CN115328054A - Intelligent manufacturing industrial software debugging system based on cloud computing - Google Patents
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Abstract
The invention discloses an intelligent manufacturing industrial software debugging system based on cloud computing, which belongs to the technical field of intelligent manufacturing, and is used for acquiring the influence of equipment aging and damage on production through analysis, so that the system is favorable for assisting intelligent manufacturing industrial software to accurately evaluate non-equipment factors in the working process, and is also favorable for timely finding the problems of low abnormal production efficiency and low production yield in equipment production, quickly locking and maintaining the equipment, and also favorable for finding the conditions of high abnormal production efficiency and high production yield in equipment production, thereby finding favorable production progress, being favorable for timely adjusting the whole production environment by a user, and fully utilizing the factors favorable for production efficiency and yield; according to the method and the system, when the industrial software is on line, part of parameters can be accurately predicted according to the actual running time and the maintenance condition of the industrial software, the running error of the industrial software at the initial stage of on line is reduced, and the accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of intelligent manufacturing, and particularly relates to an intelligent manufacturing industrial software debugging system based on cloud computing.
Background
The intelligent manufacturing means that intelligent activities such as analysis, reasoning, judgment, decision and the like can be carried out in the manufacturing process, the intelligent manufacturing can partially or completely replace mental labor of human experts in the manufacturing process, the intelligent manufacturing method has the characteristics of high production efficiency and high product consistency in production, and industrial software is an indispensable part of the intelligent manufacturing, so the accuracy of the intelligent manufacturing method is significant for the stability of the intelligent manufacturing.
In the prior art, when industrial software is put into operation, because equipment of the same model operates under different environments and different people and has different service lives, the production conditions of the equipment are obviously different, on one hand, a state value cannot be accurately preset when the software is on line, and on the other hand, software parameters cannot be further adjusted according to the actual operation condition and the average operation state.
Disclosure of Invention
The invention aims to provide an intelligent manufacturing industrial software debugging system based on cloud computing, and solves the problems that in the prior art, when industrial software is put into operation, a state value cannot be accurately preset when the software is on line, and on the other hand, software parameters cannot be further adjusted according to actual operation conditions and average operation states.
The purpose of the invention can be realized by the following technical scheme:
a cloud computing-based smart manufacturing industrial software debugging system, comprising:
the production data acquisition unit is used for acquiring the production efficiency and the production yield of each production process node;
the maintenance management unit is used for recording the maintenance times and the maintenance fund of the node equipment in the production process;
the working method of the intelligent manufacturing industrial software debugging system based on the cloud computing comprises the following steps:
s1, acquiring production efficiency data and production node data of a target product on a production process node through a production data acquisition module;
s2, acquiring a first production efficiency average value Wp and a first production yield average value Rp corresponding to production process nodes with different service lives for the same production process node of the same target product;
s3, obtaining maintenance coefficients Q of all production process nodes corresponding to the same target product, dividing the same production process node into a plurality of grades according to the maintenance coefficients from small to large, and obtaining a second production efficiency average value Wp2 and a second production yield average value Rp2 corresponding to the maintenance coefficients of all grades;
the method for calculating the maintenance coefficient Q comprises the following steps:
acquiring the maintenance times of the corresponding production procedure node, and marking the maintenance times as C;
acquiring maintenance fund corresponding to the production procedure node, and marking the maintenance fund as Z;
for one production process node, calculating a corresponding maintenance coefficient Q according to a formula Q = alpha 1+ C + Z/alpha 2, wherein Q is increased along with the increase of C and Z;
s4, according to the relation between the service life and the first production efficiency average value Wp and the first production yield average value Rp, and
the maintenance coefficient Q, the second production efficiency average value Wp2 and the second production yield average value Rp2 are obtained, and the relation between the production efficiency X of the production process node and the service life and the maintenance coefficient and the relation between the production yield of the production process node and the service life and the maintenance coefficient are obtained;
and S5, when the intelligent manufacturing industrial software is put into operation, acquiring the service life and maintenance coefficient Q corresponding to each node, predicting the current production efficiency and production yield in a future period of time according to the actual service life and maintenance coefficient, and comparing the production efficiency and production yield obtained by prediction and the actual production efficiency and production yield by the intelligent manufacturing industrial software so as to adjust the parameters.
As a further aspect of the present invention, the first average production efficiency value Wp is calculated by:
for the same production procedure node of the same target product, m production efficiency values in the production procedures with the same service life are obtained and are marked as W1, W2, 8230, wm;
according to the formula
Calculating the dispersion value of the group of data W1, W2, \8230; + Wm, and when Sw < Sy1, taking Wpp as the first average production efficiency value Wp corresponding to the service life, wherein Wpp = (W1 + W2+, \8230; + Wm)/m;
when Sw is larger than or equal to Sy1, deleting corresponding Wi values in sequence from large to small according to | Wi-Wpp | until Sw is smaller than Sy1, recording the number m1 of the deleted corresponding Wi values, and if m1/m is smaller than gamma 1, calculating the average value of the residual Wi values which are not deleted to serve as a first production efficiency average value Wp; if m1/m is larger than or equal to gamma 1, marking the first production efficiency average value Wp corresponding to the service life as a value of a to-be-determined parameter;
the gamma 1 and the Sy1 are both preset values;
and sequentially calculating to obtain first production efficiency average values Wp corresponding to different service lives, performing curve fitting on all the first production efficiency average values Wp which are not marked as the to-be-determined parameter values, and further acquiring specific numerical values of the first production efficiency average values Wp which are marked as the to-be-determined parameter values.
As a further aspect of the present invention, the calculation method of the first average production yield Rp is the same as the calculation method of the first average production efficiency Wp.
As a further scheme of the invention, for the same production process node of the same target product, the production process node with the maintenance coefficients in the same grade is obtained, and the corresponding production efficiency values are marked as W21, W22, \8230;, W2k in sequence, wherein k is the number of the maintenance coefficients in the corresponding grade;
according to the formula
Calculating the dispersion value of a group of data of W21, W22, \8230 \ 8230;, W2k, and when Sq is less than Sy3, taking W2pp as a second average production yield value Wp2 corresponding to the service life, wherein W2pp = (W21 + W22+, \8230; \ 8230;, + W2 k)/k;
when Sq is larger than or equal to Sy3, deleting corresponding W2j values in sequence from large to small according to the W2j-W2pp until the Sq is smaller than the Sy3, recording the number k3 of the deleted corresponding W2j values, and if k3/k is smaller than gamma 3, calculating the average value of the plurality of residual W2j values which are not deleted as a second production yield average value Wp2; if k3/k is larger than or equal to gamma 3, marking the average value Wp2 of the production yield corresponding to the maintenance coefficient of the gear as a value of a pending parameter;
both the gamma 3 and the Sy3 are preset values;
and sequentially calculating to obtain average values Wp2 of the production yield corresponding to the maintenance coefficients of different grades, and performing curve fitting on the average values Wp2 of the second production yield which are not marked as undetermined parameter values, so as to obtain specific values of the average values Wp2 of the second production yield which are marked as undetermined parameter values.
As a further aspect of the present invention, the method for calculating the second average production yield Rp2 is the same as the method for calculating the second average production efficiency Wp 2.
As a further aspect of the present invention, the method for calculating the relationship between the production efficiency X of the production process node and the service life and the maintenance coefficient includes: acquiring a change value X1 of the first production efficiency average value Wp along with the change of the service life according to the relationship between the service life and the first production efficiency average value Wp; obtaining a variation value X2 of the second production efficiency average value Wp2 along with the maintenance coefficient Q according to the relation between the second production efficiency average value Wp2 and the maintenance coefficient Q;
and calculating the relation between the production efficiency X of the production process node and the service life and the maintenance coefficient according to the formula X = beta (X1 + X2).
As a further aspect of the present invention, the method for obtaining β comprises: β = Xp/(X1 p + X2 p), wherein Xp is an actual production efficiency variation value of a plurality of production process nodes of the same target product; x1p is the average value of the variation value X1 of the average value of the first production efficiency corresponding to the same plurality of production process nodes of the same target product; and X2p is the average value of the variation value X2 of the second production efficiency average value Wp2 corresponding to the same plurality of production process nodes of the same target product.
The invention has the beneficial effects that:
(1) According to the invention, the influence of equipment aging and damage on production is obtained by analysis, so that the method is beneficial to assisting intelligent manufacturing industrial software to accurately evaluate non-equipment factors in the working process, is also beneficial to timely finding the problems of low abnormal production efficiency and low production yield in equipment production, is quickly locked and maintained, is also beneficial to finding the conditions of high abnormal production efficiency and high production yield in equipment production, is beneficial to finding good production progress, is beneficial to timely adjusting the overall production environment for a user, and fully utilizes the factors beneficial to the production efficiency and the yield;
(2) According to the method, the original service life data and the original production yield data are processed, the data with larger deviation values in the original data are deleted, the average value of the data with good regularity is obtained, and then the average value of the data with poor regularity is obtained through curve fitting, so that reasonable production efficiency and production yield values corresponding to each service life can be obtained, and the method is more reasonable and accurate compared with a method of directly adopting average value calculation;
(3) The invention fully considers the influence of the aging of the machine in the using process and the mechanical damage maintenance caused by different using modes and using environments on the working capacity of the mechanical equipment, and is beneficial to the accurate analysis of non-equipment influencing factors in the production process by a user;
(4) According to the method and the system, when the industrial software is on line, part of parameters can be accurately predicted according to the actual running time and the maintenance condition of the industrial software, the running error of the industrial software at the initial stage of on line is reduced, and the accuracy is improved.
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The invention will be further described with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a framework structure of an intelligent manufacturing industrial software debugging system based on cloud computing.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
A cloud computing based smart manufacturing industry software debugging system, as shown in fig. 1, comprising:
the production data acquisition unit is used for acquiring production process nodes on a target product production line, the production efficiency and the production yield of each production process node and transmitting the production efficiency and the production yield data of each production process node to the controller;
the production process node means that at the node, a product is processed from one intermediate state to another intermediate state;
the maintenance management unit is used for arranging maintenance personnel and recording the maintenance times and maintenance fund of the node equipment in the production process, wherein the maintenance fund is the amount spent in equipment maintenance;
the working method of the intelligent manufacturing industrial software debugging system based on the cloud computing comprises the following steps:
s1, acquiring production efficiency data and production node data of a target product on a production process node through a production data acquisition module;
s2, acquiring a first production efficiency average value Wp and a first production yield average value Rp corresponding to production process nodes with different service lives for the same production process node of the same target product;
the calculation method of the first average production efficiency value Wp comprises the following steps:
for the same production process node of the same target product, m production efficiency values in the production processes with the same service life are obtained and are marked as W1, W2, \8230;, wm in sequence;
according to the formula
Calculating to obtain W1,W2, \ 8230;, \ 8230; + Wm) is the set of data, and when Sw < Sy1, wpp is taken as the first average production efficiency value Wp corresponding to the service life, wherein Wpp = (W1 + W2+, \8230; + Wm)/m;
when Sw is larger than or equal to Sy1, the group of production efficiency values from W1 to Wm are considered to be relatively dispersed, at the moment, corresponding Wi values are deleted in sequence from large to small according to | Wi-Wpp | until Sw is smaller than Sy1, the number m1 of the deleted corresponding Wi values is recorded, and if m1/m is smaller than gamma 1, the average value of the remaining Wi values which are not deleted is calculated to serve as a first production efficiency average value Wp; if m1/m is larger than or equal to gamma 1, marking the first production efficiency average value Wp corresponding to the service life as a value of a to-be-determined parameter;
the gamma 1 and the Sy1 are both preset values;
sequentially calculating to obtain first production efficiency average values Wp corresponding to different service lives according to the method, performing curve fitting on all the first production efficiency average values Wp which are not marked as the to-be-determined parameter values, and further acquiring specific numerical values of the first production efficiency average values Wp which are marked as the to-be-determined parameter values;
the calculation method of the first production yield average value Rp is the same as that of the first production efficiency average value Wp;
specifically, m production yield values in the same production process with the same service life are obtained for the same production process node of the same target product, and are marked as R1, R2, \8230 \ 8230;, rm;
according to the formula
Calculating to obtain a dispersion value of a group of data of R1, R2, \8230; + Rm, and when Sr is less than Sy2, taking Rpp as a first average production yield value Rp corresponding to the service life, wherein Rpp = (R1 + R2+, \8230; + Rm)/m;
when Sr is larger than or equal to Sy2, the group of production yield values from R1 to Rm are considered to be relatively dispersed, at the moment, corresponding Ri values are deleted in sequence from big to small according to the | Ri-Rpp | until Sr is smaller than Sy2, the number m2 of the deleted corresponding Ri values is recorded, and if m2/m is smaller than gamma 2, the average value of the remaining Ri values which are not deleted is calculated to serve as a first production yield average value Rp; if m2/m is larger than or equal to gamma 2, marking the first production yield average value Rp corresponding to the service life as a undetermined parameter value;
both the gamma 2 and the Sy2 are preset values;
sequentially calculating to obtain first production yield average values Rp corresponding to different service lives according to the method, and performing curve fitting on the first production yield average values Rp which are not marked as the to-be-determined parameter values to further obtain specific numerical values of the first production yield average values Rp marked as the to-be-determined parameter values;
according to the method, original service life data and production yield data are processed, after data with large deviation values in the original data are deleted, the average value of the data with good regularity is obtained, and then the average value of the data with poor regularity is obtained through curve fitting.
S3, obtaining maintenance coefficients Q of all production process nodes corresponding to the same target product, dividing the same production process node into a plurality of grades according to the maintenance coefficients from small to large, and obtaining a second production efficiency average value Wp2 and a second production yield average value Rp2 corresponding to the maintenance coefficients of all grades;
the method for calculating the maintenance coefficient Q comprises the following steps:
acquiring the maintenance times of the corresponding production process nodes, and marking the maintenance times as C;
acquiring maintenance fund corresponding to the production procedure node, and marking the maintenance fund as Z;
for one production process node, calculating a corresponding maintenance coefficient Q according to a formula Q = alpha 1+ C + Z/alpha 2, wherein Q is increased along with the increase of C and Z;
dividing the production process nodes into a plurality of grades according to the maintenance coefficients in the sequence from small to large;
when the maintenance amount is close, the more the maintenance times are, the more the equipment damage times of the corresponding production process node is indicated, and when the maintenance times are close, the higher the maintenance amount is, the higher the damage degree of the equipment of the corresponding production process node is indicated, and by setting the maintenance coefficient Q, the aging degree of the corresponding equipment in one production node can be visually shown;
for the same production process node of the same target product, obtaining the production process node with the maintenance coefficients in the same grade, and sequentially marking the corresponding production efficiency values as W21, W22, \8230;, W2k, wherein k is the number of the maintenance coefficients in the corresponding grade;
according to the formula
Calculating the dispersion value of a group of data of W21, W22, \8230 \ 8230;, W2k, and when Sq is less than Sy3, taking W2pp as a second average production yield value Wp2 corresponding to the service life, wherein W2pp = (W21 + W22+, \8230; \ 8230;, + W2 k)/k;
when Sq is larger than or equal to Sy3, the group of production good product rate values from W21 to W2k is considered to be relatively dispersed, at the moment, corresponding W2j values are deleted in sequence from large to small according to | W2j-W2pp | until Sq is smaller than Sy3, the number k3 of the deleted corresponding W2j values is recorded, and if k3/k is smaller than gamma 3, the average value of the plurality of residual W2j values which are not deleted is calculated to serve as a second production good product rate average value Wp2; if k3/k is larger than or equal to gamma 3, marking a second production yield average value Wp2 corresponding to the maintenance coefficient of the gear as a value of a pending parameter;
both the gamma 3 and the Sy3 are preset values;
sequentially calculating according to the method to obtain second production yield average values Wp2 corresponding to different maintenance coefficients, performing curve fitting on the second production yield average values Wp2 which are not marked as undetermined parameter values, and further acquiring specific numerical values of the second production yield average values Wp2 which are marked as undetermined parameter values;
for the same production process node of the same target product, obtaining the production process node with the maintenance coefficient at the same level, and sequentially marking the corresponding production efficiency values as R21, R22, \8230;, R2k;
the calculation method of the second production yield average value Rp2 is the same as the calculation method of the second production efficiency average value Wp2;
s4, according to the relation between the service life and the first production efficiency average value Wp and the first production yield average value Rp, and
the maintenance coefficient Q, the second production efficiency average value Wp2 and the second production yield average value Rp2 are obtained, and the relation between the production efficiency X of the production process node and the service life as well as the maintenance coefficient and the relation between the production yield of the production process node and the service life as well as the maintenance coefficient are obtained;
specifically, in one embodiment of the present invention, the variation with age is obtained from the relationship between the age and the first production efficiency average value Wp, the variation value X1 of the first production efficiency average value Wp; obtaining a variation value X2 of the second production efficiency average value Wp2 along with the maintenance coefficient Q according to the relation between the second production efficiency average value Wp2 and the maintenance coefficient Q;
calculating the relation between the production efficiency X of the production process node and the service life and the maintenance coefficient according to a formula X = beta (X1 + X2);
the method for acquiring the beta comprises the following steps: β = Xp/(X1 p + X2 p), where Xp is an actual production efficiency variation value (referring to a difference from 100%) of the same target product for the same plurality of production process nodes; x1p is the average value of the variation value X1 of the average value of the first production efficiency corresponding to the same plurality of production process nodes of the same target product; x2p is the average value of the variation value X2 of the second production efficiency average value Wp2 corresponding to the same plurality of production process nodes of the same target product;
the calculation mode of the relationship between the production yield of the production process node and the service life and the maintenance coefficient is the same as the calculation mode of the relationship between the production efficiency X of the production process node and the service life and the maintenance coefficient;
s5, when the intelligent manufacturing industrial software is put into operation, service life and maintenance coefficient Q corresponding to each node are obtained, production efficiency and production yield of the intelligent manufacturing industrial software at present and in a future period are predicted according to actual service life and maintenance coefficient of the intelligent manufacturing industrial software, and the intelligent manufacturing industrial software compares the production efficiency and the production yield obtained according to prediction with the actual production efficiency and the production yield, so that partial parameters are adjusted, the influence of the production node equipment on the production efficiency and the production yield is reduced, and the intelligent manufacturing industrial software is favorable for evaluating and deciding other subjective factors of production.
The influence of equipment aging and damage on production is obtained through analysis, so that the method is beneficial to assisting intelligent manufacturing industrial software to accurately evaluate non-equipment factors in the working process, is also beneficial to timely finding the problems of low production efficiency and low production yield rate of abnormity in equipment production, is quickly locked and maintained, and is also beneficial to finding the conditions of high production efficiency and high production yield rate of abnormity in equipment production, thereby finding good production progress, being beneficial to timely adjusting the whole production environment by a user, and fully utilizing the factors beneficial to the production efficiency and the yield rate.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is illustrative and explanatory only, and it will be appreciated by those skilled in the art that various modifications, additions and substitutions can be made to the embodiments described without departing from the scope of the invention as defined in the appended claims.
Claims (7)
1. A cloud computing-based intelligent manufacturing industry software debugging system, comprising:
the production data acquisition unit is used for acquiring the production efficiency and the production yield of each production process node;
the maintenance management unit is used for recording the maintenance times and the maintenance fund of the node equipment in the production process;
the working method of the intelligent manufacturing industrial software debugging system based on the cloud computing comprises the following steps:
s1, acquiring production efficiency data and production node data of a target product on a production process node through a production data acquisition module;
s2, acquiring a first production efficiency average value Wp and a first production yield average value Rp corresponding to production process nodes with different service lives for the same production process node of the same target product;
s3, obtaining maintenance coefficients Q of all production process nodes corresponding to the same target product, dividing the same production process node into a plurality of grades according to the maintenance coefficients from small to large, and obtaining a second production efficiency average value Wp2 and a second production yield average value Rp2 corresponding to the maintenance coefficients of all grades;
the calculation method of the maintenance coefficient Q comprises the following steps:
acquiring the maintenance times of the corresponding production procedure node, and marking the maintenance times as C;
acquiring maintenance capital corresponding to the production procedure node, and marking the maintenance capital as Z;
for one production process node, calculating a corresponding maintenance coefficient Q according to a formula Q = alpha 1+ C + Z/alpha 2, wherein Q is increased along with the increase of C and Z;
s4, according to the relation between the service life and the first production efficiency average value Wp and the first production yield average value Rp, and
the maintenance coefficient Q, the second production efficiency average value Wp2 and the second production yield average value Rp2 are obtained, and the relation between the production efficiency X of the production process node and the service life and the maintenance coefficient and the relation between the production yield of the production process node and the service life and the maintenance coefficient are obtained;
s5, when the intelligent manufacturing industrial software is put into operation, obtaining the service life and the maintenance coefficient Q corresponding to each node, predicting the production efficiency and the production yield of the current and future periods according to the actual service life and the maintenance coefficient, and comparing the production efficiency and the production yield of the intelligent manufacturing industrial software obtained according to the prediction with the actual production efficiency and the production yield so as to adjust the parameters.
2. The cloud-computing-based smart manufacturing industrial software debugging system of claim 1, wherein the first production efficiency average Wp is calculated by:
for the same production procedure node of the same target product, m production efficiency values in the production procedures with the same service life are obtained and are marked as W1, W2, 8230, wm;
according to the formula
Calculating dispersion values of a group of data of W1, W2, \8230 \ 8230;, wm, and when Sw is less than Sy1, taking Wpp as a first average production efficiency value Wp corresponding to the service life, wherein Wpp = (W1 + W2+, \8230; \ 8230; + Wm)/m;
when Sw is larger than or equal to Sy1, deleting corresponding Wi values in sequence from large to small according to Wi-Wpp until Sw is smaller than Sy1, recording the number m1 of the deleted corresponding Wi values, and if m1/m is smaller than gamma 1, calculating the average value of the remaining Wi values which are not deleted to serve as a first production efficiency average value Wp; if m1/m is larger than or equal to gamma 1, marking the first production efficiency average value Wp corresponding to the service life as a value of a undetermined parameter;
the gamma 1 and the Sy1 are both preset values;
and sequentially calculating to obtain first production efficiency average values Wp corresponding to different service lives, performing curve fitting on all the first production efficiency average values Wp which are not marked as the to-be-determined parameter values, and further acquiring specific numerical values of the first production efficiency average values Wp which are marked as the to-be-determined parameter values.
3. The cloud-computing-based intelligent manufacturing industrial software debugging system of claim 2, wherein the first average production yield Rp is calculated by the same method as the first average production efficiency Wp.
4. The intelligent manufacturing industry software debugging system based on cloud computing as claimed in claim 3, wherein for the same production process node of the same target product, production process nodes with maintenance coefficients at the same level are obtained, and corresponding production efficiency values are marked as W21, W22, \8230, 8230, W2k, wherein k is the number of maintenance coefficients in the corresponding level;
according to the formula
Calculating a dispersion value of a group of data W21, W22, \8230;, W2k, and when Sq is less than Sy3, taking W2pp as a second production yield average value Wp2 corresponding to the service life, wherein W2pp = (W21 + W22+, \8230; + W2 k)/k;
when Sq is larger than or equal to Sy3, deleting corresponding W2j values in sequence from large to small according to the W2j-W2pp until the Sq is smaller than the Sy3, recording the number k3 of the deleted corresponding W2j values, and if k3/k is smaller than gamma 3, calculating the average value of the plurality of residual W2j values which are not deleted as a second production yield average value Wp2; if k3/k is larger than or equal to gamma 3, marking the average value Wp2 of the production yield corresponding to the maintenance coefficient of the gear as a value of a pending parameter;
the gamma 3 and the Sy3 are both preset values;
and sequentially calculating to obtain average values Wp2 of the production yield corresponding to the maintenance coefficients of different grades, and performing curve fitting on the average values Wp2 of the second production yield which are not marked as undetermined parameter values, so as to obtain specific values of the average values Wp2 of the second production yield which are marked as undetermined parameter values.
5. The cloud-computing-based intelligent manufacturing industrial software debugging system of claim 4, wherein the second average production yield value Rp2 is calculated in the same way as the second average production efficiency value Wp 2.
6. The cloud-computing-based intelligent manufacturing industrial software debugging system of claim 5, wherein the method for calculating the relationship between the production efficiency X of the production process node and the service life and maintenance coefficient comprises: acquiring a change value X1 of the first production efficiency average value Wp along with the change of the service life according to the relationship between the service life and the first production efficiency average value Wp; obtaining a variation value X2 of the second production efficiency average value Wp2 along with the maintenance coefficient Q according to the relation between the second production efficiency average value Wp2 and the maintenance coefficient Q;
and calculating the relation between the production efficiency X of the production process node and the service life and the maintenance coefficient according to a formula X = beta (X1 + X2).
7. The intelligent manufacturing industry software debugging system based on cloud computing of claim 6, wherein the obtaining method of β is: β = Xp/(X1 p + X2 p), where Xp is an actual production efficiency variation value of the same production process nodes of the same target product; x1p is the average value of the variation values X1 of the average values of the first production efficiency corresponding to the same plurality of production process nodes of the same target product; and X2p is the average value of the variation value X2 of the second production efficiency average value Wp2 corresponding to the same plurality of production process nodes of the same target product.
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