Method, system medium and equipment for judging sampling opportunity of molten iron in molten iron trough in tapping process of ironmaking furnace

Abstract

The invention discloses a method for judging the sampling time of molten iron in a molten iron trough in the tapping process of an iron making furnace, a system medium and equipment. Obtaining component data of a first molten iron sample sampled every time in the molten iron groove, an accumulated iron receiving amount in the molten iron container during sampling every time and component data of a second molten iron sample when the target molten iron amount is loaded into the molten iron container; determining the comprehensive component difference between the component data of the first molten iron sample and the component data of the second molten iron sample sampled each time; establishing a relation regression model between the molten iron component difference and the iron receiving amount by utilizing the comprehensive component difference and the accumulated iron receiving amount; according to the relation regression model, the accumulative iron receiving amount when the comprehensive component difference is minimum in the molten iron amount interval of [0, target molten iron amount] is determined to serve as the target accumulative iron receiving amount, and the target accumulative iron receiving amount serves as the molten iron sampling opportunity of the molten iron channel in the iron tapping process of the iron making furnace.

Description

Technical Field

[0001] The present invention relates to the technical field of steelmaking and ironmaking, and in particular to a method, system medium and equipment for determining the timing of sampling molten iron in an iron ditch during the tapping process of an ironmaking furnace. Background Art

[0002] During the tapping process in a blast furnace, molten iron is typically sampled. The resulting compositional data is then fed to various departments within the steel company for operational guidance. For example, the blast furnace's internal smelting conditions are assessed based on the molten iron's composition, guiding subsequent adjustments to the furnace's operating procedures. The transportation department also determines the molten iron's destination based on its composition. This is particularly true during periods of special blast furnace operation, when the molten iron's composition exceeds acceptable limits, determining whether it should be sent to a specific workshop for processing. The steelmaking department also determines whether production plans should be adjusted based on the molten iron's composition and makes advance production preparations.

[0003] Therefore, the sampling of molten iron composition data is of great significance to the entire steelmaking process.

[0004] As we all know, molten iron is discharged from the ironmaking furnace and flows into the molten iron container below the furnace through the molten iron ditch. Due to the spatial layout, facility safety, and production rhythm of the area below the ironmaking furnace, sampling the molten iron container is difficult. To determine the composition of the molten iron in the molten iron container in advance, molten iron sampling is usually carried out in the molten iron ditch.

[0005] However, the timing of molten iron sampling in the molten iron ditch often relies on manual experience, and the composition of the molten iron flow rate from the ironmaking furnace is constantly changing. As a result, the sampled composition of the molten iron in the ditch is only a momentary value during the tapping process, which is significantly different from the sampled composition of the molten iron in the molten iron container. This causes the molten iron sampling results in the ditch to seriously deviate from expectations, providing poor guidance for subsequent processes and easily causing significant production losses. For example, the transportation department often has to transport the molten iron to another specific steelmaking workshop because the composition exceeds the processing capacity of the delivery workshop. This increases the labor intensity of the transportation workers and exacerbates the temperature loss of the molten iron. On the other hand, the steelmaking department needs to take samples again to determine the accurate composition of the molten iron, which prolongs the operation cycle and increases production costs. Moreover, the actual composition of the molten iron often deviates significantly from the expected, requiring temporary adjustments to the steelmaking production plan, disrupting the normal production rhythm.

[0006] Therefore, the existing technology has the technical problem that the sampling results of molten iron in the iron ditch are seriously inconsistent with expectations, which provides poor guidance for subsequent processes and easily causes major production losses. Summary of the Invention

[0007] In order to solve or partially solve the technical problem that the results of molten iron sampling in the iron ditch are seriously inconsistent with expectations, have poor guidance for subsequent processes, and are likely to cause major production losses, the present invention provides a method, system medium and equipment for determining the timing of molten iron sampling in the iron ditch during the iron-making furnace tapping process, so as to determine the best time for molten iron sampling, while taking into account the guidance of the sampling results for the blast furnace itself and subsequent processes such as iron cladding, molten iron transportation, molten iron pretreatment and steelmaking.

[0008] In order to solve the above technical problems, the first aspect of the present invention discloses a method for determining the timing of sampling molten iron in a smelting furnace during the tapping process, the method comprising:

[0009] During the iron-making furnace tapping process, the composition data of the first molten iron sample taken each time in the molten iron ditch, the cumulative amount of iron received in the molten iron container at each sampling time, and the composition data of the second molten iron sample when the molten iron container is filled with the target amount of molten iron are obtained;

[0010] Determining a comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken at each time;

[0011] Establishing a relationship regression model using the comprehensive component differences and the cumulative iron intake;

[0012] The relationship regression model is used to determine the cumulative iron receiving amount with the smallest comprehensive composition difference within the molten iron amount range of [minimum molten iron amount, target molten iron amount] as the target cumulative iron receiving amount, and the target cumulative iron receiving amount is used as the timing for sampling molten iron in the molten iron ditch during the iron-making furnace tapping process.

[0013] Optionally, the obtaining of the composition data of the first molten iron sample taken each time in the molten iron ditch, the cumulative amount of iron received in the molten iron container at each sampling, and the composition data of the second molten iron sample when the molten iron container is loaded with the target amount of molten iron, specifically includes:

[0014] monitoring the cumulative iron intake in real time, sampling the molten iron in the molten iron ditch every time the cumulative iron intake increases by a preset weight, and recording the composition data of the first molten iron sample;

[0015] When the cumulative iron receiving amount reaches the target molten iron amount, the molten iron in the molten iron container is sampled and the composition data of the second molten iron sample is recorded.

[0016] Optionally, determining the comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken each time specifically includes:

[0017] For each sampling of the first molten iron sample composition data, calculate the comprehensive composition difference between the first molten iron sample composition data and the second molten iron sample composition data according to the composition difference calculation formula;

[0018] The formula for calculating the composition difference is as follows:

[0019]

[0020] Among them, C i ' is the content of the i-th chemical element in the second molten iron sample composition data, C i is the content of the i-th chemical element in the composition data of the first molten iron sample taken at a single time, a i is the coefficient.

[0021] Optionally, the relational regression model is a univariate n-order linear regression model, where 0<n≤6 and is a positive integer.

[0022] Optionally, determining the cumulative iron receiving amount with the minimum comprehensive composition difference within the molten iron amount range of [minimum molten iron amount, target molten iron amount] by the relational regression model as the target cumulative iron receiving amount, and using the target cumulative iron receiving amount as the iron ditch molten iron sampling opportunity during the iron-making furnace tapping process, specifically includes:

[0023] Derivative the relational regression model; wherein the derivative order includes first-order derivative and second-order derivative;

[0024] Obtaining all real number solutions for molten iron amounts within the molten iron amount interval of (minimum molten iron amount, target molten iron amount) when the derivative result of the relational regression model is equal to 0;

[0025] Each real number solution of the molten iron amount, the minimum molten iron amount and the target molten iron amount are respectively brought into the relational regression model to obtain the comprehensive component difference values ​​of each in the relational regression model, and the cumulative iron receiving amount corresponding to the minimum value of the comprehensive component difference values ​​is taken as the target cumulative iron receiving amount.

[0026] Optionally, the relationship regression model is:

[0027] M(q)=aq 3 +bq 2 +cq+d

[0028] Wherein, M(q) represents the comprehensive component difference value in the relational regression model, q represents the cumulative iron receiving amount, and a, b, c, and d are constants.

[0029] Optionally, the relationship regression model is:

[0030] M(q)=aq 2 +bq+c

[0031] Wherein, q represents the cumulative iron receiving amount, and a, b, c, and d are constants.

[0032] A second aspect of the present invention discloses a system for determining the timing of sampling molten iron in a smelting furnace during the tapping process, the system comprising:

[0033] an acquisition unit, configured to acquire, during the tapping process of the iron-making furnace, composition data of a first molten iron sample taken each time in the molten iron ditch, a cumulative amount of iron received in the molten iron container at each sampling time, and composition data of a second molten iron sample when the molten iron container is filled with a target amount of molten iron;

[0034] a first determining unit, configured to determine a comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken each time;

[0035] A model building unit, configured to build a regression model of the relationship between the molten iron composition difference and the iron intake amount by using the comprehensive composition difference and the cumulative iron intake amount;

[0036] The second determination unit is used to determine the cumulative iron receiving amount with the minimum comprehensive composition difference in the molten iron amount range of [0, target molten iron amount] as the target cumulative iron receiving amount through the relationship regression model, and use the target cumulative iron receiving amount as the timing for sampling molten iron in the molten iron ditch during the iron-making furnace tapping process.

[0037] According to a third aspect of the present invention, a computer-readable storage medium is disclosed, on which a computer program is stored. When the program is executed by a processor, the steps of the above method are implemented.

[0038] A fourth aspect of the present invention discloses a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0039] Through one or more technical solutions of the present invention, the present invention has the following beneficial effects or advantages:

[0040] The technical solution of the present invention is to obtain the composition data of the first molten iron sample taken in the molten iron ditch each time, the cumulative amount of iron received in the molten iron container at each time of sampling, and the composition data of the second molten iron sample when the target amount of molten iron is loaded into the molten iron container during the iron-making furnace tapping process; then determine the comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken each time, that is, the comprehensive difference between the composition of the molten iron sample taken in the ditch and the composition data of the molten iron in the iron ladle; and use the comprehensive composition difference and the cumulative amount of iron received to establish a relationship regression model and solve it, and determine the cumulative amount of iron received when the comprehensive difference is the smallest as the timing for sampling the molten iron in the molten iron ditch, so that the difference between the molten iron composition data of the molten iron ditch obtained at the molten iron sampling timing and the molten iron composition data in the molten iron container is minimized. By determining the optimal timing for molten iron sampling, this technical solution can take into account the accuracy of the sampling results and their guidance for the blast furnace itself and subsequent processes such as iron ladle iron receiving, molten iron transportation, molten iron pretreatment, and steelmaking.

[0041] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0043] Figure 1 A flow chart of a method for determining the timing of sampling molten iron in a sump during the tapping process of an ironmaking furnace according to one embodiment of the present invention is shown;

[0044] Figure 2 A schematic diagram of a system for determining the timing of molten iron sampling in a sluice during the tapping process of an ironmaking furnace according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0045] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0046] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0047] First, as Figure 1 As shown, the method for determining the timing of sampling molten iron in the iron ditch during the iron-making furnace tapping process provided by the embodiment of the present invention comprises at least the following steps:

[0048] S101, during the iron-making furnace tapping process, obtain the composition data of the first molten iron sample taken each time in the molten iron ditch, the cumulative amount of iron received in the molten iron container at each time of sampling, and the composition data of the second molten iron sample when the molten iron container is filled with the target amount of molten iron.

[0049] During the iron-tapping process of the iron-making furnace, the molten iron container is aligned to the iron receiving position below the iron-tapping platform of the iron-making furnace, and the flow direction of the molten iron in the molten iron ditch is changed so that the molten iron flows into the molten iron container.

[0050] During the iron collection process, the cumulative iron collection amount is monitored in real time. Each time the cumulative iron collection amount increases by a preset weight, the molten iron in the molten iron ditch is sampled, and the composition data of the first molten iron sample is recorded. For example, each time the cumulative iron collection amount in the molten iron container increases by m tons, the molten iron in the molten iron ditch is sampled, and the sampling of the molten iron in the molten iron ditch is recorded. m is a positive integer ≥ 1. At this point, the cumulative iron collection amount in the molten iron container at the time of sampling can also be measured.

[0051] Furthermore, when the cumulative iron receiving amount reaches the target molten iron amount, the molten iron in the molten iron container is sampled and the composition data of the second molten iron sample is recorded.

[0052] S102, determining the comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken each time.

[0053] In a specific implementation process, for the composition data of the first molten iron sample taken each time, the comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample is calculated according to the composition difference calculation formula.

[0054] The formula for calculating the composition difference is as follows:

[0055]

[0056] Among them, C i ' is the content of the i-th chemical element in the second molten iron sample composition data, Ci is the content of the i-th chemical element in the composition data of the first molten iron sample taken at a single time, a i is the coefficient.

[0057] The above composition difference calculation formula is used to calculate the comprehensive composition difference of each sampling. The comprehensive composition difference reflects the total content difference data of all elements between the composition data of the first molten iron sample and the composition data of the second molten iron sample.

[0058] S103, establishing a relationship regression model using the comprehensive component differences and the cumulative iron intake amount.

[0059] Among them, the regression model of the relationship between the comprehensive component differences and the cumulative iron intake is a univariate n-order linear regression model, 0<n≤6 and is a positive integer.

[0060] In an optional implementation, the relational regression model is:

[0061] M(q)=aq 3 +bq 2 +cq+d

[0062] Among them, M(q) represents the comprehensive component difference value in the relational regression model, q represents the cumulative iron intake, and a, b, c, and d are constants.

[0063] In an optional implementation, the relationship regression model may also be:

[0064] M(q)=aq 2 +bq+c

[0065] Wherein, q represents the cumulative iron receiving amount, and a, b, c, and d are constants.

[0066] The above are two methods listed in the relational regression model, but they do not constitute a limitation.

[0067] S104, determining the cumulative iron receiving amount with the minimum comprehensive composition difference in the molten iron amount range of [minimum molten iron amount, target molten iron amount] through the relational regression model as the target cumulative iron receiving amount, and using the target cumulative iron receiving amount as the timing for sampling molten iron in the molten iron ditch during the iron-making furnace tapping process.

[0068] In a specific implementation process, the relational regression model is derived; wherein the order of the derivative includes a first-order derivative and a second-order derivative. When the derivative result of the relational regression model is equal to 0, all the real number solutions of the molten iron amount within the molten iron amount interval of (minimum molten iron amount, target molten iron amount) are obtained; each real number solution of the molten iron amount, the minimum molten iron amount and the target molten iron amount are respectively brought into the relational regression model to obtain the comprehensive component difference value of each in the relational regression model, and the cumulative iron receiving amount corresponding to the minimum value of the comprehensive component difference value is used as the target cumulative iron receiving amount.

[0069] Using the relational regression model M(q)=aq 3 +bq 2 Take the first-order derivative of +cq+d as an example. The target molten iron quantity is set to Q and the minimum molten iron quantity is set to 0.

[0070] The result of taking the first-order derivative is:

[0071] M(q)′=3aq 2 +2bq+c

[0072] Determine the above derivative results: all real number solutions of the iron connection when M(q)′=0.

[0073] If there are two real number solutions for the iron intake amount, such as q1 and q2, where q1 ≤ q2, substitute 0, q1, q2, and Q into the original regression model to obtain their corresponding comprehensive component difference values. The cumulative iron intake amount corresponding to the minimum of these comprehensive component difference values ​​is used as the target cumulative iron intake amount. If the comprehensive component difference value obtained for q1 is the smallest, then that value is used as the target cumulative iron intake amount.

[0074] If there is no real number solution for the iron intake, the minimum molten iron amount 0 and the target molten iron amount setting Q are substituted into the original relational regression model to obtain the corresponding comprehensive component difference values. The cumulative iron intake corresponding to the minimum of the comprehensive component difference values ​​is used as the target cumulative iron intake. If the comprehensive component difference value obtained by Q is the smallest, it is used as the target cumulative iron intake.

[0075] Of course, the above-mentioned relational regression model can also be subjected to a second-order derivative. When the second-order derivative of the relational regression model is equal to 0, it is determined whether there is a real number solution for the molten iron amount in the molten iron amount interval (0, Q). If so, each real number solution for the molten iron amount, the minimum molten iron amount, and the target molten iron amount are respectively brought into the relational regression model to obtain their respective comprehensive component difference values, and the cumulative iron amount corresponding to the minimum value of the comprehensive component difference values ​​is used as the target cumulative iron amount.

[0076] If there is no real number solution for the iron intake, the minimum iron intake 0 and the target iron intake Q are substituted into the original regression model to obtain their corresponding comprehensive component difference values. The cumulative iron intake corresponding to the minimum of these comprehensive component difference values ​​is used as the target cumulative iron intake. For example, if the comprehensive component difference value obtained by Q is the smallest, then this value is used as the target cumulative iron intake.

[0077] In the second aspect, based on the same inventive concept as the method for determining the timing of sampling molten iron in the iron ditch during the iron-making furnace tapping process provided in the embodiment of the first aspect, the embodiment of the present invention further provides a system for determining the timing of sampling molten iron in the iron ditch during the iron-making furnace tapping process, see Figure 2 , the system comprising:

[0078] An acquisition unit 201 is configured to acquire, during the tapping process of the iron-making furnace, composition data of a first molten iron sample taken from the molten iron ditch at each sampling, a cumulative amount of iron received in the molten iron container at each sampling, and composition data of a second molten iron sample when the molten iron container is filled with a target amount of molten iron;

[0079] The first determining unit 202 is configured to determine a comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken at each time;

[0080] A model building unit 203 is configured to build a regression model of the relationship between the molten iron composition difference and the iron intake amount by using the comprehensive composition difference and the cumulative iron intake amount;

[0081] The second determination unit 204 is used to determine the cumulative iron receiving amount with the minimum comprehensive composition difference in the molten iron amount range of [0, target molten iron amount] as the target cumulative iron receiving amount through the relationship regression model, and use the target cumulative iron receiving amount as the timing for sampling molten iron in the molten iron ditch during the iron-making furnace tapping process.

[0082] It should be noted that the system for determining the timing of sampling molten iron in the iron ditch during the iron-making furnace tapping process provided in the embodiment of the present invention, wherein the specific manner in which each module performs operations has been described in detail in the method embodiment provided in the above-mentioned first aspect, and the specific implementation process can refer to the method embodiment provided in the above-mentioned first aspect, and will not be elaborated here.

[0083] In the third aspect, based on the same inventive concept as the method for determining the timing of sampling molten iron in the sluice during the iron-making furnace tapping process provided in the embodiment of the first aspect, the embodiment of the present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0084] In the fourth aspect, based on the same inventive concept as the method for determining the timing of sampling molten iron in the iron ditch during the iron-making furnace tapping process provided in the embodiment of the first aspect, the embodiment of the present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the aforementioned methods when executing the program.

[0085] Through one or more embodiments of the present invention, the present invention has the following beneficial effects or advantages:

[0086] The technical solution of the present invention is to obtain the composition data of the first molten iron sample taken each time in the molten iron ditch, the cumulative amount of iron received in the molten iron container at each sampling, and the composition data of the second molten iron sample when the target amount of molten iron is loaded into the molten iron container during the iron-making furnace tapping process; then determine the comprehensive composition difference between the composition data of the first molten iron sample taken each time and the composition data of the second molten iron sample taken each time, that is, the comprehensive difference between the composition of the molten iron sample taken in the ditch and the composition data of the molten iron in the ladle; and use the comprehensive composition difference and the cumulative amount of iron received to establish a regression model of the relationship between the molten iron composition difference and the amount of iron received and solve it, and determine the cumulative amount of iron received when the comprehensive difference is minimized as the timing for sampling the molten iron in the ditch, so that the difference between the molten iron composition data of the molten iron ditch obtained at the molten iron sampling timing and the molten iron composition data in the molten iron container is minimized. By determining the optimal timing for molten iron sampling, this technical solution can take into account the accuracy of the sampling results and their guidance for the blast furnace itself and subsequent processes such as iron receiving in the ladle, molten iron transportation, molten iron pretreatment, and steelmaking.

[0087] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0088] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for determining the timing of sampling molten iron in a smelting furnace during the tapping process, characterized in that: The method comprises: During the iron-making furnace tapping process, the composition data of the first molten iron sample taken each time in the molten iron ditch, the cumulative amount of iron received in the molten iron container at each sampling time, and the composition data of the second molten iron sample when the molten iron container is filled with the target amount of molten iron are obtained; Determining a comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken at each time; Establishing a relationship regression model using the comprehensive component differences and the cumulative iron intake; The relationship regression model is used to determine the cumulative iron receiving amount with the smallest comprehensive composition difference within the molten iron amount range of [minimum molten iron amount, target molten iron amount] as the target cumulative iron receiving amount, and the target cumulative iron receiving amount is used as the timing for sampling molten iron in the molten iron ditch during the iron-making furnace tapping process.

2. The method according to claim 1, wherein The obtaining of the composition data of the first molten iron sample taken each time in the molten iron ditch, the cumulative amount of iron received in the molten iron container at each sampling, and the composition data of the second molten iron sample when the molten iron container is filled with the target amount of molten iron specifically includes: monitoring the cumulative iron intake in real time, sampling the molten iron in the molten iron ditch every time the cumulative iron intake increases by a preset weight, and recording the composition data of the first molten iron sample; When the cumulative iron receiving amount reaches the target molten iron amount, the molten iron in the molten iron container is sampled and the composition data of the second molten iron sample is recorded.

3. The method according to claim 1, wherein Determining the comprehensive composition difference between the first molten iron sample composition data and the second molten iron sample composition data of each sampling specifically includes: For each sampling of the first molten iron sample composition data, calculate the comprehensive composition difference between the first molten iron sample composition data and the second molten iron sample composition data according to the composition difference calculation formula; The formula for calculating the composition difference is as follows: Among them, C i ' is the content of the i-th chemical element in the second molten iron sample composition data, C i is the content of the i-th chemical element in the composition data of the first molten iron sample taken at a single time, a i is the coefficient.

4. The method according to claim 1, wherein The relational regression model is a univariate n-order linear regression model, where 0<n≤6 and is a positive integer.

5. The method according to claim 1, wherein The method further comprises: determining the cumulative iron receiving amount with the minimum comprehensive composition difference within the molten iron amount range of [minimum molten iron amount, target molten iron amount] by the relational regression model as the target cumulative iron receiving amount, and using the target cumulative iron receiving amount as the molten iron sampling timing of the molten iron in the iron ditch during the iron-making furnace tapping process. Derivative the relational regression model; wherein the derivative order includes first-order derivative and second-order derivative; Obtaining all real number solutions for molten iron amounts within the molten iron amount interval of (minimum molten iron amount, target molten iron amount) when the derivative result of the relational regression model is equal to 0; Each real number solution of the molten iron amount, the minimum molten iron amount and the target molten iron amount are respectively brought into the relational regression model to obtain the comprehensive component difference values ​​of each in the relational regression model, and the cumulative iron receiving amount corresponding to the minimum value of the comprehensive component difference values ​​is taken as the target cumulative iron receiving amount.

6. The method according to claim 5, wherein The relationship regression model is: M(q)=aq 3 +bq 2 +cq+d Wherein, M(q) represents the comprehensive component difference value in the relational regression model, q represents the cumulative iron receiving amount, and a, b, c, and d are constants.

7. The method according to claim 5, wherein The relationship regression model is: M(q)=aq 2 +bq+c Wherein, q represents the cumulative iron receiving amount, and a, b, c, and d are constants.

8. A system for determining the timing of molten iron sampling in a molten iron ditch during the iron-making furnace tapping process, characterized in that: The system comprises: an acquisition unit, configured to acquire, during the tapping process of the iron-making furnace, composition data of a first molten iron sample taken each time in the molten iron ditch, a cumulative amount of iron received in the molten iron container at each sampling time, and composition data of a second molten iron sample when the molten iron container is filled with a target amount of molten iron; a first determining unit, configured to determine a comprehensive composition difference between the composition data of the first molten iron sample and the composition data of the second molten iron sample taken each time; A model building unit, configured to build a regression model of the relationship between the molten iron composition difference and the iron intake amount by using the comprehensive composition difference and the cumulative iron intake amount; The second determination unit is used to determine the cumulative iron receiving amount with the minimum comprehensive composition difference in the molten iron amount range of [0, target molten iron amount] as the target cumulative iron receiving amount through the relationship regression model, and use the target cumulative iron receiving amount as the timing for sampling molten iron in the molten iron ditch during the iron-making furnace tapping process.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

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