CN117057631B - Intelligent control method and system for production of tool pliers - Google Patents
Intelligent control method and system for production of tool pliers Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 72
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- 229910000831 Steel Inorganic materials 0.000 claims description 4
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- FFBHFFJDDLITSX-UHFFFAOYSA-N benzyl N-[2-hydroxy-4-(3-oxomorpholin-4-yl)phenyl]carbamate Chemical compound OC1=C(NC(=O)OCC2=CC=CC=C2)C=CC(=C1)N1CCOCC1=O FFBHFFJDDLITSX-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses an intelligent control method and system for the production of tool pliers, which relate to the technical field of intelligent control, and the method comprises the following steps: obtaining data of damage to P parts of the target tool clamp in the using process, and obtaining P damage data sets; according to the P damage data sets, P fault coefficients of the P parts are analyzed, and P key coefficients of the P parts are obtained; constructing an objective function; adjusting and optimizing preset heat treatment parameters based on an objective function to obtain optimal heat treatment parameters, wherein in the optimizing process, whether iteration is performed or not is judged according to the fitness of each solution, and the number of solutions generated by iteration is positively correlated with the fitness; and adopting optimal heat treatment parameters to carry out heat treatment in the production process of the target tool pliers. The invention solves the technical problems of low intelligent degree and poor control quality of the tool pliers production control in the prior art, and achieves the technical effects of improving the control efficiency and the control quality.
Description
Technical Field
The invention relates to the technical field of intelligent control, in particular to an intelligent control method and system for production of tool pliers.
Background
The tool pliers are widely used as tools with high daily utilization rate and huge market demand. However, the parameter control of the tool pliers during the production process often depends on the production experience of manufacturers, and the production quality cannot be reliably ensured. The tool pliers in the prior art have the technical problems of low intelligent degree of production control and poor control quality.
Disclosure of Invention
The application provides an intelligent control method and system for production of tool pliers, which are used for solving the technical problems of low intelligent degree and poor control quality of tool pliers production control in the prior art.
In view of the above problems, the present application provides a method and a system for intelligent control of tool pliers production.
In a first aspect of the present application, there is provided a method for intelligently controlling the production of a tool pliers, the method comprising:
obtaining data of damage of P parts of a target tool clamp in the using process, and obtaining P damage data sets, wherein the target tool clamp is produced by adopting preset heat treatment parameters, and the P parts in the target tool clamp are used for executing different functions;
according to the P damage data sets, P fault coefficients of the P parts are analyzed, and P key coefficients of the P parts are obtained;
constructing an objective function according to the P fault coefficients, the P key coefficients and the cost for adjusting the preset heat treatment parameters;
based on the objective function, adjusting and optimizing the preset heat treatment parameters to obtain optimal heat treatment parameters, wherein in the optimizing process, whether iteration is performed or not is judged according to the fitness of each solution, and the number of solutions generated by iteration is positively related to the fitness;
and adopting the optimal heat treatment parameters to carry out heat treatment in the production process of the target tool pliers.
In a second aspect of the present application, there is provided an intelligent control system for the production of tool pliers, the system comprising:
the damage data set obtaining module is used for obtaining data of P parts of the target tool clamp, which are damaged in the using process, so as to obtain P damage data sets, wherein the target tool clamp is produced by adopting preset heat treatment parameters, and the P parts in the target tool clamp are used for executing different functions;
the key coefficient acquisition module is used for analyzing P fault coefficients of the P parts according to the P damage data sets and acquiring P key coefficients of the P parts;
the objective function construction module is used for constructing an objective function according to the P fault coefficients, the P key coefficients and the cost for adjusting the preset heat treatment parameters;
the heat treatment parameter obtaining module is used for adjusting and optimizing the preset heat treatment parameters based on the objective function to obtain optimal heat treatment parameters, wherein in the optimizing process, whether iteration is carried out or not is judged according to the fitness of each solution, and the number of solutions generated by iteration is positively related to the fitness;
and the heat treatment module is used for carrying out heat treatment in the production process of the target tool pliers by adopting the optimal heat treatment parameters.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the method, P damage data sets are obtained by obtaining data of P parts of the target tool clamp, which are damaged in the using process, wherein the target tool clamp is produced by adopting preset heat treatment parameters, the P parts of the target tool clamp are used for executing different functions, then P fault coefficients of the P parts are analyzed according to the P damage data sets, P key coefficients of the P parts are obtained, an objective function is constructed according to the P fault coefficients, the P key coefficients and the cost for adjusting the preset heat treatment parameters, and then the preset heat treatment parameters are adjusted and optimized based on the objective function, so that optimal heat treatment parameters are obtained, whether iteration is carried out or not is judged according to the fitness of each solution in the optimizing process, the number of solutions generated by iteration is positively correlated with the fitness, and heat treatment is carried out in the production process of the target tool clamp by adopting the optimal heat treatment parameters. The technical effects of improving the production quality of the tool pliers and improving the production control efficiency are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a production intelligent control method of a tool clamp according to an embodiment of the present application;
fig. 2 is a schematic flow chart of calculating and obtaining P key coefficients in the intelligent control method for producing a tool pliers according to the embodiment of the present application;
FIG. 3 is a schematic flow chart of obtaining optimal heat treatment parameters in the intelligent control method for tool pliers according to the embodiment of the present application;
fig. 4 is a schematic structural diagram of a production intelligent control system of a tool pliers according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a damage data set obtaining module 11, a key coefficient obtaining module 12, an objective function constructing module 13, a heat treatment parameter obtaining module 14 and a heat treatment module 15.
Detailed Description
The application provides an intelligent control method and system for production of tool pliers, which are used for solving the technical problems of low intelligent degree and poor control quality of tool pliers production control in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a method for intelligently controlling production of tool pliers, wherein the method comprises:
step S100: obtaining data of damage of P parts of a target tool clamp in the using process, and obtaining P damage data sets, wherein the target tool clamp is produced by adopting preset heat treatment parameters, and the P parts in the target tool clamp are used for executing different functions;
further, obtaining data of damage to P parts of the target tool pliers in the use process, and obtaining P damage data sets, where step S100 in the embodiment of the present application further includes:
step S110: obtaining damaged data of the target tool pliers in the use process within a preset time range, and obtaining a plurality of damaged data, wherein each damaged data comprises data whether the P parts are damaged or not;
step S120: and dividing the plurality of damage data according to the P parts to obtain the P damage data sets.
In one possible embodiment, the target tool pliers are tool pliers having different functions at different locations, optionally including a nipper pliers, a vice, a cutter, a small straight screwdriver, etc. The P parts correspond to different structural positions of the target tool pliers, and each different part performs different functions, such as a sharp nose pliers is arranged at the front part of a pliers head and used for shearing single strands and multiple strands with smaller wire diameters; a vice is arranged in the middle of the clamp head and can be used for fastening or unscrewing the nut; the tail part of the clamp head, namely the knife edge, is provided with a cutting opening which can be used for cutting hard metal wires such as wires, steel wires and the like. And acquiring data of P parts of the target tool clamp, which are damaged in the using process, so as to obtain the P damaged data sets. Wherein the P damage datasets reflect the extent to which P sites were damaged during use of the target tool clamp. The target tool pliers are produced by adopting preset heat treatment parameters. The preset heat treatment parameters comprise normalizing temperature, annealing temperature, quenching time, tempering temperature and the like.
Specifically, the preset time range is a preset time period for acquiring damage data of the target tool pliers, and is set by a person skilled in the art, without limitation, and optionally, the preset time range is one month, one quarter, half year, and the like. And acquiring a plurality of damage data by acquiring damaged data of the use record data of the target tool clamp in a preset time range. Wherein each damage data includes data whether the P parts are damaged, that is, a plurality of parts may be damaged simultaneously in each damage data, and only one part may be damaged. After the plurality of damage data are obtained, the P parts are used as indexes to divide the plurality of damage data, and damage data with damage occurring in the same part are divided into the same damage data set, so that the P damage data sets are obtained. Therefore, the aim of providing basis for the subsequent analysis of key coefficients of different parts is fulfilled.
Step S200: according to the P damage data sets, P fault coefficients of the P parts are analyzed, and P key coefficients of the P parts are obtained;
further, as shown in fig. 2, according to the P damage data sets, analyzing P fault coefficients of the P parts, and obtaining P key coefficients of the P parts, step S200 in this embodiment of the present application further includes:
step S210: obtaining P first sub-fault coefficients according to the ratio of the number of damage times in the P damage data sets to the number of the plurality of damage data sets respectively;
step S220: calculating and obtaining total damage times of the P parts according to the P damage data sets, and obtaining P second sub-fault coefficients according to the ratio of the damage times in the P damage data sets to the total damage times;
step S230: calculating to obtain the P fault coefficients according to the P first sub-fault coefficients and the P second sub-fault coefficients;
step S240: collecting P using times and total using times of the P parts in the target tool clamp in a preset time range;
step S250: obtaining P sub-key coefficients according to the ratio of the P using times to the total using times;
step S260: and calculating to obtain the P key coefficients according to the P sub-key coefficients and the P fault coefficients.
In one possible embodiment, the calculated result is taken as P first sub-fault coefficients by calculating the ratio of the number of damages of P sites to the number of the plurality of damaged data from the P damaged data sets, respectively. The first sub-fault coefficient is obtained by comparing the number of times of damage to one part of the target tool clamp with the number of times of damage to the whole target tool clamp.
Specifically, the total damage times of the P parts are obtained by counting the damage times of each part in the P damage data sets, the damage times corresponding to each part in the P damage data sets are compared with the total damage times, and P second sub-fault coefficients are obtained according to the calculated ratio. The second sub-fault coefficient is the ratio of the number of times of damage to the parts to the total number of times of damage to all the parts, and reflects the ratio of the faults of one part to the faults of all the parts. The first sub-fault coefficient is inconsistent with the second sub-fault coefficient, because the first sub-fault coefficient is a ratio to the number of the plurality of damaged data, and the second sub-fault coefficient is a ratio to the total number of damage data, and because one damaged data may simultaneously contain the data whether the plurality of components are damaged, the total number of damage data is greater than or equal to the number of the plurality of damaged data. And further, weighting and calculating the P first sub-fault coefficients and the P second sub-fault coefficients according to preset fault coefficient weights, so as to obtain P fault coefficients, wherein the P fault coefficients reflect the probability of faults of the P components. The preset fault coefficient weight is a weight ratio of preset weighted calculation of P first sub-fault coefficients and P second sub-fault coefficients of P parts, and is set by a person skilled in the art, without limitation.
In one possible embodiment, the P number of times of use and the total number of times of use are obtained by collecting the number of times of use of P parts of the target tool holder in a preset time range and the total number of times of use of the target tool holder, and preferably, the P number of times of use and the total number of times of use may be obtained by recording the number of times of use of the target tool holder, for example, by tracking the recording. And obtaining P sub-key coefficients according to the ratio of the P times of use to the total times of use, wherein the more times one of the P parts is used, the more important the part is indicated, and the importance degree of the part can be reflected from the dimension of the times of use based on the P sub-key coefficients. And further, carrying out weighted calculation according to the P sub-key coefficients and the P fault coefficients, determining a weighted calculation weight ratio according to the importance degree of the two dimensions of the using times and the fault occurrence degree to the production control of the target tool pliers, and carrying out weighted calculation according to the P sub-key coefficients and the P fault coefficients so as to obtain the P key coefficients. Preferably, the higher the number of uses, the higher the production control requirement of the corresponding component, and the higher the probability of failure, the higher the production control requirement of the corresponding component, and therefore, the weight ratio is determined by one skilled in the art according to the actual demand.
Step S300: constructing an objective function according to the P fault coefficients, the P key coefficients and the cost for adjusting the preset heat treatment parameters;
further, according to the P fault coefficients, the P key coefficients, and the cost of adjusting the preset heat treatment parameters, an objective function is constructed, and step S300 of the embodiment of the present application further includes:
step S310: according to the magnitudes of the P fault coefficients and the P key coefficients, weight distribution is carried out, and P weight values are obtained;
step S320: based on the P weights, constructing the objective function according to the following formula:
wherein L is the optimization middle adjustmentFitness of heat treatment parameters, C 1 And C 2 To adapt to the degree factor, M B The processing cost of the preset heat treatment parameters is M, the processing cost of the heat treatment parameters is adjusted in optimizing, and K P As the weight of the P-th position,for the hardness of the P-th position according to the preset heat treatment parameters,/for the treatment of the steel sheet>In order to process the j-th detected hardness of the P-th position in the target tool clamp according to the heat treatment parameters adjusted in optimizing, T is the detection times.
In one possible embodiment, the objective function is a function for adjusting and optimizing a preset heat treatment parameter, and is constructed based on P failure coefficients, P key coefficients, and a cost for adjusting the preset heat treatment parameter. Preferably, the P fault coefficients and the P key coefficients are added according to the P positions to obtain P coefficient sums, and then the ratio of the P coefficient sums to the sum of the P coefficient sums is used as the P weights. Further, K in the objective function is obtained from the P weights P Values. C in the objective function 1 And C 2 Is a fitness factor, wherein C 1 C is the proportion of the cost in the optimizing process 2 In order to optimize the proportion of the hardness of the target tool pliers in the process, the hardness of the tool pliers is improved, the abrasion and damage of the tool pliers in the use process can be slowed down, and C 1 And C 2 The specific values of (C) are set by those skilled in the art, and are exemplified by C when costs are more important in the production process 1 Set to 0.75, C 2 Set to 0.45. C when hardness is more important in production and the budget of cost can be properly enlarged 1 Set to 0.35, C 2 Set to 0.85. Setting a fitness factor C corresponding to the hardness 2 The higher the hardness of the corresponding product of the produced target tool pliers, the higher the resistance to abrasion during use, and the abrasion can be effectively reduced.
Step S400: based on the objective function, adjusting and optimizing the preset heat treatment parameters to obtain optimal heat treatment parameters, wherein in the optimizing process, whether iteration is performed or not is judged according to the fitness of each solution, and the number of solutions generated by iteration is positively related to the fitness;
further, as shown in fig. 3, based on the objective function, the preset heat treatment parameters are adjusted and optimized to obtain optimal heat treatment parameters, and step S400 in the embodiment of the present application further includes:
step S410: randomly adjusting the preset heat treatment parameters in an adjustment range to obtain an initial first solution;
step S420: calculating a first fitness of the first solution according to the objective function;
step S430: according to a preset iteration number Q, randomly adjusting the first solution in a preset adjustment range, and iterating to obtain Q iteration solutions;
step S440: calculating Q second fitness of the Q iterative solutions according to the objective function;
step S450: respectively judging whether the Q second fitness is larger than the first fitness, if so, receiving a corresponding iteration solution as a second solution, if not, randomly generating random numbers in one (0, 1), judging whether the random numbers are smaller than a threshold value, if so, receiving the corresponding iteration solution as the second solution, and if not, discarding the corresponding iteration solution, and taking the first solution as the second solution;
step S460: based on the updated fitness of the Q second solutions, Q adjustment iteration numbers are obtained;
step S470: according to the Q adjustment iteration numbers, carrying out random adjustment iteration optimization on the Q second solutions in a preset adjustment range;
step S480: and outputting the solution with the maximum adaptability in the iteration process until the preset iteration times are reached, and obtaining the optimal heat treatment parameters.
Further, the threshold value is calculated by the following formula:
wherein Z is a threshold value, L 2 For the second fitness, L 1 For the first fitness, N is a positive number that decreases as the number of iterations increases.
Further, based on determining the fitness of the updated Q second solutions, Q adjustment iteration numbers are obtained, and step S460 in the embodiment of the present application further includes:
step S461: according to the updated Q second solutions, calculating to obtain average fitness;
step S462: and according to the ratio of each updated second solution to the average fitness, adjusting and calculating the preset iteration number Q to obtain the Q adjustment iteration numbers.
In a possible embodiment, after the objective function is generated, the preset heat treatment parameters are adjusted and optimized by using the objective function, so as to obtain the optimal heat treatment parameters. The optimal heat treatment parameters are optimal heat treatment parameters when the production control is carried out on the target tool pliers, the production is carried out according to the optimal heat treatment parameters, the target tool pliers with optimal quality can be obtained, and the damage speeds of P positions on the tool pliers are slowed down. In the optimizing process, the adaptability of each solution is compared to judge whether iteration is needed for the solution, and when the iteration is needed for the solution, the number of solutions generated by iteration is determined according to the adaptability of the solution, so that the iteration of the solution with higher adaptability in the optimizing process can be realized, the number of solutions generated by iteration along with the increase of the adaptability is increased, and the technical effects of improving the quality and optimizing efficiency of iterative optimization are achieved.
Specifically, according to the production requirement of the target tool pliers, a person skilled in the art determines an adjustment range of preset heat treatment parameters when the target tool pliers are subjected to production and processing, randomly adjusts the preset heat treatment parameters in the adjustment range, and obtains an initial first solution according to an adjustment result. Further, the first fitness is obtained by calculating the fitness of the target tool pliers production according to the first solution according to the objective function. The preset iteration number Q is a preset number of child solutions generated when the first solution is subjected to iterative optimization, and Q is an integer greater than 1, for example, 10. And determining the times of random adjustment of the first solution in a preset adjustment range according to the preset iteration number Q, so as to obtain Q iteration solutions. Each iterative solution corresponds to a random adjustment of the first solution within a preset adjustment range.
Specifically, according to the objective function, Q iterative solutions are substituted into the objective function to perform second fitness calculation, for example, heat treatment parameters in the Q iterative solutions may be used to perform trial production of the tool pliers, and hardness data and cost data of P positions are acquired and calculated to obtain Q second fitness. The Q second fitness values reflect fitness of the Q iteration solutions to production and processing of the target tool pliers. And judging whether the Q second fitness is larger than the first fitness according to the Q second fitness, if so, indicating that the corresponding Q iteration solutions are more suitable for the production and processing of the target tool pliers than the first solution, if not, accepting the corresponding Q iteration solutions as a new second solution according to a certain probability, preferably, judging whether the random number is smaller than a threshold value by randomly generating a random number in one (0, 1), if so, accepting the corresponding iteration solution as the second solution, if not, discarding the corresponding iteration solution, and taking the first solution as the second solution, thereby improving the optimizing precision.
Specifically, the fitness average value calculation is performed according to the updated fitness of the Q second solutions, that is, the result of the accumulation calculation of the fitness of the Q second solutions is divided by the value of Q to be used as the average fitness, and the preset iteration number Q is adjusted and calculated by judging the ratio of each updated second solution to the average fitness, preferably, the calculation result obtained by multiplying the ratio by the preset iteration number Q is used as the Q adjustment iteration number. In the process of adjusting and calculating the preset iteration quantity Q, the iteration quantity of the Q second solutions is constrained, the proportion of the offspring of each second solution in the generated iteration offspring is determined, more offspring solutions can be iteratively generated by the second solutions with larger adaptability, and optimizing efficiency and accuracy are improved.
Specifically, performing random adjustment iterative optimization on the Q second solutions within a preset adjustment range according to the Q adjustment iteration numbers until the preset iteration number set by a person skilled in the art is reached, and outputting the solution with the maximum adaptability in the multiple iteration processes to obtain the optimal heat treatment parameters. Wherein the threshold value is calculated by a threshold value calculation formula.
Step S500: and adopting the optimal heat treatment parameters to carry out heat treatment in the production process of the target tool pliers.
In the embodiment of the application, the production control is performed on the target tool clamp by performing heat treatment on the production process of the target tool clamp according to the optimal heat treatment parameters. The production control of the tool pliers is intelligently carried out by intelligently obtaining the optimal heat treatment parameters, and the technical effects of improving the control efficiency and the control quality are achieved.
In summary, the embodiments of the present application have at least the following technical effects:
according to the method, the damage data of different parts of the target tool clamp in the using process are collected and summarized, so that P damage data sets providing basis for subsequent analysis and determination of the fault coefficient and the key coefficient are obtained, and then the preset heat treatment parameters are adjusted and optimized by utilizing the objective function to obtain the optimal heat treatment parameters, so that the heat treatment parameters are controlled in the production process of the target tool clamp. The technical effect of improving the control quality and intelligently controlling the production of the tool pliers is achieved.
Example two
Based on the same inventive concept as the production intelligent control method of a tool pliers in the foregoing embodiments, as shown in fig. 4, the present application provides a production intelligent control system of a tool pliers, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
the damage data set obtaining module 11 is used for obtaining data of P parts of the target tool clamp, which are damaged in the using process, so as to obtain P damage data sets, wherein the target tool clamp is produced by adopting preset heat treatment parameters, and the P parts in the target tool clamp are used for executing different functions;
the key coefficient acquisition module 12 is configured to analyze P fault coefficients of the P parts according to the P damage data sets, and acquire P key coefficients of the P parts;
the objective function construction module 13 is configured to construct an objective function according to the P fault coefficients, the P key coefficients, and the cost of adjusting the preset heat treatment parameters;
the heat treatment parameter obtaining module 14 is configured to adjust and optimize the preset heat treatment parameters based on the objective function, so as to obtain optimal heat treatment parameters, where in the optimizing process, whether iteration is performed is determined according to the fitness of each solution, and the number of solutions generated by iteration is positively correlated with the fitness;
and a heat treatment module 15, wherein the heat treatment module 15 is used for performing heat treatment in the process of producing the target tool pliers by adopting the optimal heat treatment parameters.
Further, the damaged data set obtaining module 11 is configured to perform the following method:
obtaining damaged data of the target tool pliers in the use process within a preset time range, and obtaining a plurality of damaged data, wherein each damaged data comprises data whether the P parts are damaged or not;
and dividing the plurality of damage data according to the P parts to obtain the P damage data sets.
Further, the key coefficient obtaining module 12 is configured to perform the following method:
obtaining P first sub-fault coefficients according to the ratio of the number of damage times in the P damage data sets to the number of the plurality of damage data sets respectively;
calculating and obtaining total damage times of the P parts according to the P damage data sets, and obtaining P second sub-fault coefficients according to the ratio of the damage times in the P damage data sets to the total damage times;
calculating to obtain the P fault coefficients according to the P first sub-fault coefficients and the P second sub-fault coefficients;
collecting P using times and total using times of the P parts in the target tool clamp in a preset time range;
obtaining P sub-key coefficients according to the ratio of the P using times to the total using times;
and calculating to obtain the P key coefficients according to the P sub-key coefficients and the P fault coefficients.
Further, the objective function construction module 13 is configured to perform the following method:
according to the magnitudes of the P fault coefficients and the P key coefficients, weight distribution is carried out, and P weight values are obtained;
based on the P weights, constructing the objective function according to the following formula:
wherein L is the adaptability of adjusting the heat treatment parameters in optimizing, C 1 And C 2 To adapt to the degree factor, M B The processing cost of the preset heat treatment parameters is M, the processing cost of the heat treatment parameters is adjusted in optimizing, and K P As the weight of the P-th position,for the hardness of the P-th position according to the preset heat treatment parameters,/for the treatment of the steel sheet>In order to process the j-th detected hardness of the P-th position in the target tool clamp according to the heat treatment parameters adjusted in optimizing, T is the detection times.
Further, the heat treatment parameter obtaining module 14 is configured to perform the following method:
randomly adjusting the preset heat treatment parameters in an adjustment range to obtain an initial first solution;
calculating a first fitness of the first solution according to the objective function;
according to a preset iteration number Q, randomly adjusting the first solution in a preset adjustment range, and iterating to obtain Q iteration solutions;
calculating Q second fitness of the Q iterative solutions according to the objective function;
respectively judging whether the Q second fitness is larger than the first fitness, if so, receiving a corresponding iteration solution as a second solution, if not, randomly generating random numbers in one (0, 1), judging whether the random numbers are smaller than a threshold value, if so, receiving the corresponding iteration solution as the second solution, and if not, discarding the corresponding iteration solution, and taking the first solution as the second solution;
based on the updated fitness of the Q second solutions, Q adjustment iteration numbers are obtained;
according to the Q adjustment iteration numbers, carrying out random adjustment iteration optimization on the Q second solutions in a preset adjustment range;
and outputting the solution with the maximum adaptability in the iteration process until the preset iteration times are reached, and obtaining the optimal heat treatment parameters.
Further, the threshold value in the heat treatment parameter obtaining module 14 is calculated by the following formula:
wherein Z is a threshold value, L 2 For the second fitness, L 1 For the first fitness, N is a positive number that decreases as the number of iterations increases.
Further, the heat treatment parameter obtaining module 14 is configured to perform the following method:
according to the updated Q second solutions, calculating to obtain average fitness;
and according to the ratio of each updated second solution to the average fitness, adjusting and calculating the preset iteration number Q to obtain the Q adjustment iteration numbers.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (5)
1. An intelligent control method for the production of tool pliers, which is characterized by comprising the following steps:
obtaining data of damage of P parts of a target tool clamp in the using process, and obtaining P damage data sets, wherein the target tool clamp is produced by adopting preset heat treatment parameters, and the P parts in the target tool clamp are used for executing different functions;
according to the P damage data sets, P fault coefficients of the P parts are analyzed, and P key coefficients of the P parts are obtained;
constructing an objective function according to the P fault coefficients, the P key coefficients and the cost for adjusting the preset heat treatment parameters;
based on the objective function, adjusting and optimizing the preset heat treatment parameters to obtain optimal heat treatment parameters, wherein in the optimizing process, whether iteration is performed or not is judged according to the fitness of each solution, and the number of solutions generated by iteration is positively related to the fitness;
performing heat treatment in the production process of the target tool pliers by adopting the optimal heat treatment parameters;
obtaining data of damage to P parts of the target tool clamp in the use process, and obtaining P damage data sets, wherein the data comprises:
obtaining damaged data of the target tool pliers in the use process within a preset time range, and obtaining a plurality of damaged data, wherein each damaged data comprises data whether the P parts are damaged or not;
dividing the plurality of damage data according to the P parts to obtain the P damage data sets;
according to the P damage data sets, analyzing P fault coefficients of the P parts, and obtaining P key coefficients of the P parts, including:
obtaining P first sub-fault coefficients according to the ratio of the number of damage times in the P damage data sets to the number of the plurality of damage data sets respectively;
calculating and obtaining total damage times of the P parts according to the P damage data sets, and obtaining P second sub-fault coefficients according to the ratio of the damage times in the P damage data sets to the total damage times;
calculating to obtain the P fault coefficients according to the P first sub-fault coefficients and the P second sub-fault coefficients;
collecting P using times and total using times of the P parts in the target tool clamp in a preset time range;
obtaining P sub-key coefficients according to the ratio of the P using times to the total using times;
calculating to obtain the P key coefficients according to the P sub-key coefficients and the P fault coefficients;
constructing an objective function according to the P fault coefficients, the P key coefficients, and the cost of adjusting the preset heat treatment parameters, including:
according to the magnitudes of the P fault coefficients and the P key coefficients, weight distribution is carried out, and P weight values are obtained;
based on the P weights, constructing the objective function according to the following formula:
wherein L is the adaptability of adjusting the heat treatment parameters in optimizing, C 1 And C 2 To adapt to the degree factor, M B The processing cost of the preset heat treatment parameters is M, the processing cost of the heat treatment parameters is adjusted in optimizing, and K P As the weight of the P-th position,for the hardness of the P-th position according to the preset heat treatment parameters,/for the treatment of the steel sheet>In order to process the j-th detected hardness of the P-th position in the target tool clamp according to the heat treatment parameters adjusted in optimizing, T is the detection times.
2. The method of claim 1, wherein adjusting and optimizing the preset heat treatment parameters based on the objective function to obtain optimal heat treatment parameters comprises:
randomly adjusting the preset heat treatment parameters in an adjustment range to obtain an initial first solution;
calculating a first fitness of the first solution according to the objective function;
according to a preset iteration number Q, randomly adjusting the first solution in a preset adjustment range, and iterating to obtain Q iteration solutions;
calculating Q second fitness of the Q iterative solutions according to the objective function;
respectively judging whether the Q second fitness is larger than the first fitness, if so, receiving a corresponding iteration solution as a second solution, if not, randomly generating random numbers in one (0, 1), judging whether the random numbers are smaller than a threshold value, if so, receiving the corresponding iteration solution as the second solution, and if not, discarding the corresponding iteration solution, and taking the first solution as the second solution;
based on the updated fitness of the Q second solutions, Q adjustment iteration numbers are obtained;
according to the Q adjustment iteration numbers, carrying out random adjustment iteration optimization on the Q second solutions in a preset adjustment range;
and outputting the solution with the maximum adaptability in the iteration process until the preset iteration times are reached, and obtaining the optimal heat treatment parameters.
3. The method of claim 2, wherein the threshold value is calculated by:
wherein Z is a threshold value, L 2 For the second fitness, L 1 For the first fitness, N is a positive number that decreases as the number of iterations increases.
4. The method of claim 2, wherein obtaining Q adjustment iteration numbers based on determining fitness of the updated Q second solutions comprises:
according to the updated Q second solutions, calculating to obtain average fitness;
and according to the ratio of each updated second solution to the average fitness, adjusting and calculating the preset iteration number Q to obtain the Q adjustment iteration numbers.
5. A production intelligent control system for tool pliers, characterized in that it performs the method according to any one of claims 1-4, said system comprising:
the damage data set obtaining module is used for obtaining data of P parts of the target tool clamp, which are damaged in the using process, so as to obtain P damage data sets, wherein the target tool clamp is produced by adopting preset heat treatment parameters, and the P parts in the target tool clamp are used for executing different functions;
the key coefficient acquisition module is used for analyzing P fault coefficients of the P parts according to the P damage data sets and acquiring P key coefficients of the P parts;
the objective function construction module is used for constructing an objective function according to the P fault coefficients, the P key coefficients and the cost for adjusting the preset heat treatment parameters;
the heat treatment parameter obtaining module is used for adjusting and optimizing the preset heat treatment parameters based on the objective function to obtain optimal heat treatment parameters, wherein in the optimizing process, whether iteration is carried out or not is judged according to the fitness of each solution, and the number of solutions generated by iteration is positively related to the fitness;
and the heat treatment module is used for carrying out heat treatment in the production process of the target tool pliers by adopting the optimal heat treatment parameters.
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