A method, system and medium for controlling electric furnace charge feeding applied to brake disc

Abstract

The present invention relates to a method, system and medium for controlling the charging of electric furnace charge applied to brake discs, the method comprising: M1. adding charge of a fixed ratio into an electric furnace, obtaining data information of the amount of charge added and data information of the amount of pig iron output in real time, obtaining data information of the temperature of the charge in real time based on a temperature sensor in the furnace, and obtaining data information of the liquid level of the charge in real time based on a liquid level sensor in the furnace; M2. based on the temperature data information of the charge and the liquid level data information of the charge, using a grey prediction model algorithm based on an adaptive adjustment factor to predict the sedimentation velocity of the charge, and obtaining data information of the sedimentation velocity of the charge. The present invention not only does not require manual participation in the charging process, thereby reducing the labor intensity of manual labor, but also can perform adaptive charging adjustment according to the reaction of the charge, improve the reduction rate of the charge, and thus improve the production efficiency of the brake disc.

Description

Technical Field

[0001] The present invention relates to the technical field of brake disc processing, and in particular to an electric furnace charge feeding control method, system and medium applied to brake discs. Background Art

[0002] In recent years, with the steady development of the domestic automobile market, the continuous increase in the number of cars, and the increasing attention of consumers to 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 has shown a steady growth trend. Automobile brake discs, also known as brake discs, brake rotors, etc., are the core components of the automobile braking system, providing transmission control, conversion and other functions for the automobile braking system to ensure safe and stable driving of the car. The working principle of the brake disc is to clamp the brake disc through the brake caliper to stop the wheel from rotating, thereby achieving the braking function. In the processing of the brake disc, the raw materials of the brake disc must be processed first. In the processing of traditional brake disc charge, iron ore, coke or flux needs to be manually added into the blast furnace for pig iron reduction. This not only requires a large number of manpower and high labor intensity, but also has a low degree of intelligence and is unable to adaptively adjust the addition of raw materials, resulting in insufficient reduction of raw materials and the need for secondary reduction, which consumes a lot of energy and reduces the 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 loss has become an urgent problem to be solved.

[0003] In the prior art, the patent (application number: 201010140451.7) discloses a vanadium-titanium magnetite blast furnace smelting charge and blast furnace smelting method, the charge is composed of vanadium-titanium sintered ore and vanadium-titanium pellets, the vanadium-titanium sintered ore is a sintered ore obtained by sintering a mixture of vanadium-titanium iron concentrate and ordinary iron concentrate, the vanadium-titanium pellets are pellets obtained by roasting vanadium-titanium iron concentrate or a pellet obtained by roasting a mixture of vanadium-titanium iron concentrate and ordinary iron concentrate, and the ordinary iron concentrate is an iron concentrate that does not contain vanadium and titanium elements. However, the scheme does not intelligently optimize the charging of the charge, which will lead to the repetitiveness of the smelting process and increase energy consumption. Summary of the invention

[0004] In view of the above deficiencies in the prior art, the present invention provides an electric furnace charge feeding control method, system and medium applied to brake discs. Not only does it not require manual participation during the feeding process, thereby reducing manual labor intensity, but it can also perform adaptive feeding adjustments according to the reaction of the charge, thereby improving the reduction rate of the charge, thereby improving the production efficiency of the brake disc.

[0005] In order to achieve the above-mentioned purpose and other related purposes, the technical solution provided by the present invention is as follows:

[0006] A method for controlling charging of electric furnace charge applied to a brake disc, the method comprising:

[0007] M1. Add a fixed ratio of charge to the electric furnace, obtain real-time charge addition data information and pig iron output data information, obtain real-time charge temperature data information based on the furnace temperature sensor, and obtain real-time charge liquid level data information based on the furnace liquid level sensor;

[0008] M2. Based on the temperature data information of the charge and the liquid level data information of the charge, a gray prediction model algorithm based on an adaptive adjustment factor is used to predict the settling velocity of the charge to obtain the settling velocity data information of the charge;

[0009] M3. Based on the settling velocity data information of the charge, the charge addition data information and the pig iron output data information, the charge addition amount is optimized using an improved Osprey optimization algorithm to obtain the optimized charge addition amount data information;

[0010] M4. Based on the optimized charge addition amount data information, construct an electric furnace charge feeding control function P, control the charge feeding amount of the electric furnace, and output the control data information of the charge feeding of the electric furnace.

[0011] Furthermore, the charge includes iron ore, coke and flux, and the fixed ratio of the iron ore, coke and solvent is 1: (0.5-0.7): (0.2-0.4).

[0012] Furthermore, in step M2, the use of the grey prediction model algorithm based on the adaptive adjustment factor to predict the settling velocity of the charge includes:

[0013] M21. Based on the temperature data information of the charge and the liquid level data information of the charge, establish a relationship sequence function G of the charge settling velocity,

[0014] ,

[0015] Among them, x 1 is the temperature data information of the charge, x 2 is the liquid level data information of the charge, α 1 , α 2 and α 3 is the relational constant parameter of the charge, and the relational sequence of the charge settling velocity is deduced to obtain the relational sequence data information of the charge settling velocity;

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

[0017] ,

[0018] Among them, g i is the i-th relational sequence data information of charge settling velocity, g i+1 is the i+1th relational sequence data information of the charge settling velocity, n is the sample size, β i ,λ i and θ i is the weight coefficient of the operator of the grey model, and the operator of the grey model is calculated to obtain the operator data information of the grey model;

[0019] 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.

[0020] ,

[0021] Among them, h is the operator data information of the grey model, σ is the adaptive adjustment factor, and the settling velocity of the charge is predicted to obtain the settling velocity data information of the charge.

[0022] Furthermore, the adaptive adjustment factor σ is,

[0023] ,

[0024] Among them, h is the operator data information of the grey model.

[0025] Furthermore, according to the relationship sequence data information of the charge settling velocity, the weight coefficient β of the operator of the grey model is i ,λ i and θ i The constraints are:

[0026] .

[0027] Further, in step M3, the optimization of the amount of charge added by using the improved Osprey optimization algorithm includes:

[0028] M31. Based on the settling velocity data information of the charge, the charge addition data information and the pig iron output data information, establish the osprey population spatial function R of the charge,

[0029] ,

[0030] Among them, y 1 is the sedimentation velocity data of the charge, y 2 is the charge addition amount data information, y 3 is the output data of pig iron, µ 1 and µ2 is the spatial determining factor of the charge, the osprey population of the charge is initialized, and the data information of the osprey population of the initialized charge is obtained;

[0031] M32. Based on the osprey population data information of the initialized charge, establish the optimal osprey update position function L,

[0032] ,

[0033] Among them, r is the osprey population data information of the initialized charge, ω 1 and ω 2 is the update constant parameter of the osprey population of the charge, and obtains the optimal osprey update position data information of the osprey population of the charge;

[0034] M33. Based on the best osprey update position data information of the charge osprey population, establish the charge addition amount optimization function O,

[0035] ,

[0036] Among them, z is the best osprey update position data information of the osprey population, ρ 1 and ρ 2 The amount of charge added is optimized by the optimization factor, and the amount of charge added is optimized to obtain the optimized data information of the amount of charge added.

[0037] Furthermore, the spatial determining factor µ of the charge 1 and µ 2 The constraints are:

[0038] ;

[0039] The updated constant parameter ω of the Osprey population of the charge 1 and ω 2 The constraint function f is,

[0040] ,

[0041] ;

[0042] The optimization factor of the amount of charge added is ρ 1 and ρ 2 The constraints are:

[0043] .

[0044] Furthermore, the electric furnace charge feeding control function P is:

[0045] ,

[0046] ,

[0047] Among them, a is the data information of the amount of charge added after optimization, δ 1 , δ 2 and δ 3 It is the charging control factor of electric furnace.

[0048] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides an electric furnace charge feeding control system applied to brake discs, including a computer device, which is programmed or configured to execute any one of the steps of the electric furnace charge feeding control method applied to brake discs.

[0049] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides a computer-readable storage medium, which stores a computer program programmed or configured to execute any one of the electric furnace charge feeding control methods applied to brake discs.

[0050] The present invention has the following positive effects:

[0051] 1. The present invention predicts the settling velocity of the charge by adopting a grey prediction model algorithm based on an adaptive adjustment factor, and optimizes the amount of charge added in combination with an improved Osprey optimization algorithm, thereby obtaining optimized charge addition amount data information, which can not only adaptively adjust the charging according to the reaction of the charge, improve the reduction rate of the charge, thereby improving the production efficiency of the brake disc, but also increase the output of pig iron, reduce resource consumption, and further improve the production efficiency of the brake disc.

[0052] 2. The present invention constructs an electric furnace charge feeding control function P to control the feeding amount of the electric furnace charge and outputs control data information of the electric furnace charge feeding. It can not only perform intelligent operation on the feeding, reduce manual participation, reduce manual labor intensity, and thus reduce labor costs, but also has a low error rate, a high degree of intelligence, and improves the production efficiency of brake discs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0054] Figure 2 It is a schematic diagram of the process of the grey prediction model algorithm based on the adaptive adjustment factor of the present invention;

[0055] Figure 3 It is a schematic flow chart of the improved Osprey optimization algorithm of the present invention;

[0056] Figure 4 It is a structural schematic diagram of the present invention.

[0057] Explanation of the numbers in the figure: 1- unmanned loading trolley, 2- electric furnace, 3- liquid level sensor, 4- temperature sensor. DETAILED DESCRIPTION

[0058] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0059] Example 1: Figure 1 As shown in Figure 4, a method for controlling the charging of electric furnace charge applied to brake discs, the method comprising:

[0060] M1. Add a fixed ratio of charge to the electric furnace, obtain real-time charge addition data information and pig iron output data information, obtain real-time charge temperature data information based on the furnace temperature sensor, and obtain real-time charge liquid level data information based on the furnace liquid level sensor;

[0061] M2. Based on the temperature data information of the charge and the liquid level data information of the charge, a gray prediction model algorithm based on an adaptive adjustment factor is used to predict the settling velocity of the charge to obtain the settling velocity data information of the charge;

[0062] M3. Based on the settling velocity data information of the charge, the charge addition data information and the pig iron output data information, the charge addition amount is optimized using an improved Osprey optimization algorithm to obtain the optimized charge addition amount data information;

[0063] M4. Based on the optimized charge addition amount data information, construct an electric furnace charge feeding control function P, control the charge feeding amount of the electric furnace, and output the control data information of the charge feeding of the electric furnace.

[0064] In this embodiment, the 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).

[0065] In this embodiment, if Figure 2 As shown, in step M2, the use of the grey prediction model algorithm based on the adaptive adjustment factor to predict the settling velocity of the charge includes:

[0066] M21. Based on the temperature data information of the charge and the liquid level data information of the charge, establish a relationship sequence function G of the charge settling velocity,

[0067] ,

[0068] Among them, x 1 is the temperature data information of the charge, x 2 is the liquid level data information of the charge, α 1 , α 2 and α 3 is the relational constant parameter of the charge, and the relational sequence of the charge settling velocity is deduced to obtain the relational sequence data information of the charge settling velocity;

[0069] M22. Based on the relationship sequence data information of the charge settling velocity, construct an operator generating function H of the grey prediction model,

[0070] ,

[0071] Among them, g i is the i-th relational sequence data information of charge settling velocity, g i+1 is the i+1th relational sequence data information of the charge settling velocity, n is the sample size, β i ,λ i and θ i is the weight coefficient of the operator of the grey model, and the operator of the grey model is calculated to obtain the operator data information of the grey model;

[0072] 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.

[0073] ,

[0074] Among them, h is the operator data information of the grey model, σ is the adaptive adjustment factor, and the settling velocity of the charge is predicted to obtain the settling velocity data information of the charge.

[0075] In this embodiment, the adaptive adjustment factor σ is:

[0076] ,

[0077] Among them, h is the operator data information of the grey model.

[0078] In this embodiment, according to the relationship sequence data information of the charge settling velocity, the weight coefficient β of the operator of the grey model is i ,λ i and θ i The constraints are:

[0079] .

[0080] Embodiment 2: Based on the electric furnace charge feeding control method applied to brake discs in Embodiment 1, the present invention is further illustrated and described below.

[0081] like Figure 1 or Figure 4 As shown, a method for controlling charging of electric furnace charge applied to brake discs, the method comprising:

[0082] M1. Add a fixed ratio of charge to the electric furnace, obtain real-time charge addition data information and pig iron output data information, obtain real-time charge temperature data information based on the furnace temperature sensor, and obtain real-time charge liquid level data information based on the furnace liquid level sensor;

[0083] M2. Based on the temperature data information of the charge and the liquid level data information of the charge, a gray prediction model algorithm based on an adaptive adjustment factor is used to predict the settling velocity of the charge to obtain the settling velocity data information of the charge;

[0084] M3. Based on the settling velocity data information of the charge, the charge addition data information and the pig iron output data information, the charge addition amount is optimized using an improved Osprey optimization algorithm to obtain the optimized charge addition amount data information;

[0085] M4. Based on the optimized charge addition amount data information, construct an electric furnace charge feeding control function P, control the charge feeding amount of the electric furnace, and output the control data information of the charge feeding of the electric furnace.

[0086] In this embodiment, if Figure 3 As shown, in step M3, the optimization of the amount of charge added by using the improved Osprey optimization algorithm includes:

[0087] M31. Based on the settling velocity data information of the charge, the charge addition data information and the pig iron output data information, establish the osprey population spatial function R of the charge,

[0088] ,

[0089] Among them, y 1 is the sedimentation velocity data of the charge, y 2 is the charge addition amount data information, y 3 is the output data of pig iron, µ 1 and µ 2 is the spatial determining factor of the charge, the osprey population of the charge is initialized, and the data information of the osprey population of the initialized charge is obtained;

[0090] M32. Based on the osprey population data information of the initialized charge, establish the optimal osprey update position function L,

[0091] ,

[0092] Among them, r is the osprey population data information of the initialized charge, ω 1 and ω 2 is the update constant parameter of the osprey population of the charge, and obtains the optimal osprey update position data information of the osprey population of the charge;

[0093] M33. Based on the best osprey update position data information of the charge osprey population, establish the charge addition amount optimization function O,

[0094] ,

[0095] Among them, z is the best osprey update position data information of the osprey population, ρ 1 and ρ 2 The amount of charge added is optimized by the optimization factor, and the amount of charge added is optimized to obtain the optimized data information of the amount of charge added.

[0096] In this embodiment, the space determining factor µ of the charge is 1 and µ 2 The constraints are:

[0097] ;

[0098] The updated constant parameter ω of the Osprey population of the charge 1 and ω 2 The constraint function f is,

[0099] ,

[0100] ;

[0101] The optimization factor of the amount of charge added is ρ 1 and ρ 2 The constraints are:

[0102] .

[0103] In this embodiment, the electric furnace charge feeding control function P is:

[0104] ,

[0105] ,

[0106] Among them, a is the data information of the amount of charge added after optimization, δ1 , δ 2 and δ 3 It is the charging control factor of electric furnace.

[0107] In this embodiment, if Figure 4 As shown, the unmanned loading vehicle 1 can automatically deliver the charge into the electric furnace 2, the liquid level sensor 3 in the furnace can obtain the liquid level data information of the charge in real time, and the temperature sensor 4 in the furnace can obtain the temperature data information of the charge in real time.

[0108] In this embodiment, the present invention provides an electric furnace charge feeding control system applied to brake discs, including a computer device programmed or configured to execute any one of the steps of the electric furnace charge feeding control method applied to brake discs.

[0109] In this embodiment, the present invention provides a computer-readable storage medium storing a computer program programmed or configured to execute any one of the electric furnace charge feeding control methods for brake discs.

[0110] Any reference to memory, storage, database or other media used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), 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).

[0111] In summary, the present invention not only eliminates the need for manual intervention during the loading process, thereby reducing labor intensity, but also can adaptively adjust the loading according to the reaction of the charge, thereby improving the reduction rate of the charge and thus improving the production efficiency of the brake disc.

[0112] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for controlling the charging of electric furnace materials applied to brake discs, characterized in that: The method comprises: M1. Add a fixed ratio of charge to the electric furnace, obtain real-time charge addition data information and pig iron output data information, obtain real-time charge temperature data information based on the furnace temperature sensor, and obtain real-time charge liquid level data information based on the furnace liquid level sensor; M2. Based on the temperature data information of the charge and the liquid level data information of the charge, a gray prediction model algorithm based on an adaptive adjustment factor is used to predict the settling velocity of the charge to obtain the settling velocity data information of the charge; M3. Based on the settling velocity data information of the charge, the charge addition data information and the pig iron output data information, the charge addition amount is optimized using an improved Osprey optimization algorithm to obtain the optimized charge addition amount data information; M4. Based on the optimized charge addition amount data information, construct an electric furnace charge feeding control function P, control the charge feeding amount of the electric furnace charge, and output the control data information of the electric furnace charge feeding; In step M2, the method of predicting the settling velocity of the charge using the grey prediction model algorithm based on the adaptive adjustment factor includes: M21. Based on the temperature data information of the charge and the liquid level data information of the charge, establish a relationship sequence function G of the charge settling velocity, ， Among them, x1 is the temperature data information of the charge, x2 is the liquid level data information of the charge, α1, α2 and α3 are the relational constant parameters of the charge, and the relational sequence of the charge settling velocity is calculated to obtain the relational sequence data information of the charge settling velocity; M22. Based on the relationship sequence data information of the charge settling velocity, construct an operator generating function H of the grey prediction model, ， Among them, g i is the i-th relational sequence data information of charge settling velocity, g i+1 is the i+1th relational sequence data information of the charge settling velocity, n is the sample size, β i ,λ i and θ i is the weight coefficient of the operator of the grey model, and the operator of the grey model is calculated to obtain the operator data information of the grey model; 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. ， Among them, h is the operator data information of the grey model, σ is the adaptive adjustment factor, and the settling velocity of the charge is predicted to obtain the settling velocity data information of the charge; The adaptive adjustment factor σ is, ， Among them, h is the operator data information of the grey model; According to the relationship sequence data information of the charge settling velocity, the weight coefficient β of the operator of the grey model is i ,λ i and θ i The constraints are: ； In step M3, the optimization of the amount of charge added by using the improved Osprey optimization algorithm includes: M31. Based on the settling velocity data information of the charge, the charge addition data information and the pig iron output data information, establish the osprey population spatial function R of the charge, ， Among them, y1 is the sedimentation velocity data information of the charge, y2 is the charge addition data information, y3 is the pig iron output data information, µ1 and µ2 are the spatial determining factors of the charge, and the osprey population of the charge is initialized to obtain the initialized osprey population data information of the charge; M32. Based on the osprey population data information of the initialized charge, establish the optimal osprey update position function L, ， Among them, r is the data information of the osprey population of the charge after initialization, ω1 and ω2 are the update constant parameters of the osprey population of the charge, and the optimal osprey update position data information of the osprey population of the charge is obtained; M33. Based on the best osprey update position data information of the charge osprey population, establish the charge addition amount optimization function O, ， Among them, z is the best osprey update position data information of the charge osprey population, ρ1 and ρ2 are the optimization factors of the charge addition amount, and the charge addition amount is optimized to obtain the optimized charge addition amount data information; The constraints of the spatial determining factors µ1 and µ2 of the charge are: ； The constraint function f for updating the constant parameters ω1 and ω2 of the osprey population of the charge is, ， ； The constraints of the optimization factors ρ1 and ρ2 of the charge addition amount are: ； The electric furnace charge feeding control function P is: ， ， Among them, a is the data information of the added amount of the optimized charge, and δ1, δ2 and δ3 are the charging control factors of the electric furnace charge.

2. The method for controlling the charging of electric furnace materials applied to brake discs according to claim 1 is characterized in that: The furnace charge includes iron ore, coke and flux, and the fixed ratio of the iron ore, coke and solvent is 1: (0.5-0.7): (0.2-0.4).

3. A control system for feeding electric furnace charge applied to brake disc, comprising computer equipment, characterized in that: The computer device is programmed or configured to execute the steps of the electric furnace charge feeding control method applied to brake discs as described in any one of claims 1 to 2.

4. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program programmed or configured to execute the electric furnace charge feeding control method for brake discs as described in any one of claims 1 to 2.

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