN network or a DNN network, a corresponding training set is built through a manual mode to train, implementation fees are set according to cost fees required for changing the existing process, the efficiency improvement value refers to a lifting value of the to-be-selected improvement scheme relative to the efficiency and other beneficial effects of the current to-be-improved step, the process connection value is set according to the connection degree between the to-be-selected improvement scheme and the front and back production steps, the operation change value is set according to the degree of whether a worker in the current to-be-improved step can operate the to-be-selected improvement scheme, and the training set is specifically set through a manual mode to train.
The recommending module is used for recommending the target improvement scheme to a corresponding manager, and acquiring one or more target improvement schemes; setting corresponding management personnel, performing discussion setting in the enterprise, and transmitting the obtained target improvement scheme to the corresponding management personnel; and finally, judging whether the target improvement scheme is implemented or not by adopting a manual auditing mode.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.

Claims (6)

1. The intelligent manufacturing flow management system based on big data is characterized by comprising a marking module, a scheme module, a recommending module and a server;
the marking module is used for marking flow steps to be improved in the intelligent manufacturing flow;
the scheme module is used for setting an improvement scheme corresponding to the step to be improved, obtaining corresponding step information to be improved, setting a corresponding retrieval formula based on the obtained step information to be improved, and carrying out corresponding retrieval in the Internet according to the obtained retrieval formula to obtain a to-be-selected improvement scheme; carrying out adaptability calculation on the obtained to-be-selected improved scheme to obtain a corresponding suitable value, and marking the to-be-selected improved scheme corresponding to the suitable value being greater than a threshold value X3 as a target improved scheme;
the recommending module is used for recommending the target improvement scheme to a corresponding manager, acquiring the target improvement scheme, setting the corresponding manager and sending the acquired target improvement scheme to the corresponding manager.
2. The intelligent manufacturing flow management system based on big data of claim 1, wherein the method of operation of the marking module comprises:
acquiring an intelligent manufacturing process adopted at present, dividing the intelligent manufacturing process into a plurality of production steps according to production nodes, establishing a corresponding step data record table according to the acquired production steps, acquiring advice data of the corresponding production steps in real time, and inputting the acquired advice data into the step data record table; analyzing the data in the step data record table to obtain the steps to be improved, and carrying out corresponding marks in the intelligent manufacturing flow according to the obtained steps to be improved.
3. The intelligent manufacturing flow management system based on big data according to claim 2, wherein the method of analyzing the data in the step data record table comprises:
identifying suggested data in the step data record table, classifying the identified suggested data to obtain a plurality of classified data, obtaining the number of suggested stripes in the classified data, and marking the corresponding classified data as i according to the number of the obtained suggested stripes matched with the corresponding number value, wherein i=1, 2, … …, n and n are positive integers; marking the obtained quantity value as PLi, analyzing the suggested content corresponding to each classified data to obtain the corresponding content value, marking the obtained content value as NRi, and according to the formula
Figure FDA0004091104550000021
Calculating a corresponding suggested value, marking the corresponding production step as a step to be improved when the suggested value exceeds a threshold value X1, and marking the corresponding emphasis classification; when the recommended value is not greater than the threshold value X1, no operation is performed.
4. The intelligent manufacturing flow management system based on big data of claim 3, wherein the method of marking the corresponding stress classification comprises:
and calculating the emphasis value corresponding to each classified data according to the formula b1×PLi+b2×NRi, and listing the classified data with the emphasis value larger than the threshold value X2 as the emphasis classification, wherein when no emphasis value is larger than the threshold value X2, no emphasis classification exists.
5. The intelligent manufacturing flow management system based on big data according to claim 1, wherein the method for adaptively calculating the obtained candidate improvement comprises:
acquiring flow data of a current step to be improved, integrating the acquired flow data and a to-be-selected improvement scheme into comprehensive analysis data, establishing a comprehensive value analysis model, inputting the comprehensive analysis data into the comprehensive value analysis model, and obtaining corresponding implementation values, efficiency improvement values, flow engagement values and operation change values, which are respectively marked as SZ, GZ, LX and BZ, and calculating corresponding suitable values according to a suitability calculation formula SYZ=β1×SZ+β2×GZ+β3×LX+β4×BZ, wherein β1, β2, β3 and β4 are weight coefficients of the implementation values, the efficiency improvement values, the flow engagement values and the operation change values respectively.
6. The intelligent manufacturing flow management system based on big data of claim 5, wherein the integrated value analysis model is built based on a CNN network or a DNN network.
CN202310151877.XA 2023-02-22 2023-02-22 Intelligent manufacturing flow management system based on big data Pending CN116307664A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116823193A (en) * 2023-08-31 2023-09-29 深圳市永迦电子科技有限公司 Intelligent manufacturing flow management system based on big data
CN116846161A (en) * 2023-07-04 2023-10-03 湖南贝特新能源科技有限公司 Motor stator production system
CN117196399A (en) * 2023-09-21 2023-12-08 深圳市科荣软件股份有限公司 Customer service center operation supervision optimization system based on data analysis

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116846161A (en) * 2023-07-04 2023-10-03 湖南贝特新能源科技有限公司 Motor stator production system
CN116823193A (en) * 2023-08-31 2023-09-29 深圳市永迦电子科技有限公司 Intelligent manufacturing flow management system based on big data
CN116823193B (en) * 2023-08-31 2023-11-03 深圳市永迦电子科技有限公司 Intelligent manufacturing flow management system based on big data
CN117196399A (en) * 2023-09-21 2023-12-08 深圳市科荣软件股份有限公司 Customer service center operation supervision optimization system based on data analysis
CN117196399B (en) * 2023-09-21 2024-05-31 深圳市科荣软件股份有限公司 Customer service center operation supervision optimization system based on data analysis

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