A method and system for automatically controlling the temperature of an electric furnace for forging brake discs

Abstract

The present invention relates to an automatic temperature rise control method and system for an electric furnace used for brake disc forging, the method comprising: U1. adding iron material and other auxiliary materials into an electric furnace for melting, collecting data information of the current intensity at different stages of the iron material, obtaining data information of the current intensity at the stage when the iron material begins to melt, data information of the current intensity at the stage when the iron material and molten iron coexist, and data information of the current intensity at the molten iron stage; U2. based on the data information of the current intensity at the stage when the iron material begins to melt and data information of the current intensity at the stage when the iron material and molten iron coexist. The present invention not only accurately controls the temperature according to the different states of the iron material, prevents excessive or insufficient heating, thereby reducing energy consumption and improving the efficiency of brake disc forging, but also saves energy and reduces emissions, ensuring sufficient melting of the iron material and other auxiliary materials.

Description

Technical Field

[0001] The present invention relates to the technical field of brake disc forging, and in particular to an automatic temperature rise control method and system of an electric furnace used for brake disc forging. Background Art

[0002] With the continuous development of automation technology, it is affecting the technological updates in various fields. Automation replacing human physical or mental labor is only a part of the automation functional goal system. The functional goals of automation are multifaceted and have formed an organic system. In terms of scope, manufacturing automation not only involves specific production and manufacturing processes, but also involves all processes in the product life cycle.

[0003] Among them, in the traditional brake disc forging process, the control of the electric furnace will judge the time when the iron material is melted based on manual visual observation. Not only is the error large, but it will also cause insufficient melting of the iron material. The generated slag needs to be melted again, wasting energy. At the same time, the end point of complete melting of the iron material cannot be accurately controlled, resulting in further waste of energy. Summary of the invention

[0004] In view of the above problems, the present invention provides an automatic temperature rise control method and system for an electric furnace used for brake disc forging. The method not only accurately controls the temperature according to the different states of the iron materials to prevent excessive or insufficient heating, thereby reducing energy consumption and improving the efficiency of brake disc forging, but also saves energy and reduces emissions, ensuring the full melting of the iron materials and other auxiliary materials.

[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] An automatic temperature rise control method for an electric furnace used in brake disc forging, the method comprising:

[0007] U1. Add iron and other auxiliary materials to an electric furnace for melting, collect data on the current intensity of the iron at different stages, obtain data on the current intensity at the beginning of the melting stage of the iron, data on the current intensity at the stage when the iron and molten iron coexist, and data on the current intensity at the molten iron stage;

[0008] U2. Based on the current intensity data information of the iron material at the beginning of the melting stage and the current intensity data information of the iron material and the molten iron coexistence stage, the current intensity of the iron material at different melting stages is fitted using a Fourier fitting algorithm based on the golden sine to obtain the current intensity data information of the first state change of the iron material;

[0009] U3. Based on the current intensity data information of the coexistence stage of the iron material and the molten iron and the current intensity data information of the molten iron stage, the current intensity of the iron material at different stages is processed by the Kriging interpolation algorithm based on the adaptive adjustment factor to obtain the current intensity data information of the second state change of the iron material;

[0010] U4. Based on the current intensity data information of the second state change of the iron material and the current intensity data information of the first state change of the iron material, a brake disc forging current intensity optimization model constructed by a multi-layer neural network is constructed to optimize the current intensity of the electric furnace at different stages to obtain the optimized current intensity data information of the electric furnace at different stages.

[0011] Furthermore, the method further comprises:

[0012] U5. Based on the current intensity data information of the electric furnace at different stages after optimization, establish the electric furnace automatic temperature rise control function P,

[0013] ,

[0014] Among them, x is the current intensity data information of the electric furnace at different stages after optimization, α 1 , α 2 and α 3 It is the constant parameter of the temperature control of the electric furnace, controls the temperature of the current in different stages, and outputs the temperature control data information of the electric furnace.

[0015] Furthermore, the temperature control constant parameter α of the electric furnace 1 , α 2 and α 3 The constraints are:

[0016] .

[0017] Furthermore, in step U2, the fitting of the current intensity at different stages of the iron material melting using the Fourier fitting algorithm based on the golden sine includes:

[0018] U21. Based on the current intensity data information of the iron material at the beginning of the melting stage and the current intensity data information of the iron material and the molten iron coexistence stage, establish the probability density function f of the electric furnace current intensity,

[0019] ,

[0020] in, This is the current intensity data information when the iron material begins to melt. is the current intensity data information of the coexistence stage of iron material and molten iron, β 1 and β 2is the weight coefficient, and the probability density of different electric furnace current intensities is estimated to obtain the probability density data information of the current intensity at different stages of the electric furnace;

[0021] U22. Based on the probability density data information of the current intensity at different stages of the electric furnace, a Fourier fitting function F of the current intensity at different stages is established.

[0022] ,

[0023] Among them, b i is the probability density data information of the current intensity at different stages of the electric furnace, n is the sample size, λ i is the golden sine factor of the probability density of the current intensity at different stages of the electric furnace;

[0024] U23. Based on the Fourier fitting function F of the current intensity at different stages, the current intensity at different stages of the melting of the iron material is fitted to obtain the current intensity data information of the first state change of the iron material.

[0025] Furthermore, the golden sine factor λ of the probability density of the current intensity at different stages of the electric furnace is i for,

[0026] ,

[0027] Among them, b i It is the probability density data information of the current intensity at different stages of the electric furnace.

[0028] Furthermore, in step U3, the process of processing the current intensity of the iron material at different stages by using the Kriging interpolation algorithm based on the adaptive adjustment factor includes:

[0029] U31. Based on the current intensity data information of the iron material and molten iron coexistence stage and the current intensity data information of the molten iron stage, a semivariogram function G of the current intensity of the electric furnace at different stages is established,

[0030] ,

[0031] in, It is the current intensity data information when iron material and molten iron coexist. is the current intensity data information of the molten iron stage, ω 1 ,ω 2 and ω 3 is the correlation learning factor of the current intensity, characterizes the correlation of the current intensity at different stages, and obtains the correlation data information of the current intensity at different stages of the electric furnace;

[0032] U32. Based on the relevant data information of the current intensity at different stages of the electric furnace, establish the current intensity interpolation function H at different stages of the electric furnace,

[0033] ,

[0034] Among them, g is the relevant data information of the current intensity of the electric furnace at different stages, σ 1 , σ 2 and σ 3 The interpolation determining factor of the current intensity is used to calculate the interpolation points of the current intensity at different stages, and the data information of the interpolation points of the current intensity at different stages of the electric furnace is obtained;

[0035] U33. Based on the current intensity interpolation point data information of the electric furnace at different stages, a current intensity smoothing function Q based on an adaptive adjustment factor is established.

[0036] ,

[0037] Among them, y is the data information of the current intensity interpolation points at different stages of the electric furnace, ρ 1 , 2 and ρ 3 is the adaptive adjustment factor of the current intensity. The current intensity of the iron material at different stages is processed to obtain the current intensity data information of the second state change of the iron material.

[0038] Furthermore, the adaptive adjustment factor ρ of the current intensity 1 , 2 and ρ 3 for,

[0039] ,

[0040] ,

[0041] ,

[0042] Among them, y is the data information of the current intensity interpolation points at different stages of the electric furnace.

[0043] Furthermore, in step U4, the brake disc forging current intensity optimization model constructed using a multi-layer neural network optimizes the current intensity of the electric furnace at different stages, including:

[0044] U41. Based on the current intensity data information of the second state change of the iron material and the current intensity data information of the first state change of the iron material, construct a current intensity data set of the state change of the iron material;

[0045] U42. Input the current intensity data set of the iron material state change into the brake disc forging current intensity optimization model constructed by the multi-layer neural network for training and learning, and determine the brake disc forging current intensity optimization function R,

[0046] ,

[0047] Among them, v is the current intensity data set of the iron material state change, θ 1 ,θ 2 and θ 3 The optimization factor of the multi-layer neural network is used to obtain the brake disc forging current intensity optimization model constructed by the trained multi-layer neural network;

[0048] U43. Based on the brake disc forging current intensity optimization model constructed based on the trained multi-layer neural network, the current intensity dataset of the iron material state change is input, the current intensity of the electric furnace at different stages is optimized, and the current intensity data information of the electric furnace at different stages after optimization is obtained.

[0049] Furthermore, the optimization factor θ of the multi-layer neural network 1 ,θ 2 and θ 3 The constraint function r is,

[0050] ,

[0051] The value range of r is (1,2).

[0052] In order to achieve the above-mentioned purpose and other related purposes, the present invention also provides an electric furnace automatic temperature rise control system applied to brake disc forging, including a computer device, which is programmed or configured to execute any one of the steps of the electric furnace automatic temperature rise control method applied to brake disc forging.

[0053] The present invention has the following positive effects:

[0054] 1. The present invention adopts a Fourier fitting algorithm based on the golden sine to fit the current intensity at different stages of iron melting, and combines it with a Kriging interpolation algorithm based on an adaptive adjustment factor to process the current intensity at different stages of the iron. It can not only accurately fit the current intensity under different states of the iron, so as to accurately control the temperature, prevent excessive or insufficient heating, thereby reducing energy consumption and improving the forging efficiency of the brake disc, but also save energy and reduce emissions, and ensure the full melting of the iron and other auxiliary materials.

[0055] 2. The present invention optimizes the current intensity of the electric furnace at different stages by constructing a brake disc forging current intensity optimization model using a multi-layer neural network. This not only solves the problem of insufficient melting of iron materials due to large errors, the need to re-melt the generated slag and waste energy, but also the entire process does not require human intervention, thereby reducing labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0057] Figure 2 Schematic diagram of the process of the Fourier fitting algorithm based on the golden sine of the present invention;

[0058] Figure 3 The Kriging interpolation algorithm based on the adaptive adjustment factor of the present invention;

[0059] Figure 4 A schematic diagram of a process for optimizing the current intensity of an electric furnace at different stages by constructing a brake disc forging current intensity optimization model constructed by a multi-layer neural network according to the present invention;

[0060] Figure 5 It is a structural schematic diagram of the electric furnace of the present invention.

[0061] Explanation of the numbers in the figure: 1-automatic feeding trolley, 2-electric furnace. DETAILED DESCRIPTION

[0062] 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.

[0063] Example 1: Figure 1 or Figure 5 As shown, a method for automatically controlling the temperature rise of an electric furnace used for forging a brake disc comprises:

[0064] U1. The automatic feeding trolley 1 adds iron and other auxiliary materials to the electric furnace 2 for melting, collects data information of the current intensity of the iron at different stages, obtains data information of the current intensity at the stage when the iron begins to melt, data information of the current intensity at the stage when the iron and molten iron coexist, and data information of the current intensity at the molten iron stage;

[0065] U2. Based on the current intensity data information of the iron material at the beginning of the melting stage and the current intensity data information of the iron material and the molten iron coexistence stage, the current intensity of the iron material at different melting stages is fitted using a Fourier fitting algorithm based on the golden sine to obtain the current intensity data information of the first state change of the iron material;

[0066] U3. Based on the current intensity data information of the coexistence stage of the iron material and the molten iron and the current intensity data information of the molten iron stage, the current intensity of the iron material at different stages is processed by the Kriging interpolation algorithm based on the adaptive adjustment factor to obtain the current intensity data information of the second state change of the iron material;

[0067] U4. Based on the current intensity data information of the second state change of the iron material and the current intensity data information of the first state change of the iron material, a brake disc forging current intensity optimization model constructed by a multi-layer neural network is constructed to optimize the current intensity of the electric furnace at different stages to obtain the optimized current intensity data information of the electric furnace at different stages.

[0068] In this embodiment, the method further includes:

[0069] U5. Based on the current intensity data information of the electric furnace at different stages after optimization, establish the electric furnace automatic temperature rise control function P,

[0070] ,

[0071] Among them, x is the current intensity data information of the electric furnace at different stages after optimization, α 1 , α 2 and α 3 It is the constant parameter of the temperature control of the electric furnace, controls the temperature of the current in different stages, and outputs the temperature control data information of the electric furnace.

[0072] In this embodiment, the temperature control constant parameter α of the electric furnace 1 , α 2 and α 3 The constraints are:

[0073] .

[0074] In this embodiment, if Figure 2 As shown, in step U2, the use of the golden sine-based Fourier fitting algorithm to fit the current intensity at different stages of iron material melting includes:

[0075] U21. Based on the current intensity data information of the iron material at the beginning of the melting stage and the current intensity data information of the iron material and the molten iron coexistence stage, establish the probability density function f of the electric furnace current intensity,

[0076] ,

[0077] in, This is the current intensity data information when the iron material begins to melt. is the current intensity data information of the coexistence stage of iron material and molten iron, β 1 and β 2 is the weight coefficient, and the probability density of different electric furnace current intensities is estimated to obtain the probability density data information of the current intensity at different stages of the electric furnace;

[0078] U22. Based on the probability density data information of the current intensity at different stages of the electric furnace, a Fourier fitting function F of the current intensity at different stages is established.

[0079] ,

[0080] Among them, b i is the probability density data information of the current intensity at different stages of the electric furnace, n is the sample size, λ i is the golden sine factor of the probability density of the current intensity at different stages of the electric furnace;

[0081] U23. Based on the Fourier fitting function F of the current intensity at different stages, the current intensity at different stages of the melting of the iron material is fitted to obtain the current intensity data information of the first state change of the iron material.

[0082] In this embodiment, the golden sine factor λ of the probability density of the current intensity at different stages of the electric furnace is i for,

[0083] ,

[0084] Among them, b i It is the probability density data information of the current intensity at different stages of the electric furnace.

[0085] Example 2: Based on the method for automatic temperature rise control of an electric furnace applied to brake disc forging in Example 1, the present invention is further illustrated and described below.

[0086] like Figure 1 or Figure 5 As shown, a method for automatically controlling the temperature rise of an electric furnace used for forging a brake disc comprises:

[0087] U1. The automatic feeding trolley 1 adds iron and other auxiliary materials to the electric furnace 2 for melting, collects data information of the current intensity of the iron at different stages, obtains data information of the current intensity at the stage when the iron begins to melt, data information of the current intensity at the stage when the iron and molten iron coexist, and data information of the current intensity at the molten iron stage;

[0088] U2. Based on the current intensity data information of the iron material at the beginning of the melting stage and the current intensity data information of the iron material and the molten iron coexistence stage, the current intensity of the iron material at different melting stages is fitted using a Fourier fitting algorithm based on the golden sine to obtain the current intensity data information of the first state change of the iron material;

[0089] U3. Based on the current intensity data information of the coexistence stage of the iron material and the molten iron and the current intensity data information of the molten iron stage, the current intensity of the iron material at different stages is processed by the Kriging interpolation algorithm based on the adaptive adjustment factor to obtain the current intensity data information of the second state change of the iron material;

[0090] U4. Based on the current intensity data information of the second state change of the iron material and the current intensity data information of the first state change of the iron material, a brake disc forging current intensity optimization model constructed by a multi-layer neural network is constructed to optimize the current intensity of the electric furnace at different stages to obtain the optimized current intensity data information of the electric furnace at different stages.

[0091] In this embodiment, if Figure 3 As shown, in step U3, the process of processing the current intensity of the iron material at different stages by using the Kriging interpolation algorithm based on the adaptive adjustment factor includes:

[0092] U31. Based on the current intensity data information of the iron material and molten iron coexistence stage and the current intensity data information of the molten iron stage, a semivariogram function G of the current intensity of the electric furnace at different stages is established,

[0093] ,

[0094] in, It is the current intensity data information when iron material and molten iron coexist. is the current intensity data information of the molten iron stage, ω 1 ,ω 2 and ω 3 is the correlation learning factor of the current intensity, characterizes the correlation of the current intensity at different stages, and obtains the correlation data information of the current intensity at different stages of the electric furnace;

[0095] U32. Based on the relevant data information of the current intensity of the electric furnace at different stages, establish the current intensity interpolation function H of the electric furnace at different stages,

[0096] ,

[0097] Among them, g is the relevant data information of the current intensity of the electric furnace at different stages, σ 1 , σ 2 and σ 3The interpolation determining factor of the current intensity is used to calculate the interpolation points of the current intensity at different stages, and the data information of the interpolation points of the current intensity at different stages of the electric furnace is obtained;

[0098] U33. Based on the current intensity interpolation point data information of the electric furnace at different stages, a current intensity smoothing function Q based on an adaptive adjustment factor is established.

[0099] ,

[0100] Among them, y is the data information of the current intensity interpolation points at different stages of the electric furnace, ρ 1 , 2 and ρ 3 is the adaptive adjustment factor of the current intensity. The current intensity of the iron material at different stages is processed to obtain the current intensity data information of the second state change of the iron material.

[0101] In this embodiment, the adaptive adjustment factor ρ of the current intensity 1 , 2 and ρ 3 for,

[0102] ,

[0103] ,

[0104] ,

[0105] Among them, y is the data information of the current intensity interpolation points at different stages of the electric furnace.

[0106] In this embodiment, if Figure 4 As shown, in step U4, the brake disc forging current intensity optimization model constructed by the multi-layer neural network optimizes the current intensity of the electric furnace at different stages, including:

[0107] U41. Based on the current intensity data information of the second state change of the iron material and the current intensity data information of the first state change of the iron material, construct a current intensity data set of the state change of the iron material;

[0108] U42. Input the current intensity data set of the iron material state change into the brake disc forging current intensity optimization model constructed by the multi-layer neural network for training and learning, and determine the brake disc forging current intensity optimization function R,

[0109] ,

[0110] Among them, v is the current intensity data set of the iron material state change, θ 1 ,θ 2 and θ 3is the optimization factor of the multi-layer neural network, and the brake disc forging current intensity optimization model constructed by the trained multi-layer neural network is obtained;

[0111] U43. Based on the brake disc forging current intensity optimization model constructed based on the trained multi-layer neural network, the current intensity dataset of the iron material state change is input, the current intensity of the electric furnace at different stages is optimized, and the current intensity data information of the electric furnace at different stages after optimization is obtained.

[0112] In this embodiment, the optimization factor θ of the multi-layer neural network 1 ,θ 2 and θ 3 The constraint function r is,

[0113] ,

[0114] The value range of r is (1,2).

[0115] In this embodiment, the present invention provides an automatic temperature rise control system for an electric furnace applied to brake disc forging, including a computer device programmed or configured to execute any one of the steps of the automatic temperature rise control method for an electric furnace applied to brake disc forging.

[0116] In this embodiment, the present invention further provides a computer-readable storage medium, on which is stored a computer program programmed or configured to execute any one of the improved Yolov5 pedestrian detection methods described above.

[0117] 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).

[0118] In summary, the present invention not only accurately controls the temperature according to the different states of the iron materials to prevent excessive or insufficient heating, thereby reducing energy consumption and improving the forging efficiency of the brake disc, but also saves energy and reduces emissions, ensuring the full melting of the iron materials and other auxiliary materials.

[0119] 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. An automatic temperature rise control method for an electric furnace used in brake disc forging, characterized in that: The method comprises: U1. Add iron and other auxiliary materials to an electric furnace for melting, collect data on the current intensity of the iron at different stages, obtain data on the current intensity at the beginning of the melting stage of the iron, data on the current intensity at the stage when the iron and molten iron coexist, and data on the current intensity at the molten iron stage; U2. Based on the current intensity data information of the iron material at the beginning of the melting stage and the current intensity data information of the iron material and the molten iron coexistence stage, the current intensity of the iron material at different melting stages is fitted using a Fourier fitting algorithm based on the golden sine to obtain the current intensity data information of the first state change of the iron material; U3. Based on the current intensity data information of the coexistence stage of the iron material and the molten iron and the current intensity data information of the molten iron stage, the current intensity of the iron material at different stages is processed by the Kriging interpolation algorithm based on the adaptive adjustment factor to obtain the current intensity data information of the second state change of the iron material; U4. Based on the current intensity data information of the second state change of the iron material and the current intensity data information of the first state change of the iron material, a brake disc forging current intensity optimization model constructed by a multi-layer neural network is constructed to optimize the current intensity of the electric furnace at different stages to obtain the optimized current intensity data information of the electric furnace at different stages; The method further comprises: U5. Based on the current intensity data information of the electric furnace at different stages after optimization, establish the electric furnace automatic temperature rise control function P, ， Among them, x is the current intensity data information of the electric furnace at different stages after optimization, α1, α2 and α3 are the temperature control constant parameters of the electric furnace, which control the temperature of the current at different stages and output the temperature control data information of the electric furnace.

2. The automatic temperature rise control method of an electric furnace for forging a brake disc according to claim 1 is characterized in that: The constraints of the temperature control constant parameters α1, α2 and α3 of the electric furnace are: 。 3. The automatic temperature rise control method of an electric furnace for brake disc forging according to claim 1 is characterized in that: In step U2, the method of fitting the current intensity at different stages of iron material melting using the golden sine-based Fourier fitting algorithm includes: U21. Based on the current intensity data information of the iron material at the beginning of the melting stage and the current intensity data information of the iron material and the molten iron coexistence stage, establish the probability density function f of the electric furnace current intensity, ， in, This is the current intensity data information when the iron material begins to melt. is the current intensity data information in the stage where iron material and molten iron coexist, β1 and β2 are weight coefficients, and the probability density of different electric furnace current intensities is calculated to obtain the probability density data information of the current intensity in different stages of the electric furnace; U22. Based on the probability density data information of the current intensity at different stages of the electric furnace, a Fourier fitting function F of the current intensity at different stages is established. ， Among them, b i is the probability density data information of the current intensity at different stages of the electric furnace, n is the sample size, λ i is the golden sine factor of the probability density of the current intensity at different stages of the electric furnace; U23. Based on the Fourier fitting function F of the current intensity at different stages, the current intensity at different stages of the melting of the iron material is fitted to obtain the current intensity data information of the first state change of the iron material.

4. The automatic temperature rise control method for an electric furnace used for brake disc forging according to claim 3 is characterized in that: The golden sine factor λ of the probability density of the current intensity at different stages of the electric furnace i for, ， Among them, b i It is the probability density data information of the current intensity at different stages of the electric furnace.

5. The automatic temperature rise control method of an electric furnace for brake disc forging according to claim 1 is characterized in that: In step U3, the process of processing the current intensity of the iron material at different stages by using the Kriging interpolation algorithm based on the adaptive adjustment factor includes: U31. Based on the current intensity data information of the iron material and molten iron coexistence stage and the current intensity data information of the molten iron stage, a semivariogram function G of the current intensity of the electric furnace at different stages is established, ， in, It is the current intensity data information when iron material and molten iron coexist. is the current intensity data information of the molten iron stage, ω1, ω2 and ω3 are the correlation learning factors of the current intensity, the correlation of the current intensity in different stages is characterized, and the correlation data information of the current intensity in different stages of the electric furnace is obtained; U32. Based on the relevant data information of the current intensity of the electric furnace at different stages, establish the current intensity interpolation function H of the electric furnace at different stages, ， Among them, g is the relevant data information of the current intensity at different stages of the electric furnace, σ1, σ2 and σ3 are the interpolation determining factors of the current intensity, and the interpolation points of the current intensity at different stages are calculated to obtain the data information of the interpolation points of the current intensity at different stages of the electric furnace; U33. Based on the current intensity interpolation point data information of the electric furnace at different stages, a current intensity smoothing function Q based on an adaptive adjustment factor is established. ， Among them, y is the data information of the current intensity interpolation points at different stages of the electric furnace, ρ1, ρ2 and ρ3 are adaptive adjustment factors of the current intensity, and the current intensity of the iron material at different stages is processed to obtain the current intensity data information of the second state change of the iron material.

6. The automatic temperature rise control method for an electric furnace used for brake disc forging according to claim 5 is characterized in that: The adaptive adjustment factors ρ1, ρ2 and ρ3 of the current intensity are: ， ， ， Among them, y is the data information of the current intensity interpolation points at different stages of the electric furnace.

7. The automatic temperature rise control method of an electric furnace for brake disc forging according to claim 1 is characterized in that: In step U4, the brake disc forging current intensity optimization model constructed using a multi-layer neural network optimizes the current intensity of the electric furnace at different stages, including: U41. Based on the current intensity data information of the second state change of the iron material and the current intensity data information of the first state change of the iron material, construct a current intensity data set of the state change of the iron material; U42. Input the current intensity data set of the iron material state change into the brake disc forging current intensity optimization model constructed by the multi-layer neural network for training and learning, and determine the brake disc forging current intensity optimization function R, ， Among them, v is the current intensity data set of the iron material state change, θ1, θ2 and θ3 are the optimization factors of the multi-layer neural network, and the brake disc forging current intensity optimization model constructed by the trained multi-layer neural network is obtained; U43. Based on the brake disc forging current intensity optimization model constructed based on the trained multi-layer neural network, the current intensity dataset of the iron material state change is input, the current intensity of the electric furnace at different stages is optimized, and the current intensity data information of the electric furnace at different stages after optimization is obtained.

8. The automatic temperature rise control method for an electric furnace used for brake disc forging according to claim 7 is characterized in that: The constraint function r of the optimization factors θ1, θ2 and θ3 of the multi-layer neural network is, ， The value range of r is (1,2).

9. An automatic temperature rise control system for an electric furnace used for brake disc forging, comprising a computer device, characterized in that: The computer device is programmed or configured to execute the steps of the electric furnace automatic temperature rise control method for brake disc forging as described in any one of claims 1 to 8.

Images