CN114781730A - Production technology intelligent management system based on intelligent production - Google Patents
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Abstract
The invention relates to the technical field of intelligent factories, and aims to solve the problems that the management mode of the production process of the existing intelligent factory has larger one-sidedness and inaccuracy, and the reasonable distribution and utilization of the production process are difficult to realize, so that the burden of factory production resources is increased, and the high-efficiency development of the factory is greatly hindered; the invention realizes the classification and division of the operation difficulty of the equipment and the technical quality capability of the workers, and utilizes the modes of cross integration, variable verification and signal output, thereby improving the accuracy and comprehensiveness of the production process management, realizing the effect of maximum and reasonable distribution and selection of the production process, reducing the burden of factory production resources and promoting the high-efficiency management of the production process.
Description
Technical Field
The invention relates to the technical field of intelligent factories, in particular to an intelligent production process management system based on intelligent production.
Background
The intelligent factory is characterized in that various modern technologies are utilized to realize the automation of office, management and production of the factory, and the aims of strengthening and standardizing enterprise management, reducing working errors, blocking various loopholes, improving working efficiency, carrying out safety production and providing decision reference are fulfilled;
the intelligent management of the production process is an important determining factor influencing the operation quality of an intelligent factory, so that the realization of the efficient and reasonable management of the production process is very important;
however, in the existing management process of the production process of the intelligent factory, the traditional manpower management is mainly adopted, the software system management is taken as an auxiliary mode, the management mode has larger one-sidedness and inaccuracy, the production management trend of the intelligent factory is difficult to adapt, and the reasonable distribution and utilization of the production process are difficult to realize, so the burden of the production resource of the factory is increased, and the high-efficiency development of the factory is greatly hindered;
in order to solve the above-mentioned drawbacks, a technical solution is now provided.
Disclosure of Invention
The invention aims to solve the problems that the management mode of the production process of the existing intelligent factory has larger one-sidedness and inaccuracy, is difficult to adapt to the production management trend of the intelligent factory, and is difficult to reasonably distribute and utilize the production process, thereby increasing the burden of factory production resources and greatly hindering the efficient development of the factory, through the modes of symbolic calibration, formulated analysis and model establishment, the operation difficulty of equipment and the technical quality capability of workers are clearly classified and divided, and the modes of cross integration, variable verification and signal output are utilized, thereby obtaining the priority sequence of the production process scheme, improving the accuracy and the comprehensiveness of the production process management, and taking the priority sequence of the production process scheme as the basis, utilizing the grade division, The effects of maximum reasonable distribution and selection of the production process are realized by means of sequence sequencing and gear setting training, so that the intelligent management of the production process is greatly promoted, the burden of factory production resources is reduced, the efficient development of a production workshop is promoted, and the intelligent management system of the production process based on intelligent production is provided.
The purpose of the invention can be realized by the following technical scheme:
a production process intelligent management system based on intelligent production comprises a data acquisition unit, an equipment supervision unit, a personnel supervision unit, an integration matching unit, a process production execution unit and a display terminal;
the data acquisition unit is used for acquiring operation data information of equipment and state data information of staff in a factory production workshop and respectively sending the operation data information and the state data information to the equipment supervision unit and the staff supervision unit;
the equipment supervision unit is used for receiving operation data information of each equipment in a factory workshop, carrying out operation difficulty quantitative analysis processing according to the operation data information, generating a set A, a set B and a set C according to the operation difficulty quantitative analysis processing, and sending the sets A, B and C to the integration matching unit;
the personnel supervision unit is used for receiving the state data information of each employee in the factory production workshop, carrying out quantitative evaluation and analysis processing on the skill quality according to the state data information, and generating a set A according to the quantitative evaluation and analysis processing*Set B*And set C*And sending it to the integration matching unit;
the integration matching unit is used for performing matching training analysis processing on the received various types of sets, generating a production process priority ordering set F according to the matching training analysis processing, and sending the production process priority ordering set F to the process production execution unit;
and the process production execution unit is used for performing production process grading analysis processing according to the received production process priority ordering set F, generating a first-grade instruction, a second-grade instruction and a third-grade instruction according to the production process priority ordering set F, and sending the first-grade instruction, the second-grade instruction and the third-grade instruction to the display terminal for displaying.
Furthermore, the operation data information of the equipment comprises an operation duration value, a service life limit value, a key control value, a failure number value and a maintenance number value, and the state data information comprises an actual operation value, a qualification number value and an assessment number value.
Further, the specific operation steps of the operation difficulty quantitative analysis treatment are as follows:
s1: obtaining operation time length value, service life limit value, key control value, failure number value and maintenance number value in operation data information of each device in factory production workshop, and respectively marking as tyli、tsli、kali、guliAnd jaliAccording to formula Czxi＝e1×tyli÷（e2×tsli＋e3×kali＋e4×guli＋jali e5) And i is {1, 2, 3 … n }, and the operation difficulty coefficient Czx of each device is obtainediWherein e1, e2, e3, e4 and e5 are weight factor coefficients of the operation duration value, the service life limit value, the key control quantity value, the fault number value and the overhaul number value respectively, and e1 > e4 > e2 > e5 > e3 > 0;
s2: the operation difficulty coefficient Czx of each deviceiPerforming mean value processing to obtain mean value operation difficulty coefficient JCzx, and calculating operation difficulty coefficient Czx of each equipmentiPerforming difference judgment analysis with the mean operation difficulty coefficient JCzx to obtain a quantitative coefficient pdi；
S3: according to step S2, three preset thresholds Yu1, Yu2 and Yu3 are set, and the quantitative coefficient pd is determinediAnd substituting the signals into a preset threshold value to perform signal comparison, classification and analysis processing, and generating a set A, a set B and a set C according to the signal comparison, classification and analysis processing.
Further, the specific operation steps of signal comparison, classification, analysis and processing are as follows:
the quantitative coefficient pdiSubstituting into preset threshold values Yu1, Yu2 and Yu3 for comparison analysis, and if the quantitative coefficient pd isiWhen the current value is within the range of the preset threshold value Yu1, a high difficulty operation signal is generated, and if the quantitative coefficient pd is within the range of the preset threshold value Yu1iIf the signal is within the range of the preset threshold value Yu2, a medium-difficulty operation signal is generated, and if the quantitative coefficient pd is within the range of the preset threshold value Yu2iWhen the signal is within the range of the preset threshold value Yu3, generating a normal difficulty operation signal;
classifying the devices according to difficulty operation level signals, classifying the devices according to difficulty operation levels, regulating the devices generating high-difficulty operation signals into a set A, regulating the set A to {1, 2, 3 … n1}, regulating the devices generating medium-difficulty operation signals into a set B, regulating the set B to {1, 2, 3 … n2}, regulating the devices generating normal-difficulty operation signals into a set C, regulating the set C to {1, 2, 3 … n3}, wherein n1 + n2+ n3 is n.
Further, the specific operation steps of the quantitative assessment and analysis processing of the skill quality are as follows:
SS 1: acquiring actual operation values, qualification values and assessment values in state data information of all employees in a factory production workshop, and respectively marking the actual operation values, the qualification values and the assessment values as sclj、zgljAnd khljThen, normalization processing is performed to obtain skill coefficients Jnx of the employeesjJ ═ 1, 2, 3 … m }, wherein f1, f2 and f3 are correction factor coefficients of the real operation quantity value, the qualification quantity value and the qualification quantity value respectively, and f1 > f2 > f3 > 0;
SS 2: using the employee as the abscissa and the skill coefficient as the ordinate, and establishing a rectangular coordinate system based on the abscissa, the skill coefficient Jnx of each employee will be obtainedjDrawing on a rectangular coordinate system in a point drawing mode, carrying out modeling analysis processing according to the rectangular coordinate system, and generating a set A according to the modeling analysis processing*Set B*And set C*。
Further, the concrete operation steps of the modeling analysis processing are as follows:
setting a first reference line and a second reference line on a rectangular coordinate system, wherein the position of the first reference line in the rectangular coordinate system is positioned on the second reference line, calibrating employees with skill coefficients positioned between the first reference line and the second reference line as medium-class employees, calibrating employees with skill coefficients positioned on and above the first reference line as high-class employees, and calibrating employees with skill coefficients positioned on and below the second reference line as primary employees;
classifying and classifying the employees according to the job title grades according to the job title grade signals, and regulating the employees generating the high-grade job titles into a set A*And set A*Each employee who generates the intermediate staff member is normalized to the set B {1, 2, 3 … m1}*And set B*1, 2, 3 … m2, which will yield the job title of the first timeEach employee is normalized to a set C*And set C*1, 2, 3 … m3, wherein m1 + m2+ m3 is m.
Further, the specific operation steps of the matching training analysis processing are as follows:
obtaining device class classification sets A, B and C, obtaining worker class classification set A*、B*And C*And matching the devices in the set A, the set B and the set C in sequence in the set A according to the device matching rules*Set B*And set C*And form a production process performance queue assembly Ldz based on the workers in the groupkAnd a production process performance queue assembly set LdzkK = {1, 2, 3 … 9}, where O = { a, B, C }, P = { a }, and P = { a }, respectively*，B*，C*}；
And (4) analyzing and processing the process priority values of the production process efficiency queue combinations, and generating a production process priority ordering set F according to the process priority ordering set.
Further, the specific operation steps of the process priority value analysis treatment are as follows:
queue composition set Ldz according to production process performancekRespectively setting the same production task value rw for each production process efficiency queue combination, and accordingly obtaining the time hat taken by each production process efficiency queue combination to reach the production task valuekAnd the spent time hat of the queue combination of the production process effects is arranged according to the descending order modekSorting is carried out, and a descending sequence H is obtained according to the sortingk*And k is an*＝{1，2，3…9}；
According to descending sequence Hk*The production process performance queue combination with the time spent at the first position in the descending sequence is marked as the optimal process scheme, the production process performance queue combination with the time spent at the second position in the descending sequence is marked as the second process scheme, and so on, the production process performance queue combination with the time spent at the ninth position in the descending sequence is marked as the latest process scheme, and the production process priority ordering set F is generated according to the latest process scheme.
Further, the specific operation steps of the production process grading analysis treatment are as follows:
acquiring a production task value rw of a factory workshop, setting a task threshold value Yu4 according to the production task value rw, substituting the production task value rw into the task threshold value Yu4 for comparison and analysis, and generating a primary level setting signal, a secondary level setting signal and a tertiary level setting signal according to the result;
acquiring a production process priority set F, grading the process schemes in the production process priority set F by taking three process schemes as a group, and generating a first-gear instruction, a second-gear instruction and a third-gear instruction;
and executing the production process according to the level of the grade setting signal, executing the first-grade instruction according to the first-grade setting signal when the first-grade setting signal is generated, executing the second-grade instruction according to the second-grade setting signal when the second-grade setting signal is generated, and executing the third-grade instruction according to the third-grade setting signal when the third-grade setting signal is generated.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, data is used as a decision basis, and the accurate analysis of the operation difficulty of equipment is realized through symbolic calibration, formulated analysis and model establishment, and meanwhile, the technical quality and capability of workers are definitely classified and divided, so that the efficient management of the production process is promoted;
2. according to the invention, through the modes of cross integration, variable verification and signal output, the accuracy and comprehensiveness of production process management are improved while the priority sequence of the production process scheme is obtained, and the development of an intelligent factory is promoted.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a general block diagram of the system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1, an intelligent management system for a production process based on intelligent production comprises a data acquisition unit, an equipment supervision unit, a personnel supervision unit, an integration matching unit, a process production execution unit and a display terminal;
the data acquisition unit is used for acquiring operation data information of equipment and state data information of staff in a factory production workshop and respectively transmitting the operation data information and the state data information to the equipment supervision unit and the staff supervision unit;
the operation data information of the equipment is used for representing a class of data information of the operation state condition of each production tool and equipment in a factory production workshop, and comprises an operation duration value, a service life value, a key control value, a fault number value and a maintenance number value;
the operation duration value refers to a data quantity value of one-time operation time of the machine equipment from startup to shutdown, and it needs to be stated that when an expression numerical value of the operation duration value is larger, the current working operation performance of the machine equipment is higher;
the service life limit value refers to a data quantity value of total service time of the machine equipment from purchase to use, and it should be noted that, when the expression numerical value of the service life limit value is larger, the service time of the machine equipment is longer, and the service time of the machine equipment is longer, on the other hand, the old degree of the machine equipment is highlighted, and the operation difficulty of the machine equipment is increased from different degrees;
the key control quantity value is a data quantity value used for representing the operation complexity of control buttons on the machine equipment, the key control quantity value refers to the product of the number of the buttons distributed on the machine equipment and the distribution spacing of the adjacent buttons, the failure number quantity value refers to the data quantity value of the failure times of shutdown interruption occurring in one operating time from startup to shutdown of the machine equipment, and the overhaul number quantity value refers to the data quantity value of the times of shutdown overhaul of the machine equipment since the machine equipment is purchased and put into use;
the state data information of the staff is used for representing a class of data quantity values of the working state conditions of all the staff in the factory production workshop, and the state data information comprises real operation quantity values, qualification quantity values and assessment quantity values;
the real operation value is used for expressing a data quantity value of proficiency degree of workers in a production workshop when the workers actually operate a machine, the real operation value refers to the percentage of correct operation times to wrong operation times, the qualification value is used for expressing a data quantity value of personal culture quality of the workers in the production workshop, the qualification value refers to a product value of working year time and educated year time, the assessment value is used for expressing a data quantity value of examination qualification times in annual examination qualification times of the workers in the production workshop, and the higher the real operation value, the qualification value and the performance value of the assessment value are, the higher the work quality level of the workers are;
the equipment supervision unit is used for receiving operation data information of each equipment in the factory workshop, carrying out quantitative analysis processing on operation difficulty according to the operation data information, generating a set A, a set B and a set C according to the operation data information, and sending the sets A, the set B and the set C to the integration matching unit;
the personnel supervision unit is used for receiving the state data information of each employee in the factory production workshop, carrying out quantitative evaluation and analysis processing on the skill quality according to the state data information, and generating a set A according to the skill quality*Set B*And set C*And sending it to the integrated matching unit;
the integration matching unit is used for performing matching training analysis processing on the received various types of sets, generating a production process priority set F according to the received various types of sets, and sending the production process priority set F to the process production execution unit;
the process production execution unit is used for performing production process grading analysis processing according to the received production process priority ordering set F, generating a first-grade instruction, a second-grade instruction and a third-grade instruction according to the production process priority ordering set F, and sending the first-grade instruction, the second-grade instruction and the third-grade instruction to the display terminal for displaying.
Example two:
as shown in fig. 1, when the device supervision unit receives the operation data information of each device in the factory production workshop, the operation difficulty quantitative analysis processing is performed according to the operation data information, and the specific operation process is as follows:
obtaining the operation duration value, the service life limit value, the key control value, the failure number value and the maintenance number value in the operation data information of each device in the factory production workshop, and respectively marking the values as tyli、tsli、kali、guliAnd jaliAccording to the formula Czxi＝e1×tyli÷（e2×tsli＋e3×kali＋e4×guli＋jali e5) And i is {1, 2, 3 … n }, and an operation difficulty coefficient Czx of each device is obtainediWherein e1, e2, e3, e4 and e5 are weight factor coefficients of an operation duration value, a service life limit value, a key control quantity value, a fault number value and a maintenance number value respectively, e1 > e4 > e2 > e5 > e3 > 0, and e1 + e 2+ e3 + e4 + e5= 7.0281;
it should be noted that i is a positive integer greater than or equal to 1, i is used for representing the number of devices, and the weighting factor coefficient is used for balancing the proportion weight of each item of data in the formula calculation, so as to promote the accuracy of the calculation result;
the operation difficulty coefficient Czx of each deviceiPerforming mean value processing according to formula JCzx = (Czx)1＋Czx2＋……＋Czxn) Dividing n, calculating the mean operation difficulty coefficient JCzx, and dividing the operation difficulty coefficient Czx of each deviceiPerforming difference judgment analysis with the mean value operation difficulty coefficient JCzx according to the formula pdiCzx |)i-JCzx I, obtaining a quantitative coefficient pdi；
Three preset thresholds Yu1, Yu2 and Yu3 are set and the quantitative coefficient pd is determinediSubstituting into a preset threshold value to perform signal comparison, classification, analysis and processing, wherein the specific operation process is as follows:
the quantitative coefficient pdiSubstituting into preset thresholds Yu1, Yu2 and Yu3 for comparison analysis, and if the quantitative coefficient pd isiWhen the signal is within the range of the preset threshold value Yu1, a high difficulty operation signal is generated, and if the quantitative coefficient pd is within the range of the preset threshold value Yu1iIf the signal is within the range of the preset threshold value Yu2, a medium-difficulty operation signal is generated, and if the quantitative coefficient pd is within the range of the preset threshold value Yu2iWhen the signal is within the range of the preset threshold value Yu3, generating a normal difficulty operation signal;
classifying and dividing the devices according to difficulty operation levels according to difficulty operation level signals, regulating the devices generating high-difficulty operation signals into a set A, regulating the set A to {1, 2, 3 … n1}, regulating the devices generating medium-difficulty operation signals into a set B, regulating the set B to {1, 2, 3 … n2}, regulating the devices generating normal-difficulty operation signals into a set C, regulating the set C to {1, 2, 3 … n3}, wherein n1 + n2+ n3 is n, and sending the generated set A, set B and set C to an integration matching unit;
it should be noted that the preset thresholds Yu1, Yu2 and Yu3 are respectively represented as three different ranges of thresholds, wherein the specific threshold value settings of the preset thresholds Yu1, Yu2 and Yu3 are specifically set by those skilled in the art according to specific process conditions.
Example three:
as shown in fig. 1, when the supervision unit receives status data information of each employee in the factory production workshop, and performs quantitative assessment and analysis of skill quality according to the status data information, the specific operation process is as follows:
acquiring actual operation values, qualification values and assessment values in state data information of all employees in a factory production workshop, and respectively marking the actual operation values, the qualification values and the assessment values as sclj、zgljAnd khljAnd normalizing the real operation value, the qualification value and the qualification value according to a formula Jnxj＝f1×sclj＋f2×zglj＋f3×khljDetermining skill coefficient Jnx of each employeejJ ═ 1, 2, 3 … m, where f1, f2, and f3 are divided intoCorrection factor coefficients of the real exercise value, the qualification value and the assessment value, wherein f1 is more than f2 is more than f3 is more than 0, and f1 + f 2+ f3 is 5.4081;
it should be noted that j is a positive integer greater than or equal to 1, and j is used for representing the number of employees, and the correction factor coefficient is used for correcting the deviation of each parameter in the formula calculation process, so that the calculation is more accurate and the parameter data are obtained;
using the employee as the abscissa and the skill coefficient as the ordinate, and establishing a rectangular coordinate system based on the abscissa, the skill coefficient Jnx of each employee will be obtainedjDrawing on a rectangular coordinate system in a point drawing mode, and carrying out modeling analysis processing according to the coordinate system, wherein the specific operation process is as follows:
setting a first reference line and a second reference line on a rectangular coordinate system, wherein the position of the first reference line in the rectangular coordinate system is positioned on the second reference line, calibrating employees with skill coefficients positioned between the first reference line and the second reference line as middle-level employees, calibrating employees with skill coefficients positioned on and above the first reference line as advanced-level employees, and calibrating employees with skill coefficients positioned on and below the second reference line as primary-level employees;
classifying and classifying the employees according to the job title grades according to the job title grade signals, and regulating the employees generating the high-grade job titles into a set A*And set A*Each employee who generates the intermediate staff member is normalized to the set B {1, 2, 3 … m1}*And set B*Each employee who generated the primary staff member is normalized to the set C {1, 2, 3 … m2}*And set C*(1, 2, 3 … m 3), where m1 + m2+ m3 is m, and the generated set a is*Set B*And set C*All the data are sent to an integration matching unit;
the first reference line and the second reference line are set to clearly classify the skill and job title levels of the entire staff, and the specific positions of the first reference line and the second reference line in the rectangular coordinate system are specifically analyzed by the skilled person according to a specific production workshop.
Example four:
as shown in fig. 1, when the integration matching unit receives each type set, the matching training analysis processing is performed according to the received type set, and the specific operation process is as follows:
obtaining device class classification sets A, B and C, obtaining worker class classification set A*、B*And C*And matching the devices regulated in the set A, the set B and the set C in sequence respectively*Set B*And set C*And forming a production process performance queue combination Ldz according to the workers in the production linekAnd a production process performance queue assembly set LdzkK = {1, 2, 3 … 9}, where O = { a, B, C }, P = { a }, and P = { a }, respectively*，B*，C*Ldz should be notedk＝{A→A*，A→B*，A→C*，B→A*，B→B*，B→C*，C→A*，C→B*，C→C*Ldz when k is 11＝A→A*When k is 2, Ldz2＝A→B*And so on, when k is 9, Ldz9＝C→C*；
The process priority value analysis treatment is carried out on each production process efficiency queue combination, and the specific operation process is as follows:
queue composition set Ldz according to production process performancekSetting the same production task value rw for each production process efficiency queue combination, and accordingly obtaining the time hat taken for each production process efficiency queue combination to reach the production task valuekAnd the spent time hat of the queuing combination of the production process effects is arranged in a descending orderkSorting is carried out, and a descending sequence H is obtained according to the sortingk*And k is*＝{1，2，3…9}；
When k is in the specification*When 1, it indicates in descending order Hk*When k is the first bit of*When the sequence is 2, the sequence is in descending order Hk*The second digit in (1) takes time, and so on, when k is*When the value is 9, the sequence is in descending orderHk*The time spent in the ninth bit of (1);
according to descending sequence Hk*Calibrating the production process performance queue combination with the time spent at the first position in the descending sequence as the optimal process scheme, calibrating the production process performance queue combination with the time spent at the second position in the descending sequence as the second process scheme, and so on, calibrating the production process performance queue combination with the time spent at the ninth position in the descending sequence as the latest process scheme, and sending the latest process scheme to the process production execution unit;
when the process production execution unit receives the production process prioritization set F, the production process is subjected to production process grading analysis processing according to the production process prioritization set F, and the specific operation process is as follows:
the method comprises the steps that a production task value rw of a factory workshop is obtained, a task threshold value Yu4 is set according to the production task value rw, the production task value rw is substituted into the comparison analysis, when the production task value rw is larger than the maximum value of the task threshold value Yu4, a primary grade setting signal is generated, when the production task value rw is within a task threshold value Yu4, a secondary grade setting signal is generated, and when the production task value rw is smaller than the minimum value of the task threshold value Yu4, a tertiary grade setting signal is generated;
obtaining a production process priority set F, classifying the process schemes in the production process priority set F into a group of three process schemes, classifying the best process scheme, the second process scheme and the third process scheme in the production process priority set F into a first-grade instruction, classifying the fourth process scheme, the fifth process scheme and the sixth process scheme in the production process priority set F into a second-grade instruction, and classifying the seventh process scheme, the eighth process scheme and the latest process scheme in the production process priority set F into a third-grade instruction;
executing the production process according to the level gear setting signals, executing a first gear instruction according to the first gear instruction when the first level gear setting signals are generated, executing the first gear instruction according to the first gear instruction when the first level gear setting signals are generated, executing a second gear instruction according to the second gear instruction when the second level gear setting signals are generated, and executing a third gear instruction according to the third gear instruction when the third level gear setting signals are generated;
and the generated first-gear instruction, second-gear instruction and third-gear instruction are sent to a display terminal for analysis by a manager.
The formulas are all obtained by acquiring a large amount of data and performing software simulation, and a formula close to a true value is selected, and coefficients in the formulas are set by a person skilled in the art according to actual conditions;
such as the formula: czxi＝e1×tyli÷（e2×tsli＋e3×kali＋e4×guli＋jali e5）；
Collecting multiple groups of sample data by technicians in the field and setting a corresponding weight factor coefficient for each group of sample data; substituting the set weight factor coefficient and the collected sample data into a formula, forming a five-membered equation set by any five formulas, screening the calculated coefficients and taking the average value to obtain values of e1, e2, e3, e4 and e5 which are 0.7548, 2.1051, 0.4203, 2.389 and 1.3762 respectively;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and a corresponding weight factor coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relationship between the parameters and the quantized values is not affected.
When the intelligent analysis system is used, the operation difficulty of each device in a production workshop is accurately judged and analyzed by collecting operation data information of the devices in the production workshop of a factory, utilizing symbolic calibration, formulated analysis, mean value analysis and difference substitution comparison modes, and each device is definitely calibrated and analyzed according to the level of the operation difficulty by utilizing threshold substitution comparison, classification and set regulation modes, so that the operation difficulty degree of production tools and devices is accurately and definitely analyzed in an intelligent factory, and the efficient management of a production process is promoted;
by collecting state data information of staff in a factory production workshop, and utilizing symbolic calibration, normalization processing and model establishment comparison modes, the work quality level of each worker in the production workshop is accurately analyzed and output in a grade mode, so that the technical quality capability of the workers in the production workshop is clarified, and meanwhile, the efficient management of the production process is further promoted;
by means of matching and integrating, equipment groups classified according to operation difficulty levels and worker groups classified according to skill quality levels are subjected to matching training analysis, and a cross integration, variable verification and signal output mode is utilized, so that the accuracy and comprehensiveness of production process management are improved while a priority sequence of a production process scheme is obtained, and the development of an intelligent factory is promoted;
on the basis of the priority sequence of the production process scheme, the effect of maximizing reasonable selection of the production process is realized by means of grade division, sequence sequencing and gear setting training, so that the intelligent management of the production process is greatly promoted, and the efficient development of a production workshop is promoted.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (9)
1. The production process intelligent management system based on intelligent production is characterized by comprising a data acquisition unit, an equipment supervision unit, a personnel supervision unit, an integration matching unit, a process production execution unit and a display terminal;
the data acquisition unit is used for acquiring operation data information of equipment and state data information of staff in a factory production workshop and respectively sending the operation data information and the state data information to the equipment supervision unit and the staff supervision unit;
the equipment supervision unit is used for receiving operation data information of each equipment in a factory workshop, carrying out operation difficulty quantitative analysis processing according to the operation data information, generating a set A, a set B and a set C according to the operation difficulty quantitative analysis processing, and sending the sets A, B and C to the integration matching unit;
the personnel supervision unit is used for receiving the state data information of each employee in the factory production workshop, carrying out quantitative evaluation and analysis processing on the skill quality according to the state data information, and generating a set A according to the skill quality*Set B*And set C*And sending it to the integrated matching unit;
the integration matching unit is used for performing matching training analysis processing on the received various types of sets, generating a production process priority ordering set F according to the matching training analysis processing, and sending the production process priority ordering set F to the process production execution unit;
the process production execution unit is used for performing production process grading analysis processing according to the received production process priority set F, generating a first-grade instruction, a second-grade instruction and a third-grade instruction according to the production process priority set F, and sending the first-grade instruction, the second-grade instruction and the third-grade instruction to the display terminal for displaying.
2. The intelligent management system for the production process based on the intelligent production as claimed in claim 1, wherein the operation data information of the equipment comprises an operation duration value, an age limit value, a key control value, a failure number value and a maintenance number value, and the state data information comprises an actual operation value, a qualification value and a qualification value.
3. The intelligent management system for the production process based on the intelligent production, as claimed in claim 1, wherein the specific operation steps of the quantitative analysis and treatment of the operation difficulty are as follows:
s1: obtaining a run length magnitude tyliAge limit value tsliKey control quantity kaliFault count value guliAnd overhaul number value jaliAccording to the formula Czxi＝e1×tyli÷（e2×tsli＋e3×kali＋e4×guli＋jali e5) And i is {1, 2, 3 … n }, and the operation difficulty coefficient Czx of each device is obtainediWherein e1, e2, e3, e4 and e5 are respectivelyThe weight factor coefficients are the operation duration value, the service life limit value, the key control value, the fault number value and the overhaul number value, and e1 is more than e4 is more than e2 is more than e5 is more than e3 is more than 0, and e1 + e 2+ e3 + e4 + e5= 7.0281;
s2: the operation difficulty coefficient Czx of each deviceiPerforming mean value processing to obtain a mean value operation difficulty coefficient JCzx, and performing operation difficulty coefficient Czx of each deviceiPerforming difference judgment analysis with the mean operation difficulty coefficient JCzx to obtain a quantitative coefficient pdi；
S3: according to the step S2, three preset thresholds Yu1, Yu2 and Yu3 are set, and the quantitative coefficient pd is determinediAnd substituting the signals into a preset threshold value to perform signal comparison, classification and analysis processing, and generating a set A, a set B and a set C according to the signal comparison, classification and analysis processing.
4. The intelligent management system for the production process based on the intelligent production as claimed in claim 3, wherein the specific operation steps of the signal comparison, classification, analysis and processing are as follows:
the quantitative coefficient pdiSubstituting into preset threshold values Yu1, Yu2 and Yu3 for comparison analysis, and if the quantitative coefficient pd isiWhen the current value is within the range of the preset threshold value Yu1, a high difficulty operation signal is generated, and if the quantitative coefficient pd is within the range of the preset threshold value Yu1iIf the signal is within the range of the preset threshold value Yu2, a medium-difficulty operation signal is generated, and if the quantitative coefficient pd is within the range of the preset threshold value Yu2iWhen the signal is within the range of the preset threshold value Yu3, generating a normal difficulty operation signal;
classifying the devices according to difficulty operation grades according to the difficulty operation grades, regulating the devices generating high difficulty operation signals into a set A, regulating the set A to be {1, 2, 3 … n1}, regulating the devices generating medium difficulty operation signals into a set B, regulating the set B to be {1, 2, 3 … n2}, regulating the devices generating normal difficulty operation signals into a set C, regulating the set C to be {1, 2, 3 … n3}, wherein n1 + n2+ n3 is n.
5. The intelligent management system for the production process based on the intelligent production as claimed in claim 1, wherein the specific operation steps of the quantitative evaluation and analysis processing of the skill quality are as follows:
SS 1: obtaining the real operation value scljQualification value zgljAnd assessment magnitude khljThen, normalization processing is performed to obtain a skill coefficient Jnx of each employeej，j＝{1，2，3…m}；
SS 2: using the employee as the abscissa and the skill coefficient as the ordinate, and establishing a rectangular coordinate system based on the abscissa, the skill coefficient Jnx of each employee will be obtainedjDrawing on a rectangular coordinate system in a point drawing mode, carrying out modeling analysis processing according to the rectangular coordinate system, and generating a set A according to the modeling analysis processing*Set B*And set C*。
6. The intelligent management system for the production process based on the intelligent production as claimed in claim 5, wherein the specific operation steps of the modeling analysis processing are as follows:
setting a first reference line and a second reference line on a rectangular coordinate system, wherein the position of the first reference line in the rectangular coordinate system is positioned on the second reference line, calibrating employees with skill coefficients positioned between the first reference line and the second reference line as middle-level employees, calibrating employees with skill coefficients positioned on and above the first reference line as advanced-level employees, and calibrating employees with skill coefficients positioned on and below the second reference line as primary-level employees;
classifying and classifying the employees according to the job title grades according to the job title grade signals, and regulating the employees generating the high-grade job titles into a set A*And set A*Each employee who generates a middle-ranked employee is normalized to set B {1, 2, 3 … m1}*And set B*Each employee who generates the primary staff member is normalized to the set C {1, 2, 3 … m2}*And set C*1, 2, 3 … m3, wherein m1 + m2+ m3 is m.
7. The intelligent management system for the production process based on the intelligent production as claimed in claim 1, wherein the specific operation steps of the matching training analysis processing are as follows:
obtaining device class classification sets A, B and C, obtaining worker class classification set A*、B*And C*And matching the devices in the set A, the set B and the set C in sequence in the set A according to the device matching rules*Set B*And set C*And forming a production process performance queue combination Ldz according to the workers in the production linekAnd a production process performance queue assembly set LdzkK = {1, 2, 3 … 9}, where O = { a, B, C }, and P = { a }, where a =*，B*，C*}；
And (4) analyzing and processing the process priority values of the production process efficiency queue combinations, and generating a production process priority ordering set F according to the process priority ordering set.
8. The intelligent management system for the production process based on the intelligent production as claimed in claim 7, wherein the specific operation steps of the process priority value analysis processing are as follows:
queue composition set Ldz according to production process performancekRespectively setting the same production task value rw for each production process efficiency queue combination, and accordingly obtaining the time hat taken by each production process efficiency queue combination to reach the production task valuekAnd the spent time hat of the queuing combination of the production process effects is arranged in a descending orderkSorting is carried out, and a descending sequence H is obtained according to the sortingk*And k is an*＝{1，2，3…9}；
According to descending sequence Hk*The production process performance queue combination with the time spent at the first position in the descending sequence is marked as the optimal process scheme, the production process performance queue combination with the time spent at the second position in the descending sequence is marked as the second process scheme, and so on, the production process performance queue combination with the time spent at the ninth position in the descending sequence is marked as the latest process scheme, and the production process priority ordering set F is generated according to the latest process scheme.
9. The intelligent management system for the production process based on the intelligent production as claimed in claim 1, wherein the specific operation steps of the production process profile analysis processing are as follows:
acquiring a production task value rw of a factory workshop, setting a task threshold value Yu4 according to the production task value rw, substituting the production task value rw into the task threshold value Yu4 for comparison and analysis, and generating a primary level setting signal, a secondary level setting signal and a tertiary level setting signal according to the result;
acquiring a production process priority set F, grading the process schemes in the production process priority set F by taking three process schemes as a group, and generating a first-gear instruction, a second-gear instruction and a third-gear instruction;
and executing the production process according to the level of the grade setting signal, executing the first-grade instruction according to the first-grade setting signal when the first-grade setting signal is generated, executing the second-grade instruction according to the second-grade setting signal when the second-grade setting signal is generated, and executing the third-grade instruction according to the third-grade setting signal when the third-grade setting signal is generated.
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| CN115237079A (en) * | 2022-09-15 | 2022-10-25 | 双阳化工淮安有限公司 | Intelligent control system and control method for equipment for chemical production |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115237079A (en) * | 2022-09-15 | 2022-10-25 | 双阳化工淮安有限公司 | Intelligent control system and control method for equipment for chemical production |
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