CN117148804A - Special steel smelting control method and system combining application requirements - Google Patents
Special steel smelting control method and system combining application requirements Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 263
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 142
- 239000010959 steel Substances 0.000 title claims abstract description 142
- 238000003723 Smelting Methods 0.000 title claims abstract description 93
- 230000008569 process Effects 0.000 claims abstract description 238
- 238000004519 manufacturing process Methods 0.000 claims abstract description 36
- 238000011156 evaluation Methods 0.000 claims description 44
- 238000005457 optimization Methods 0.000 claims description 18
- 238000012360 testing method Methods 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 5
- 230000007423 decrease Effects 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 25
- 239000000463 material Substances 0.000 abstract description 4
- 230000035945 sensitivity Effects 0.000 description 6
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 230000007797 corrosion Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010206 sensitivity analysis Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- WFKWXMTUELFFGS-UHFFFAOYSA-N tungsten Chemical compound [W] WFKWXMTUELFFGS-UHFFFAOYSA-N 0.000 description 2
- 229910052721 tungsten Inorganic materials 0.000 description 2
- 239000010937 tungsten Substances 0.000 description 2
- 229910000975 Carbon steel Inorganic materials 0.000 description 1
- 229910000976 Electrical steel Inorganic materials 0.000 description 1
- 229910000617 Mangalloy Inorganic materials 0.000 description 1
- ZOKXTWBITQBERF-UHFFFAOYSA-N Molybdenum Chemical compound [Mo] ZOKXTWBITQBERF-UHFFFAOYSA-N 0.000 description 1
- 229910018487 Ni—Cr Inorganic materials 0.000 description 1
- 229910045601 alloy Inorganic materials 0.000 description 1
- 239000000956 alloy Substances 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000007664 blowing Methods 0.000 description 1
- 239000010962 carbon steel Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- QNHZQZQTTIYAQM-UHFFFAOYSA-N chromium tungsten Chemical compound [Cr][W] QNHZQZQTTIYAQM-UHFFFAOYSA-N 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- PYLLWONICXJARP-UHFFFAOYSA-N manganese silicon Chemical compound [Si].[Mn] PYLLWONICXJARP-UHFFFAOYSA-N 0.000 description 1
- 229910052750 molybdenum Inorganic materials 0.000 description 1
- 239000011733 molybdenum Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 229910001220 stainless steel Inorganic materials 0.000 description 1
- 239000010935 stainless steel Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
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- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
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Abstract
The invention provides a special steel smelting control method and system combining application requirements, and relates to the technical field of intelligent control, wherein the method comprises the following steps: p performance indexes of target special steel materials and Q technological parameter indexes are obtained; acquiring a sensitive performance index and P-1 first association degrees; calculating to obtain Q second association degrees; constructing an objective function; acquiring a historical process parameter set, and expanding to acquire a plurality of sample process parameter sets according to the Q second association degrees; the method comprises the steps of obtaining an optimal technological parameter set, controlling smelting production of target special steel by adopting the optimal technological parameter set, and solving the technical problems that the quality of the steel is poor due to unreasonable and inaccurate setting of the smelting technological parameter, and the operation effect of the industry is affected after the steel is further applied to the industry, so that the smelting technological parameter is optimized, the smelting quality of the special steel is improved, and the application performance of the special steel is guaranteed.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to a special steel smelting control method and system combining application requirements.
Background
The special steel is prepared by adding one or more alloy elements into carbon steel in proper amount to change the structure of the steel, so that the steel has various special properties such as high strength and hardness, good plasticity and toughness, wear resistance and corrosion resistance, and many other excellent properties such as tungsten steel, manganese steel and the like. The special steel can be used in extremely severe environments and is generally used in special industries, such as the ship industry, so that the special steel has higher performance requirements, and when special steel is smelted, the smelting process parameters need to be strictly controlled.
In the prior art, the smelting of special steel is mostly carried out according to the unified smelting process parameters set according to the type of the steel, and the technical problems that the quality of the steel is poor due to unreasonable and inaccurate setting of the smelting process parameters, and the operation effect of the industry is affected after the steel is further applied to the industry are solved.
Disclosure of Invention
The invention provides a special steel smelting control method and system combining application requirements, which are used for solving the technical problems that in the prior art, the smelting of special steel is carried out by setting uniform smelting process parameters according to the type of the steel, and the quality of the steel is poor due to unreasonable and inaccurate smelting process parameter setting, and the operation effect of the industry is affected after the steel is further applied to the industry.
According to a first aspect of the present invention, there is provided a special steel smelting control method in combination with application requirements, comprising: p performance indexes of target special steel are obtained, Q technological parameter indexes of smelting production are carried out on the target special steel, and P and Q are integers larger than 1; according to the data of the target special steel in use, analyzing and acquiring sensitive performance indexes in the P performance indexes, and P-1 first association degrees of other P-1 performance indexes and the sensitive performance indexes; respectively analyzing the association degrees of the Q technological parameters and the P performance indexes, and combining the P-1 first association degrees to calculate and obtain Q second association degrees; constructing an objective function according to the sensitive performance index and the P-1 first association degrees; acquiring a historical process parameter set according to smelting production data in the historical time of the target special steel, and expanding to obtain a plurality of sample process parameter sets according to the Q second relevancy, wherein each sample process parameter set comprises process parameters of the Q process parameter indexes; and optimizing in the plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set, and controlling smelting production of the target special steel by adopting the optimal process parameter set, wherein the optimizing is performed according to a preset optimizing rule.
According to a second aspect of the present invention, there is provided a special steel smelting control system incorporating application requirements, comprising: the index acquisition module is used for acquiring P performance indexes of target special steel and Q technological parameter indexes for smelting and producing the target special steel, wherein P and Q are integers larger than 1; the first association degree acquisition module is used for analyzing and acquiring sensitive performance indexes in the P performance indexes and other P-1 performance indexes and P-1 first association degrees of the sensitive performance indexes according to the data of the target special steel in use; the second association degree calculation module is used for respectively analyzing the association degrees of the Q technological parameters and the P performance indexes, and calculating to obtain Q second association degrees by combining the P-1 first association degrees; the objective function construction module is used for constructing an objective function according to the sensitive performance index and the P-1 first relevancy; the process parameter expansion module is used for acquiring a historical process parameter set according to smelting production data in the historical time of the target special steel, expanding and acquiring a plurality of sample process parameter sets according to the Q second relevancy, wherein each sample process parameter set comprises process parameters of the Q process parameter indexes; and the parameter optimizing module is used for optimizing in the plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set, and the optimal process parameter set is adopted to control smelting production of the target special steel, wherein the optimizing is performed according to a preset optimizing rule.
According to the special steel smelting control method combining application requirements, the sensitivity analysis is carried out on the performance indexes during special steel smelting to obtain the sensitivity performance indexes, and then the first association degree between the sensitivity performance indexes and other performance indexes is calculated, so that when the steel smelting process parameters are optimized, the sensitivity performance indexes are focused, the process parameter optimizing precision and accuracy are improved, and the technical effect of improving the steel smelting quality is achieved. Further, a plurality of sample process parameter sets are obtained by calculating a second association degree between the process parameter indexes and the performance indexes and expanding the historical process parameter sets, so that the expansion of sample data is realized, and the technical effect of improving the optimizing precision of the process parameters is achieved. And finally, optimizing in a plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set, and controlling smelting production of the target special steel by adopting the optimal process parameter set to achieve the technical effects of improving the rationality of the process parameters, further improving the smelting quality and ensuring the application performance of the special steel.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a special steel smelting control method combining application requirements according to an embodiment of the present invention;
FIG. 2 is a flow chart of a process for obtaining a plurality of sample process parameter sets according to an embodiment of the present invention;
FIG. 3 is a flow chart of obtaining an optimal process parameter set according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a special steel smelting control system according to an embodiment of the present invention, which combines application requirements.
Reference numerals illustrate: the system comprises an index acquisition module 11, a first relevance acquisition module 12, a second relevance calculation module 13, an objective function construction module 14, a process parameter expansion module 15 and a parameter optimizing module 16.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problems that in the prior art, the smelting of special steel is carried out according to the unified smelting process parameters set according to the type of the steel, and the quality of the steel is poor due to unreasonable and inaccurate smelting process parameters, and the operation effect of the industry is affected after the steel is further applied to the industry, the inventor obtains the special steel smelting control method and system combining the application requirements through creative labor.
Example 1
Fig. 1 is a diagram of a special steel smelting control method combining application requirements, which is provided by an embodiment of the invention, and the method comprises the following steps:
step S100: p performance indexes of target special steel are obtained, Q technological parameter indexes of smelting production are carried out on the target special steel, and P and Q are integers larger than 1;
specifically, the target special steel material refers to any type of special steel material, such as tungsten steel, manganese silicon steel, molybdenum steel, tungsten-chromium steel-nickel-chromium steel (stainless steel) and the like, and the performance indexes comprise indexes such as strength, hardness, plasticity, toughness, wear resistance, corrosion resistance and the like which can evaluate the quality of the steel material; the technological parameter indexes refer to technological indexes which influence the smelting quality of the steel during steel smelting, and comprise indexes such as raw material molten iron components, current density, current time, furnace temperature, furnace pressure, softening temperature, blowing requirement and the like. The special steel is different in type, corresponding performance indexes and process indexes are different, P performance indexes are determined according to the type of the target special steel, and Q process parameter indexes for smelting and producing the target special steel are determined, wherein P and Q are integers larger than 1.
Step S200: according to the data of the target special steel in use, analyzing and acquiring sensitive performance indexes in the P performance indexes, and P-1 first association degrees of other P-1 performance indexes and the sensitive performance indexes;
as shown in fig. 2, step S200 of the embodiment of the present invention includes:
step S210: acquiring a plurality of evaluation data of a plurality of users using the target special steel for the target special steel, wherein each evaluation data comprises P evaluation results with the P performance indexes being qualified or unqualified;
step S220: acquiring P total times of unqualified evaluation results of the P performance indexes in the plurality of evaluation data, and taking the performance index corresponding to the maximum total times as the sensitive performance index;
step S230: and obtaining the ratio of the times of unqualified occurrence of other P-1 performance indexes and the evaluation results of the sensitive performance indexes to the plurality of evaluation data, and obtaining the P-1 first association degrees.
Specifically, the sensitive performance index refers to a performance index with the largest number of unqualified user evaluation in a historical use condition, and the specific acquisition process is as follows: obtaining multiple evaluation data of the target special steel by multiple users using the target special steel, wherein each evaluation data comprises P evaluation results with P performance indexes being qualified or unqualified, for example, a user evaluation questionnaire can be set, such as a user evaluation link or a website is generated, the performance indexes of the target steel are subjected to qualified or unqualified evaluation by the users using the target steel through the link, the evaluation results of the user evaluation questionnaire of the multiple users are subjected to statistical analysis through a background, multiple evaluation data are obtained, one evaluation data represents the evaluation results of P performances of the target special steel by one user, and one evaluation data comprises P evaluation results with P performance indexes being qualified or unqualified, such as qualified hardness, unqualified brittleness, unqualified toughness and the like.
And further analyzing the plurality of evaluation data to obtain P total times when the evaluation result of the P performance indexes is unqualified, and taking the performance index corresponding to the maximum total times, namely the performance index with the maximum unqualified times of user evaluation, as the sensitive performance index.
Further calculating the first correlation between other P-1 performance indexes and P-1 first correlation between the other P-1 performance indexes and the sensitive performance indexes, that is, the disqualification of the sensitive performance indexes may have a correlation with the disqualification of other performance indexes, so that the ratio of the number of times of disqualification of other P-1 performance indexes and the evaluation result of the sensitive performance indexes to the plurality of evaluation data is obtained, in popular terms, one evaluation data may contain a plurality of disqualification performance indexes, the number of evaluation data of which any performance index except the sensitive performance indexes is simultaneously disqualified is obtained respectively, then the ratio of the number to the total number of the plurality of evaluation data is used as the first correlation between any performance index and the sensitive performance indexes, that is, the first correlation between P-1 first correlation is required, and it is to be noted that in this embodiment, by obtaining the sensitive performance indexes according to the disqualification times of the performance indexes, in practical application, the user may also obtain the sensitive performance indexes by analyzing the importance of the performance indexes through other data, that is the important performance indexes that need to be focused in the smelting process. Therefore, when the smelting control of the target special steel is carried out subsequently, the smelting effect of the steel smelting process parameters can be evaluated through the first association degree, the optimal process parameters can be conveniently obtained to control the smelting of the target special steel, the use requirement is met to the maximum extent, the smelting quality of the steel is improved, and the subsequent use effect of the steel is guaranteed to be good.
Step S300: respectively analyzing the association degrees of the Q technological parameters and the P performance indexes, and combining the P-1 first association degrees to calculate and obtain Q second association degrees;
the step S300 of the embodiment of the present invention includes:
step S310: respectively obtaining the ratio of the times of unqualified P performance indexes to the total times when the Q technological parameters are abnormal according to the unqualified production data in the historical time of the target special steel, and obtaining Q ratio sets;
step S320: according to the sensitive performance index and the P-1 first association degrees, weight distribution is carried out to obtain P weight values, wherein the weight value of the sensitive performance index is 0.5;
step S330: and respectively carrying out weighted calculation on the P ratios in the Q ratio sets by adopting the P weight values to obtain the Q second association degrees.
Specifically, the association degrees of the Q technological parameters and the P performance indexes are respectively analyzed, the P-1 first association degrees are combined, Q second association degrees are obtained through calculation, the influence degree of the Q technological parameters on the P performance indexes is represented by the Q second association degrees, for example, the influence degree of furnace temperature, softening temperature and the like on the toughness of target special steel during smelting is represented, and the calculation process of the Q second association degrees is as follows:
According to the unqualified production data in the historical time (such as the past month, half year and the like) of the target special steel, the unqualified production data comprises historical abnormal process parameters and unqualified performance indexes during smelting, the unqualified times of the P performance indexes when the Q process parameters are abnormal, such as the unqualified times of the P performance indexes of steel hardness, brittleness, toughness and the like when the softening temperature is abnormal, are respectively obtained through statistical analysis on the unqualified production data, the ratio of the unqualified times of the P performance indexes to the total unqualified times of the P performance indexes is carried out, a ratio set corresponding to one process parameter is obtained through P ratio result combination, and Q ratio sets corresponding to the Q process parameters are obtained based on the ratio set.
And carrying out weight distribution according to the sensitive performance index and the P-1 first relevancy to obtain P weight values, wherein the most unqualified times of the sensitive performance index are required to pay important attention to the sensitive performance index in the use process of the target special steel, the weight value of the sensitive performance index is 0.5, then carrying out weight setting on other P-1 performance indexes according to the P-1 first relevancy of the sensitive performance index and other P-1 performance indexes, wherein the larger the first relevancy is, the larger the corresponding weight value is, so that the P-1 weight value corresponding to other P-1 performance indexes is obtained, the weight value of the P-1 weight value is smaller than the weight value corresponding to the sensitive performance index, so that the P weight value is required to be explained, the weight value of the sensitive performance index is set to be 0.5 based on the general condition, and if the unqualified times or the importance of the sensitive performance index is larger in the actual smelting process of the steel is required to be subjected to actual adjustment, the P weight value is not limited. And finally, respectively carrying out weighted calculation on P ratios in the Q ratio sets by adopting the P weight values to obtain the Q second association degrees, and providing data support for subsequent smelting control.
Step S400: constructing an objective function according to the sensitive performance index and the P-1 first association degrees;
specifically, according to the sensitive performance index and the P-1 first relevancy, an objective function is constructed, wherein the objective function is used for evaluating the advantages and disadvantages of different process parameter sets, and the objective function has the following formula:
；
wherein,for the adaptability of the ith process parameter set, the steel smelting effect by adopting the ith process parameter set can be simply understood as that one process parameter set comprises process parameters of a plurality of process parameter indexes, namely>The larger the value of (2) is, the better the smelting effect of the steel is; />The first performance index is a first weight value of the first performance index, and the first performance index is any performance index in the P performance indexes; t (T)For testing the number of tests of the first performance indicator +.>For the j-th test result of the first performance index, i.e. the index value corresponding to the first performance index,/-, is given>The first test standard is the first performance index, namely the index value when the first performance index is qualified.
By constructing the objective function, the smelting effect bias evaluation of a plurality of process parameter sets is realized, and the effect of facilitating the optimizing of the process parameters is achieved.
Step S500: acquiring a historical process parameter set according to smelting production data in the historical time of the target special steel, and expanding to obtain a plurality of sample process parameter sets according to the Q second relevancy, wherein each sample process parameter set comprises process parameters of the Q process parameter indexes;
As shown in fig. 2, step S500 in the embodiment of the present invention includes:
step S510: acquiring a plurality of preset process parameter expansion noise values;
step S520: dividing the plurality of preset process parameter expansion noise values according to the Q second association degrees to obtain Q preset process parameter expansion noise value sets;
step S530: acquiring a historical technological parameter set according to smelting production data in the historical time of the target special steel;
step S540: adopting the Q preset process parameter expansion noise value sets to perform noise data expansion processing on the process parameters of the Q process parameter indexes in the historical process parameter sets to obtain Q expansion historical process parameter sets;
step S550: and randomly selecting and combining the historical process parameters in the Q extended historical process parameter sets to obtain the plurality of sample process parameter sets.
Specifically, according to smelting production data in the historical time of the target special steel, a historical process parameter set is obtained, wherein the historical process parameter set comprises process parameters of a plurality of process parameter indexes. According to the Q second association degrees, a plurality of sample process parameter sets are obtained through expansion, and each sample process parameter set comprises process parameters of the Q process parameter indexes, so that data expansion is carried out on historical process parameters, and conventionally, the process parameters in the historical process parameter sets may not reach the optimal smelting effect, therefore, the data expansion is carried out, more sample process parameters are obtained through data expansion under the condition that the smelting effect of the optimal process parameters in the historical process parameter sets is poor, the sample data size is improved, the screening accuracy of the optimal process parameter sets is improved, the control accuracy of steel smelting is improved, and the effect of steel quality is guaranteed.
Specifically, a plurality of preset process parameter expansion noise values are obtained, and the preset process parameter expansion noise values refer to noise values obtained by expanding or contracting a plurality of process parameters, for example, noise values obtained by expanding or contracting a process parameter by 1%, 2%, 5% or the like, which are used for expanding historical process parameters. Dividing the plurality of preset process parameter expansion noise values according to the magnitude of the Q second association degrees to obtain Q preset process parameter expansion noise value sets, wherein the larger the second association degree is, the more the expansion noise values obtained by dividing are, or more noise is added, so that more process parameters can be conveniently obtained by expansion, and more historical process parameter data are obtained for the process parameter types with the second association degree, so that the precision of process parameter optimization of the corresponding process parameter types can be improved, for example, 500 noise expansion data are added for the process parameter types with the second association degree being small, 1000 noise expansion data are added for the process parameter types with the association degree being large, and the more process parameters of the process parameter types with the association degree being large are added, so that the precision of subsequent process parameter optimization is larger.
According to smelting production data in the target special steel historical time, a historical process parameter set is obtained, then the Q preset process parameter expansion noise value sets are adopted, noise data expansion processing is carried out on the process parameters of the Q process parameter indexes in the historical process parameter set, noise calculation is randomly added in the noise data expansion processing, for example, the process parameters which are increased by 1% or reduced by 5% are obtained based on the noise data expansion processing, the Q expansion historical process parameter sets are obtained, and then the historical process parameters in the Q expansion historical process parameter sets are randomly selected and combined to obtain a plurality of sample process parameter sets, wherein each sample process parameter set comprises the process parameters of the Q process parameter indexes. By expanding the process parameters, the optimizing precision of the process parameters is improved, and the technical effect of improving the control precision of steel smelting is further achieved.
Step S600: and optimizing in the plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set, and controlling smelting production of the target special steel by adopting the optimal process parameter set, wherein the optimizing is performed according to a preset optimizing rule.
As shown in fig. 3, step S600 of the embodiment of the present invention further includes:
step S610: dividing the plurality of sample process parameter sets to obtain M optimizing domains, wherein each optimizing domain comprises a plurality of sample process parameter sets, and obtaining the serial numbers 1,2,3 and … M of the M optimizing domains, wherein M is a positive integer;
step S620: randomly selecting a sample process parameter set as an initial solution S, and calculating to obtain initial fitness according to the objective function and the production detection data of the target special steel under the sample process parameter set in the initial solution;
step S630: traversing a plurality of sample process parameter sets in an optimizing domain with the number of 1, calculating fitness according to the objective function, optimizing according to the preset optimizing rule, and judging whether a new solution S' is obtained;
step S640: if yes, continuing to perform optimization in the optimizing domain with the number of 1, and if not, performing optimization in the optimizing domain with the number of 2;
Step S650: and continuing optimizing until the preset optimizing times are reached, and outputting a final solution to obtain the optimal technological parameter set.
The step S630 of the embodiment of the present invention includes:
step S631: randomly selecting and obtaining a first sample technological parameter set in an optimizing domain with the number of 1, and calculating and obtaining a first fitness according to the objective function;
step S632: judging whether the first fitness is larger than the initial fitness, if so, taking the first sample technological parameter set as a new solution S ', and if not, taking the first sample technological parameter set as a new solution S' according to the probability, wherein the probability is calculated by the following formula:
；
wherein,for the first fitness->For initial fitness, R is a positive number that decreases as the number of optimizations increases.
Specifically, optimizing is performed in the plurality of sample process parameter sets according to a preset optimizing rule based on the objective function, an optimal process parameter set is obtained, the function value of the objective function is the adaptability of the plurality of sample process parameter sets, the steel smelting effect of the plurality of sample process parameter sets is represented, the sample process parameter set with the largest adaptability is used as the optimal process parameter set, finally the smelting production of the target special steel is controlled through the optimal process parameter set, the technical objective of optimizing the process parameters is achieved, the reasonable and accurate setting of the smelting process parameters is achieved, the quality of the special steel is further improved, and the technical effect of the application performance of the special steel is guaranteed.
Specifically, the multiple sample process parameter sets are randomly divided to obtain M optimizing domains, each optimizing domain comprises multiple sample process parameter sets, and numbers 1,2,3 and … M of the M optimizing domains are obtained, wherein M is a positive integer, and M can be set by itself. And randomly selecting a sample process parameter set from a plurality of sample process parameter sets as an initial solution S, and calculating and obtaining initial fitness corresponding to the initial solution S according to the objective function and production detection data of the target special steel under the sample process parameter set in the initial solution. The production detection data comprise test results corresponding to P performance indexes of target special steels respectively, the test results are simply understood to be the sizes of the performance index values, and the performance index values are substituted into the target function to obtain the initial fitness.
Further traversing the plurality of sample process parameter sets in the optimizing domain with the number of 1, calculating the fitness according to the objective function, optimizing according to the preset optimizing rule, judging whether to obtain a new solution S ', in a simple way, sequentially calculating the fitness of the plurality of sample process parameter sets in the optimizing domain with the number of 1, if any one of the obtained fitness is greater than or equal to the initial fitness, or according to the preset optimizing rule, obtaining the new solution S ', wherein the new solution S ' is the sample process parameter set corresponding to the fitness greater than or equal to the initial fitness, at the moment, replacing the initial fitness with the new solution S ', continuing optimizing in the optimizing domain with the number of 1, and continuing to find the sample process parameter set with the fitness greater than or equal to the new solution S '. If the sample technological parameter set with the adaptability being greater than or equal to the initial adaptability is not found in the optimizing domain with the number being 1, optimizing is carried out in the optimizing domain with the number being 2, and the like, if a new solution is found in the optimizing domain with the number being 2, optimizing is carried out in the optimizing domain with the number being 1 again, otherwise, optimizing is carried out in the optimizing domain with the number being 3, and the like, optimizing is continued until the preset optimizing times are reached, the preset optimizing times are set according to the actual situation, and finally, the final solution is outputted, so that the optimal technological parameter set is obtained, the trapping of local optimization is avoided, and the effect of improving the optimizing efficiency and accuracy is achieved.
Specifically, the procedure for judging whether to obtain a new solution S' is as follows: randomly selecting and obtaining a first sample technological parameter set in the optimizing domain with the number of 1, wherein the first sample technological parameter set is any sample technological parameter set in the optimizing domain with the number of 1, and calculating and obtaining a first sample by utilizing the objective functionThe first fitness corresponding to the process parameter set. Judging whether the first fitness is greater than the initial fitness, if so, taking the first sample process parameter set as a new solution S ', if not, taking the first sample process parameter set as a new solution S ' according to probability, that is, if the first fitness is less than the initial fitness, indicating that the smelting effect of the first sample process parameter set is poor, at this time, in order to quickly perform optimizing iteration while avoiding sinking into local optimization, taking the poor first sample process parameter set as a temporary optimal process parameter set according to probability, namely, a temporary new solution S ', and taking the poor first sample process parameter set as the temporary optimal process parameter set according to probability, wherein the probability is high (for example, 0.9) at the beginning, improving optimizing iteration efficiency, gradually reducing the probability, and taking the poor sample process parameter set as the temporary optimal process parameter set at the later stage of iterative optimization (for example, 0.1), thereby achieving the effect of ensuring the accuracy of the later iterative optimization, wherein the probability is improved by Calculated, wherein->For the first fitness->For initial fitness, R is a positive number that decreases as the number of optimizations increases.
Based on the analysis, the invention provides a special steel smelting control method combining application requirements, in the embodiment, the sensitivity analysis is carried out on the performance index during special steel smelting to obtain the sensitivity performance index, and then the first association degree between the sensitivity performance index and other performance indexes is calculated, so that when the steel smelting process parameter is optimized, the sensitivity performance index is focused on, and the technical effects of improving the optimizing precision and accuracy of the process parameter and further improving the steel smelting quality are achieved. Further, a plurality of sample process parameter sets are obtained by calculating a second association degree between the process parameter indexes and the performance indexes and expanding the historical process parameter sets, so that the expansion of sample data is realized, and the technical effect of improving the optimizing precision of the process parameters is achieved. And finally, optimizing in a plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set, and controlling smelting production of the target special steel by adopting the optimal process parameter set to achieve the technical effects of improving the rationality of the process parameters, further improving the smelting quality and ensuring the application performance of the special steel.
Example 2
Based on the same inventive concept as the special steel smelting control method combining application requirements in the foregoing embodiment, as shown in fig. 4, the present invention further provides a special steel smelting control system combining application requirements, where the system includes:
the index acquisition module 11 is used for acquiring P performance indexes of target special steel and Q technological parameter indexes for smelting and producing the target special steel, wherein P and Q are integers larger than 1;
the first association degree acquiring module 12 is configured to analyze and acquire a sensitive performance index of the P performance indexes, and other P-1 performance indexes and P-1 first association degrees of the sensitive performance indexes according to data of the target special steel in use;
the second association degree calculating module 13 is configured to analyze association degrees of the Q process parameters and the P performance indexes, and calculate Q second association degrees by combining the P-1 first association degrees;
the objective function construction module 14 is configured to construct an objective function according to the sensitive performance index and the P-1 first correlations;
The process parameter expansion module 15 is used for acquiring a historical process parameter set according to smelting production data in the historical time of the target special steel, and expanding to obtain a plurality of sample process parameter sets according to the Q second relevancy, wherein each sample process parameter set comprises process parameters of the Q process parameter indexes;
and the parameter optimizing module 16 is configured to perform optimizing within the plurality of sample process parameter sets based on the objective function, obtain an optimal process parameter set, and control smelting production of the target special steel by adopting the optimal process parameter set, where the optimizing is performed according to a preset optimizing rule.
Further, the first association degree obtaining module 12 is further configured to:
acquiring a plurality of evaluation data of a plurality of users using the target special steel for the target special steel, wherein each evaluation data comprises P evaluation results with the P performance indexes being qualified or unqualified;
acquiring P total times of unqualified evaluation results of the P performance indexes in the plurality of evaluation data, and taking the performance index corresponding to the maximum total times as the sensitive performance index;
And obtaining the ratio of the times of unqualified occurrence of other P-1 performance indexes and the evaluation results of the sensitive performance indexes to the plurality of evaluation data, and obtaining the P-1 first association degrees.
Further, the second association degree calculating module 13 is further configured to:
respectively obtaining the ratio of the times of unqualified P performance indexes to the total times when the Q technological parameters are abnormal according to the unqualified production data in the historical time of the target special steel, and obtaining Q ratio sets;
according to the sensitive performance index and the P-1 first association degrees, weight distribution is carried out to obtain P weight values, wherein the weight value of the sensitive performance index is 0.5;
and respectively carrying out weighted calculation on the P ratios in the Q ratio sets by adopting the P weight values to obtain the Q second association degrees.
Further, the objective function construction module 14 is further configured to:
and constructing the objective function according to the P weight values, wherein the objective function comprises the following formula:
；
wherein,for the i-th process parameter set, < +.>A first weight value for the first performance index, T is the number of tests for testing the first performance index, < - >For the j-th test result of the first performance index,/for the test result of the j-th test of the first performance index>A first test criterion that is a first performance indicator.
Further, the process parameter expansion module 15 is further configured to:
acquiring a plurality of preset process parameter expansion noise values;
dividing the plurality of preset process parameter expansion noise values according to the Q second association degrees to obtain Q preset process parameter expansion noise value sets;
acquiring a historical technological parameter set according to smelting production data in the historical time of the target special steel;
adopting the Q preset process parameter expansion noise value sets to perform noise data expansion processing on the process parameters of the Q process parameter indexes in the historical process parameter sets to obtain Q expansion historical process parameter sets;
and randomly selecting and combining the historical process parameters in the Q extended historical process parameter sets to obtain the plurality of sample process parameter sets.
Further, the parameter optimizing module 16 is further configured to:
dividing the plurality of sample process parameter sets to obtain M optimizing domains, wherein each optimizing domain comprises a plurality of sample process parameter sets, and obtaining the serial numbers 1,2,3 and … M of the M optimizing domains, wherein M is a positive integer;
Randomly selecting a sample process parameter set as an initial solution S, and calculating to obtain initial fitness according to the objective function and the production detection data of the target special steel under the sample process parameter set in the initial solution;
traversing a plurality of sample process parameter sets in an optimizing domain with the number of 1, calculating fitness according to the objective function, optimizing according to the preset optimizing rule, and judging whether a new solution S' is obtained;
if yes, continuing to perform optimization in the optimizing domain with the number of 1, and if not, performing optimization in the optimizing domain with the number of 2;
and continuing optimizing until the preset optimizing times are reached, and outputting a final solution to obtain the optimal technological parameter set.
Further, the parameter optimizing module 16 is further configured to:
randomly selecting and obtaining a first sample technological parameter set in an optimizing domain with the number of 1, and calculating and obtaining a first fitness according to the objective function;
judging whether the first fitness is larger than the initial fitness, if so, taking the first sample technological parameter set as a new solution S ', and if not, taking the first sample technological parameter set as a new solution S' according to the probability, wherein the probability is calculated by the following formula:
；
Wherein,for the first fitness->For initial fitness, R is a positive number that decreases as the number of optimizations increases.
The specific example of the special steel smelting control method according to the first embodiment is also applicable to the special steel smelting control system according to the present embodiment, and the specific description of the special steel smelting control method according to the first embodiment is clearly known to those skilled in the art, so that the detailed description of the special steel smelting control system according to the first embodiment is omitted herein for brevity.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. The special steel smelting control method combining application requirements is characterized by comprising the following steps of:
p performance indexes of target special steel are obtained, Q technological parameter indexes of smelting production are carried out on the target special steel, and P and Q are integers larger than 1;
according to the data of the target special steel in use, analyzing and acquiring sensitive performance indexes in the P performance indexes, and P-1 first association degrees of other P-1 performance indexes and the sensitive performance indexes;
respectively analyzing the association degrees of the Q technological parameters and the P performance indexes, and combining the P-1 first association degrees to calculate and obtain Q second association degrees;
constructing an objective function according to the sensitive performance index and the P-1 first association degrees;
acquiring a historical process parameter set according to smelting production data in the historical time of the target special steel, and expanding to obtain a plurality of sample process parameter sets according to the Q second relevancy, wherein each sample process parameter set comprises process parameters of the Q process parameter indexes;
and optimizing in the plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set, and controlling smelting production of the target special steel by adopting the optimal process parameter set, wherein the optimizing is performed according to a preset optimizing rule.
2. The method according to claim 1, wherein analyzing and acquiring the sensitive performance index of the P performance indexes and the other P-1 performance indexes and P-1 first correlations of the sensitive performance indexes according to the data of the target special steel in use comprises:
acquiring a plurality of evaluation data of a plurality of users using the target special steel for the target special steel, wherein each evaluation data comprises P evaluation results with the P performance indexes being qualified or unqualified;
acquiring P total times of unqualified evaluation results of the P performance indexes in the plurality of evaluation data, and taking the performance index corresponding to the maximum total times as the sensitive performance index;
and obtaining the ratio of the times of unqualified occurrence of other P-1 performance indexes and the evaluation results of the sensitive performance indexes to the plurality of evaluation data, and obtaining the P-1 first association degrees.
3. The method of claim 1, wherein analyzing the association between the Q process parameters and the P performance metrics, and calculating Q second associations in combination with the P-1 first associations, respectively, comprises:
Respectively obtaining the ratio of the times of unqualified P performance indexes to the total times when the Q technological parameters are abnormal according to the unqualified production data in the historical time of the target special steel, and obtaining Q ratio sets;
according to the sensitive performance index and the P-1 first association degrees, weight distribution is carried out to obtain P weight values, wherein the weight value of the sensitive performance index is 0.5;
and respectively carrying out weighted calculation on the P ratios in the Q ratio sets by adopting the P weight values to obtain the Q second association degrees.
4. A method according to claim 3, wherein constructing an objective function based on the sensitive performance indicators and the P-1 first correlations comprises:
and constructing the objective function according to the P weight values, wherein the objective function comprises the following formula:
；
wherein,for the i-th process parameter set, < +.>A first weight value for the first performance index, T is the number of tests for testing the first performance index, < ->For the j-th test result of the first performance index,/for the test result of the j-th test of the first performance index>A first test criterion that is a first performance indicator.
5. The method of claim 1, wherein obtaining a historical set of process parameters from the smelting production data for the historical time of the target specialty steel and expanding a plurality of sample sets of process parameters from the Q second correlations comprises:
Acquiring a plurality of preset process parameter expansion noise values;
dividing the plurality of preset process parameter expansion noise values according to the Q second association degrees to obtain Q preset process parameter expansion noise value sets;
acquiring a historical technological parameter set according to smelting production data in the historical time of the target special steel;
adopting the Q preset process parameter expansion noise value sets to perform noise data expansion processing on the process parameters of the Q process parameter indexes in the historical process parameter sets to obtain Q expansion historical process parameter sets;
and randomly selecting and combining the historical process parameters in the Q extended historical process parameter sets to obtain the plurality of sample process parameter sets.
6. The method of claim 1, wherein optimizing within the plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set comprises:
dividing the plurality of sample process parameter sets to obtain M optimizing domains, wherein each optimizing domain comprises a plurality of sample process parameter sets, and obtaining the serial numbers 1,2,3 and … M of the M optimizing domains, wherein M is a positive integer;
Randomly selecting a sample process parameter set as an initial solution S, and calculating to obtain initial fitness according to the objective function and the production detection data of the target special steel under the sample process parameter set in the initial solution;
traversing a plurality of sample process parameter sets in an optimizing domain with the number of 1, calculating fitness according to the objective function, optimizing according to the preset optimizing rule, and judging whether a new solution S' is obtained;
if yes, continuing to perform optimization in the optimizing domain with the number of 1, and if not, performing optimization in the optimizing domain with the number of 2;
and continuing optimizing until the preset optimizing times are reached, and outputting a final solution to obtain the optimal technological parameter set.
7. The method of claim 6, wherein traversing the plurality of sample process parameter sets in the optimization domain numbered 1, calculating fitness according to the objective function, optimizing according to the preset optimization rule, and determining whether to obtain a new solution S', comprises:
randomly selecting and obtaining a first sample technological parameter set in an optimizing domain with the number of 1, and calculating and obtaining a first fitness according to the objective function;
Judging whether the first fitness is larger than the initial fitness, if so, taking the first sample technological parameter set as a new solution S ', and if not, taking the first sample technological parameter set as a new solution S' according to the probability, wherein the probability is calculated by the following formula:
；
wherein,for the first fitness->For initial fitness, R is a positive number that decreases as the number of optimizations increases.
8. A specialty steel smelting control system that incorporates application requirements, said system comprising:
the index acquisition module is used for acquiring P performance indexes of target special steel and Q technological parameter indexes for smelting and producing the target special steel, wherein P and Q are integers larger than 1;
the first association degree acquisition module is used for analyzing and acquiring sensitive performance indexes in the P performance indexes and other P-1 performance indexes and P-1 first association degrees of the sensitive performance indexes according to the data of the target special steel in use;
the second association degree calculation module is used for respectively analyzing the association degrees of the Q technological parameters and the P performance indexes, and calculating to obtain Q second association degrees by combining the P-1 first association degrees;
The objective function construction module is used for constructing an objective function according to the sensitive performance index and the P-1 first relevancy;
the process parameter expansion module is used for acquiring a historical process parameter set according to smelting production data in the historical time of the target special steel, expanding and acquiring a plurality of sample process parameter sets according to the Q second relevancy, wherein each sample process parameter set comprises process parameters of the Q process parameter indexes;
and the parameter optimizing module is used for optimizing in the plurality of sample process parameter sets based on the objective function to obtain an optimal process parameter set, and the optimal process parameter set is adopted to control smelting production of the target special steel, wherein the optimizing is performed according to a preset optimizing rule.
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