CN111986318A - Method and device for selecting steel to be processed - Google Patents
Method and device for selecting steel to be processed Download PDFInfo
- Publication number
- CN111986318A CN111986318A CN202010925442.2A CN202010925442A CN111986318A CN 111986318 A CN111986318 A CN 111986318A CN 202010925442 A CN202010925442 A CN 202010925442A CN 111986318 A CN111986318 A CN 111986318A
- Authority
- CN
- China
- Prior art keywords
- information
- material information
- matching
- order information
- size specification
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 94
- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 66
- 239000010959 steel Substances 0.000 title claims abstract description 66
- 239000000463 material Substances 0.000 claims abstract description 180
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 56
- 238000005520 cutting process Methods 0.000 claims abstract description 45
- 238000012216 screening Methods 0.000 claims abstract description 14
- 238000012545 processing Methods 0.000 claims description 33
- 230000002068 genetic effect Effects 0.000 claims description 27
- 238000002922 simulated annealing Methods 0.000 claims description 24
- 230000008569 process Effects 0.000 claims description 21
- 238000005457 optimization Methods 0.000 claims description 20
- 238000003860 storage Methods 0.000 claims description 13
- 238000001914 filtration Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 7
- 230000004044 response Effects 0.000 claims description 4
- 239000002699 waste material Substances 0.000 abstract description 14
- 238000004458 analytical method Methods 0.000 abstract description 4
- 108090000623 proteins and genes Proteins 0.000 description 17
- 230000004083 survival effect Effects 0.000 description 12
- 238000004364 calculation method Methods 0.000 description 9
- 238000011156 evaluation Methods 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 7
- 230000035772 mutation Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000009471 action Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000008030 elimination Effects 0.000 description 3
- 238000003379 elimination reaction Methods 0.000 description 3
- 238000010295 mobile communication Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 238000000137 annealing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 210000000349 chromosome Anatomy 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000004566 building material Substances 0.000 description 1
- 230000002559 cytogenic effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 229910003460 diamond Inorganic materials 0.000 description 1
- 239000010432 diamond Substances 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 230000002650 habitual effect Effects 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008929 regeneration Effects 0.000 description 1
- 238000011069 regeneration method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 244000017160 saligot Species 0.000 description 1
- 235000009165 saligot Nutrition 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Linguistics (AREA)
- Economics (AREA)
- Geometry (AREA)
- General Health & Medical Sciences (AREA)
- Finance (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Genetics & Genomics (AREA)
- Computer Graphics (AREA)
- Physiology (AREA)
- Accounting & Taxation (AREA)
- Molecular Biology (AREA)
- Development Economics (AREA)
- Databases & Information Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Strategic Management (AREA)
Abstract
The invention discloses a method for selecting steel to be processed, which comprises the steps of responding to received order information, and acquiring pre-stored stock material information; screening the stock material information to obtain first material information; and matching and optimizing the order information and the first material information to obtain a material selection result and output the material selection result. According to the method and the device provided by the invention, the most appropriate material and cutting method can be quickly selected by utilizing intelligent algorithm analysis, so that the problems of serious material waste, high labor and high management cost in the traditional enterprise are solved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a method and a device for selecting steel to be processed.
Background
According to the statistical report of the research data of prospective industry, 63 die steel production enterprises are in total in China, 17 provinces and cities in China are covered, 89 production lines are in total, the total capacity is about 236.2 ten thousand tons, and the total net import amount of die steel in China is about 10 ten thousand tons each year; mold steel processing enterprises of more than medium scale are nearly thousands of, and mainly provide services such as mold steel processing and the like for industries such as automobiles, household appliances, communication, building materials and the like.
However, since the current die steel processing service is basically developed according to the traditional operation mode and is not combined with the current rapidly developed intelligent analysis and intelligent technologies such as big data technology, the following problems are generally present in the whole industry and need to be solved:
1. because of various steel products, after an order comes, material comparison and material selection need to be carried out manually, the most appropriate material needed cannot be found in numerous master batches and excess materials, most people can preferentially select the master batches or large excess materials due to habitual selection of people, and the truly appropriate excess materials are easily discarded, so that the labor cost is consumed, and the material waste is easily caused;
2. because no fixed standard exists, the standards of each person for the residual materials are different, so that the phenomenon that the residual materials are discarded as the residual materials often occurs, and the material waste is serious.
Disclosure of Invention
In order to solve the problems mentioned in the background art, the inventor thinks that the intelligent algorithm analysis technology is combined with the traditional die steel processing industry to realize the rapid selection of the most appropriate material by utilizing the intelligent algorithm analysis, so as to solve the problems of serious material waste and high labor and management cost in the traditional enterprises.
In a first aspect, an embodiment of the present invention provides a method for selecting a steel material to be processed, including:
in response to the received order information, acquiring pre-stored stock material information;
screening the stock material information to obtain first material information;
and matching and optimizing the order information and the first material information to obtain a material selection result and output the material selection result.
In a second aspect, an embodiment of the present invention provides a device for selecting a steel material to be processed, including:
the information acquisition module is used for responding to the received order information and acquiring pre-stored stock material information;
the filtering module is used for screening the stock material information to obtain first material information;
and the matching optimization module is used for performing matching optimization processing on the order information and the first material information to obtain a material selection result and outputting the material selection result.
In a third aspect, an embodiment of the present invention provides a storage medium, where one or more programs including execution instructions are stored in the storage medium, and the execution instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to perform any method for selecting a steel product to be processed according to any one of the foregoing methods.
In a fourth aspect, an electronic device is provided, comprising: the steel product sorting system comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for sorting the steel product to be processed according to any one of the methods.
The embodiment of the invention has the beneficial effects that: firstly, based on the screening processing of cutting order information and prestored stock materials, the comparison data amount is reduced, and then the material selection result is quickly obtained through matching optimization processing, so that the problem of difficulty in material selection of business personnel of a steel processing enterprise is effectively solved, and an optimal material selection scheme can be obtained through matching optimization processing, so that the problems of low residual material utilization rate, low cutting efficiency, high cost, more waste materials and material waste of the steel processing enterprise are effectively solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of a method for selecting a steel material to be processed according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for performing a matching optimization process according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for selecting an optimal combination scenario using a genetic algorithm according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for performing a matching optimization process according to yet another embodiment of the present invention;
FIG. 5 is a schematic block diagram of an apparatus for selecting a steel material to be processed according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an embodiment of an electronic device according to the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
As used in this disclosure, "module," "device," "system," and the like are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, or software in execution. In particular, for example, an element may be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. Also, an application or script running on a server, or a server, may be an element. One or more elements may be in a process and/or thread of execution and an element may be localized on one computer and/or distributed between two or more computers and may be operated by various computer-readable media. The elements may also communicate by way of local and/or remote processes based on a signal having one or more data packets, e.g., from a data packet interacting with another element in a local system, distributed system, and/or across a network in the internet with other systems by way of the signal.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising", without further limitation, means that the element so defined is not excluded from the group of processes, methods, articles, or devices that include the element.
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 schematically shows a process flow of selecting a steel material to be processed, and as shown in fig. 1, an embodiment of the present invention provides a method for selecting a steel material to be processed, which includes the following steps:
step S101: in response to the received order information, pre-stored stock material information is obtained.
Step S102: and screening the stock material information to obtain first material information.
Step S103: and matching and optimizing the order information and the first material information to obtain a material selection result and output the material selection result.
The stock material information configured and stored in advance in the embodiment of the present invention may be implemented to include a surplus material shape and a size specification, and the acquired order information may be, for example, cutting order information, which may be implemented to include a cutting number, a cutting shape, and a cutting size specification. The screening of the stock material information in step S102 to obtain the first material information may be implemented by performing the screening according to the cutting size specification in the order information and the size specification in the stock material information, for example, filtering material information with a size specification smaller than the cutting size specification from the stock material information, and outputting the remaining material information after the filtering as the first material information.
In step S103, the matching optimization processing of the order information and the first material information may be implemented by selecting an optimal solution from the first material information as a material selection result using a genetic algorithm and a simulated annealing algorithm, or may also be implemented by selecting an optimal solution from the first material information as a material selection result using a genetic algorithm, a 3D digital model matching method, and a simulated annealing algorithm. Fig. 2 and fig. 3 respectively provide a specific implementation method of matching optimization processing, for example, an optimal solution is selected from first material information by using a genetic algorithm and a simulated annealing algorithm as a material selection result, as shown in fig. 2, the method includes:
step S201: and selecting an optimal combination scheme matched with the cutting order information by using a genetic algorithm. The number of data combinations in the conventional combination method is very large, the number of combinations of 100 pieces of data is about 3 × 10275, which is larger than the amount of all dust on the earth, and the amount of data processed in this way is too large, and the efficiency of calculation processing is very low. Based on this, the inventors conceived a method of simulating inheritance using a gene combination technique to achieve combination and optimization. The use of gene combination technology enables the combination of individuals by creating gene combination sequences and customizing combination rules. In addition, under the condition of adopting the gene combination technology, cytogenetics, variation and the like can be simulated based on the gene sequence, so that individuals most suitable for survival conditions can be automatically obtained through selection of superior and inferior by putting the generated individuals into a simulated living environment for survival training competition, namely, the optimal combination scheme is selected, and the optimal combination scheme can be selected very efficiently. Fig. 3 schematically shows a flow of a method for selecting an optimal combination scheme by using a genetic algorithm according to an embodiment of the present invention, which is specifically implemented as shown in fig. 3 and includes the following steps:
step S201A: gene sequences and evolution conditions were initialized. In order to realize the selection of the optimal steel material combination for the received cutting order by using the genetic algorithm, so as to solve the problems of material selection time consuming, excess material searching time consuming, excess material unreasonable utilization, material waste and the like, the phenotype of the steel material needs to be mapped into a gene sequence, as a specific implementation example, in order to realize the purpose of the present invention, the phenotype of the steel material is preferably determined as the characteristic that the information of the cutting order is adapted, for example: the steel can be only stacked, the number of the combinations is not more than S, the combination volume is not more than V, the combinations cannot be spaced, gaps L1-L2 can be configured, at least one binding surface and at most six binding surfaces are provided, and the phenotype is mapped to a chromosome gene sequence of [1, S, 0, V, 0, 1, 6, 6, L1 and L2], so that steel individuals are formed. Thereafter, steel individuals are randomly initialized to create an initial population comprising a plurality of steel individuals having such gene sequences. The initialization of the evolution condition may be to set an evolution algebra counter T and a maximum evolution algebra T, and initialize the evolution algebra to T-0, so that the evolution condition is that T is smaller than T (i.e., when the evolution algebra T is a value smaller than T, evolution iteration continues), and the termination condition is determined to be that T-T. Illustratively, M individuals are randomly generated as an initial population P (t), wherein the initial population has an evolution passage number t of 0, i.e., the initial population is P (0), and the initial population includes M individuals, each of which has a chromosome gene sequence.
Step S201B: the evolution termination condition judgment is performed, and if the evolution condition is satisfied, the processing of step S201C to step S201D is performed in a loop, and if the termination condition is satisfied, the processing of step S201E is performed. The evolution termination condition is determined according to the content of initialization of the evolution condition, and for example, in the case where the evolution condition is initialized to set an evolution algebra counter T and set the maximum evolution algebra to T, the termination condition is determined to be T ═ T.
Step S201C: and evaluating the fitness of each individual in the group P (t), and calculating the living environment according to the fitness evaluation result. The fitness is designed according to the problem to be solved by the genetic algorithm, is adaptive to the survival condition and can reflect the adaptive condition of the current individual to the survival condition. In the embodiment of the invention, because the problem to be solved by the genetic algorithm is to select the steel material which is most suitable for the current cutting order form from the first material information, wherein the most suitable for the current cutting order form means that when the selected steel material is used for carrying out the cutting task in the current cutting order form, the selected steel material can meet the requirements of highest utilization rate and lowest scrap rate, and the survival condition is generated according to the set simulation
Where t is the current evolution algebra, y is the utilization, and the function Δ (t, y) returns [0, y]And the probability that the delta (t, y) tends to 0 along with the increase of t is increased, so that the effects of improving the utilization rate of steel and reducing excess material waste are achieved, therefore, in the embodiment of the invention, the survival condition is the individual meeting the highest utilization rate and the lowest waste material rate, the individual meeting the survival condition can survive, and otherwise the individual is eliminated. Based on the purpose of the invention, the fitness evaluation method designed in the embodiment of the invention specifically comprises the following steps: followed byExtracting an individual A and an individual B by a machine, comparing PK, configuring each weight into K alpha, Kc, Kb, Ks and K beta according to a formula according to PK rules (water caltrop alpha, outline c, binding surface B, hollow quantity s and azimuth beta) in the preset survival conditions
Evaluation and scoring are carried out, and half of each round can be eliminated. For the population P (t) with the current algebra, namely the evolution algebra, being t, the fitness of each individual in the population P (t) is calculated by the fitness evaluation method, and the survival environment is operated according to the fitness to achieve the effect of superior and inferior, so that the individuals meeting the survival conditions can survive, and the individuals not meeting the survival conditions are eliminated.
The operation of performing the living environment operation refers to an operation of selecting a superior individual from a population and eliminating an inferior individual, and is implemented by a selection operator (also called a regeneration operator). The purpose of selection is to inherit optimized individuals directly to the next generation or to generate new individuals by pairwise crossing and then to inherit them to the next generation. The selection operation is based on fitness evaluation of individuals in a group, and the following selection operators are commonly used: a fitness proportion method, a random traversal sampling method and a local selection method.
Step S201D: and carrying out genetic operation on individuals in the group left after the survival environment operation. After the selection is realized by the operation of living environment, the genetic operation is carried out on the selected (i.e. not eliminated) population, so that the optimized individuals are directly inherited to the next generation or new individuals are generated by pairing and crossing and then inherited to the next generation. The optimized individuals are directly inherited to the next generation through a copying mode, for example, the same steel individuals are added to the next generation on the basis of the combined three steels. And the generation of new individuals through pairing and crossing and the inheritance to the next generation are realized through crossing operation and mutation operation. Wherein, the crossover operation is realized by applying a crossover operator to the population, and the mutation operation is realized by applying a mutation operator to the population. Specifically, the crossover operation may be to exchange gene sequences of individuals in the population according to a preset rule to obtain a crossover gene sequence as a next generation of individuals. After the crossover operation, the value of a certain gene in the population may be randomly changed, for example, the value of a certain gene in the gene sequence may be randomly changed to realize the mutation, and the individuals after the crossover mutation operation may be the next generation individuals after the evolution. Thus, the next-generation population P (t ═ t +1) can be obtained after direct inheritance, crossover, and mutation operations.
Step S201E: and outputting the obtained individual with the maximum fitness as an optimal solution. And if T is equal to T, outputting the individual with the maximum fitness obtained in the evolution process as the optimal solution, and stopping the calculation. This makes it possible to obtain, by means of genetic algorithms, an optimal combination scheme which describes the steel individuals having the most suitable characteristic requirements.
Step S203: and matching an optimal scheme matched with the optimal combination scheme of the order information from the first material information by using a simulated annealing algorithm, and outputting the optimal scheme serving as a material selection result. After the steel individual with the most suitable characteristic requirement, namely the optimal combination scheme, is obtained by utilizing the genetic algorithm, the optimal scheme can be preferably selected by matching the optimal combination scheme with the steel in the first material information, and the optimal material information output meeting the requirement is obtained. The optimal combination scheme and the steel material in the first material information can be matched by respectively taking the optimal combination scheme and the steel material in the first material information as input values, inputting the input values into a simulated annealing algorithm, and determining a preferred scheme which is most matched with the optimal combination scheme by using the simulated annealing algorithm, namely, taking the optimal solution as a final material selection result. When the simulated annealing algorithm is used for matching, the optimal combination scheme can be used as an initial solution state, a new solution is generated based on the first material information, and the current solution is updated based on the evaluation result of the solution. The specific implementation process may refer to the description of the simulated annealing algorithm in the prior art, and the embodiment of the present invention is not described herein again. Fig. 4 shows another implementation example of selecting an optimal solution from the first material information as a material selection result, for example, selecting an optimal solution from the first material information as a material selection result by using a genetic algorithm, a 3D digital model matching method, and a simulated annealing algorithm, as shown in fig. 4, the method includes:
step S201: and selecting an optimal combination scheme matched with the cutting order information by using a genetic algorithm.
Step S202: and respectively establishing corresponding 3D digital models for the optimal combination scheme and the first material information.
Step S203: and matching an optimal scheme matched with the optimal combination scheme of the order information from the first material information by using a simulated annealing algorithm and the established 3D digital model.
In this embodiment, the implementation manner of step S201 may refer to the description of the method part, and step S202 is implemented by building a 3D model by the BIM virtual model modeling algorithm on the first material information and the selected optimal combination scheme information respectively. In step S203, the established 3D digital model is used as input data of simulated annealing, and the 3D model is matched through the simulated annealing algorithm to obtain an optimal solution output. When the simulated annealing algorithm is used for matching, the 3D digital model of the optimal combination scheme can be used as an initial solution state, a new solution is generated based on the 3D digital model of the first material information, and the current solution is updated based on the evaluation result of the solution. The specific implementation process may refer to the description of the simulated annealing algorithm in the prior art, and the embodiment of the present invention is not described herein again.
Preferably, in the embodiment of the present invention, the process of selecting the optimal combination scheme by using the genetic algorithm and performing the optimal matching by using the simulated annealing algorithm is performed by a CPU (for example, the process is performed by using a CPU using an API of an operating system), and the process of constructing the 3D digital model is performed by a GPU (for example, the process is performed by using a graphics card-driven API, such as DirectX/opengloncl/rendercript, using a GPU). The GPU has higher space computing capacity and efficiency, so that the processing speed for constructing the 3D model is higher, the resources of the CPU are saved, the CPU can do other work, the resources of the CPU and the GPU are fully utilized for computing, and the running performance of the equipment is improved.
In other embodiments, after the optimal combination scheme matched with the cutting order information is selected by using the genetic algorithm, a simulated annealing algorithm may not be used, but a BIM virtual model modeling algorithm is directly used to respectively establish corresponding 3D digital models for the optimal combination scheme and the first material information, and the existing graph matching algorithm is used to perform graph matching on the 3D digital models, so that the material information with the highest matching degree with the optimal combination scheme is selected from the first material information and output as the optimal scheme.
Therefore, after the cutting order is received, the optimal cutting scheme can be found out by combining, matching and optimizing the cutting order and stock materials stored in the database in advance, and the optimal result and the cutting surface are displayed through the 3D graph. Illustratively, for example, five steels of 30 × 50 × 100 are required in the received cutting order, according to the above method steps, stock materials smaller than this are pre-stored in the database are filtered according to the conventional calculation of volume, length, width and height, and the first material information is obtained. And then, selecting the optimal combination scheme as a material selection result for output by the matching and optimization method. Therefore, the steel material selection and the optimal cutting method which have the advantages of small cutting quantity, high steel material utilization rate and less cutting residual materials as far as possible can be obtained, and the problems of difficult steel material selection and high material waste rate in the existing mode are solved. Preferably, the output material selection result includes the residual material shape and size specification of the selected material information, for example, the selected material information is characterized by the feature description of the optimal combination scheme obtained by the genetic algorithm, or is represented by the diamond angle α, the contour c, the attaching face b, the hollow number s and the orientation β, and in the latter case, each feature may be assigned with a weight, for example, each weight is configured as K α, Kc, Kb, Ks and K β, respectively, so that for the obtained optimal scheme, the evaluation scoring is performed according to the integral weight of each condition, for example, according to a formula
And evaluating and scoring, and selecting the highest score as the optimal solution to obtain the optimal solution. The method provided by the embodiment of the invention utilizes the gene combination technology and the gene elimination technology to carry out combination elimination of a large amount of data, and finally selects an effective combination method, and utilizes GPU to carry out 3D model buildingThe method comprises the steps of utilizing a CPU to carry out annealing calculation, fully utilizing system calculation resources, and finally calculating the optimal cutting method by using a 3D model matching method, thereby effectively solving the problems of low utilization rate of residual materials, low cutting efficiency, high cost, more waste materials and material waste of steel processing enterprises. In addition, the scheme of the embodiment of the invention can automatically give the optimal combination method and the number of the cutting knives for material selection through the advanced 3D algorithm and big data comparison, and can effectively solve the problem of difficult material selection of business personnel. Fig. 5 schematically shows a device for selecting a steel material to be processed according to an implementation manner of the embodiment of the present invention, and as shown in fig. 5, the device includes:
an information obtaining module 60, configured to obtain pre-stored stock material information in response to the received order information;
the filtering module 61 is used for screening the stock material information to obtain a first material information; and
and a matching optimization module 62, configured to perform matching optimization processing on the order information and the first material information to obtain a material selection result and output the material selection result.
The stock material information can be stored in a database entry mode, the stock material information can be realized to include the shape and the size specification of the surplus material, the acquired order information can also be received by the device in the embodiment of the invention in a user interface entry or uploading mode, and the order information can be realized to be cutting order information, illustratively cutting order information including the cutting quantity, the cutting shape and the cutting size specification. In a specific implementation, as described in the above method section, the filtering module 61 may perform a screening process by comparing the cutting size specification in the order information with the size specification in the stock material information, so as to obtain the first material information that includes the steel material with the size specification larger than the cutting size specification required by the order information. Therefore, the data processing amount during the optimal matching processing can be reduced through the contrast filtering, the noise is reduced, and the processing efficiency of the algorithm is improved.
The matching preference module 62 may be implemented to perform the matching preference process on the order information and the first material information by using a genetic algorithm and a simulated annealing algorithm. In a preferred embodiment, the matching preference module 62 can also be implemented to perform the matching preference process on the order information and the first material information by using a genetic algorithm, a 3D digital model matching method, and a simulated annealing algorithm. In another preferred embodiment, the matching preference module may be further implemented to perform matching preference processing on the order information and the first material information by using a genetic algorithm and a 3D digital model matching method. The specific implementation process of the matching optimization module 62 for performing the matching optimization processing can be described with reference to the foregoing method part, and is not described herein again.
Preferably, the device of the embodiment of the invention utilizes the GPU to perform 3D digital model modeling and utilizes the CPU to perform calculation of genetic algorithm and simulated annealing algorithm. The device provided by the invention can simulate the genetic rule to carry out the selection and elimination treatment, and finally selects an effective combination scheme. The device provided by the embodiment of the invention can also utilize the GPU to carry out 3D model modeling and utilize the CPU to carry out annealing calculation, fully utilizes system calculation resources and finally utilizes a 3D model matching method to calculate the optimal cutting method. Therefore, the problems of low utilization rate of residual materials, low cutting efficiency, high cost, more waste materials and material waste of steel processing enterprises are effectively solved. In addition, the scheme of the embodiment of the invention can automatically give the optimal combination method and the number of the cutting knives for material selection through the advanced 3D algorithm and big data comparison, and can effectively solve the problem of difficult material selection of business personnel.
In some embodiments, the present invention provides a non-transitory computer-readable storage medium, in which one or more programs including executable instructions are stored, where the executable instructions can be read and executed by an electronic device (including but not limited to a computer, a server, or a network device, etc.) to perform any one of the methods for selecting a steel product to be processed according to the present invention.
In some embodiments, the present invention further provides a computer program product comprising a computer program stored on a non-volatile computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform any of the above methods for selecting a steel material to be processed.
In some embodiments, an embodiment of the present invention further provides an electronic device, which includes: the steel processing system comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for selecting the steel to be processed.
In some embodiments, the present invention further provides a storage medium having a computer program stored thereon, where the computer program is capable of implementing the method for selecting a steel material to be processed when the computer program is executed by a processor.
The device for selecting the steel to be processed according to the embodiment of the present invention may be used to execute the method for selecting the steel to be processed according to the embodiment of the present invention, and accordingly achieve the technical effect achieved by the method for selecting the steel to be processed according to the embodiment of the present invention, which is not described herein again. In the embodiment of the present invention, the relevant functional module may be implemented by a hardware processor (hardware processor).
Fig. 6 is a schematic hardware structure diagram of an electronic device for executing a method for selecting a steel material to be processed according to another embodiment of the present application, and as shown in fig. 6, the electronic device includes:
one or more processors 410 and a memory 420, with one processor 410 being an example in fig. 6.
The apparatus for performing the method of selecting a steel material to be processed may further include: an input device 430 and an output device 440.
The processor 410, the memory 420, the input device 430, and the output device 440 may be connected by a bus or other means, such as the bus connection in fig. 6.
The memory 420 is used as a non-volatile computer-readable storage medium, and may be used to store a non-volatile software program, a non-volatile computer-executable program, and modules, such as program instructions/modules corresponding to the method for selecting a steel material to be processed in the embodiment of the present application. The processor 410 executes various functional applications and data processing of the server by running the nonvolatile software program, instructions and modules stored in the memory 420, that is, implements the method for selecting steel materials to be processed according to the above method embodiment.
The memory 420 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of a device that selects a steel material to be processed, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 420 may optionally include memory located remotely from processor 410, and such remote memory may be connected to a device for selecting steel to be processed via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may receive input numerical or character information and generate signals related to user settings and function controls of the device for selecting a steel material to be processed. The output device 440 may include a display device such as a display screen.
The one or more modules are stored in the memory 420 and, when executed by the one or more processors 410, perform a method of selecting a steel material to be processed in any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the methods provided in the embodiments of the present application.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to:
(1) a mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., iphones), multimedia phones, functional phones, and low-end phones, among others.
(2) Ultra mobile personal computer device: the equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as ipads.
(3) A portable entertainment device: such devices can display and play multimedia content. This type of device comprises: audio, video players (e.g., ipods), handheld game consoles, electronic books, and smart toys and portable car navigation devices.
(4) A server: the device for providing the computing service comprises a processor, a hard disk, a memory, a system bus and the like, and the server is similar to a general computer architecture, but has higher requirements on processing capacity, stability, reliability, safety, expandability, manageability and the like because of the need of providing high-reliability service.
(5) And other electronic devices with data interaction functions.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the above technical solutions substantially or contributing to the related art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. The method for selecting the steel to be processed comprises the following steps:
in response to the received order information, acquiring pre-stored stock material information;
screening the stock material information to obtain first material information;
and matching and optimizing the order information and the first material information to obtain a material selection result and output the material selection result.
2. The method according to claim 1, wherein the stock material information includes a log shape and a size specification, and the acquired order information includes a cut number, a cut shape, and a cut size specification; the screening of the stock material information to obtain the first material information includes:
and screening according to the cutting size specification in the order information and the size specification in the stock material information, filtering material information with the size specification smaller than the cutting size specification from the stock material information, and outputting the remaining material information after filtering as first material information.
3. The method according to claim 1 or 2, wherein the matching and optimizing the order information and the first material information to obtain the material selection result output comprises
Selecting an optimal combination scheme of the order information by using a genetic algorithm;
and matching an optimal scheme matched with the optimal combination scheme of the order information from the first material information by using a simulated annealing algorithm, and outputting the optimal scheme serving as a material selection result.
4. The method of claim 3, further comprising, before matching the optimal solution adapted to the optimal combination solution of the order information from the first material information using a simulated annealing algorithm:
respectively establishing corresponding 3D digital models for the optimal combination scheme of the order information and the first material information;
and taking the 3D digital model as an input parameter of a simulated annealing algorithm.
5. The method of claim 4, wherein the selection result includes a log shape and size specification of the selected material information.
6. Treat the device that processes steel carries out the selection material, include:
the information acquisition module is used for responding to the received order information and acquiring pre-stored stock material information;
the filtering module is used for screening the stock material information to obtain first material information; and
and the matching optimization module is used for performing matching optimization processing on the order information and the first material information to obtain a material selection result and outputting the material selection result.
7. The apparatus according to claim 6, wherein the stock material information includes a surplus material shape and a size specification, and the acquired order information includes a cutting number, a cutting shape, and a cutting size specification; and the filtering module is used for screening by using the cutting size specification in the order information and the size specification in the stock material information.
8. The apparatus of claim 7, wherein the matching preference module performs matching preference processing on the order information and the first material information using a genetic algorithm and a simulated annealing algorithm; or
The matching optimization module performs matching optimization processing on the order information and the first material information by using a genetic algorithm, a 3D digital model matching method and a simulated annealing algorithm; or
And the matching optimization module performs matching optimization processing on the order information and the first material information by using a genetic algorithm and a 3D digital model matching method.
9. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method of any one of claims 1-5.
10. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010925442.2A CN111986318A (en) | 2020-09-04 | 2020-09-04 | Method and device for selecting steel to be processed |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010925442.2A CN111986318A (en) | 2020-09-04 | 2020-09-04 | Method and device for selecting steel to be processed |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111986318A true CN111986318A (en) | 2020-11-24 |
Family
ID=73448215
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010925442.2A Pending CN111986318A (en) | 2020-09-04 | 2020-09-04 | Method and device for selecting steel to be processed |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111986318A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113554234A (en) * | 2021-07-27 | 2021-10-26 | 张玉丽 | Material striping method, computer equipment and storage medium |
| CN113689384A (en) * | 2021-07-27 | 2021-11-23 | 北京市机械施工集团有限公司 | Steel plate excess material intelligent statistics and cyclic utilization technology based on photogrammetry |
| CN114386770A (en) * | 2021-12-16 | 2022-04-22 | 方智模具技术(昆山)有限公司 | Intelligent management system and method for steel processing, computer storage medium and equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488271A (en) * | 2015-11-27 | 2016-04-13 | 天津博迈科海洋工程有限公司 | Automatic matching method for oil gas module section steel |
| CN107657361A (en) * | 2017-08-28 | 2018-02-02 | 梁彪 | Materials intelligent management and Intelligent Production System |
| CN109657354A (en) * | 2018-12-20 | 2019-04-19 | 华中科技大学 | A kind of the mixed-model assembly workshop method for fast reconstruction and system twin based on number |
| CN110091133A (en) * | 2019-05-28 | 2019-08-06 | 广东三维家信息科技有限公司 | Shape extrusion optimization method and device |
| CN110222937A (en) * | 2019-05-09 | 2019-09-10 | 山东钢铁集团有限公司 | A kind of novel production and marketing emerging system applied to steel and iron manufacturing |
| CN111553625A (en) * | 2020-04-08 | 2020-08-18 | 山东鹰联光电科技有限公司 | Raw material management method and system for cable distribution |
-
2020
- 2020-09-04 CN CN202010925442.2A patent/CN111986318A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105488271A (en) * | 2015-11-27 | 2016-04-13 | 天津博迈科海洋工程有限公司 | Automatic matching method for oil gas module section steel |
| CN107657361A (en) * | 2017-08-28 | 2018-02-02 | 梁彪 | Materials intelligent management and Intelligent Production System |
| CN109657354A (en) * | 2018-12-20 | 2019-04-19 | 华中科技大学 | A kind of the mixed-model assembly workshop method for fast reconstruction and system twin based on number |
| CN110222937A (en) * | 2019-05-09 | 2019-09-10 | 山东钢铁集团有限公司 | A kind of novel production and marketing emerging system applied to steel and iron manufacturing |
| CN110091133A (en) * | 2019-05-28 | 2019-08-06 | 广东三维家信息科技有限公司 | Shape extrusion optimization method and device |
| CN111553625A (en) * | 2020-04-08 | 2020-08-18 | 山东鹰联光电科技有限公司 | Raw material management method and system for cable distribution |
Non-Patent Citations (1)
| Title |
|---|
| 赵华: "一种海洋钻井平台二维零件切割排料技术方案", 《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》, no. 2, 15 December 2011 (2011-12-15), pages 1 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113554234A (en) * | 2021-07-27 | 2021-10-26 | 张玉丽 | Material striping method, computer equipment and storage medium |
| CN113689384A (en) * | 2021-07-27 | 2021-11-23 | 北京市机械施工集团有限公司 | Steel plate excess material intelligent statistics and cyclic utilization technology based on photogrammetry |
| CN113689384B (en) * | 2021-07-27 | 2024-03-15 | 北京市机械施工集团有限公司 | Intelligent steel plate residue counting and recycling technology based on photogrammetry |
| CN113554234B (en) * | 2021-07-27 | 2024-05-07 | 张玉丽 | Material striping method, computer equipment and storage medium |
| CN114386770A (en) * | 2021-12-16 | 2022-04-22 | 方智模具技术(昆山)有限公司 | Intelligent management system and method for steel processing, computer storage medium and equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111986318A (en) | Method and device for selecting steel to be processed | |
| CN108339270B (en) | The processing method, rendering method and device of static component in scene of game | |
| CN106981015A (en) | The implementation method of interactive present | |
| CN107485859A (en) | Game invitation method, device and electronic equipment | |
| CN109731332B (en) | Method, device, equipment and storage medium for controlling non-player character | |
| CN105869053A (en) | Two-stage memetic based social network influence maximizing method | |
| CN108304853B (en) | Game correlation obtaining method and device, storage medium and electronic device | |
| CN111260220B (en) | Group control equipment identification method and device, electronic equipment and storage medium | |
| CN111160624B (en) | User intention prediction method, user intention prediction device and terminal equipment | |
| Xiao et al. | A combined zone-LP and simulated annealing algorithm for unequal-area facility layout problem. | |
| CN109727070A (en) | A kind of determination method and device of potential any active ues | |
| Sun et al. | Method for forecasting DI based on TRIZ technology system evolution theory | |
| CN109741029A (en) | The building method and device in a kind of power grid enterprises' audit regulation storehouse | |
| CN111078742A (en) | User classification model training method, user classification method and device | |
| CN112001782A (en) | Method and system for intelligently matching live-broadcast goods information based on vermicelli portrait | |
| WO2020252751A1 (en) | Resource pushing method, device, server, and storage medium | |
| CN106327343B (en) | Initial user selection method in social network influence propagation | |
| CN109460930B (en) | Method for determining risk account and related equipment | |
| CN111931106A (en) | Data processing method and related device | |
| CN110728428A (en) | Method and device for determining cloud effect maturity of enterprise | |
| CN109173259A (en) | Audio optimization method, device and equipment in a kind of game | |
| CN110069772A (en) | Predict device, method and the storage medium of the scoring of question and answer content | |
| CN112337093B (en) | Virtual object clustering method and device, storage medium and electronic device | |
| CN108256694A (en) | Based on Fuzzy time sequence forecasting system, the method and device for repeating genetic algorithm | |
| CN110996158B (en) | Virtual article display method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |