CN114708935A

CN114708935A - Crystallizer copper plate quality optimization method and system based on heat conduction performance evaluation

Info

Publication number: CN114708935A
Application number: CN202210631749.0A
Authority: CN
Inventors: 李培忠; 安玉华; 付琦; 贾红
Original assignee: Jinan Eastern Crystallizer Co ltd
Current assignee: Jinan Eastern Crystallizer Co ltd
Priority date: 2022-06-07
Filing date: 2022-06-07
Publication date: 2022-07-05
Anticipated expiration: 2042-06-07
Also published as: CN114708935B

Abstract

The invention provides a crystallizer copper plate quality optimization method and system based on heat conduction performance evaluation, and relates to the technical field of data processing, wherein the method comprises the following steps: acquiring parameter information of a crystallizer copper plate; carrying out partition processing on the crystallizer copper plate through the crystallizer copper plate parameter information to obtain a plurality of block parameters; acquiring a heat conduction performance parameter set of the crystallizer copper plate through a plurality of block parameters; acquiring first temperature information through application scene parameter information of a crystallizer copper plate; carrying out partition processing to obtain a first temperature information set; determining a heat conduction performance parameter test set; evaluating the heat-conducting performance parameter set to obtain a heat-conducting performance evaluation result; and correspondingly optimizing the quality of the crystallizer copper plate. The technical problem that the surface and the internal quality of the continuous casting billet are affected due to poor heat conductivity or too fast heat conductivity is solved, reasonable evaluation on the heat conductivity is achieved, the quality of the crystallizer copper plate is optimized by utilizing the heat conductivity requirement, and the technical effect of the casting billet quality is further improved.

Description

Crystallizer copper plate quality optimization method and system based on heat conduction performance evaluation

Technical Field

The invention relates to the technical field of data processing, in particular to a crystallizer copper plate quality optimization method and system based on heat conduction performance evaluation.

Background

The crystallizer is a key component of a continuous casting machine, a copper plate is a core component of the crystallizer, molten steel leads heat outwards through a steel plate of the crystallizer to solidify the molten steel into a blank shell with a certain thickness, and the copper plate bears thermal stress generated by high and low temperature, plastic deformation caused by high-temperature thermal expansion, huge tensile stress caused by cooling shrinkage and friction force generated by relative movement of a steel billet and the copper plate in the process, so that the crystallizer copper plate is required to have higher thermal conductivity, higher tensile strength, higher recrystallization temperature, softening temperature and thermal strength to resist denaturation and thermal fatigue, and simultaneously has the capacity of resisting abrasion and improving the steel passing amount, so the crystallizer copper plate plays an indispensable role in continuous casting.

In the continuous casting production, a crystallizer copper plate needs to carry away heat generated by molten steel in a short time, so that a crystallizer steel plate needs to have good heat conductivity, if the heat conductivity is poor, a casting blank shell of a crystallizer becomes thin, in order to prevent pulling leakage, the pulling speed needs to be reduced, the production efficiency is reduced, and adverse effects on the casting blank quality are caused; particularly, at the meniscus position of the crystallizer, the heat conductivity is good, the heat dissipation is too fast, the thickness of a casting blank shell is uneven, and cracks are easy to generate.

Therefore, the heat conductivity of the crystallizer copper plate is an important factor which significantly affects the surface and internal quality of the continuous casting billet, and how to obtain the crystallizer copper plate with good heat conductivity becomes a technical problem which needs to be solved urgently at present.

Disclosure of Invention

The embodiment of the application provides a crystallizer copper plate quality optimization method and system based on heat conduction performance evaluation, and the method and system are used for solving the high requirement of a crystallizer copper plate on the heat conduction performance of the crystallizer copper plate in the prior art, achieving reasonable evaluation on the heat conduction performance by the technical problem that the heat conduction is poor or the heat conduction can influence the surface and the internal quality of a continuous casting billet too fast, optimizing the quality of the crystallizer copper plate by utilizing the heat conduction performance requirement, and further improving the technical effect of the quality of the casting billet.

In view of the above problems, the present application provides a method and a system for optimizing the quality of a crystallizer copper plate based on heat conduction performance evaluation.

In a first aspect of the application, a crystallizer copper plate quality optimization method based on heat conduction performance evaluation is provided, and the method comprises the following steps: acquiring parameter information of a crystallizer copper plate; partitioning the crystallizer copper plate according to the crystallizer copper plate parameter information to obtain a plurality of block parameters; acquiring a heat conduction performance parameter set of the crystallizer copper plate according to the block parameters; acquiring first temperature information according to the application scene parameter information of the crystallizer copper plate; partitioning the first temperature information based on the structural information of the crystallizer to obtain a first temperature information set; determining a test set of thermal conductivity parameters based on the first set of temperature information; evaluating the heat-conducting performance parameter set by using the heat-conducting performance parameter test set to obtain a heat-conducting performance evaluation result; and correspondingly optimizing the quality of the crystallizer copper plate according to the heat conduction performance evaluation result.

In a second aspect of the application, a crystallizer copper plate quality optimization system based on heat conduction performance evaluation is provided, and the system comprises: the first obtaining unit is used for obtaining the parameter information of the crystallizer copper plate; the first processing unit is used for carrying out partition processing on the crystallizer copper plate according to the crystallizer copper plate parameter information to obtain a plurality of block parameters; a second obtaining unit, configured to obtain a heat conduction performance parameter set of the mold copper plate according to the plurality of block parameters; a third obtaining unit, configured to obtain first temperature information according to application scene parameter information of the crystallizer copper plate; the second processing unit is used for carrying out partition processing on the first temperature information based on the structural information of the crystallizer to obtain a first temperature information set; a first determination unit for determining a test set of thermal conductivity parameters based on the first set of temperature information; the first evaluation unit is used for evaluating the heat conduction performance parameter set by the heat conduction performance parameter test set to obtain a heat conduction performance evaluation result; and the third processing unit is used for correspondingly optimizing the quality of the crystallizer copper plate according to the heat-conducting performance evaluation result.

In a third aspect of the present application, there is provided a crystallizer copper plate quality optimization system based on heat conductivity evaluation, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes a system to perform the steps of the method according to the first aspect.

In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method according to the first aspect.

One or more technical solutions provided in the present application have at least the following technical effects or advantages:

the crystallizer copper plate quality optimization method and system based on heat conduction performance evaluation, provided by the application, are used for obtaining parameter information of a crystallizer copper plate; carrying out partition processing on the crystallizer copper plate through the crystallizer copper plate parameter information to obtain a plurality of block parameters; the heat-conducting performance parameter set of the crystallizer copper plate is obtained through the plurality of block parameters, the obtained crystallizer copper plate parameter information can be preprocessed before partition processing is carried out, data are normalized, abnormal values are eliminated, and the processing speed and accuracy of the data are accelerated; acquiring first temperature information through application scene parameter information of the crystallizer copper plate, and bringing the crystallizer copper plate into the crystallizer for temperature analysis, thereby effectively ensuring the comprehensiveness of temperature data and providing a data basis for heat conductivity analysis; partitioning the first temperature information based on the structural information of the crystallizer to obtain a first temperature information set; determining a heat conduction performance parameter test set based on the first temperature information set; evaluating the heat conduction performance parameter set by using the heat conduction performance parameter test set to obtain a heat conduction performance evaluation result; and correspondingly optimizing the quality of the crystallizer copper plate according to the heat conduction performance evaluation result. The technical problems that in the prior art, the crystallizer copper plate has high requirements on the heat conductivity, the surface and the internal quality of the continuous casting billet are affected due to poor heat conductivity or too fast heat conductivity are solved, reasonable evaluation on the heat conductivity is achieved, the quality of the crystallizer copper plate is optimized by utilizing the heat conductivity requirements, and the quality of the casting billet is further improved.

The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.

Drawings

FIG. 1 is a schematic flow chart of a crystallizer copper plate quality optimization method based on heat conduction performance evaluation provided by the present application;

fig. 2 is a schematic flow chart of a crystallizer copper plate quality optimization process chain determining method for crystallizer copper plate quality optimization based on heat conductivity evaluation according to the present application;

FIG. 3 is a schematic flow chart of excitation information determination of a crystallizer copper plate quality optimization method based on heat conduction performance evaluation provided by the application;

FIG. 4 is a schematic structural diagram of a crystallizer copper plate quality optimization system based on heat conduction performance evaluation provided by the application;

fig. 5 is a schematic structural diagram of an exemplary electronic device of the present application.

Description of reference numerals: a first obtaining unit 11, a first processing unit 12, a second obtaining unit 13, a third obtaining unit 14, a second processing unit 15, a first determining unit 16, a first evaluating unit 17, a third processing unit 18, an electronic device 300, a memory 301, a processor 302, a communication interface 303, a bus architecture 304.

Detailed Description

This application is through providing a crystallizer copper quality optimization method and system based on heat conductivility aassessment for solve prior art crystallizer copper and to its high requirement of heat conductivility, the heat conductivity is poor or the heat conductivity can all influence the technical problem of continuous casting billet surface and internal quality too fast, reach the reasonable aassessment to the heat conductivility, utilize the heat conductivility requirement to optimize crystallizer copper quality, and then improve the technological effect of casting blank quality.

In view of the above technical problems, the technical solution provided by the present application has the following general idea:

the application provides a crystallizer copper plate quality optimization method and system based on heat conduction performance evaluation, wherein the method comprises the following steps: acquiring parameter information of a crystallizer copper plate; carrying out partition processing on the crystallizer copper plate through the crystallizer copper plate parameter information to obtain a plurality of block parameters; acquiring a heat conduction performance parameter set of the crystallizer copper plate through a plurality of block parameters; acquiring first temperature information through application scene parameter information of a crystallizer copper plate; the first temperature information is subjected to partition processing based on the structural information of the crystallizer, and a first temperature information set is obtained; determining a heat conduction performance parameter test set; evaluating the heat conduction performance parameter set by using the heat conduction performance parameter test set to obtain a heat conduction performance evaluation result; and correspondingly optimizing the quality of the crystallizer copper plate.

Having described the basic principles of the present application, the technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments of the present application, and the present application is not limited to the exemplary embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the elements relevant to the present application are shown in the drawings.

Example one

As shown in fig. 1, the present application provides a crystallizer copper plate quality optimization method based on heat conduction performance evaluation, the method comprises:

s100: acquiring parameter information of a crystallizer copper plate;

specifically, the crystallizer is a key tool in horizontal continuous casting, the copper plate is a main component of the crystallizer and mainly used for taking away heat of pouring molten steel, so that molten metal is solidified and crystallized in the crystallizer, and finally a casting blank is formed.

The parameter information comprises parameter information such as geometric parameters, material types, preparation processes and the like of the crystallizer copper plate, wherein the geometric parameters comprise geometric parameters such as narrow-edge copper plates, wide-edge copper plates and the like; the material types comprise red copper, phosphor copper, Cu-Cr-Zr alloy, Cu-Ag alloy and other material types; the preparation process comprises preparation process information such as chemical component proportion of raw materials in the preparation process, preparation process steps, corresponding preparation process parameters and the like; the method for acquiring the parameter information of the crystallizer copper plate can acquire the parameter information of the crystallizer copper plate from a preparation system for preparing the crystallizer copper plate, or acquire images of geometric parameters, material types and preparation processing processes of the crystallizer copper plate by using an image acquisition device to acquire the parameter information of the crystallizer copper plate, so as to provide a data base for subsequent data processing.

S200: partitioning the crystallizer copper plate according to the crystallizer copper plate parameter information to obtain a plurality of block parameters;

s300: acquiring a heat conduction performance parameter set of the crystallizer copper plate according to the block parameters;

specifically, the crystallizer copper plate is subjected to partition processing by utilizing geometrical parameter information, material type information and a preparation process of the crystallizer copper plate, wherein the geometrical parameter information comprises thickness information of the crystallizer copper plate and shape information of the crystallizer copper plate, the material type information comprises component information and proportion information corresponding to the components of the crystallizer copper plate, and the preparation process comprises an electroplating process and a hot forging process to obtain a plurality of block parameters; the partition processing method can utilize a clustering method to process, so that the similarity of data information in the same class or cluster is as large as possible, and the difference of the data information which is not in the same cluster is also as large as possible; the clustering method can comprise clustering algorithms such as a K-means algorithm, a K-means algorithm, a neural network and the like; furthermore, before the partition processing, the obtained crystallizer copper plate parameter information can be preprocessed, data is normalized, abnormal values are eliminated, and the processing speed and accuracy of the data are accelerated.

Specifically, in order to optimize the quality of the crystallizer copper plate, the obtained parameters of the plurality of blocks are subjected to heat conduction performance parameters, preferably, the heat conduction performance parameters may be combined with the material characteristics of the crystallizer copper plates of the plurality of blocks, wherein the heat conduction performance parameters include parameters such as heat conduction coefficient, working temperature, and thermal resistance coefficient, which constitute a set of heat conduction performance parameters of the crystallizer copper plate, so as to optimize the quality of the crystallizer copper plate.

S400: acquiring first temperature information according to the application scene parameter information of the crystallizer copper plate;

specifically, the application scenario of the copper plate of the crystallizer needs to be specifically analyzed by the crystallizer, and simply speaking, the copper plate is used as a metal material with excellent heat conductivity in the crystallizer, and can effectively ensure the heat conductivity of the crystallizer, the first temperature information includes but is not limited to the temperature of the casting liquid in the cooling bin, the temperature information of the cooling water channel of the crystallizer, and the temperature information of the crystallizer, and generally, the information of the cooling end state of the casting liquid changes, the casting liquid can be a metal liquid, a non-metal liquid, and an example of the non-metal liquid is a solution of sodium bicarbonate (Na2CO 3.10H 2O), and the sodium bicarbonate solution is cooled to obtain sodium bicarbonate (crystals). In the embodiment of the present application, the copper plate appearing in the step description is a mold copper plate, and is a simplified term, the mold temperature information includes temperature information of the copper plate for the mold, temperature information of the cooling bin, and temperature information of other mold components, specifically, the mold copper plate and other related materials are used for casting the mold, a plurality of temperature detection devices are present in the mold, the temperature detection devices may be alcohol thermometers or other related temperature detection instruments, the temperature information of a plurality of positions of the mold is detected by the temperature detection devices, the temperature information is the first temperature information, the copper plate is brought into the mold for temperature analysis, the comprehensiveness of the temperature data is effectively ensured, and a data base is provided for heat conduction performance analysis.

S500: the first temperature information is subjected to partition processing based on the structural information of the crystallizer, and a first temperature information set is obtained;

s600: determining a test set of thermal conductivity parameters based on a plurality of the first temperature information sets;

specifically, the structural information of the crystallizer includes a plurality of components of the crystal and the connection relationship of the components, the crystal components include but are not limited to a cooling block, a cooling water channel and a cooling bin, the first temperature information is processed in a partitioning mode through the structural information, all temperature data of the same component are divided into the same zone, all temperature data of the same connection relationship are divided into the same zone, the used structural information is a label, the first temperature information is processed in a partitioning mode, data sorting is carried out on partitioning results, a first temperature information set is obtained, the first temperature information set includes a plurality of temperature data zones, the first temperature information set corresponds to real-time temperature data, a plurality of first temperature information sets are obtained through a cooling test process of the crystallizer, detection times corresponding to the plurality of first temperature information sets are inconsistent, and external environment interference is not considered in the test process, the data is laboratory test data, laboratory temperature, humidity, air current are rational state data, confirm the thermal conductivity parameter test set through the change of a plurality of first temperature information sets.

More specifically, the heat conduction performance parameters are determined through the change of a plurality of first temperature information sets, the plurality of first temperature information sets have corresponding relations, the temperature information change of the same subarea has a certain rule, the rule can reflect the heat conductivity or the stability of the connection relation of the components corresponding to the subarea to a certain extent, for example, the heat conduction performance parameters include but are not limited to heat conduction coefficient, working temperature and thermal resistance coefficient, the temperature information change is fast, the cooling time is short, the determined heat conduction coefficient value is large, the working temperature needs to be correspondingly determined through a cooled object, in a simple way, the working temperatures of coolers for cooling different types of metal liquid are inconsistent, the working temperatures of the coolers for cooling different types of non-metal liquid are inconsistent, particularly, the temperature information change fast, the temperature gradient of the solidification front of the continuous casting slab is too large, the temperature change in the cooling bin is not uniform, which can cause that the tendency of forming columnar crystals and growing the columnar crystals in the solidification structure is obviously increased, particularly, cracks are easy to generate at the meniscus of the crystallizer, and can cause the existence of bubbles in the cooled object or the uneven surface of the cooled object, determine the thermal resistance coefficient, lead the heat conduction in the crystallizer to be too fast, cause the temperature gradient of the solidification front of the continuous casting slab to be too large, can effectively avoid the related equipment problems caused by quick temperature information change, the heat conduction coefficient, the working temperature and the thermal resistance coefficient are sequentially determined according to the temperature change rule, the heat conduction performance parameter is comprehensively determined by using the heat conduction coefficient, the working temperature and the thermal resistance coefficient, the heat conduction performance parameter is the test data information of the laboratory environment state, and (4) extracting data of the heat-conducting performance parameters, and determining a heat-conducting performance parameter test set as the heat-conducting performance parameters of the crystallizer copper plate.

S700: evaluating the heat-conducting performance parameter set by using the heat-conducting performance parameter test set to obtain a heat-conducting performance evaluation result;

s800: and correspondingly optimizing the quality of the crystallizer copper plate according to the heat conduction performance evaluation result.

Specifically, the heat conduction performance parameter test set is used for evaluating the heat conduction performance parameter set, the heat conduction performance parameter test set is data information obtained by actual test, the heat conduction performance parameter set is data information determined by characteristic parameters of the crystallizer copper plate, test data is used for evaluating the material performance of the crystallizer copper plate, a quality evaluation result of the crystallizer copper plate is determined, the quality evaluation result is a heat conduction performance evaluation result, a crystallizer copper plate quality optimization scheme is determined through the heat conduction performance evaluation result and based on heat conduction performance parameter standards, the quality of the crystallizer copper plate is optimized correspondingly, and the stability of an execution result of the crystallizer copper plate quality optimization scheme is guaranteed.

Further, the step S300 of obtaining the heat conductivity parameter set of the mold copper plate according to the parameters of the plurality of blocks further includes:

s310: acquiring copper plate parameter historical data and copper plate heat-conducting property historical data, wherein the copper plate parameter historical data comprises a plurality of block material characteristic sets of crystallizers of the same type;

s320: performing dimensionless analysis based on the historical data of the crystallizer copper plate parameters to obtain a plurality of characteristic parameter indexes;

s330: carrying out supervised training by combining the plurality of characteristic parameter indexes with the historical heat-conducting performance data of the crystallizer copper plate, and constructing a heat-conducting performance index model of the crystallizer copper plate material;

s340: acquiring a plurality of block material characteristic sets by combining the material characteristics of the crystallizer copper plate through the plurality of block parameters;

s350: and inputting the data information of the characteristic sets of the block materials into the heat-conducting performance index model of the copper plate material to obtain a heat-conducting performance parameter set of the crystallizer copper plate.

Specifically, a data information base of a crystallizer with the same model as a crystallizer copper plate is obtained through big data, and copper plate parameter historical data and copper plate heat conduction performance historical data are obtained through the data information base of the crystallizer with the same model, wherein the copper plate parameter historical data comprise a plurality of block material characteristic sets of the crystallizer with the same model, and the copper plate heat conduction performance historical data comprise heat conduction coefficients, working temperatures and thermal resistance coefficients of the crystallizer with the same model; based on the crystallizer copper plate parameter historical data, simplifying the crystallizer copper plate parameter historical data through dimensionless analysis, and obtaining a plurality of characteristic parameter indexes; acquiring multiple groups of historical training data, wherein each group of historical training data comprises multiple characteristic parameter indexes, determining the multiple groups of characteristic parameter indexes as input data of a training data set, taking the corresponding crystallizer copper plate heat conductivity historical data as identification data for verifying the output accuracy, performing supervised training based on an artificial intelligent machine learning model as a model base, and determining a crystallizer copper plate material heat conductivity index model by verifying the identification data for outputting the accuracy on a model output result so as to provide a reliable model base for data analysis.

Specifically, a plurality of block material characteristic sets are obtained by combining the material characteristics of the crystallizer copper plate through the plurality of block parameters, wherein the material characteristics comprise the molecular structure and chemical composition of the material, density and weight, humidity, temperature, pore characteristics and granularity; and inputting the data information of the characteristic sets of the block materials into the heat conduction performance index model of the copper plate material to obtain the heat conduction performance parameter set of the crystallizer copper plate, thereby effectively ensuring the stability of the heat conduction performance parameter set.

Further, as shown in fig. 2, the method further includes:

s910: performing parameter analysis on the heat conduction performance parameter set, and determining parameter analysis information corresponding to the heat conduction performance parameter set as current state information;

s920: performing parameter analysis on a heat conduction performance parameter standard set, and determining parameter analysis information corresponding to the heat conduction performance parameter standard set as target state information;

s930: performing parameter analysis on the heat conduction performance evaluation result, and determining parameter analysis information corresponding to the heat conduction performance evaluation result as action information;

s940: and determining a crystallizer copper plate quality optimization process chain according to the current state information, the target state information and the action information.

Specifically, based on a Markov chain, constructing a crystallizer copper plate quality optimization process chain in a mode of determining an excitation function in a reverse recursion manner, specifically, performing parameter analysis on the heat conduction performance parameter set, specifically determining the parameter analysis in combination with parameter characteristics, and determining parameter analysis information corresponding to the heat conduction performance parameter set as current state information; performing parameter analysis on the heat conduction performance parameter standard set, and determining parameter analysis information corresponding to the heat conduction performance parameter standard set as target state information, wherein the target state information corresponds to the final state of a crystallizer copper plate quality optimization process chain; performing parameter analysis on the heat conduction performance evaluation result, and determining parameter analysis information corresponding to the heat conduction performance evaluation result as action information; and determining a crystallizer copper plate quality optimization process chain according to the current state information, the target state information and the action information, so as to provide a model basis for obtaining the crystallizer copper plate quality optimization scheme and ensure the rationality of the crystallizer copper plate quality optimization scheme.

Further, the step S920 of performing parameter analysis on the heat conductivity parameter standard set and determining parameter analysis information corresponding to the heat conductivity parameter standard set as target state information includes:

s921: performing data analysis on the historical data of the heat conductivity of the copper plate to obtain a historical parameter set of the heat conductivity of the copper plate;

s922: data sorting is carried out on the copper plate heat-conducting property historical parameter set, and a heat-conducting property parameter standard set is obtained;

s923: and carrying out parameter analysis on the heat conduction performance parameter standard set to determine target state information.

Specifically, data analysis and extraction are carried out on the historical data of the heat conductivity of the copper plate, a historical parameter set of the heat conductivity of the copper plate is obtained, and the data distribution of the historical parameter set of the heat conductivity of the copper plate corresponds to the partition processing of the crystallizer copper plate by utilizing the geometric parameter information, the material type information and the preparation process of the crystallizer copper plate; data sorting is carried out on the copper plate heat-conducting property historical parameter set, abnormal data is removed, the abnormal data can be the maximum value or the minimum value, and a heat-conducting property parameter standard set is obtained; and performing parameter analysis on the heat conduction performance parameter standard set, and determining index threshold information corresponding to the heat conduction performance parameter standard set as target state information, thereby effectively ensuring the stability of the target state information.

Further, as shown in fig. 3, after performing parameter analysis on the heat conductivity evaluation result and determining parameter analysis information corresponding to the heat conductivity evaluation result as action information, the embodiment of the present application further includes:

s931: determining difference value information of the heat conduction performance parameter test set and the heat conduction performance parameter set, wherein the difference value information is a first difference value set;

s932: determining a first optimization scheme through the first difference value set;

s933: adjusting the first optimization scheme by optimizing the complexity of the process flow to obtain a second optimization scheme;

s934: determining a plurality of optimization nodes through the second optimization scheme;

s935: obtaining an excitation function through the plurality of optimization nodes;

s936: determining the excitation information by an excitation function.

Specifically, determining difference value information of the heat conduction performance parameter test set and the heat conduction performance parameter set, wherein the difference value information is a first difference value set, and the first difference value set is arranged in sequence from large to small through difference values; determining a first optimization scheme through the first difference set, wherein the first optimization scheme is an optimization scheme corresponding to an index with the largest difference in the first difference set; performing scheme optimization on the first optimization scheme by optimizing the complexity of the process flow to obtain a second optimization scheme, wherein the complexity of the optimization flow of the second optimization scheme is lower than that of the optimization flow of the first optimization scheme; determining a plurality of optimization nodes through the second optimization scheme, wherein the optimization nodes correspond to the state change of the crystallizer copper plate quality optimization process chain, and the second optimization scheme can preferably adjust the thickness information and can also define the preferred mode in a self-defining way; acquiring an excitation function through the plurality of optimization nodes, wherein the excitation function is an excitation function corresponding to the first difference set and corresponds to a crystallizer copper plate quality optimization scheme; and determining a plurality of pieces of excitation information through the excitation function, wherein each piece of excitation information execution corresponds to one piece of action information.

Further specifically, after the index with the largest difference in the first difference set is optimized, other index difference data may also change, the optimization process corresponds to multiple optimization nodes, and after the index with the largest difference in the first difference set is optimized, the index with the second difference in the first difference set may not be consistent with the index to be optimized of the next node.

Further, the method further comprises:

s932-1: determining first action information according to the current state information and the excitation information, wherein the first action information corresponds to the current state information;

s932-2: performing state prediction by combining the first action information with the target state information to determine next state information;

s932-3: traversing the state prediction process, and determining a plurality of action information;

s932-4: and determining the first optimization scheme through the first difference value set and a plurality of action information.

Specifically, first action information is determined through the current state information and the excitation information, in short, the current state information changes under the excitation action of the excitation information, the change corresponds to the first action information, the first action information is substantially data change of a heat conduction performance parameter test set, and the first action information corresponds to the current state information; the state prediction is carried out by combining the first action information with the target state information, the state prediction result and the heat conduction performance parameter test set data are possibly different, the data prediction can be carried out for multiple times, the stability of the prediction result is continuously optimized, and the next state information is determined; repeating the steps, traversing the state prediction process, and determining a plurality of action information; and determining a crystallizer copper plate quality optimization step through the first difference set and the plurality of action information, and determining a first optimization scheme based on a crystallizer copper plate quality optimization process chain through the quality optimization step, thereby effectively ensuring the rationality of the first optimization scheme.

Further, the determining, by the second optimization scheme, a plurality of optimization nodes, step S934 further includes:

s934-1: determining an optimization process according to the second optimization scheme, wherein the optimization process corresponds to the crystallizer copper plate quality optimization process chain;

s934-2: performing hierarchical division through the optimization process, wherein the hierarchical division result is a first hierarchical set, and the first hierarchical set corresponds to the first difference set;

s934-3: through the first hierarchical set, a plurality of optimization nodes are determined.

Specifically, an optimization process is determined through the second optimization scheme, the optimization process preferentially optimizes an index with the largest difference in the first difference set, the optimization process corresponds to the crystallizer copper plate quality optimization process chain, the simple description shows that next state information is obtained based on state prediction information of the crystallizer copper plate quality optimization process chain, the prediction steps are repeated, and a plurality of pieces of state information can be determined, the plurality of pieces of state information correspond to the crystallizer copper plate quality optimization process chain, the plurality of pieces of state information correspond to the plurality of pieces of excitation information, and the plurality of pieces of excitation information correspond to the optimization process; and carrying out hierarchical division through the optimization process, wherein the hierarchical division corresponds to parameter indexes in the first difference set, exemplarily, the parameter index related to the heat conduction coefficient is determined as a first layer parameter index, the parameter index related to the working temperature is determined as a second layer parameter index, and the parameter index related to the thermal resistance coefficient is determined as a third layer parameter index. The example is that for scheme understanding and implementation, the actual data division is not limited, the corresponding determination should be actually performed in combination with the actual data features, the hierarchical division result is a first hierarchical set, and the first hierarchical set corresponds to the first difference set; through the first level set, further explained by referring to the above example, the first level set corresponds to the first level parameter index, the first level set includes the parameter index related to the heat conduction coefficient, and the plurality of optimization nodes are determined through the plurality of first level sets, so that the execution efficiency of the crystallizer copper plate quality optimization scheme can be effectively ensured.

In summary, the visual scheduling method and system for big data of a manufacturing plant provided by the present application have the following technical effects:

1. the method comprises the steps of obtaining the parameter information of the crystallizer copper plate; carrying out partition processing on the crystallizer copper plate through the crystallizer copper plate parameter information to obtain a plurality of block parameters; acquiring a heat conduction performance parameter set of the crystallizer copper plate through a plurality of block parameters; acquiring first temperature information through application scene parameter information of a crystallizer copper plate; partitioning the first temperature information based on the structural information of the crystallizer to obtain a first temperature information set; determining a heat conduction performance parameter test set; evaluating the heat conduction performance parameter set by using the heat conduction performance parameter test set to obtain a heat conduction performance evaluation result; the quality of the crystallizer copper plate is correspondingly optimized, the method and the system for optimizing the quality of the crystallizer copper plate based on heat conduction performance evaluation achieve reasonable evaluation of heat conduction performance, the quality of the crystallizer copper plate is optimized by utilizing the heat conduction performance requirement, and further the technical effect of improving the quality of a casting blank is achieved.

2. The parameter analysis is carried out on the heat conduction performance parameter set, and the parameter analysis information corresponding to the heat conduction performance parameter set is determined as the current state information; performing parameter analysis on the heat conduction performance parameter standard set, and determining parameter analysis information corresponding to the heat conduction performance parameter standard set as target state information; performing parameter analysis on the heat conduction performance evaluation result, and determining parameter analysis information corresponding to the heat conduction performance evaluation result as action information; and determining a crystallizer copper plate quality optimization process chain according to the current state information, the target state information and the action information. And a model foundation is provided for obtaining a crystallizer copper plate quality optimization scheme, and the reasonability of the crystallizer copper plate quality optimization scheme is ensured.

Example two

Based on the same inventive concept as the crystallizer copper plate quality optimization method based on heat conduction performance evaluation in the previous embodiment, as shown in fig. 4, the present application provides a crystallizer copper plate quality optimization system based on heat conduction performance evaluation, wherein the system comprises:

the first obtaining unit 11 is used for obtaining the parameter information of the crystallizer copper plate;

the first processing unit 12 is configured to perform partition processing on the mold copper plate according to the mold copper plate parameter information to obtain a plurality of block parameters;

a second obtaining unit 13, where the second obtaining unit 13 is configured to obtain a heat conduction performance parameter set of the mold copper plate according to the plurality of block parameters;

a third obtaining unit 14, wherein the third obtaining unit 14 is configured to obtain first temperature information according to application scene parameter information of the mold copper plate;

a second processing unit 15, where the second processing unit 15 is configured to perform partition processing on the first temperature information based on the structural information of the crystallizer to obtain a first temperature information set;

a first determination unit 16, said first determination unit 16 being configured to determine a test set of thermal conductivity parameters based on said first set of temperature information;

a first evaluation unit 17, where the first evaluation unit 17 is configured to evaluate the set of heat conduction performance parameters with the set of heat conduction performance parameter tests to obtain a heat conduction performance evaluation result;

a third processing unit 18, wherein the third processing unit 18 is used for performing corresponding optimization on the quality of the crystallizer copper plate according to the heat conduction performance evaluation result.

Further, the system comprises:

a fourth obtaining unit, configured to obtain copper plate parameter historical data and copper plate heat conductivity historical data, where the copper plate parameter historical data includes a plurality of block material characteristic sets of crystallizers of the same model;

a fifth obtaining unit, configured to perform dimensionless analysis based on the crystallizer copper plate parameter historical data to obtain a plurality of characteristic parameter indexes;

the first construction unit is used for carrying out supervised training by combining the historical data of the heat-conducting performance of the crystallizer copper plate through the characteristic parameter indexes and constructing a model of the heat-conducting performance index of the crystallizer copper plate material;

a fourth processing unit, configured to obtain a plurality of block material characteristic sets by combining the material characteristics of the mold copper plate with the plurality of block parameters;

a sixth obtaining unit, configured to respectively input data information of the multiple block material characteristic sets into the copper plate material heat conduction performance index model, and obtain a heat conduction performance parameter set of the mold copper plate.

Further, the system comprises:

a second determining unit, configured to perform parameter analysis on the heat conductivity parameter set, and determine parameter analysis information corresponding to the heat conductivity parameter set as current state information;

a third determining unit, configured to perform parameter analysis on the heat conductivity parameter standard set, and determine parameter analysis information corresponding to the heat conductivity parameter standard set as target state information;

a fourth determination unit, configured to perform parameter analysis on the heat conduction performance evaluation result, and determine parameter analysis information corresponding to the heat conduction performance evaluation result as excitation information;

and the fifth determining unit is used for determining a crystallizer copper plate quality optimization process chain according to the current state information, the target state information and the excitation information.

Further, the system comprises:

a seventh obtaining unit, configured to perform data analysis on the copper plate heat conductivity history data to obtain a copper plate heat conductivity history parameter set;

an eighth obtaining unit, configured to perform data arrangement on the copper plate heat conductivity history parameter set to obtain a heat conductivity parameter standard set;

and the sixth determining unit is used for carrying out parameter analysis on the heat-conducting performance parameter standard set and determining target state information.

Further, the system comprises:

a seventh determining unit, configured to determine difference information between the heat conductivity parameter test set and the heat conductivity parameter set, where the difference information is a first difference set, and the first difference set is arranged in order from large to small through differences;

an eighth determining unit, configured to determine a first optimization scheme according to the first difference set;

a ninth obtaining unit, configured to adjust the first optimization scheme by optimizing process complexity, and obtain a second optimization scheme;

a ninth determining unit, configured to determine, according to the second optimization scheme, a plurality of optimization nodes corresponding to state changes of the mold copper plate quality optimization process chain;

a tenth obtaining unit, configured to obtain, through the plurality of optimization nodes, an excitation function;

a tenth determining unit for determining the excitation information by an excitation function.

Further, the system comprises:

an eleventh determining unit, configured to determine first action information according to the current state information and the excitation information, where the first action information corresponds to the current state information;

a twelfth determining unit, configured to perform state prediction by combining the first action information and the target state information, and determine next state information;

a thirteenth determining unit, configured to determine a plurality of action information by traversing the state prediction process;

a fourteenth determining unit, configured to determine the first optimization scheme by using the first difference set and a plurality of action information.

Further, the system comprises:

a fifteenth determining unit, configured to determine an optimization flow according to the second optimization scheme, where the optimization flow corresponds to the crystallizer copper plate quality optimization flow chain;

a fourth processing unit, configured to perform hierarchical division through the optimization process, where the hierarchical division result is a first hierarchical set, and the first hierarchical set corresponds to the first difference set;

a sixteenth determining unit configured to determine a plurality of optimized nodes through the first hierarchical set.

Exemplary electronic device

The electronic device of the present application is described below with reference to figure 5,

based on the same inventive concept as the crystallizer copper plate quality optimization method based on heat conduction performance evaluation in the previous embodiment, the application also provides a crystallizer copper plate quality optimization system based on heat conduction performance evaluation, which comprises: a processor coupled to a memory, the memory to store a program that, when executed by the processor, causes a system to perform the method of any of the embodiments.

The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.

Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of programs in accordance with the teachings of the present application.

The communication interface 303 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a wired access network, and the like.

The memory 301 may be, but is not limited to, ROM or other type of static storage device that can store static information and instructions, RAM or other type of dynamic storage device that can store information and instructions, EEPROM, CD-ROM or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.

The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is used for executing computer-executed instructions stored in the memory 301, so as to implement the method for optimizing the quality of the copper plate of the mold based on the heat conduction performance evaluation provided by the above-mentioned embodiments of the present application.

Alternatively, the computer executable instructions may also be referred to as application code, and the application is not limited thereto.

The application provides a crystallizer copper plate quality optimization method based on heat conduction performance evaluation, wherein the method comprises the following steps: acquiring parameter information of a crystallizer copper plate; partitioning the crystallizer copper plate according to the crystallizer copper plate parameter information to obtain a plurality of block parameters; acquiring a heat conduction performance parameter set of the crystallizer copper plate according to the block parameters; acquiring first temperature information according to the application scene parameter information of the crystallizer copper plate; partitioning the first temperature information based on the structural information of the crystallizer to obtain a first temperature information set; determining a test set of thermal conductivity parameters based on the first set of temperature information; evaluating the heat-conducting performance parameter set by using the heat-conducting performance parameter test set to obtain a heat-conducting performance evaluation result; and correspondingly optimizing the quality of the crystallizer copper plate according to the heat conduction performance evaluation result.

Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are for convenience of description and are not intended to limit the scope of this application nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the present application are generated, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.

The steps of a method or algorithm described in this application may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may 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 claims of the present application and their equivalents, the present application is intended to include such modifications and variations.

Claims

1. A crystallizer copper plate quality optimization method based on heat conduction performance evaluation is characterized by comprising the following steps:

acquiring parameter information of a crystallizer copper plate;

partitioning the crystallizer copper plate according to the crystallizer copper plate parameter information to obtain a plurality of block parameters;

acquiring a heat conduction performance parameter set of the crystallizer copper plate according to the block parameters;

acquiring first temperature information according to the application scene parameter information of the crystallizer copper plate;

the first temperature information is subjected to partition processing based on the structural information of the crystallizer, and a first temperature information set is obtained;

determining a test set of thermal conductivity parameters based on the first set of temperature information;

evaluating the heat-conducting performance parameter set by using the heat-conducting performance parameter test set to obtain a heat-conducting performance evaluation result;

and correspondingly optimizing the quality of the crystallizer copper plate according to the heat conduction performance evaluation result.

2. The method as claimed in claim 1, wherein the parameter set of the heat-conducting property of the mold copper plate is obtained by the plurality of block parameters, and the method comprises:

acquiring copper plate parameter historical data and copper plate heat-conducting property historical data, wherein the copper plate parameter historical data comprises a plurality of block material characteristic sets of crystallizers of the same type;

performing dimensionless analysis based on the historical data of the crystallizer copper plate parameters to obtain a plurality of characteristic parameter indexes;

carrying out supervised training by combining the plurality of characteristic parameter indexes with the historical heat-conducting performance data of the crystallizer copper plate, and constructing a heat-conducting performance index model of the crystallizer copper plate material;

acquiring a plurality of block material characteristic sets by combining the material characteristics of the crystallizer copper plate through the plurality of block parameters;

and respectively inputting the data information of the plurality of block material characteristic sets into the copper plate material heat conduction performance index model to obtain the heat conduction performance parameter set of the crystallizer copper plate.

3. The method of claim 1, wherein the method further comprises:

performing parameter analysis on the heat conduction performance parameter set, and determining parameter analysis information corresponding to the heat conduction performance parameter set as current state information;

performing parameter analysis on a heat conduction performance parameter standard set, and determining parameter analysis information corresponding to the heat conduction performance parameter standard set as target state information;

performing parameter analysis on the heat conduction performance evaluation result, and determining parameter analysis information corresponding to the heat conduction performance evaluation result as excitation information;

and determining a crystallizer copper plate quality optimization process chain according to the current state information, the target state information and the excitation information.

4. The method of claim 3, wherein the analyzing the parameters of the set of heat transfer performance parameters determines parametric analysis information corresponding to the set of heat transfer performance parameters as target state information, the method comprising:

performing data analysis on the historical data of the heat conductivity of the copper plate to obtain a historical parameter set of the heat conductivity of the copper plate;

data sorting is carried out on the copper plate heat-conducting property historical parameter set, and a heat-conducting property parameter standard set is obtained;

and carrying out parameter analysis on the heat conduction performance parameter standard set to determine target state information.

5. The method of claim 3, wherein the heat conduction performance evaluation result is subjected to parametric analysis, and parametric analysis information corresponding to the heat conduction performance evaluation result is determined as excitation information, and the method includes:

determining difference value information of the heat conduction performance parameter test set and the heat conduction performance parameter set, wherein the difference value information is a first difference value set, and the first difference value set is sequentially arranged from large to small through difference values;

determining a first optimization scheme through the first difference set;

adjusting the first optimization scheme by optimizing the complexity of the process flow to obtain a second optimization scheme;

determining a plurality of optimization nodes through the second optimization scheme, wherein the optimization nodes correspond to the state change of the crystallizer copper plate quality optimization process chain;

obtaining an excitation function through the plurality of optimization nodes;

determining the excitation information by an excitation function.

6. The method of claim 5, wherein the method further comprises:

determining first action information according to the current state information and the excitation information, wherein the first action information corresponds to the current state information;

predicting the state by combining the first action information with the target state information to determine the next state information;

traversing the state prediction process, and determining a plurality of action information;

and determining the first optimization scheme through the first difference value set and a plurality of action information.

7. The method of claim 5, wherein the determining a plurality of optimization nodes via the second optimization scheme, the method comprising:

determining an optimization process according to the second optimization scheme, wherein the optimization process corresponds to the crystallizer copper plate quality optimization process chain;

performing hierarchical division through the optimization process, wherein the hierarchical division result is a first hierarchical set, and the first hierarchical set corresponds to the first difference set;

through the first hierarchical set, a plurality of optimization nodes are determined.

8. A crystallizer copper plate quality optimization system based on heat conduction performance evaluation is characterized by comprising the following components:

the first obtaining unit is used for obtaining the parameter information of the crystallizer copper plate;

the first processing unit is used for carrying out partition processing on the crystallizer copper plate according to the crystallizer copper plate parameter information to obtain a plurality of block parameters;

a second obtaining unit, configured to obtain a heat conduction performance parameter set of the mold copper plate according to the plurality of block parameters;

a third obtaining unit, configured to obtain first temperature information through application scenario parameter information of the crystallizer;

the second processing unit is used for carrying out partition processing on the first temperature information based on the structural information of the crystallizer to obtain a first temperature information set;

a first determination unit for determining a test set of thermal conductivity parameters based on the first set of temperature information;

the first evaluation unit is used for evaluating the heat conduction performance parameter set by the heat conduction performance parameter test set to obtain a heat conduction performance evaluation result;

and the third processing unit is used for carrying out corresponding optimization on the quality of the crystallizer copper plate according to the heat conduction performance evaluation result.

9. A crystallizer copper plate quality optimization system based on heat conduction performance evaluation is characterized by comprising: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the steps of the method of any of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.