Model prediction-based gray cast iron performance regulation method and device and electronic equipment

Abstract

The present disclosure provides a model prediction-based gray cast iron performance regulation method and device and electronic equipment, and relates to the technical field of material processing. The method comprises: in response to the content of molybdenum element in gray cast iron used for an engine cylinder being reduced, determining a tensile strength related value of the gray cast iron according to a first preset model, the tensile strength related value being a reduced value of the tensile strength or the tensile strength after reduction; determining a niobium element content related value corresponding to the tensile strength related value according to a second preset model; and improving the content value of niobium element in the gray cast iron according to the niobium element content related value, so that the tensile strength of the gray cast iron reaches an expected value, wherein the difference between the expected value and the tensile strength of the gray cast iron before the content of molybdenum element is reduced is less than a target difference threshold. The present disclosure compensates the tensile strength of the gray cast iron by supplementing niobium element, so that the tensile strength of the gray cast iron can be ensured to be relatively stable in the case of reducing molybdenum element to save cost.

Description

Technical Field

[0001] This disclosure relates to the field of materials processing technology, and in particular to a method, apparatus and electronic equipment for controlling the properties of gray cast iron based on model prediction. Background Technology

[0002] When the grade of gray cast iron used for engine cylinder blocks and cylinder heads is upgraded to HT300 or HT350, it is necessary to add molybdenum (Mo), a micro-alloying element.

[0003] However, in related technologies, molybdenum causes significant shrinkage porosity and voids in castings, leading to air and water leaks during machining and causing leaks in engines, resulting in high production and operating costs. Furthermore, molybdenum is a precious metal, expensive and subject to market fluctuations, resulting in high raw material procurement costs. Therefore, based on the above analysis, it is necessary to develop a microalloying technology to replace molybdenum in order to reduce production, operating, and procurement costs. Summary of the Invention

[0004] In view of this, the purpose of this disclosure is to propose a method, device and electronic equipment for controlling the performance of gray cast iron based on model prediction, which can specifically solve existing problems.

[0005] Based on the above objectives, in a first aspect, this disclosure proposes a model-based prediction method for controlling the performance of gray cast iron, comprising: responding to a decrease in the molybdenum content in gray cast iron used in engine cylinders, determining a tensile strength correlation value of the gray cast iron according to a first preset model, wherein the tensile strength correlation value is the decrease in tensile strength or the tensile strength obtained after the decrease; determining a niobium content correlation value corresponding to the tensile strength correlation value according to a second preset model; and increasing the niobium content value in the gray cast iron according to the niobium content correlation value, so that the tensile strength of the gray cast iron reaches a desired value, wherein the difference between the desired value and the tensile strength of the gray cast iron before the decrease in molybdenum content is less than a target difference threshold.

[0006] Secondly, a model-based gray cast iron performance control device is also provided, comprising: a first determining unit configured to, in response to a decrease in the molybdenum content in gray cast iron used in engine cylinders, determine a tensile strength correlation value of the gray cast iron according to a first preset model, wherein the tensile strength correlation value is the decrease in tensile strength or the tensile strength obtained after the decrease; a second determining unit configured to, according to a second preset model, determine a niobium content correlation value corresponding to the tensile strength correlation value; and an enhancing unit configured to, according to the niobium content correlation value, enhance the niobium content value in the gray cast iron to make the tensile strength of the gray cast iron reach a desired value, wherein the difference between the desired value and the tensile strength of the gray cast iron before the decrease in molybdenum content is less than a target difference threshold.

[0007] Thirdly, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor running the computer program to implement the method of the first aspect.

[0008] Fourthly, a computer-readable storage medium is also provided, on which a computer program is stored, the computer program being executed by a processor to implement the method described in any one of the first aspects.

[0009] Fifthly, a computer program product is also provided, comprising a computer program that is executed by a processor to implement the method described in any one of the first aspects.

[0010] In summary, this disclosure offers at least the following advantages: By supplementing with niobium, the tensile strength of gray cast iron can be compensated, thereby ensuring relatively stable tensile strength while reducing molybdenum content to save costs. Furthermore, the reduced molybdenum content helps prevent casting shrinkage porosity and other problems, and facilitates airtightness testing during machining and prevents leaks such as air and water during engine operation. Attached Figure Description

[0011] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this disclosure and should not be construed as limiting the scope of this disclosure.

[0012] Figure 1 A flowchart of a model-predictive-based method for controlling the properties of gray cast iron according to an embodiment of the present disclosure is shown; Figure 2 Another flowchart of a model-prediction-based method for controlling the properties of gray cast iron according to an embodiment of the present disclosure is shown; Figure 3 A schematic diagram of the microstructure of gray cast iron after adding Nb is shown in the model prediction-based gray cast iron performance control method according to an embodiment of the present disclosure. Figure 4 A schematic diagram of a model-based prediction-based gray cast iron performance control device according to an embodiment of the present disclosure is shown; Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of a storage medium provided according to an embodiment of the present disclosure is shown. Detailed Implementation

[0013] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0014] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0015] Figure 1 This disclosure illustrates a model-based prediction-based method for controlling the properties of gray cast iron. In embodiments of this disclosure, the method includes: Step S101: In response to the decrease in the molybdenum content in the gray cast iron used in the engine cylinder, the tensile strength correlation value of the gray cast iron is determined according to the first preset model. The tensile strength correlation value is the decrease in tensile strength or the tensile strength obtained after the decrease.

[0016] Step S102: Determine the niobium content correlation value corresponding to the tensile strength correlation value according to the second preset model.

[0017] Step S103: Based on the niobium content related value, increase the niobium content value in the gray cast iron so that the tensile strength of the gray cast iron reaches the desired value, wherein the difference between the desired value and the tensile strength of the gray cast iron before the molybdenum content is reduced is less than the target difference threshold.

[0018] In this embodiment, the entity implementing the model-predictive gray cast iron performance control method can increase the niobium content while reducing the molybdenum content, ensuring that the tensile strength of the gray cast iron remains essentially unchanged compared to before the reduction in molybdenum content.

[0019] The relevant value for niobium content can be the niobium content itself (i.e., the content value to be achieved after the increase) or the change value of niobium content, which can be directly used to increase the niobium content value.

[0020] The first and second preset models here can be various models. For example, they can be correspondences, formulas, neural network models, or other models.

[0021] This embodiment of the invention can compensate for the tensile strength of gray cast iron by supplementing it with niobium, thereby ensuring relatively stable tensile strength of gray cast iron while reducing molybdenum content to save costs. Furthermore, the reduced molybdenum content helps avoid problems such as shrinkage porosity and shrinkage cavities in castings, as well as issues related to airtightness testing during machining and leaks of air or water during engine operation.

[0022] In some optional implementations of any embodiment of this disclosure, the step of generating the first preset model includes: generating a correlation model between the change in molybdenum content in gray cast iron and the change in tensile strength of gray cast iron based on the influence of the change in molybdenum content in gray cast iron on the change in tensile strength of gray cast iron.

[0023] Among these optional implementations, for typical products with main wall thicknesses ranging from 20mm to 100mm, simulations or experiments are used to evaluate the effect of Mo content on the tensile strength of HT250, HT300, and HT350 gray cast iron. The correlation model demonstrates the relationship between variations in Mo content in gray cast iron and variations in its tensile strength.

[0024] For example, when the Mo content changes by 0.10%, the corresponding tensile strength σ of gray cast iron... b The change value is also the amplitude δ ([Mo]→σ) b The tensile strength can range from 8MPa to 12MPa. That is, the correlation model can be a correspondence. In this case, the tensile strength correlation value can be the decrease in tensile strength.

[0025] Among these optional implementations, the change in strength of gray cast iron corresponding to the change in molybdenum content can be determined quickly and accurately using an association model.

[0026] In some optional implementations of any embodiment of this disclosure, the step of generating the second preset model includes at least one of the following: generating a correlation model between the niobium content in gray cast iron and the tensile strength of gray cast iron based on the influence of the niobium content in gray cast iron on the tensile strength of gray cast iron; generating a correlation model between the change in the niobium content in gray cast iron and the change in the tensile strength of gray cast iron based on the influence of the change in the niobium content in gray cast iron on the tensile strength of gray cast iron; and generating a correlation model between the niobium content in gray cast iron and the molybdenum content in gray cast iron based on the influence of the niobium content in gray cast iron on the tensile strength of gray cast iron and the influence of the molybdenum content on the tensile strength of gray cast iron.

[0027] Among these optional implementations, the correlation model between niobium content and the tensile strength of gray cast iron can be a direct correspondence. Furthermore, for typical products with main wall thicknesses ranging from 20mm to 100mm, the influence of Nb content on the tensile strength of HT250, HT300, and HT350 gray cast iron is obtained based on simulation or experimental data to derive a correlation model. For example, when the Nb content changes by 0.10%, the corresponding change in the tensile strength σb of gray cast iron, δ ([Nb]→σb), is 20MPa~40MPa. The correlation model can be F(x)=aX+b, where X is the [Nb] element, and F(x) is the change in the tensile strength σb of gray cast iron, δ.

[0028] Based on the influence amplitude of Mo content on the tensile strength of HT250, HT300, and HT350 gray cast iron, i.e., δ([Mo]→σb), and the influence amplitude of Nb content on the tensile strength of HT250, HT300, and HT350 gray cast iron, i.e., δ([Nb]→σb), the correlation model δ([Mo]→σb) is obtained: δ([Nb]→σb) = α, α∈(0.5, 3.5). It can be derived and solved that the reduction in tensile strength of HT250, HT300, and HT350 gray cast iron caused by the removal or reduction of Mo content [Mo] requires the addition or increase of Nb content to compensate for the decrease in tensile strength of HT250, HT300, and HT350 gray cast iron.

[0029] In determining the niobium content correlation value corresponding to the tensile strength correlation value based on the second preset model, one of the three correlation models mentioned above can be used. When using two or more correlation models, the results of these models can be processed, such as inputting the results into the preset model or averaging the results.

[0030] These methods can accurately determine the amount of niobium to supplement gray cast iron using various correlation models that show the relationship between niobium content and tensile strength.

[0031] In some optional application scenarios of these implementation methods, the step of generating a correlation model between the niobium content and the tensile strength of gray cast iron based on the influence of niobium content on the tensile strength of gray cast iron includes: determining the maximum equilibrium solubility of niobium in the gray cast iron; determining the content of effective niobium in the cast iron based on the maximum equilibrium solubility, wherein the niobium state corresponding to the content of effective niobium includes solid solution state and hard phase formed by solidification precipitation; and generating a correlation model between the content of effective niobium and the tensile strength of gray cast iron based on the content of effective niobium.

[0032] In these potential application scenarios, the maximum equilibrium solubility of Nb in cast iron is 0.05%-0.07%. Part of it exists in a free solid solution state, exhibiting solid solution strengthening. The remaining part solidifies and precipitates to form hard Nb (C, N) phases, providing dispersion strengthening, increasing the number of eutectic clusters, refining dendrite size, and reducing lamellar spacing, thereby improving the tensile strength of HT250, HT300, and HT350 gray cast iron. Therefore, based on the maximum equilibrium solubility of Nb, an effective [Nb] content correlation model for solid solution strengthening and refining lamellar spacing can be determined.

[0033] These methods can determine the effective niobium content that enhances the tensile strength of gray cast iron by maximizing equilibrium solubility, thereby helping to improve the accuracy of determining the correlation model.

[0034] In some optional implementations of any embodiment of this disclosure, the step of generating the second preset model includes: generating a linear relationship between the niobium content function and the tensile strength of gray cast iron based on the influence of the niobium content in gray cast iron on the tensile strength of gray cast iron, wherein the content function is represented by a target coefficient and the niobium content.

[0035] In these optional implementations, the generation step of the second preset model includes: generating a linear relationship between the niobium content function and the tensile strength of gray cast iron based on the influence of the niobium content in gray cast iron on the tensile strength of gray cast iron, wherein the content function is represented by a target coefficient and the niobium content.

[0036] These implementation methods utilize linear relationships to improve the expressive power and regularity of the association model.

[0037] Optionally, the linear relationship generation step includes: determining the linear relationship between the niobium content function and the tensile strength of gray cast iron based on engineering process factors related to niobium during the processing of gray cast iron, wherein the engineering process factors include at least one of the following: feeding method, ferro-niobium alloy state, and effective dissolution rate of niobium in ferro-niobium alloy.

[0038] In this process, the Nb element content [Nb] can be determined not only through the [Mo]→σb mathematical model, the [Nb]→σb mathematical model, and the [Mo]→σb and [Nb]→σb correlation model, but is also affected by factors such as the charging method (inside the smelting furnace or in the ladle), the state of the ferro-niobium alloy (large block size or small particle size distribution), and the effective solubility rate of Nb in the ferro-niobium alloy. Therefore, considering the engineering process factors, a factor incorporating the process index k is used to adjust the influence of [Nb]→σb, and a [Nb]-σb correlation model is established. σ b = k . f （[Nb]） + C Mathematical model, k∈(0.5,1).

[0039] In some optional implementations of any embodiment of this disclosure, the step of determining the tensile strength-related value of the gray cast iron according to a first preset model in response to a decrease in the molybdenum content in the gray cast iron used in the engine cylinder includes: inputting the product structure of the product to be processed into a control model, wherein the control model is a simulation model or an experimental model; using the control model to perform a main wall thickness analysis on the product to be processed; and when the analysis results indicate that the main wall thickness of the product to be processed is within a preset range, determining the tensile strength-related value of the gray cast iron according to the first preset model in response to a decrease in the molybdenum content in the gray cast iron used in the engine cylinder.

[0040] In these optional implementations, the product structure can serve as input to the control model, which can be simulation software or an experimental model, i.e., an experimental evaluation model. Specifically, the preset range can be a main wall thickness of 20mm to 100mm.

[0041] These implementation methods can quantify the main wall thickness of the engine gray cast iron through main wall thickness analysis, thereby improving the targeting of performance control and avoiding inaccurate control results due to differences in main wall thickness.

[0042]

[0043] Table 1 Table 1 shows the design and verification of the alloy composition and bulk properties of the casting. When [Nb] ∈ (0.05, 0.25), the tensile strength deviation | Δσ b | / σ b ≤5 indicates that the model predictions are accurate and reliable.

[0044] Figure 2 Another flowchart of a model-based prediction method for controlling the properties of gray cast iron according to an embodiment of the present disclosure is shown.

[0045] Figure 3 A schematic diagram of the metallographic structure of gray cast iron is shown. The diagram illustrates that the addition of Nb promotes the formation of the Nb (C, N) phase.

[0046] This disclosure provides a model-predictive-based gray cast iron performance control device, which is used to execute the model-predictive-based gray cast iron performance control method described in the above embodiments, such as... Figure 4 As shown, the device 400 includes: a first determining unit 401, configured to determine a tensile strength related value of the gray cast iron according to a first preset model in response to a decrease in the molybdenum content in the gray cast iron used in the engine cylinder, wherein the tensile strength related value is the decrease in tensile strength or the tensile strength obtained after the decrease; a second determining unit 402, configured to determine a niobium content related value corresponding to the tensile strength related value according to a second preset model; and an enhancing unit 403, configured to enhance the niobium content value in the gray cast iron according to the niobium content related value, so that the tensile strength of the gray cast iron reaches a desired value, wherein the difference between the desired value and the tensile strength of the gray cast iron before the decrease in molybdenum content is less than a target difference threshold.

[0047] The gray cast iron performance control device based on model prediction provided in the above embodiments of this disclosure and the gray cast iron performance control method based on model prediction provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0048] This disclosure also provides an electronic device corresponding to the model-prediction-based gray cast iron performance control method provided in the foregoing embodiments, for executing the aforementioned model-prediction-based gray cast iron performance control method. This disclosure does not limit the scope of the embodiments.

[0049] Please refer to Figure 5 This illustrates a schematic diagram of an electronic device provided by some embodiments of the present disclosure. For example... Figure 5 As shown, the electronic device 50 includes: a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, the communication interface 503, and the memory 501 are connected via the bus 502. The memory 501 stores a computer program that can run on the processor 500. When the processor 500 runs the computer program, it executes the method provided in any of the foregoing embodiments of this disclosure.

[0050] The memory 501 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 503 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0051] Bus 502 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 501 is used to store programs. After receiving an execution instruction, the processor 500 executes the program. The model-prediction-based gray cast iron performance control method disclosed in any of the foregoing embodiments of this disclosure can be applied to the processor 500, or implemented by the processor 500.

[0052] The processor 500 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 500 or by instructions in software form. The processor 500 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 501. The processor 500 reads the information in memory 501 and, in conjunction with its hardware, completes the steps of the above method.

[0053] The electronic device provided in this disclosure and the model-prediction-based gray cast iron performance control method provided in this disclosure are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0054] This disclosure also provides a computer-readable storage medium corresponding to the model-prediction-based gray cast iron performance control method provided in the foregoing embodiments. Please refer to... Figure 6 The computer-readable storage medium shown is an optical disc 60, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the model prediction-based gray cast iron performance control method provided in any of the foregoing embodiments.

[0055] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0056] The computer-readable storage medium provided in the above embodiments of this disclosure and the model-prediction-based gray cast iron performance control method provided in the embodiments of this disclosure are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0057] It should be noted that: In the foregoing text, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in this disclosure is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0058] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0059] The embodiments of this disclosure have been described above with reference to the accompanying drawings. These are merely specific implementations of this disclosure, but this disclosure is not limited to the specific implementations described above. The specific implementations described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this disclosure without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this disclosure.

Claims

1. A method for controlling the properties of gray cast iron based on model prediction, characterized in that, include: In response to the reduction in the molybdenum content in the gray cast iron used in engine cylinders, a tensile strength correlation value of the gray cast iron is determined according to a first preset model. The tensile strength correlation value is the reduction value of the tensile strength or the tensile strength obtained after the reduction. According to the second preset model, the niobium content correlation value corresponding to the tensile strength correlation value is determined, wherein the niobium content correlation value is the niobium content or the change value of the niobium content; Based on the niobium content value, the niobium content in the gray cast iron is increased to make the tensile strength of the gray cast iron reach the desired value, wherein the difference between the desired value and the tensile strength of the gray cast iron before the molybdenum content is reduced is less than the target difference threshold. In response to the decrease in molybdenum content in the gray cast iron used in engine cylinders, the tensile strength related values ​​of the gray cast iron are determined according to a first preset model, including: The product structure of the product to be processed is input into the control model, which is a simulation model or an experimental model. Using the control model, the main wall thickness of the product to be processed is analyzed. When the analysis results indicate that the main wall thickness of the product to be processed is within a preset range, in response to the decrease in the molybdenum content in the gray cast iron used in the engine cylinder, the tensile strength related value of the gray cast iron is determined according to the first preset model. The generation step of the second preset model includes at least one of the following: Based on the influence of niobium content in gray cast iron on its tensile strength, a correlation model between niobium content and tensile strength in gray cast iron is generated; based on the influence of changes in niobium content in gray cast iron on changes in tensile strength, a correlation model between niobium content and tensile strength in gray cast iron is generated; based on the influence of niobium content and molybdenum content on changes in tensile strength in gray cast iron, a correlation model between niobium content and molybdenum content in gray cast iron is generated. The step of determining the niobium content correlation value corresponding to the tensile strength correlation value according to the second preset model includes: Use one of the three association models mentioned above; or When using two or more association models, the results of the association models used are processed.

2. The method according to claim 1, characterized in that, The generation steps of the first preset model include: Based on the influence of the change in molybdenum content in gray cast iron on the change in tensile strength of gray cast iron, a correlation model between the change in molybdenum content and the change in tensile strength of gray cast iron is generated.

3. The method according to claim 1, characterized in that, The process involves generating a correlation model between niobium content and tensile strength in gray cast iron, based on the influence of niobium content on the tensile strength of gray cast iron. This model includes: Determine the maximum equilibrium solubility of niobium in the gray cast iron; The content of effective niobium in cast iron is determined based on the maximum equilibrium solubility, wherein the niobium state corresponding to the content of effective niobium includes solid solution state and hard phase formed by solidification precipitation; Based on the effective niobium content, a correlation model is generated between the effective niobium content in gray cast iron and the tensile strength of gray cast iron.

4. The method according to claim 1, characterized in that, The generation steps of the second preset model include: Based on the influence of niobium content in gray cast iron on its tensile strength, a linear relationship between the niobium content function and the tensile strength of gray cast iron is generated, wherein the content function is represented by a target coefficient and the niobium content.

5. The method according to claim 4, characterized in that, The steps for generating the linear relationship include: Based on the engineering process factors related to niobium during the processing of gray cast iron, the linear relationship between the niobium content function in gray cast iron and the tensile strength of gray cast iron is determined. The engineering process factors include at least one of the following: feeding method, ferro-niobium alloy state, and effective dissolution rate of niobium in ferro-niobium alloy.

6. A model-predictive-based gray cast iron performance control device, characterized in that, include: The first determining unit is configured to, in response to a decrease in the molybdenum content in the gray cast iron used in the engine cylinder, determine a tensile strength correlation value of the gray cast iron according to a first preset model, wherein the tensile strength correlation value is the decrease in tensile strength or the tensile strength obtained after the decrease. The second determining unit is configured to determine the niobium content related value corresponding to the tensile strength related value according to the second preset model, wherein the niobium content related value is the niobium content or the change value of the niobium content. The enhancement unit is configured to increase the niobium content in the gray cast iron according to the niobium content-related value, so that the tensile strength of the gray cast iron reaches a desired value, wherein the difference between the desired value and the tensile strength of the gray cast iron before the molybdenum content is reduced is less than a target difference threshold. The first determining unit is further configured to perform the following action in response to a decrease in the molybdenum content in the gray cast iron used in the engine cylinder: determining the tensile strength related value of the gray cast iron according to a first preset model. The product structure of the product to be processed is input into the control model, which is a simulation model or an experimental model. Using the control model, the main wall thickness of the product to be processed is analyzed. When the analysis results indicate that the main wall thickness of the product to be processed is within a preset range, in response to the decrease in the molybdenum content in the gray cast iron used in the engine cylinder, the tensile strength related value of the gray cast iron is determined according to the first preset model. The generation step of the second preset model includes at least one of the following: Based on the influence of niobium content in gray cast iron on its tensile strength, a correlation model between niobium content and tensile strength in gray cast iron is generated; based on the influence of changes in niobium content in gray cast iron on changes in tensile strength, a correlation model between niobium content and tensile strength in gray cast iron is generated; based on the influence of niobium content and molybdenum content on changes in tensile strength in gray cast iron, a correlation model between niobium content and molybdenum content in gray cast iron is generated. The second determining unit is further configured to perform the determination of the niobium content related value corresponding to the tensile strength related value according to the second preset model in the following manner: Use one of the three association models mentioned above; or When using two or more association models, the results of the association models used are processed.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the method as described in any one of claims 1-5.

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