Method and system for controlling charging of electric furnace burden used in brake disc production, and medium

Abstract

A method and system for controlling charging of electric furnace burden used in brake disc production, and a medium. The method comprises: M1, adding a fixed ratio of burden into an electric furnace, acquiring addition amount data information of the burden and output amount data information of pig iron in real time, acquiring temperature data information of the burden in real time on the basis of an in-furnace temperature sensor, and acquiring liquid level data information of the burden in real time on the basis of an in-furnace liquid level sensor; M2, on the basis of the temperature data information of the burden and the liquid level data information of the burden, using a grey prediction model algorithm based on an adaptive adjustment factor to predict the settling velocity of the burden to obtain settling velocity data information of the burden; M3, on the basis of the settling velocity data information of the burden, the addition amount data information of the burden, and the output amount data information of the pig iron, using an improved Osprey optimization algorithm to optimize the addition amount of the burden to obtain optimized addition amount data information of the burden; and M4, on the basis of the optimized addition amount data information of the burden, constructing an electric furnace burden charging control function P, controlling the charging amount of the electric furnace burden, and outputting control data information of the charging of the electric furnace burden.

Description

A furnace charge feeding control method and system applied to a brake disc and a medium TECHNICAL FIELD

[0001] The present application relates to the technical field of brake disc processing, in particular to a furnace charge feeding control method and system applied to a brake disc and a medium. BACKGROUND

[0002] In recent years, with the steady development of the domestic automobile market, the continuous increase of automobile ownership, and the increasing emphasis of consumers on driving safety, the automobile brake disc industry has ushered in an opportunity for rapid development. As an important part of the automobile braking system, the market demand for automobile brake discs is showing a steady growth trend. Automobile brake discs, also known as brake discs, brake rotors, etc., are core components of the automobile braking system, providing transmission control, conversion, etc. for the automobile braking system to ensure safe and stable driving of the automobile. The working principle of the brake disc is to stop the rotation of the wheel by clamping the brake disc with the brake caliper, thereby realizing the brake function. In the processing of the brake disc, the raw material of the brake disc needs to be processed first. In the traditional processing of brake disc furnace charge, iron ore, coke or flux need to be manually added to the blast furnace for the reduction of pig iron. Not only does this require a large number of manual labor, but also has low intelligence and cannot adaptively adjust the addition of raw materials, resulting in insufficient reduction of raw materials and the need for secondary reduction, high energy consumption, and low production efficiency of the brake disc. Therefore, how to intelligently control and adaptively adjust the feeding of the brake disc to reduce labor intensity, improve the production efficiency of the brake disc and reduce energy consumption has become a problem that we need to solve.

[0003] In the prior art, patent (application number: 201010140451.7) discloses a vanadium-titanium magnetite blast furnace smelting furnace charge and a blast furnace smelting method. The furnace charge is composed of vanadium-titanium sinter and vanadium-titanium pellet. The vanadium-titanium sinter is a sinter obtained by sintering a mixture containing vanadium-titanium iron concentrate and ordinary iron concentrate. The vanadium-titanium pellet is a pellet obtained by roasting vanadium-titanium iron concentrate or a pellet obtained by roasting a mixture containing vanadium-titanium iron concentrate and ordinary iron concentrate. The ordinary iron concentrate is an iron concentrate without vanadium and titanium elements. However, this scheme does not intelligently optimize the feeding of the furnace charge, which may lead to repetition in the smelting process and increase energy consumption.

[0004] SUMMARY

[0005] In view of the above deficiencies of the prior art, the present application provides a furnace charge feeding control method and system applied to a brake disc and a medium. The method does not require manual intervention during the feeding process, reduces the labor intensity of manual labor, and can adaptively adjust the feeding according to the reaction of the furnace charge to improve the reduction rate of the furnace charge and the production efficiency of the brake disc.

[0006] To achieve the above object and other related objects, the technical solutions of the present application are as follows.

[0007] A furnace charge feeding control method applied to a brake disc electric furnace, the method comprising:

[0008] M1. Adding fixed proportion of furnace charge in the electric furnace, acquiring real-time furnace charge addition amount data information and pig iron output amount data information, acquiring real-time furnace charge temperature data information based on the furnace temperature sensor, and acquiring real-time furnace charge liquid level data information based on the furnace liquid level sensor;

[0009] M2. Based on the furnace charge temperature data information and the furnace charge liquid level data information, using a grey prediction model algorithm based on an adaptive adjustment factor to predict the settling velocity of the furnace charge, and obtaining the settling velocity data information of the furnace charge;

[0010] M3. Based on the settling velocity data information of the furnace charge, the furnace charge addition amount data information and the pig iron output amount data information, using an improved fish-eagle optimization algorithm to optimize the addition amount of the furnace charge, and obtaining the optimized furnace charge addition amount data information;

[0011] M4. Based on the optimized furnace charge addition amount data information, constructing an electric furnace furnace charge feeding control function P to control the feeding amount of the electric furnace furnace charge, and outputting the control data information of the electric furnace furnace charge feeding.

[0012] Further, the furnace charge includes iron ore, coke and flux, and the fixed proportion of the iron ore, coke and flux is 1:(0.5-0.7):(0.2-0.4).

[0013] Further, in step M2, the prediction of the settling velocity of the furnace charge using the grey prediction model algorithm based on the adaptive adjustment factor comprises:

[0014] M21. Based on the furnace charge temperature data information and the furnace charge liquid level data information, establishing a relationship sequence function G of the furnace charge settling velocity,

[0015] wherein x1 is the furnace charge temperature data information, x2 is the furnace charge liquid level data information, and α1, α2 and α3 are the relationship constant parameters of the furnace charge, the relationship sequence of the furnace charge settling velocity is calculated to obtain the relationship sequence data information of the furnace charge settling velocity;

[0016] M22. Based on the relationship sequence data information of the furnace charge settling velocity, constructing an operator generating function H of the grey prediction model,

[0017] wherein gi is the i-th relational sequence data information of the settling velocity of the furnace charge i+1 is the i+1-th relational sequence data information of the settling velocity of the furnace charge, n is the sample capacity, β i , λ i and θ i are weight coefficients of the operators of the grey model, the operators of the grey model are calculated to obtain operator data information of the grey model;

[0018] M23. Based on the operator data information of the grey model, a grey model prediction function W based on an adaptive adjustment factor is established,

[0019] wherein h is the operator data information of the grey model, and σ is the adaptive adjustment factor, the settling velocity of the furnace charge is predicted to obtain settling velocity data information of the furnace charge.

[0020] Further, the adaptive adjustment factor σ is

[0021] wherein h is the operator data information of the grey model.

[0022] Further, according to the relational sequence data information of the settling velocity of the furnace charge, the constraint conditions of the weight coefficients β i , λ i and θ i of the operators of the grey model are

[0023] Further, in step M3, the improved fish-eagle optimization algorithm is used to optimize the addition amount of the furnace charge, which includes:

[0024] M31. Based on the settling velocity data information of the furnace charge, the addition amount data information of the furnace charge and the output data information of the pig iron, a fish-eagle population space function R of the furnace charge is established,

[0025] wherein y1 is the settling velocity data information of the furnace charge, y2 is the addition amount data information of the furnace charge, y3 is the output data information of the pig iron, and μ1 and μ2 are space determining factors of the furnace charge, the fish-eagle population of the furnace charge is initialized to obtain initialized fish-eagle population data information of the furnace charge;

[0026] M32. Based on the initialized fish-eagle population data information of the furnace charge, a best fish-eagle update position function L is established,

[0027] Wherein, r is the fish eagle population data information of the initialized furnace charge, omega 1 and omega 2 are the updating constant parameters of the fish eagle population of the furnace charge, and the optimal fish eagle updating position data information of the furnace charge fish eagle population is obtained.

[0028] M33. Based on the optimal fish eagle updating position data information of the furnace charge fish eagle population, an adding amount optimization function O of the furnace charge is established,

[0029] Wherein, z is the optimal fish eagle updating position data information of the furnace charge fish eagle population, rho 1 and rho 2 are the adding amount optimization factors of the furnace charge, the adding amount of the furnace charge is optimized, and the adding amount data information of the optimized furnace charge is obtained.

[0030] Further, the constraint condition of the space determining factors mu 1 and mu 2 of the furnace charge is,

[0031] The constraint function f of the updating constant parameters omega 1 and omega 2 of the fish eagle population of the furnace charge is,

[0032] The constraint condition of the adding amount optimization factors rho 1 and rho 2 of the furnace charge is,

[0033] Further, the furnace charge feeding control function P of the electric furnace is,

[0034] Wherein, a is the adding amount data information of the optimized furnace charge, delta 1, delta 2 and delta 3 are the electric furnace furnace charge feeding control factors.

[0035] In order to achieve the above-mentioned purpose and other related purposes, the application further provides an electric furnace charge feeding control system applied to a brake disc, comprising a computer device programmed or configured to execute the steps of any one of the electric furnace charge feeding control methods applied to the brake disc.

[0036] In order to achieve the above-mentioned purpose and other related purposes, the application further provides a computer readable storage medium, which stores a computer program programmed or configured to execute the electric furnace charge feeding control method applied to the brake disc.

[0037] The application has the following positive effects:

[0038] 1. The present application predicts the settling velocity of the charge by using a grey prediction model algorithm based on adaptive adjustment factors, and optimizes the addition amount of the charge by combining an improved fish eagle optimization algorithm, to obtain the optimized addition amount data information of the charge, which can not only adaptively adjust the feeding according to the reaction of the charge, improve the reduction rate of the charge, and thus improve the production efficiency of the brake disc, but also increase the output of pig iron and reduce resource consumption, further improving the production efficiency of the brake disc.

[0039] 2. The present application controls the feeding amount of the electric furnace charge by constructing an electric furnace charge feeding control function P, and outputs the control data information of the electric furnace charge feeding, which can not only intelligently operate the feeding, reduce the participation of manual labor, and reduce the labor intensity of manual labor, thereby reducing the labor cost, but also has a low error rate and a high degree of intelligence, and improves the production efficiency of the brake disc. BRIEF DESCRIPTION OF DRAWINGS

[0040] Fig. 1 is a method flowchart of the present application;

[0041] Fig. 2 is a flowchart of the grey prediction model algorithm based on adaptive adjustment factors of the present application;

[0042] Fig. 3 is a flowchart of the improved fish eagle optimization algorithm of the present application;

[0043] Fig. 4 is a structural schematic diagram of the present application.

[0044] Explanation of reference numerals in the drawings: 1 - unmanned feeding trolley, 2 - electric furnace, 3 - liquid level sensor, 4 - temperature sensor. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, which include various details of the embodiments of the present disclosure to assist in understanding, and should be considered only as exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Also, in order to be clear and concise, descriptions of well-known functions and structures are omitted in the following description.

[0046] Embodiment 1: As shown in Fig. 1 or 4, an electric furnace charge feeding control method applied to a brake disc, the method comprising:

[0047] M1. Add a fixed proportion of charge in the electric furnace, real-time acquire the addition amount data information of the charge and the output data information of the pig iron, real-time acquire the temperature data information of the charge based on the furnace temperature sensor, and real-time acquire the liquid level data information of the charge based on the furnace liquid level sensor;

[0048] M2. Based on the temperature data information of the furnace charge and the liquid level data information of the furnace charge, a grey prediction model algorithm based on an adaptive adjustment factor is used to predict the settling velocity of the furnace charge, and the settling velocity data information of the furnace charge is obtained;

[0049] M3. Based on the settling velocity data information of the furnace charge, the addition amount data information of the furnace charge, and the output amount data information of the pig iron, an improved fish-eagle optimization algorithm is used to optimize the addition amount of the furnace charge, and the optimized addition amount data information of the furnace charge is obtained.

[0050] M4. Based on the optimized addition amount data information of the furnace charge, a furnace charge feeding control function P is constructed to control the feeding amount of the furnace charge, and the control data information of the furnace charge feeding is output.

[0051] In this embodiment, the furnace charge includes iron ore, coke and flux, and the fixed ratio of the iron ore, coke and flux is 1:(0.5-0.7):(0.2-0.4).

[0052] In this embodiment, as shown in FIG. 2, in step M2, the prediction of the settling velocity of the furnace charge by using the grey prediction model algorithm based on the adaptive adjustment factor includes:

[0053] M21. Based on the temperature data information of the furnace charge and the liquid level data information of the furnace charge, a relationship sequence function G of the furnace charge settling velocity is established,

[0054] wherein x1 is the temperature data information of the furnace charge, x2 is the liquid level data information of the furnace charge, and α1, α2 and α3 are the relationship constant parameters of the furnace charge, the relationship sequence of the furnace charge settling velocity is calculated, and the relationship sequence data information of the furnace charge settling velocity is obtained.

[0055] M22. Based on the relationship sequence data information of the furnace charge settling velocity, an operator generating function H of the grey prediction model is constructed,

[0056] wherein g i is the i-th relationship sequence data information of the furnace charge settling velocity, g i+1 is the i+1-th relationship sequence data information of the furnace charge settling velocity, n is the sample capacity, β i , λ i and θ i are the weight coefficients of the operators of the grey model, the operators of the grey model are calculated, and the operator data information of the grey model is obtained.

[0057] M23. Based on the operator data information of the grey model, a grey model prediction function W based on an adaptive adjustment factor is established,

[0058] wherein h is the operator data information of the grey model, and σ is an adaptive adjustment factor, the settling velocity of the furnace charge is predicted to obtain the settling velocity data information of the furnace charge.

[0059] In this embodiment, the adaptive adjustment factor σ is,

[0060] wherein h is the operator data information of the grey model.

[0061] In this embodiment, according to the relational sequence data information of the settling velocity of the furnace charge, the weight coefficient β of the operator of the grey model is adjusted to obtain the settling velocity data information of the furnace charge. i , λ i and θ i The constraint conditions are,

[0062] Embodiment 2: Based on the electric furnace furnace charge feeding control method applied to the brake disc in embodiment 1, the present application is further described and explained as follows.

[0063] As shown in FIG. 1 or FIG. 4, an electric furnace furnace charge feeding control method applied to the brake disc, the method comprises:

[0064] M1. A fixed proportion of furnace charge is added to the electric furnace, the adding amount data information of the furnace charge and the output amount data information of the pig iron are obtained in real time, the temperature data information of the furnace charge is obtained in real time based on the furnace temperature sensor, and the liquid level data information of the furnace charge is obtained in real time based on the furnace liquid level sensor;

[0065] M2. Based on the temperature data information of the furnace charge and the liquid level data information of the furnace charge, the settling velocity of the furnace charge is predicted by using the grey prediction model algorithm based on the adaptive adjustment factor to obtain the settling velocity data information of the furnace charge;

[0066] M3. Based on the settling velocity data information of the furnace charge, the adding amount data information of the furnace charge and the output amount data information of the pig iron, the adding amount of the furnace charge is optimized by using the improved fish-eagle optimization algorithm to obtain the optimized adding amount data information of the furnace charge;

[0067] M4. Based on the optimized adding amount data information of the furnace charge, an electric furnace furnace charge feeding control function P is constructed to control the feeding amount of the electric furnace furnace charge, and the control data information of the electric furnace furnace charge feeding is output.

[0068] In this embodiment, as shown in FIG. 3, in step M3, the optimization of the adding amount of the furnace charge by using the improved fish-eagle optimization algorithm comprises:

[0069] M31. Establishing a fish eagle population space function R of the burden based on the settling velocity data information of the burden, the burden addition amount data information and the pig iron output data information,

[0070] wherein y1 is the settling velocity data information of the burden, y2 is the burden addition amount data information, y3 is the pig iron output data information, μ1 and μ2 are the space determinants of the burden, the fish eagle population of the burden is initialized to obtain the initialized fish eagle population data information of the burden;

[0071] M32. Establishing an optimal fish eagle update position function L based on the initialized fish eagle population data information of the burden,

[0072] wherein r is the initialized fish eagle population data information of the burden, ω1 and ω2 are the update constant parameters of the fish eagle population of the burden, and the optimal fish eagle update position data information of the burden fish eagle population is obtained;

[0073] M33. Establishing a burden addition amount optimization function O based on the optimal fish eagle update position data information of the burden fish eagle population,

[0074] wherein z is the optimal fish eagle update position data information of the burden fish eagle population, ρ1 and ρ2 are the burden addition amount optimization factors, the burden addition amount is optimized to obtain the optimized burden addition amount data information.

[0075] In the embodiment, the constraint conditions of the space determinants μ1 and μ2 of the burden are,

[0076] The constraint function f of the update constant parameters ω1 and ω2 of the fish eagle population of the burden is,

[0077] The constraint conditions of the burden addition amount optimization factors ρ1 and ρ2 are,

[0078] In the embodiment, the electric furnace burden feeding control function P is,

[0079] wherein a is the optimized burden addition amount data information, δ1, δ2 and δ3 are the electric furnace burden feeding control factors.

[0080] In the embodiment, as shown in FIG. 4, the unmanned charging trolley 1 can automatically charge the furnace charge into the electric furnace 2, the liquid level sensor 3 in the furnace can obtain the liquid level data information of the furnace charge in real time, and the temperature sensor 4 in the furnace can obtain the temperature data information of the furnace charge in real time.

[0081] In the embodiment, the application provides an electric furnace charge charging control system applied to a brake disc, comprising a computer device programmed or configured to perform the steps of any one of the electric furnace charge charging control methods applied to the brake disc.

[0082] In the embodiment, the application provides a computer readable storage medium, which stores a computer program programmed or configured to perform any one of the electric furnace charge charging control methods applied to the brake disc.

[0083] Any reference to memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0084] In summary, the application not only reduces the labor intensity of manual work without manual intervention in the charging process, but also can adaptively adjust the charging according to the reaction of the furnace charge, improve the reduction rate of the furnace charge, and thus improve the production efficiency of the brake disc.

[0085] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modification, equivalent replacement, and improvement within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for controlling the charging of an electric furnace with a charge for a brake disc, characterized in that, The method comprises: M1. Adding fixed proportion of furnace charge in the electric furnace, acquiring real-time furnace charge adding amount data information and pig iron output data information, acquiring real-time furnace charge temperature data information based on the furnace temperature sensor, and acquiring real-time furnace charge liquid level data information based on the furnace liquid level sensor; M2. Based on the furnace charge temperature data information and the furnace charge liquid level data information, using a grey prediction model algorithm based on an adaptive adjustment factor to predict the settling velocity of the furnace charge, to obtain the settling velocity data information of the furnace charge; M3. Based on the settling velocity data information of the furnace charge, the furnace charge adding amount data information and the pig iron output data information, using an improved fish-eagle optimization algorithm to optimize the adding amount of the furnace charge, to obtain the optimized furnace charge adding amount data information; M4. Based on the optimized furnace charge adding amount data information, constructing an electric furnace furnace charge feeding control function P to control the feeding amount of the electric furnace furnace charge, and outputting the control data information of the electric furnace furnace charge feeding.

2. The electric furnace burden charging control method for a brake disc according to claim 1, characterized by, In step M2, the prediction of the settling velocity of the furnace charge using the grey prediction model algorithm based on the adaptive adjustment factor comprises: M21. establishing a relationship sequence function G of the settling velocity of the burden based on the temperature data information of the burden and the level data information of the burden, Wherein x1 is the temperature data information of the furnace charge, x2 is the liquid level data information of the furnace charge, and α1, α2 and α3 are the relationship constant parameters of the furnace charge. The relationship sequence of the settling velocity of the furnace charge is calculated to obtain the relationship sequence data information of the settling velocity of the furnace charge; M22. Constructing an operator generating function H of a grey prediction model based on the relational sequence data information of the burden settling velocity, wherein g i is the i-th relational sequence data information of the settling velocity of the furnace charge, g i+1 is the i+1-th relational sequence data information of the settling velocity of the furnace charge, n is the sample capacity, β i , λ i and θ i are weight coefficients of the operators of the grey model, and the operators of the grey model are calculated to obtain the operator data information of the grey model. M23. establishing a grey model prediction function W based on an adaptive adjustment factor, based on the operator data information of the grey model, Wherein h is the operator data information of the grey model, and σ is the adaptive adjustment factor. The settling velocity of the furnace charge is predicted to obtain the settling velocity data information of the furnace charge.

3. The method for controlling the charging of the electric furnace charge for a brake disc according to claim 2, characterized in that: The adaptive adjustment factor σ is, Wherein h is the operator data information of the grey model.

4. The method for controlling the charging of the electric furnace charge for a brake disc according to claim 2, characterized in that: According to the relationship sequence data information of the burden settling velocity, the constraint conditions of the weight coefficients β i , λ i and θ i of the operators of the grey model are 5. The method for controlling the charging of the electric furnace burden for a brake disc according to claim 1, characterized in that, In step M3, the optimization of the adding amount of the furnace charge using the improved fish-eagle optimization algorithm comprises: M31. Based on the settling velocity data information of the burden, the addition amount data information of the burden, and the output data information of the pig iron, a fish eagle population space function R of the burden is established, Wherein y1 is the settling velocity data information of the furnace charge, y2 is the adding amount data information of the furnace charge, y3 is the output data information of the pig iron, and μ1 and μ2 are the space determining factors of the furnace charge. The fish-eagle population of the furnace charge is initialized to obtain the initialized fish-eagle population data information of the furnace charge; M32. Based on the initialized fish eagle population data information of the furnace charge, an optimal fish eagle update position function L is established, Wherein r is the initialized fish-eagle population data information of the furnace charge, ω1 and ω2 are the update constant parameters of the fish-eagle population of the furnace charge, and the best fish-eagle update position data information of the fish-eagle population of the furnace charge is obtained; M33. Based on the best fish-eagle update position data information of the fish-eagle population of the furnace charge, establishing The charge addition amount optimization function O, Wherein z is the best fish-eagle update position data information of the fish-eagle population of the furnace charge, and ρ1 and ρ2 are the adding amount optimization factors of the furnace charge. The adding amount of the furnace charge is optimized to obtain the optimized adding amount data information of the furnace charge.

6. The method for controlling the charging of the electric furnace charge for a brake disc according to claim 5, characterized in that: The constraints on the spatial determinants μ1 and μ2 of the charge are, The constraint function f of the renewal constant parameters ω1 and ω2 of the osprey population of the furnace charge is, The constraint conditions of the addition amount optimization factors p1 and p2 of the furnace charge are, 7. The method for controlling the charging of the electric furnace burden for a brake disc according to claim 1, characterized in that: The electric furnace charge loading control function P is, Wherein a is the optimized adding amount data information of the furnace charge, and δ1, δ2 and δ3 are the electric furnace furnace charge feeding control factors. The furnace charge comprises iron ore, coke and flux, and the fixed proportion of the iron ore, coke and flux is 1:(0.5-0.7):(0.2-0.4).

8. The method for controlling the charging of the electric furnace charge for a brake disc according to claim 1, characterized in that: The computer device is programmed or configured to perform the steps of the electric furnace furnace charge feeding control method applied to the brake disc according to any one of claims 1-8.

9. An electric furnace burden charging control system applied to a brake disc, comprising a computer device, characterized in that, ​ 10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program programmed or configured to perform the electric furnace burden charging control method for a brake disc according to any one of claims 1 to 8.

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