WO2023070691A1 - Blast furnace burden trajectory model construction method - Google Patents

Blast furnace burden trajectory model construction method Download PDF

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WO2023070691A1
WO2023070691A1 PCT/CN2021/128114 CN2021128114W WO2023070691A1 WO 2023070691 A1 WO2023070691 A1 WO 2023070691A1 CN 2021128114 W CN2021128114 W CN 2021128114W WO 2023070691 A1 WO2023070691 A1 WO 2023070691A1
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charge
population
trajectory model
shape data
surface shape
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PCT/CN2021/128114
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French (fr)
Chinese (zh)
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叶理德
高田翔
李清忠
余成明
刘书文
梁小兵
梅超凡
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中冶南方工程技术有限公司
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    • 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
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • 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
    • G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
    • 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
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G06T3/4007—Scaling of whole images or parts thereof, e.g. expanding or contracting based on interpolation, e.g. bilinear interpolation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

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  • the invention relates to the technical field of blast furnace ironmaking, in particular to a method for constructing a blast furnace charge trajectory model.
  • the charge enters from the bunker at the top of the blast furnace, passes through the material flow valve, fork pipe, throat, and chute, and finally falls to the throat of the furnace. Then, as the charge is consumed, the charge at the throat of the furnace gradually falls To the furnace shaft to complete the conversion process.
  • the shape of the charge surface is crucial to the normal operation of the blast furnace. A good charge surface can make the gas flow distribution more reasonable and effectively fill the consumption of charge in different parts.
  • the traditional manual method is to measure the depth of several parts with a probe, and then make a rough estimate of the material level in combination with the gas composition and furnace top temperature, which requires the operator to have rich experience in material distribution, and it is difficult to accurately describe the continuous distribution of the material level .
  • the material level distribution of the furnace throat can be directly measured by technical means such as radar, but in practical applications, the service life of the radar is limited, and it is inconvenient to replace and maintain, and most radars can only measure one at a time. Point, to complete the scanning of the entire material surface needs to be measured tens of hundreds of times, if you want to scan both before and after the cloth, it will take a long time.
  • the present invention provides a method for building a blast furnace charge trajectory model, which includes the following steps,
  • the initial charge trajectory model formula is as follows:
  • v is the speed when the charge slides out of the chute
  • ⁇ is the inclination angle of the chute
  • r is the hydraulic radius of the material flow valve
  • H is the distance from the outlet of the chute to the material surface
  • t is the time for the charge to move in the air
  • R is The lateral distance between the charging point and the outlet of the chute
  • a, b, c, d, e are model parameters
  • acquiring the real material surface shape data in S3 specifically includes detecting the real material surface shape data of the furnace top material surface through radar scanning, and the real material surface shape data is a discrete point vector S with a fixed length.
  • model parameters in the initial charge trajectory model formula are optimized through a genetic algorithm, and the initial charge trajectory model formula
  • the model parameters in are iteratively updated as follows:
  • the construction of the initial population by the chromosome coding method in S301 specifically includes: the chromosome coding method adopts floating-point number coding, and the population individuals of the initial population include model parameters a, b, c, d, and e.
  • the cross mutation operation is performed on the reserved population individuals to obtain the next generation population operation, specifically: performing genetic crossover processing on the retained population individuals through a crossover operator, and performing genetic crossover processing on the two paired population individuals
  • the next generation of population individuals is obtained by exchanging genes with each other through single-point crossover; the reserved population individuals are subjected to gene mutation processing through a mutation operator, and after determining the gene mutation position in the population individuals, the mutation is performed by means of basic bit mutation Operation, inverting the original gene at the gene mutation position according to the preset probability.
  • Another object of the present invention is to provide a computer system, which is configured to perform the following steps: first, model the process of blast furnace charge descent to obtain the initial charge trajectory model formula; then obtain a charge level data set, and convert the The material surface data set is input to the initial furnace charge trajectory model formula to calculate the set of the drop position of the charge at the furnace throat, and the simulated charge is obtained according to the set of the charge surface data set and the drop position of the charge at the furnace throat. surface shape data; finally obtain the real material surface shape data, according to the real material surface shape data and the simulated material surface shape data, the model parameters in the initial charge trajectory model formula are optimized by genetic algorithm, and the The model parameters in the formula of the initial charge trajectory model described above are iteratively updated to obtain the charge trajectory model.
  • Another object of the present invention is to provide a computer-readable storage medium, which includes a memory, and a computer program is stored in the memory.
  • the computer program is executed by a processor, the above-mentioned method for building a blast furnace charge trajectory model is realized.
  • the beneficial effects of the present invention are: continuously adjust and optimize multiple parameters of the furnace charge trajectory model according to the radar scanning data, and finally obtain the most suitable furnace charge trajectory model for the current blast furnace, so that without the need for radar, through the distribution matrix directly
  • the shape of the material surface after the material distribution is calculated, and by simulating the genetic evolution of nature, the optimal model parameters of the furnace material trajectory model can be efficiently found in the solution space, which not only makes the modeling process simple but also improves the accuracy of the model.
  • Fig. 1 is a method block diagram of a method for constructing a blast furnace charge trajectory model of the present invention
  • Fig. 2 is the structural representation of blast furnace throat place of the present invention
  • Fig. 3 is a flow chart of the method for optimizing the model parameters of the blast furnace charge trajectory model of the present invention.
  • the present invention provides a method for building a blast furnace charge trajectory model, comprising the following steps,
  • the present invention continuously adjusts and optimizes multiple parameters of the furnace material trajectory model, and finally obtains the most suitable furnace material trajectory model for the current blast furnace, so that the material distribution matrix can be directly calculated through the material distribution matrix without the need for radar.
  • the shape of the charge surface by simulating the genetic evolution of nature, can efficiently find the optimal model parameters of the charge trajectory model in the solution space, which not only makes the modeling process simple but also improves the accuracy of the model.
  • the charge descending process is modeled using classical mechanical formulas to obtain a charge trajectory model formula.
  • the structure at the throat of the blast furnace is shown in Figure 2. Modeling is performed on the blast furnace charging process to obtain the initial charging trajectory model formula.
  • the initial charging trajectory model formula is as follows:
  • v is the speed when the charge slides out of the chute
  • ⁇ is the inclination angle of the chute
  • r is the hydraulic radius of the material flow valve
  • H is the distance from the outlet of the chute to the material surface
  • t is the time for the charge to move in the air
  • R is The lateral distance between the charging point and the outlet of the chute
  • a, b, c, d, e are model parameters.
  • the position of the drop point of the charge on the furnace throat can be calculated.
  • the material level data set includes the hydraulic radius of the material flow valve, the angle of the chute and the drop height, the total weight, density, and distribution matrix of the material batch.
  • the temperature of each gear of the blast furnace can be calculated. Cloth volume.
  • the shape of the material surface of the gear can be simulated. Repeat the above method to calculate the shape of each gear of the current fabric, and then obtain the simulated material surface shape data of the material surface after the current material batch is finished.
  • the real material surface shape data of the furnace top material surface is detected by radar scanning, and the real material surface shape data is a discrete point vector S with a fixed length.
  • the preset number of generations is generally set to 100-500 generations.
  • the chromosome encoding method adopts floating-point number encoding
  • the population individuals of the initial population include model parameters a, b, c, d, and e.
  • Set the lower limit of the solution space and the upper limit of the solution space of the model parameters set the lower limit of the solution space of the five parameters to ⁇ -50,-50,-50,-50,-50 ⁇ , and the upper limit of the solution space of the parameters to ⁇ 50 ,50,50,50,50 ⁇ , generate the first generation population ⁇ a1,b1,c1,d1,e1 ⁇ , ⁇ a2,b2,c2,d2,e2 ⁇ ,... ⁇ according to the chromosome coding method, the population size is 100.
  • the initial population is constructed by the chromosome coding method, and the population individuals in the initial population are input into the formula of the initial charge trajectory model to obtain the simulated material surface shape data of the population individual, wherein the simulated material surface shape data is a fixed-length discrete point vector C.
  • Carry out interpolation or elimination processing to described discrete point vector S obtain the applicability of described simulated material level shape data according to applicability function, make the discrete point vector S length of described real material level shape data and described simulated material level shape
  • the discrete point vector C of the data has the same length, and the applicability function is the opposite number of the two norm of the discrete point vector, then the applicability function f is specifically:
  • the first N population individuals are retained; the cross-mutation operation is performed on the retained population individuals to obtain the next generation Population, after inputting the population individuals in the current population into the formula of the initial charge trajectory model to obtain the simulated material surface shape data of the population individuals, carry out genetic crossover processing on the reserved population individuals through the crossover operator, and perform genetic crossover processing on the two mutual
  • the paired population individuals exchange genes with each other through single-point crossover, thereby forming the next generation of population individuals; the reserved population individuals are subjected to gene mutation processing through mutation operators, and after determining the gene mutation position in the population individuals, through the basic position
  • the mutation operation is carried out in the way of mutation, and the original gene at the mutation position of the gene is reversed according to the preset probability.
  • the population uses the fitness ratio method of the selection operation to select individuals with high applicability and keep them.
  • the optimized individuals in the population are directly inherited to the next generation through the selection operator selection, or new individuals are generated through pairing and crossover, and then passed on to the next generation.
  • using the crossover operator of single point crossover by selecting two chromosomes, splitting at a randomly selected position point and exchanging the right part, so as to obtain two different daughter chromosomes.
  • mutation and crossover operations are performed on the basis of selected individuals to generate the next generation of populations. Through crossover operations, two paired chromosomes exchange genes in a certain way to form a new population. individual.
  • the crossover operation plays a key role in the genetic algorithm, and changing the gene values at some loci of the individual strings in the population is the main method to generate new individuals.
  • Mutation operation is to change the gene value on a certain locus according to a small probability, and it is also an operation method to generate new individuals.
  • the present invention adopts the method of basic bit mutation to carry out the variation operation, and its specific operation process is: firstly determine the gene variation position of each individual, and then reverse the original gene of the variation point according to a certain probability. After the population P(t) undergoes selection, crossover and mutation operations, the next generation population P(t+1) is obtained. Genetic algorithm has local random search ability.
  • the local random search ability of the mutation operator can be used to accelerate the convergence to the parameters in the formula of the optimal charge trajectory model, and it can also make the genetic algorithm Population diversity can be maintained to prevent premature convergence. Select the population individual with the greatest applicability to iteratively update the model parameters in the initial charge trajectory model formula to obtain the charge trajectory model.
  • Another object of the present invention is to provide a computer system, which is configured to perform the following steps: first, model the process of blast furnace charge descent to obtain the initial charge trajectory model formula; then obtain a charge level data set, and convert the The material surface data set is input to the initial furnace charge trajectory model formula to calculate the set of the drop position of the charge at the furnace throat, and the simulated charge is obtained according to the set of the charge surface data set and the drop position of the charge at the furnace throat. surface shape data; finally obtain the real material surface shape data, according to the real material surface shape data and the simulated material surface shape data, the model parameters in the initial charge trajectory model formula are optimized by genetic algorithm, and the The model parameters in the formula of the initial charge trajectory model described above are iteratively updated to obtain the charge trajectory model.
  • Another object of the present invention is to provide a computer-readable storage medium, which includes a memory, and a computer program is stored in the memory.
  • the computer device mainly includes a processor, a memory 302 and a bus, the memory stores at least one program, and the program is executed by the processor to realize the configuration of the computer as described above.
  • the processor includes one or more processing cores.
  • the processor is connected to the memory through the bus.
  • the memory is used to store program instructions.
  • the memory can be realized by any type of volatile or non-volatile storage device or their combination, such as Static Anytime Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
  • SRAM Static Anytime Access Memory
  • EEPROM Electrically Erasable Programmable Read Only Memory
  • EPROM Erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Flash Memory Magnetic Disk or Optical Disk.
  • the present invention proposes a computer-readable storage medium, at least one program is stored in the storage medium, and the at least one program is executed during operation to realize the configuration of the above-mentioned computer system and/or execute the above-mentioned construction method of the blast furnace charge trajectory model .

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Abstract

A blast furnace burden trajectory model construction method, comprising: modeling a blast furnace burden descending process to obtain an initial furnace burden trajectory model formula; acquiring real material surface shape data; according to the real material surface shape data and the simulated material surface shape data, optimizing the model parameters in the initial furnace burden trajectory model formula by means of a genetic algorithm to compare the error value of the optimal individuals; and if the error value is less than the previous minimum error, updating the parameters in the furnace burden trajectory model formula by using the parameters of chromosomes of the individual. According to the method, the modeling process is simple, and the accuracy of the model is improved.

Description

一种高炉炉料轨迹模型构建方法A Blast Furnace Charge Trajectory Model Construction Method 技术领域technical field
本发明涉及高炉炼铁技术领域，具体涉及一种高炉炉料轨迹模型构建方法。The invention relates to the technical field of blast furnace ironmaking, in particular to a method for constructing a blast furnace charge trajectory model.
背景技术Background technique
在高炉生产过程中，炉料从高炉顶部的料仓处进入，经由料流阀、叉形管、喉管、溜槽最后落至炉喉部位，然后随着炉料的消耗，炉喉部位的炉料渐渐下落至炉身部位完成转化过程。料面的形状对高炉的正常运行至关重要，良好的料面可以使煤气流分布更加合理，有效填充不同部位炉料的消耗。During the production process of the blast furnace, the charge enters from the bunker at the top of the blast furnace, passes through the material flow valve, fork pipe, throat, and chute, and finally falls to the throat of the furnace. Then, as the charge is consumed, the charge at the throat of the furnace gradually falls To the furnace shaft to complete the conversion process. The shape of the charge surface is crucial to the normal operation of the blast furnace. A good charge surface can make the gas flow distribution more reasonable and effectively fill the consumption of charge in different parts.
传统的人工方法是通过探尺进行几个部位的深度测量，然后结合煤气成分、炉顶温度等进行料面的粗略估计，需要操作人员拥有丰富的布料经验，且难以准确描述料面的连续分布。随着测量设备的发展，可以通过雷达等技术手段直接测量得到炉喉的料面分布，但在实际应用中，雷达的使用寿命有限，更换及维修都不方便，且多数雷达一次只能测量一个点，完成整个料面的扫描需要测量数十上百次，如果要在布料前后都进行扫描要耗费较长时间。The traditional manual method is to measure the depth of several parts with a probe, and then make a rough estimate of the material level in combination with the gas composition and furnace top temperature, which requires the operator to have rich experience in material distribution, and it is difficult to accurately describe the continuous distribution of the material level . With the development of measuring equipment, the material level distribution of the furnace throat can be directly measured by technical means such as radar, but in practical applications, the service life of the radar is limited, and it is inconvenient to replace and maintain, and most radars can only measure one at a time. Point, to complete the scanning of the entire material surface needs to be measured tens of hundreds of times, if you want to scan both before and after the cloth, it will take a long time.
发明内容Contents of the invention
为解决上述现有建模方法过程繁琐耗时较长的问题，本发明提供一种高炉炉料轨迹模型构建方法，包括以下步骤，In order to solve the above-mentioned problem that the existing modeling method is cumbersome and time-consuming, the present invention provides a method for building a blast furnace charge trajectory model, which includes the following steps,
S1，对高炉炉料下降过程进行建模，得到初始炉料轨迹模型公式，所述初始炉料轨迹模型公式如下：S1, modeling the descending process of the blast furnace charge, and obtaining the initial charge trajectory model formula, the initial charge trajectory model formula is as follows:
Figure PCTCN2021128114-appb-000001
Figure PCTCN2021128114-appb-000001
H＝vtcosα+0.5gt 2； H=vtcosα+0.5gt 2 ;
R＝vtsinα；R = vtsinα;
其中，v为炉料滑出溜槽时的速度，α为溜槽的倾斜角度，r为料流阀的水力学半径，H为溜槽出口到料面的距离，t为炉料在空中运动的时间，R为炉料落点距溜槽出口的横向距离，a、b、c、d、e为模型参数；Among them, v is the speed when the charge slides out of the chute, α is the inclination angle of the chute, r is the hydraulic radius of the material flow valve, H is the distance from the outlet of the chute to the material surface, t is the time for the charge to move in the air, and R is The lateral distance between the charging point and the outlet of the chute, a, b, c, d, e are model parameters;
S2，获取料面数据集，并将所述料面数据集输入至所述初始炉料轨迹模型公式计算得到炉料在炉喉部位落点位置的集合，根据所述料面数据集以及所述炉料在炉喉部位落点位置的集合得到模拟料面形状数据；S2. Obtain a data set of the material level, and input the data set of the material level into the formula of the initial charge trajectory model to calculate the set of the position of the charge at the furnace throat. According to the data set of the charge level and the position of the charge The shape data of the simulated material surface is obtained by the collection of the drop point positions of the furnace throat;
S3，获取真实料面形状数据，根据所述真实料面形状数据以及所述模拟料面形状数据通过遗传算法对所述初始炉料轨迹模型公式中的模型参数进行寻优操作，对所述初始炉料轨迹模型公式中的模型参数进行迭代更新，得到炉料轨迹模型。S3. Obtain the real material surface shape data, perform an optimization operation on the model parameters in the initial charge trajectory model formula through a genetic algorithm according to the real charge surface shape data and the simulated charge surface shape data, and perform an optimization operation on the initial charge surface The model parameters in the trajectory model formula are iteratively updated to obtain the charge trajectory model.
在上述方案的基础上本发明还可以做如下改进，On the basis of the above scheme, the present invention can also be improved as follows,
进一步，所述S3中获取所述真实料面形状数据具体为，通过雷达扫描检测出炉顶料面的真实料面形状数据，所述真实料面形状数据为固定长度的离散点向量S。Further, acquiring the real material surface shape data in S3 specifically includes detecting the real material surface shape data of the furnace top material surface through radar scanning, and the real material surface shape data is a discrete point vector S with a fixed length.
进一步，所述S3中根据所述真实料面形状数据以及所述模拟料面形状数据通过遗传算法对所述初始炉料轨迹模型公式中的模型参数进行寻优操作，对所述初始炉料轨迹模型公式中的模型参数进行迭代更新具体为：Further, in the S3, according to the real material surface shape data and the simulated material surface shape data, the model parameters in the initial charge trajectory model formula are optimized through a genetic algorithm, and the initial charge trajectory model formula The model parameters in are iteratively updated as follows:
S301、设定所述模型参数的解空间下限以及解空间上限，通过染色体编码方法构建初始种群，将所述初始种群中的种群个体输入至所述初始炉料轨迹模型公式中得到种群个体的模拟料面形状数据，其中所述模拟料面形状数据为固定长度的离散点向量C；S301. Set the lower limit of the solution space and the upper limit of the solution space of the model parameters, construct the initial population through the chromosome coding method, input the population individuals in the initial population into the formula of the initial charge trajectory model to obtain the simulated material of the population individual Surface shape data, wherein the simulated material surface shape data is a fixed-length discrete point vector C;
S302、对所述离散点向量S进行插值或剔除处理，使所述真实料面形状数据的离散点向量S长度与所述模拟料面形状数据的离散点向量C长度相同，将所述离散点向量S、C输入至适用度函数得到该种群个体的适用度，所述适用度函数为离散点向量的二范数的相反数，所述适用度函数f具体为：S302. Perform interpolation or elimination processing on the discrete point vector S, so that the length of the discrete point vector S of the real material surface shape data is the same as the length of the discrete point vector C of the simulated material surface shape data, and the discrete point Vectors S and C are input to the fitness function to obtain the fitness of the population individual, the fitness function is the opposite number of the two norm of the discrete point vector, and the fitness function f is specifically:
Figure PCTCN2021128114-appb-000002
Figure PCTCN2021128114-appb-000002
S303、将种群个体的适用度按照降序后，保留前N个所述种群个体；S303. After sorting the applicability of the population individuals in descending order, retain the first N population individuals;
S304、对保留的所述种群个体进行交叉变异操作，得到下一代种群，将当前种群中的种群个体输入至所述初始炉料轨迹模型公式中得到种群个体的模拟料面形状数据后，重复进行上述S303、S304操作，直至迭代次数等于预设迭代次数进入步骤S305；S304. Perform cross mutation operation on the reserved population individuals to obtain the next generation population, input the population individuals in the current population into the initial charge trajectory model formula to obtain the simulated material surface shape data of the population individuals, and repeat the above S303, S304 operation, until the number of iterations is equal to the preset number of iterations to enter step S305;
S305、选取适用度最大的种群个体对所述初始炉料轨迹模型公式中的模型参数进行迭代更新，得到炉料轨迹模型。S305. Select the population individual with the greatest applicability to iteratively update the model parameters in the initial charge trajectory model formula to obtain a charge trajectory model.
进一步，所述S301中通过染色体编码方法构建初始种群具体为：染色体编码方法采用浮点数编码，所述初始种群的种群个体包括有模型参数a、b、c、d、e。Further, the construction of the initial population by the chromosome coding method in S301 specifically includes: the chromosome coding method adopts floating-point number coding, and the population individuals of the initial population include model parameters a, b, c, d, and e.
进一步，所述S304中对保留的所述种群个体进行交叉变异操作，得到下一代种群操作具体为：对保留的所述种群个体通过交叉算子进行基因交叉处理，对两个相互配对的种群个体通过单点交叉的方式相互交换基因，得到下一代种群个体；对保留的所述种群个体通过变异算子进行基因变异处理，确定种群个体中的基因变异位置后，通过基本位变异的方式进行变异运算，依照预设概率的将所述基因变异位置的原有基因取反。Further, in the S304, the cross mutation operation is performed on the reserved population individuals to obtain the next generation population operation, specifically: performing genetic crossover processing on the retained population individuals through a crossover operator, and performing genetic crossover processing on the two paired population individuals The next generation of population individuals is obtained by exchanging genes with each other through single-point crossover; the reserved population individuals are subjected to gene mutation processing through a mutation operator, and after determining the gene mutation position in the population individuals, the mutation is performed by means of basic bit mutation Operation, inverting the original gene at the gene mutation position according to the preset probability.
本发明另一个目的在于提供一种计算机***，所述计算机***被配制为执行以下步骤：首先对高炉炉料下降过程进行建模，得到初始炉料轨迹模型公式；然后获取料面数据集，并将所述料面数据集输入至所述初始炉料轨迹 模型公式计算得到炉料在炉喉部位落点位置的集合，根据所述料面数据集以及所述炉料在炉喉部位落点位置的集合得到模拟料面形状数据；最后获取真实料面形状数据，根据所述真实料面形状数据以及所述模拟料面形状数据通过遗传算法对所述初始炉料轨迹模型公式中的模型参数进行寻优操作，对所述初始炉料轨迹模型公式中的模型参数进行迭代更新，得到炉料轨迹模型。Another object of the present invention is to provide a computer system, which is configured to perform the following steps: first, model the process of blast furnace charge descent to obtain the initial charge trajectory model formula; then obtain a charge level data set, and convert the The material surface data set is input to the initial furnace charge trajectory model formula to calculate the set of the drop position of the charge at the furnace throat, and the simulated charge is obtained according to the set of the charge surface data set and the drop position of the charge at the furnace throat. surface shape data; finally obtain the real material surface shape data, according to the real material surface shape data and the simulated material surface shape data, the model parameters in the initial charge trajectory model formula are optimized by genetic algorithm, and the The model parameters in the formula of the initial charge trajectory model described above are iteratively updated to obtain the charge trajectory model.
本发明再一个目的在于提供一种计算机可读存储介质，包括存储器，所述存储器内存储有计算机程序，所述计算机程序被处理器执行时，实现上述的高炉炉料轨迹模型构建方法。Another object of the present invention is to provide a computer-readable storage medium, which includes a memory, and a computer program is stored in the memory. When the computer program is executed by a processor, the above-mentioned method for building a blast furnace charge trajectory model is realized.
本发明的有益效果是：根据雷达扫描的料面数据，不断调整优化炉料轨迹模型的多个参数，最终得到最合适当前高炉的炉料轨迹模型，从而在不需要雷达的情况下，通过布料矩阵直接计算得到布料后的料面形状，通过模拟自然界的遗传演变，可以在解空间中高效寻找炉料轨迹模型最优模型参数，不仅建模过程简洁且提高了模型的准确率。The beneficial effects of the present invention are: continuously adjust and optimize multiple parameters of the furnace charge trajectory model according to the radar scanning data, and finally obtain the most suitable furnace charge trajectory model for the current blast furnace, so that without the need for radar, through the distribution matrix directly The shape of the material surface after the material distribution is calculated, and by simulating the genetic evolution of nature, the optimal model parameters of the furnace material trajectory model can be efficiently found in the solution space, which not only makes the modeling process simple but also improves the accuracy of the model.
附图说明Description of drawings
图1为本发明一种高炉炉料轨迹模型构建方法方法流程框图；Fig. 1 is a method block diagram of a method for constructing a blast furnace charge trajectory model of the present invention;
图2为本发明高炉炉喉处的结构示意图；Fig. 2 is the structural representation of blast furnace throat place of the present invention;
图3为本发明高炉炉料轨迹模型的模型参数寻优方法流程图。Fig. 3 is a flow chart of the method for optimizing the model parameters of the blast furnace charge trajectory model of the present invention.
具体实施方式Detailed ways
以下结合附图对本发明的原理和特征进行描述，所举实例只用于解释本发明，并非用于限定本发明的范围。The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.
如图1所示本发明提供一种高炉炉料轨迹模型构建方法，包括以下步骤，As shown in Figure 1, the present invention provides a method for building a blast furnace charge trajectory model, comprising the following steps,
S1，对高炉炉料下降过程进行建模，得到初始炉料轨迹模型公式，S1, modeling the blast furnace charging process, and obtaining the initial charging trajectory model formula,
S2，获取料面数据集，并将所述料面数据集输入至所述初始炉料轨迹模 型公式计算得到炉料在炉喉部位落点位置的集合，根据所述料面数据集以及所述炉料在炉喉部位落点位置的集合得到模拟料面形状数据；S2. Obtain a data set of the material level, and input the data set of the material level into the formula of the initial charge trajectory model to calculate the set of the position of the charge at the furnace throat. According to the data set of the charge level and the position of the charge The shape data of the simulated material surface is obtained by the collection of the drop point positions of the furnace throat;
S3，获取真实料面形状数据，根据所述真实料面形状数据以及所述模拟料面形状数据通过遗传算法对所述初始炉料轨迹模型公式中的模型参数进行寻优操作，对所述初始炉料轨迹模型公式中的模型参数进行迭代更新，得到炉料轨迹模型。S3. Obtain the real material surface shape data, perform an optimization operation on the model parameters in the initial charge trajectory model formula through a genetic algorithm according to the real charge surface shape data and the simulated charge surface shape data, and perform an optimization operation on the initial charge surface The model parameters in the trajectory model formula are iteratively updated to obtain the charge trajectory model.
本发明根据雷达扫描的料面数据，不断调整优化炉料轨迹模型的多个参数，最终得到最合适当前高炉的炉料轨迹模型，从而在不需要雷达的情况下，通过布料矩阵直接计算得到布料后的料面形状，通过模拟自然界的遗传演变，可以在解空间中高效寻找炉料轨迹模型最优模型参数，不仅建模过程简洁且提高了模型的准确率。According to the material surface data scanned by the radar, the present invention continuously adjusts and optimizes multiple parameters of the furnace material trajectory model, and finally obtains the most suitable furnace material trajectory model for the current blast furnace, so that the material distribution matrix can be directly calculated through the material distribution matrix without the need for radar. The shape of the charge surface, by simulating the genetic evolution of nature, can efficiently find the optimal model parameters of the charge trajectory model in the solution space, which not only makes the modeling process simple but also improves the accuracy of the model.
在本实施例中具体地，利用经典力学公式对炉料下降过程进行建模，得到炉料轨迹模型公式。高炉炉喉处的结构如图2所示，对高炉炉料下降过程进行建模，得到初始炉料轨迹模型公式，所述初始炉料轨迹模型公式如下：Specifically, in this embodiment, the charge descending process is modeled using classical mechanical formulas to obtain a charge trajectory model formula. The structure at the throat of the blast furnace is shown in Figure 2. Modeling is performed on the blast furnace charging process to obtain the initial charging trajectory model formula. The initial charging trajectory model formula is as follows:
Figure PCTCN2021128114-appb-000003
Figure PCTCN2021128114-appb-000003
H＝vtcosα+0.5gt 2； H=vtcosα+0.5gt 2 ;
R＝vtsinα；R = vtsinα;
其中，v为炉料滑出溜槽时的速度，α为溜槽的倾斜角度，r为料流阀的水力学半径，H为溜槽出口到料面的距离，t为炉料在空中运动的时间，R为炉料落点距溜槽出口的横向距离，a、b、c、d、e为模型参数。Among them, v is the speed when the charge slides out of the chute, α is the inclination angle of the chute, r is the hydraulic radius of the material flow valve, H is the distance from the outlet of the chute to the material surface, t is the time for the charge to move in the air, and R is The lateral distance between the charging point and the outlet of the chute, a, b, c, d, e are model parameters.
在本实施例中通过初始炉料轨迹模型公式，如图3所示在获取料面数据集后，可以计算出炉料在炉喉部位的落点位置。其中料面数据集包括料流阀水力学半径、溜槽角度和下落高度、料批的总重量、密度、布料矩阵，通过料批的总重量、密度、布料矩阵可以计算出高炉每个档位的布料体积。基于轨迹模型计算出的落点位置，和布料体积可以模拟出该档位料面形状。重复 上述方法可以计算出当前布料每个档位的形状，就得到了当前料批布完后料面的模拟料面形状数据。In this embodiment, by using the formula of the initial charge trajectory model, as shown in FIG. 3 , after obtaining the charge surface data set, the position of the drop point of the charge on the furnace throat can be calculated. The material level data set includes the hydraulic radius of the material flow valve, the angle of the chute and the drop height, the total weight, density, and distribution matrix of the material batch. Through the total weight, density, and distribution matrix of the material batch, the temperature of each gear of the blast furnace can be calculated. Cloth volume. Based on the position of the landing point calculated by the trajectory model, and the volume of the cloth, the shape of the material surface of the gear can be simulated. Repeat the above method to calculate the shape of each gear of the current fabric, and then obtain the simulated material surface shape data of the material surface after the current material batch is finished.
在本实施例中具体地，通过雷达扫描检测出炉顶料面的真实料面形状数据，所述真实料面形状数据为固定长度的离散点向量S。设置进化代数计数器(预设迭代次数)t＝0，设置最大进化代数T，随机生成M个个体作为初始种群。若当前已完成的迭代次数t＝T时,则选取整个进化过程(所有迭代)中所得到的具有最大适应度个体作为最优解输出，终止遗传计算的计算。当最优个体的适应度达到给定的阈值，或者最优个体的适应度和种群适应度不再上升时，或者迭代次数达到预设的代数时，算法终止。预设的代数一般设置为100-500代。Specifically, in this embodiment, the real material surface shape data of the furnace top material surface is detected by radar scanning, and the real material surface shape data is a discrete point vector S with a fixed length. Set the evolution algebra counter (preset number of iterations) t=0, set the maximum evolution algebra T, and randomly generate M individuals as the initial population. If the number of iterations currently completed is t=T, the individual with the maximum fitness obtained in the entire evolution process (all iterations) is selected as the output of the optimal solution, and the calculation of the genetic calculation is terminated. When the fitness of the optimal individual reaches a given threshold, or when the fitness of the optimal individual and the fitness of the population no longer increase, or when the number of iterations reaches the preset number of generations, the algorithm terminates. The preset number of generations is generally set to 100-500 generations.
在本实施例中具体地，染色体编码方法采用浮点数编码，所述初始种群的种群个体包括有模型参数a、b、c、d、e。设定所述模型参数的解空间下限以及解空间上限，设定五个参数的解空间下限为{-50,-50,-50,-50,-50}，参数的解空间上限为{50,50,50,50,50},根据染色体编码方式生成初代种群{{a1,b1,c1,d1,e1},{a2,b2,c2,d2,e2},...}，种群规模为100。通过染色体编码方法构建初始种群，将所述初始种群中的种群个体输入至所述初始炉料轨迹模型公式中得到种群个体的模拟料面形状数据，其中所述模拟料面形状数据为固定长度的离散点向量C。对所述离散点向量S进行插值或剔除处理，根据适用度函数得到所述模拟料面形状数据的适用度，使所述真实料面形状数据的离散点向量S长度与所述模拟料面形状数据的离散点向量C长度相同，所述适用度函数为离散点向量的二范数的相反数，则所述适用度函数f具体为：Specifically, in this embodiment, the chromosome encoding method adopts floating-point number encoding, and the population individuals of the initial population include model parameters a, b, c, d, and e. Set the lower limit of the solution space and the upper limit of the solution space of the model parameters, set the lower limit of the solution space of the five parameters to {-50,-50,-50,-50,-50}, and the upper limit of the solution space of the parameters to {50 ,50,50,50,50}, generate the first generation population {{a1,b1,c1,d1,e1},{a2,b2,c2,d2,e2},...} according to the chromosome coding method, the population size is 100. The initial population is constructed by the chromosome coding method, and the population individuals in the initial population are input into the formula of the initial charge trajectory model to obtain the simulated material surface shape data of the population individual, wherein the simulated material surface shape data is a fixed-length discrete point vector C. Carry out interpolation or elimination processing to described discrete point vector S, obtain the applicability of described simulated material level shape data according to applicability function, make the discrete point vector S length of described real material level shape data and described simulated material level shape The discrete point vector C of the data has the same length, and the applicability function is the opposite number of the two norm of the discrete point vector, then the applicability function f is specifically:
Figure PCTCN2021128114-appb-000004
Figure PCTCN2021128114-appb-000004
在本实施例中具体地，根据种群个体的所述模拟料面形状数据的适用度 按照降序后，保留前N个所述种群个体；对保留的所述种群个体进行交叉变异操作，得到下一代种群，将当前种群中的种群个体输入至所述初始炉料轨迹模型公式中得到种群个体的模拟料面形状数据后，对保留的所述种群个体通过交叉算子进行基因交叉处理，对两个相互配对的种群个体通过单点交叉的方式相互交换基因，从而形成下一代种群个体；对保留的所述种群个体通过变异算子进行基因变异处理，确定种群个体中的基因变异位置后，通过基本位变异的方式进行变异运算，依照预设概率的将所述基因变异位置的原有基因取反。Specifically, in this embodiment, according to the applicability of the simulated material surface shape data of the population individuals in descending order, the first N population individuals are retained; the cross-mutation operation is performed on the retained population individuals to obtain the next generation Population, after inputting the population individuals in the current population into the formula of the initial charge trajectory model to obtain the simulated material surface shape data of the population individuals, carry out genetic crossover processing on the reserved population individuals through the crossover operator, and perform genetic crossover processing on the two mutual The paired population individuals exchange genes with each other through single-point crossover, thereby forming the next generation of population individuals; the reserved population individuals are subjected to gene mutation processing through mutation operators, and after determining the gene mutation position in the population individuals, through the basic position The mutation operation is carried out in the way of mutation, and the original gene at the mutation position of the gene is reversed according to the preset probability.
种群通过选择运算的适应度比例方法来选择适用度较高的个体予以保留，通过选择算子选择将种群中优化的个体直接遗传到下一代或通过配对交叉产生新的个体再遗传到下一代。然后运用单点交叉的交叉算子，通过选取两条染色体，在随机选择的位置点上进行分割并交换右侧的部分，从而得到两个不同的子染色体。单点交叉可以造成较小的破坏的情况下，在选取的个体基础上进行变异、交叉操作产生下一代种群，通过交叉运算对两个相互配对的染色体按某种方式相互交换基因，从而形成新的个体。交叉运算在遗传算法中起着关键作用，对种群中的个体串的某些基因座上的基因值作变动是产生新个体的主要方法。变异运算是对某一个基因座上的基因值按一较小概率进行改变,它也是产生新个体的一种操作方法。本发明采用基本位变异的方法来进行变异运算,其具体操作过程是:首先确定出各个个体的基因变异位置，然后依照某一概率将变异点的原有基因取反。种群P(t)经过选择、交叉、变异运算之后得到下一代种群P(t+1)。遗传算法具有局部的随机搜索能力。当遗传算法通过交叉算子已接近炉料轨迹模型公式中的参数邻域时，利用变异算子的这种局部随机搜索能力可以加速向最优炉料轨迹模型公式中的参数收敛，还可以使遗传算法可维持种群多样性，以防止出现未成熟收敛现象。选取适用度最大的种群个体对所述初始炉料轨迹模型公式中的模型参 数进行迭代更新，得到炉料轨迹模型。The population uses the fitness ratio method of the selection operation to select individuals with high applicability and keep them. The optimized individuals in the population are directly inherited to the next generation through the selection operator selection, or new individuals are generated through pairing and crossover, and then passed on to the next generation. Then, using the crossover operator of single point crossover, by selecting two chromosomes, splitting at a randomly selected position point and exchanging the right part, so as to obtain two different daughter chromosomes. When single-point crossover can cause minor damage, mutation and crossover operations are performed on the basis of selected individuals to generate the next generation of populations. Through crossover operations, two paired chromosomes exchange genes in a certain way to form a new population. individual. The crossover operation plays a key role in the genetic algorithm, and changing the gene values at some loci of the individual strings in the population is the main method to generate new individuals. Mutation operation is to change the gene value on a certain locus according to a small probability, and it is also an operation method to generate new individuals. The present invention adopts the method of basic bit mutation to carry out the variation operation, and its specific operation process is: firstly determine the gene variation position of each individual, and then reverse the original gene of the variation point according to a certain probability. After the population P(t) undergoes selection, crossover and mutation operations, the next generation population P(t+1) is obtained. Genetic algorithm has local random search ability. When the genetic algorithm is close to the parameter neighborhood in the formula of the charge trajectory model through the crossover operator, the local random search ability of the mutation operator can be used to accelerate the convergence to the parameters in the formula of the optimal charge trajectory model, and it can also make the genetic algorithm Population diversity can be maintained to prevent premature convergence. Select the population individual with the greatest applicability to iteratively update the model parameters in the initial charge trajectory model formula to obtain the charge trajectory model.
本发明另一个目的在于提供一种计算机***，所述计算机***被配制为执行以下步骤：首先对高炉炉料下降过程进行建模，得到初始炉料轨迹模型公式；然后获取料面数据集，并将所述料面数据集输入至所述初始炉料轨迹模型公式计算得到炉料在炉喉部位落点位置的集合，根据所述料面数据集以及所述炉料在炉喉部位落点位置的集合得到模拟料面形状数据；最后获取真实料面形状数据，根据所述真实料面形状数据以及所述模拟料面形状数据通过遗传算法对所述初始炉料轨迹模型公式中的模型参数进行寻优操作，对所述初始炉料轨迹模型公式中的模型参数进行迭代更新，得到炉料轨迹模型。Another object of the present invention is to provide a computer system, which is configured to perform the following steps: first, model the process of blast furnace charge descent to obtain the initial charge trajectory model formula; then obtain a charge level data set, and convert the The material surface data set is input to the initial furnace charge trajectory model formula to calculate the set of the drop position of the charge at the furnace throat, and the simulated charge is obtained according to the set of the charge surface data set and the drop position of the charge at the furnace throat. surface shape data; finally obtain the real material surface shape data, according to the real material surface shape data and the simulated material surface shape data, the model parameters in the initial charge trajectory model formula are optimized by genetic algorithm, and the The model parameters in the formula of the initial charge trajectory model described above are iteratively updated to obtain the charge trajectory model.
本发明再一个目的在于提供一种计算机可读存储介质，包括存储器，所述存储器内存储有计算机程序，所述计算机程序被处理器执行时，实现上述的高炉炉料轨迹模型构建方法。该计算机装置主要包括处理器、存储器302和总线，所述存储器存储有至少一段程序，所述程序由所述处理器执行以实现如上述所述的计算机的配置。处理器包括一个或一个以上处理核心，处理器通过总线与存储器相连，存储器用于存储程序指令，处理器执行存储器中的程序指令时实现上述方法实施例提供的高炉炉料轨迹模型构建方法。存储器可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随时存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM),可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。Another object of the present invention is to provide a computer-readable storage medium, which includes a memory, and a computer program is stored in the memory. When the computer program is executed by a processor, the above-mentioned method for building a blast furnace charge trajectory model is realized. The computer device mainly includes a processor, a memory 302 and a bus, the memory stores at least one program, and the program is executed by the processor to realize the configuration of the computer as described above. The processor includes one or more processing cores. The processor is connected to the memory through the bus. The memory is used to store program instructions. When the processor executes the program instructions in the memory, the method for constructing the blast furnace charge trajectory model provided by the above method embodiments is realized. The memory can be realized by any type of volatile or non-volatile storage device or their combination, such as Static Anytime Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic Disk or Optical Disk.
本发明提出一种计算机可读存储介质，所述存储介质中存储有至少一段程序，所述至少一段程序运行时执行以实现如上述的计算机***的配置和/或执行上述高炉炉料轨迹模型构建方法。The present invention proposes a computer-readable storage medium, at least one program is stored in the storage medium, and the at least one program is executed during operation to realize the configuration of the above-mentioned computer system and/or execute the above-mentioned construction method of the blast furnace charge trajectory model .
读者应理解，在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该 实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。Readers should understand that in the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" mean that the embodiments or examples are combined A particular feature, structure, material, or characteristic is described as included in at least one embodiment or example of the invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the described specific features, structures, materials or characteristics may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without conflicting with each other.
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的方法实施例仅仅是示意性的，例如，步骤的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个步骤可以结合或者可以集成到另一个步骤，或一些特征可以忽略，或不执行。In the several embodiments provided in this application, it should be understood that the disclosed devices and methods may be implemented in other ways. For example, the method embodiments described above are only illustrative. For example, the division of steps is only a logical function division. In actual implementation, there may be other division methods. For example, multiple steps can be combined or integrated into another A step, or some features, can be ignored, or not performed.
以上，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到各种等效的修改或替换，这些修改或替换都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or modifications within the technical scope disclosed in the present invention. Replacement, these modifications or replacements shall all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (7)

  1. 一种高炉炉料轨迹模型构建方法，其特征在于，包括以下步骤，A method for building a blast furnace charge trajectory model, characterized in that it comprises the following steps,
    S1，对高炉炉料下降过程进行建模，得到初始炉料轨迹模型公式，所述初始炉料轨迹模型公式如下：S1, modeling the descending process of the blast furnace charge, and obtaining the initial charge trajectory model formula, the initial charge trajectory model formula is as follows:
    Figure PCTCN2021128114-appb-100001
    Figure PCTCN2021128114-appb-100001
    H＝vtcosα+0.5gt 2； H=vtcosα+0.5gt 2 ;
    R＝vtsinα；R = vtsinα;
    其中，v为炉料滑出溜槽时的速度，α为溜槽的倾斜角度，r为料流阀的水力学半径，H为溜槽出口到料面的距离，t为炉料在空中运动的时间，R为炉料落点距溜槽出口的横向距离，a、b、c、d、e为模型参数；Among them, v is the speed when the charge slides out of the chute, α is the inclination angle of the chute, r is the hydraulic radius of the material flow valve, H is the distance from the outlet of the chute to the material surface, t is the time for the charge to move in the air, and R is The lateral distance between the charging point and the outlet of the chute, a, b, c, d, e are model parameters;
    S2，获取料面数据集，并将所述料面数据集输入至所述初始炉料轨迹模型公式中计算得到炉料在炉喉部位落点位置的集合，根据所述料面数据集以及所述炉料在炉喉部位落点位置的集合得到模拟料面形状数据；S2. Obtain a data set of material level, and input the data set of material level into the formula of the initial charge trajectory model to calculate a set of the location of the charge at the furnace throat, according to the data set of charge level and the charge The shape data of the simulated material surface is obtained by collecting the drop point positions at the throat of the furnace;
    S3，获取真实料面形状数据，根据所述真实料面形状数据以及所述模拟料面形状数据通过遗传算法对所述初始炉料轨迹模型公式中的模型参数进行寻优操作，对所述初始炉料轨迹模型公式中的模型参数进行迭代更新，得到炉料轨迹模型。S3. Obtain the real material surface shape data, perform an optimization operation on the model parameters in the initial charge trajectory model formula through a genetic algorithm according to the real charge surface shape data and the simulated charge surface shape data, and perform an optimization operation on the initial charge surface The model parameters in the trajectory model formula are iteratively updated to obtain the charge trajectory model.
  2. 根据权利要求1所述的高炉炉料轨迹模型构建方法，其特征在于，所述S3中获取所述真实料面形状数据具体为，通过雷达扫描检测出炉顶料面的真实料面形状数据，所述真实料面形状数据为固定长度的离散点向量S。The method for constructing a blast furnace charge trajectory model according to claim 1, wherein the acquisition of the real charge surface shape data in the S3 is specifically detecting the real charge surface shape data of the top charge surface through radar scanning, and the The real surface shape data is a fixed-length discrete point vector S.
  3. 根据权利要求2所述的高炉炉料轨迹模型构建方法，其特征在于，所述S3中根据所述真实料面形状数据以及所述模拟料面形状数据通过遗传算法对所述初始炉料轨迹模型公式中的模型参数进行寻优操作，对所述初始炉料轨迹模型公式中的模型参数进行迭代更新具体为：The method for constructing a blast furnace charge trajectory model according to claim 2, wherein in said S3, the initial charge trajectory model formula is calculated by a genetic algorithm based on the real charge surface shape data and the simulated charge surface shape data Optimizing operation is performed on the model parameters, and the model parameters in the initial charge trajectory model formula are iteratively updated as follows:
    S301、设定所述模型参数的解空间下限以及解空间上限，通过染色体编 码方法构建初始种群，将所述初始种群中的所有种群个体分别输入至所述初始炉料轨迹模型公式中得到种群个体的模拟料面形状数据，其中所述模拟料面形状数据为固定长度的离散点向量C；S301. Set the lower limit of the solution space and the upper limit of the solution space of the model parameters, construct the initial population by the chromosome coding method, input all the population individuals in the initial population into the formula of the initial charge trajectory model to obtain the population individual Simulating material surface shape data, wherein the simulated material surface shape data is a discrete point vector C of fixed length;
    S302、对所述离散点向量S进行插值或剔除处理，使所述真实料面形状数据的离散点向量S长度与所述模拟料面形状数据的离散点向量C长度相同，将所述离散点向量S、C输入至适用度函数得到该种群个体的适用度，所述适用度函数为离散点向量的二范数的相反数，所述适用度函数f具体为：S302. Perform interpolation or elimination processing on the discrete point vector S, so that the length of the discrete point vector S of the real material surface shape data is the same as the length of the discrete point vector C of the simulated material surface shape data, and the discrete point Vectors S and C are input to the fitness function to obtain the fitness of the population individual, the fitness function is the opposite number of the two norm of the discrete point vector, and the fitness function f is specifically:
    Figure PCTCN2021128114-appb-100002
    Figure PCTCN2021128114-appb-100002
    S303、将种群个体的适用度按照降序后，保留前N个所述种群个体；S303. After sorting the applicability of the population individuals in descending order, retain the first N population individuals;
    S304、对保留的所述种群个体进行交叉变异操作，得到下一代种群，将当前种群中的种群个体输入至所述初始炉料轨迹模型公式中得到种群个体的模拟料面形状数据后，重复进行上述S303、S304操作，直至迭代次数等于预设迭代次数进入步骤S305；S304. Perform cross mutation operation on the reserved population individuals to obtain the next generation population, input the population individuals in the current population into the initial charge trajectory model formula to obtain the simulated material surface shape data of the population individuals, and repeat the above S303, S304 operation, until the number of iterations is equal to the preset number of iterations to enter step S305;
    S305、选取适用度最大的种群个体并根据选取的种群个体对所述初始炉料轨迹模型公式中的模型参数进行迭代更新，得到炉料轨迹模型。S305. Select the population individual with the greatest applicability and iteratively update the model parameters in the initial charge trajectory model formula according to the selected population individual to obtain a charge trajectory model.
  4. 根据权利要求3所述的高炉炉料轨迹模型构建方法，其特征在于，所述S301中通过染色体编码方法构建初始种群具体为：染色体编码方法采用浮点数编码，所述初始种群的种群个体包括有模型参数a、b、c、d、e。The method for constructing a blast furnace charge trajectory model according to claim 3, wherein the construction of the initial population by the chromosome coding method in S301 is specifically: the chromosome coding method adopts floating-point number coding, and the population individuals of the initial population include a model Parameters a, b, c, d, e.
  5. 根据权利要求4所述的高炉炉料轨迹模型构建方法，其特征在于，所述S304中对保留的所述种群个体进行交叉变异操作，得到下一代种群操作具体为：对保留的所述种群个体通过交叉算子进行基因交叉处理，将两个相互配对的种群个体通过单点交叉的方式相互交换基因，得到下一代种群个体；对保留的所述种群个体通过变异算子进行基因变异处理，确定种群个体中的基因变异位置后，通过基本位变异的方式进行变异运算，依照预设概率 的将所述基因变异位置的原有基因取反。The method for constructing a blast furnace charge trajectory model according to claim 4, characterized in that, in said S304, performing a cross-mutation operation on the retained population individuals to obtain the next generation population operation is specifically: performing a cross mutation operation on the retained population individuals The crossover operator performs gene crossover processing, and the two paired population individuals exchange genes with each other through a single-point crossover method to obtain the next generation of population individuals; the reserved population individuals are subjected to gene mutation processing through the mutation operator to determine the population After the mutation position of the gene in the individual, the mutation operation is performed by means of basic bit mutation, and the original gene at the mutation position of the gene is reversed according to the preset probability.
  6. 一种计算机***，其特征在于：所述计算机***被配制为能执行如权利要求1至5任一项所述的高炉炉料轨迹模型构建方法。A computer system, characterized in that the computer system is configured to execute the method for constructing a blast furnace charge trajectory model according to any one of claims 1 to 5.
  7. 一种计算机可读存储介质，其特征在于：包括存储器，所述存储器内存储有计算机程序，所述计算机程序被处理器执行时，实现如权利要求1至5任一项所述的高炉炉料轨迹模型构建方法。A computer-readable storage medium, characterized in that it includes a memory, and a computer program is stored in the memory, and when the computer program is executed by a processor, the blast furnace charge trajectory according to any one of claims 1 to 5 is realized Model building method.
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