Experimental data adjusting method and device for multi-metal mineral separation experiment, medium and equipment
By constructing a matrix of yield and grade parameters and combining the target coupling function with constraints, the problem of inaccurate adjustment of multi-metal beneficiation experimental data was solved, and the synchronous optimization and accuracy of the experimental data were achieved.
Patent Information
- Application Number
- CN202510501974.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-21
- Publication Date
- 2025-09-12
AI Technical Summary
In the prior art, the experimental data adjustment of multi-metal beneficiation experiments is not reasonable and accurate enough, resulting in the final adjustment results seriously deviating from the reasonable process parameters.
By constructing the yield parameter matrix and the grade parameter matrix, based on actual experimental data, the target coupling function is constructed, and the constraints are used to solve it, multi-objective collaborative optimization is achieved, and the initial yield and grade matrices are adjusted.
The synchronous optimization and adjustment of multi-metal mineral processing experimental data was achieved, ensuring the accuracy of the final adjustment results of the experimental data.
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Figure CN120632247A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of experimental data processing, and in particular to an experimental data adjustment method, device, medium and equipment for a multi-metal mineral processing experiment. Background Art
[0002] During mineral processing tests, deviations and inconsistencies often exist in the experimental data and indicator data for various products. If the test data for certain material lines have significant deviations, or if test data is missing due to unexpected circumstances during the test process, these special experimental data need to be adjusted to ensure compatibility among all experimental data.
[0003] At present, the optimization and adjustment of test data are all based on global equivalence considerations, and special test data cannot be processed compatibly, which will cause the final adjustment results to deviate seriously from reasonable process parameters.
[0004] Therefore, there is an urgent need for an experimental data adjustment method for multi-metal mineral processing experiments to solve the problem that the adjustment of experimental data in the existing technology is not reasonable and accurate enough. Summary of the Invention
[0005] In view of this, the present invention provides a method, device, medium and equipment for adjusting experimental data of a multi-metal mineral processing experiment to solve the problem in the prior art that the adjustment of experimental data is not reasonable and accurate enough.
[0006] To solve the above problems, the present application provides a method for adjusting experimental data of a multi-metal beneficiation experiment, comprising:
[0007] Based on the actual experimental data of each logistics line in the mineral processing experiment, a yield parameter matrix about yield and a grade parameter matrix about grade are constructed, wherein the actual experimental data include actual yield values and actual grade values;
[0008] Constructing an initial yield matrix to be adjusted and an initial grade matrix to be adjusted;
[0009] constructing a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix;
[0010] Based on the pre-constructed constraint conditions and the target coupling function, the initial yield matrix and the initial grade matrix are solved to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
[0011] Optionally, constructing a yield parameter matrix for yield and a grade parameter matrix for grade based on actual experimental data of each logistics line in the mineral processing experiment specifically includes:
[0012] Based on the actual yield values of each logistics line in the mineral processing experiment, the yield parameter matrix is constructed;
[0013] The grade parameter matrix is constructed based on the actual grade value of each metal component in each logistics line in the mineral processing experiment.
[0014] Optionally, the constructing of the initial yield matrix to be adjusted and the initial grade matrix to be adjusted specifically includes:
[0015] Based on each logistics line in the mineral processing experiment, determining an initial yield variable corresponding to each logistics line, and constructing the initial yield matrix based on each of the initial yield variables;
[0016] Based on each metal component in each logistics line in the mineral processing experiment, the initial grade variable corresponding to each metal component in each logistics line is determined, and the initial grade matrix is constructed based on each of the initial grade variables.
[0017] Optionally, constructing a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix specifically includes:
[0018] Determining a first yield objective function based on the initial yield matrix and the yield parameter matrix;
[0019] Determining a first full-grade target function including each metal component based on the initial grade matrix and the grade parameter matrix;
[0020] The target coupling function is constructed based on the first yield target function and the first full-grade target function.
[0021] Optionally, before solving the initial yield matrix and the initial grade matrix based on the pre-established constraint conditions and the target coupling function, the method further includes:
[0022] Construct a correlation matrix in advance based on the relationship between each equipment node and logistics line in the mineral processing experiment;
[0023] The constraint conditions are constructed based on the association matrix, and the constraint conditions include: a yield balance constraint condition and an overall grade balance constraint condition.
[0024] Optionally, before constructing the target coupling function, the method further includes:
[0025] Based on the weight coefficients of each logistics line, a weight matrix is pre-built;
[0026] The constructing of a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix specifically includes:
[0027] The target coupling function is constructed based on the weight matrix, the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix.
[0028] Optionally, constructing the target coupling function based on the weight matrix, the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix specifically includes:
[0029] Determining a second yield objective function based on the weight matrix, the initial yield matrix, and the yield parameter matrix;
[0030] Determining a second full-grade objective function including each metal component based on the weight matrix, the initial grade matrix, and the grade parameter matrix;
[0031] The target coupling function is constructed based on the second yield target function and the second overall grade target function.
[0032] To solve the above problems, the present application provides an experimental data adjustment device for a multi-metal beneficiation experiment, comprising:
[0033] A first construction module is used to construct a yield parameter matrix related to yield and a grade parameter matrix related to grade based on actual experimental data of each logistics line in the mineral processing experiment, wherein the actual experimental data includes actual yield values and actual grade values;
[0034] The second construction module is used to construct an initial yield matrix to be adjusted and an initial grade matrix to be adjusted;
[0035] a third construction module, configured to construct a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix;
[0036] An adjustment module is used to solve the initial yield matrix and the initial grade matrix based on pre-established constraints and the target coupling function to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
[0037] To solve the above problems, the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the experimental data adjustment method of any of the above-mentioned multi-metallic mineral processing experiments.
[0038] To solve the above problems, the present application provides an electronic device, which includes at least a memory and a processor, wherein a computer program is stored on the memory, and when the processor executes the computer program on the memory, it implements the steps of the experimental data adjustment method of the multi-metal mineral processing experiment described in any of the above items.
[0039] The present application provides a method, device, medium and equipment for adjusting experimental data of a multi-metal mineral processing experiment. By constructing a yield parameter matrix and a grade parameter matrix based on actual experimental data, a target coupling function including an initial yield matrix and an initial grade matrix can be constructed based on the yield parameter matrix and the grade parameter matrix. Then, based on the constraint conditions, the initial yield matrix and the initial grade matrix in the target coupling function can be solved, thereby realizing multi-objective collaborative optimization and adjustment, that is, realizing the synchronous optimization and adjustment of the mineral processing multi-metal yield experimental data and the grade experimental data, and ensuring the accuracy of the final adjustment results of the experimental data.
[0040] 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
[0041] 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:
[0042] Figure 1 This is a flow chart of a method for adjusting experimental data in a multi-metal beneficiation experiment according to an embodiment of the present application;
[0043] Figure 2 This is a structural block diagram of an experimental data adjustment device for a multi-metal ore dressing experiment according to another embodiment of the present application;
[0044] Figure 3 This is a structural block diagram of an electronic device according to another embodiment of the present application. DETAILED DESCRIPTION
[0045] Various aspects and features of the present application are described herein with reference to the accompanying drawings.
[0046] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but merely as an example of an embodiment. Other modifications within the scope and spirit of the present application will occur to those skilled in the art.
[0047] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.
[0048] These and other characteristics of the present application will become apparent from the following description of a preferred form of embodiment given as a non-limiting example with reference to the accompanying drawings.
[0049] It should also be understood that although the present application has been described with reference to certain specific examples, those skilled in the art will readily be able to implement many other equivalent forms of the present application.
[0050] The above and other aspects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.
[0051] Specific embodiments of the present application will be described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments described are merely examples of the present application and may be implemented in a variety of ways. Familiar and / or repetitive functions and structures are not described in detail to avoid obscuring the present application with unnecessary or redundant details. Therefore, the specific structural and functional details described herein are not intended to be limiting, but rather serve merely as a basis and representative basis for the claims to teach those skilled in the art to variously utilize the present application with substantially any suitable detailed structure.
[0052] This specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may all refer to one or more of the same or different embodiments according to the present application.
[0053] The present application embodiment provides a method for adjusting experimental data of a multi-metal beneficiation experiment, which can be applied to electronic devices such as terminals and servers, such as Figure 1 As shown, the method in this embodiment includes the following steps:
[0054] Step S101, constructing a yield parameter matrix for yield and a grade parameter matrix for grade based on actual experimental data of each logistics line in the mineral processing experiment, wherein the actual experimental data includes actual yield values and actual grade values;
[0055] In this embodiment, for mineral processing experiments, the actual yield values of each logistics line and the actual grade values of each metal component in each logistics line can be detected to obtain actual experimental data. Furthermore, a yield parameter matrix RY' can be constructed based on the actual yield values in the actual experimental data, and a yield parameter matrix RG' can be constructed based on the actual grade values in the actual experimental data.
[0056] Step S102, constructing an initial yield matrix to be adjusted and an initial grade matrix to be adjusted;
[0057] In this step, in order to simultaneously optimize the yield of each logistics line / material line and the grade of each component, the yield variable and the grade variable can be defined according to the same rule. Let the yield of the pth material line be x p , where one logistics line corresponds to one yield variable, and there are n material lines in total. At the same time, let the grade of each component (assuming it contains k metal components) on the pth material line be y p 1 、y p 2 ,...,y p k-1 、y p k Thus, an initial yield matrix RY containing n yield variables can be constructed. Similarly, an initial grade matrix RG containing n×k grade variables can be constructed, where the k columns correspond to the k metal components and the n rows correspond to the n physical lines.
[0058] Step S103, constructing a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix;
[0059] During the specific implementation of this step, a first yield target function can be determined based on the initial yield matrix and the yield parameter matrix; then, a first full-grade target function including each metal component can be determined based on the initial grade matrix and the grade parameter matrix; finally, the target coupling function can be constructed based on the first yield target function and the first full-grade target function.
[0060] Step S104: Solve the initial yield matrix and the initial grade matrix based on the pre-established constraint conditions and the target coupling function to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
[0061] In this step, after constructing the target coupling function, the constraints can be used to solve the initial yield matrix and the initial grade matrix in the target coupling function, thereby obtaining the target yield matrix containing the target yield value and the target grade matrix containing the target grade value, thereby obtaining the adjusted experimental data.
[0062] The method in this embodiment constructs a yield parameter matrix and a grade parameter matrix based on actual experimental data. Subsequently, a target coupling function including an initial yield matrix and an initial grade matrix can be constructed based on the yield parameter matrix and the grade parameter matrix. Then, based on the constraints, the initial yield matrix and the initial grade matrix in the target coupling function can be solved, thereby realizing multi-objective collaborative optimization and adjustment, that is, realizing the synchronous optimization and adjustment of the mineral processing multi-metal yield experimental data and the grade experimental data, thereby ensuring the accuracy of the final adjustment results of the experimental data.
[0063] Another embodiment of the present application provides a method for adjusting experimental data of a multi-metal beneficiation experiment, comprising the following steps:
[0064] Step S201, constructing a correlation matrix based on the relationship between each equipment node and the logistics line in the mineral processing experiment;
[0065] During the specific implementation of this step, the association matrix, node sequence and flow sequence / logistics line sequence are obtained based on the process structure data.
[0066] In other words, the association matrix is an m×n matrix consisting of the relationships between m equipment units (nodes) and n flow streams (n logistics lines). The rows correspond to all equipment units, and the columns correspond to all flow streams / logistics lines. If flow stream j flows into equipment z, the element in row z, column j is 1; if it flows out, it is -1. No association is recorded as 0.
[0067] Step S202, constructing the yield parameter matrix based on the actual yield value of each logistics line in the mineral processing experiment; constructing the grade parameter matrix based on the actual grade value of each metal component in each logistics line in the mineral processing experiment;
[0068] In this step, for the mineral processing experiment, the actual yield values of each logistics line and the actual grade values of each metal component in each logistics line can be detected to obtain actual experimental data. Then, the yield parameter matrix RY' can be constructed based on the actual yield values in the actual experimental data, and the yield parameter matrix RG' can be constructed based on the actual grade values in the actual experimental data.
[0069] Step S203, determining the initial yield variables corresponding to the logistics lines in the mineral processing experiment, and constructing the initial yield matrix based on the initial yield variables;
[0070] In this step, the initial yield matrix RY contains an n×1 matrix consisting of the yield variables of n logistics lines. Among them, one logistics line corresponds to one yield variable, and there are n material lines in total. That is, the rows correspond to the logistics lines, and the columns represent the yield variables of each logistics line. It describes the yield values of all logistics lines in the mineral processing process. The initial yield matrix RY is shown in the following formula (1):
[0071]
[0072] Step S204, determining the initial grade variables corresponding to the metal components in each logistics line in the ore dressing experiment, and constructing the initial grade matrix based on each of the initial grade variables;
[0073] In this step, the initial grade matrix RG contains an n×k matrix consisting of n logistics lines and k components. The k columns correspond to the k metal components, and the n rows correspond to the n logistics lines. The element in the pth row and jth column represents the grade variable of metal component j on logistics line p, describing the grade values of all components on all logistics lines in the mineral processing process. The initial grade matrix RG is shown in the following formula (2):
[0074]
[0075] Step S205: constructing a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix;
[0076] Step S2051, determining a first yield objective function based on the initial yield matrix and the yield parameter matrix;
[0077] In this step, the first yield objective function can be expressed as the following formula (3):
[0078] [RY-RY']T×[RY-RY'](3)
[0079] Step S20512: determining a first full-grade objective function including each metal component based on the initial grade matrix and the grade parameter matrix;
[0080] In this step, first, the grade objective function of the i-th component is calculated. From the grade matrix RG and the grade parameter RG', the first grade objective function of the i-th metal component can be obtained. Then, the first full-grade objective function can be determined based on the first grade objective functions of each metal component. The first grade objective function is shown in the following formula (4):
[0081] [RG-RG'].col(i)T×[RG-RG'].col(i)(4)
[0082] The first full-grade objective function is shown in the following formula (5):
[0083] SUM([RG-RG'].col(i)T×[RG-RG'].col(i))(5)
[0084] Step S2053: constructing the target coupling function based on the first yield target function and the first overall grade target function.
[0085] In this step, since the yield objective function in the prior art only considers the optimization calculation of yield alone, and does not involve the grade value, there will be a contradiction between yield balance and grade after the calculation is completed. The grade objective function only considers the optimization calculation of grade, and is obtained on the basis of yield balance through the grade balance constraint condition, which is not able to meet the simultaneous optimization of yield and grade. For this reason, the present embodiment adopts a calculation method based on the simultaneous optimization of yield and grade. Therefore, by coupling the first yield objective function with the first full-grade objective function, a coupled objective function is obtained; the target coupling function is shown in the following formula (6):
[0086] [RY-RY']T×[RY-RY']+SUM([RG-RG'].col(i)T×[RG-RG'].col(i))(6)
[0087] Step S206: constructing the constraint conditions based on the association matrix, wherein the constraint conditions include: a yield balance constraint condition and a full grade balance constraint condition;
[0088] In this step, the association matrix contains all equipment unit / equipment node information, but there are two special types of equipment units that need to be identified and eliminated. One is the input equipment unit, that is, the equipment unit with no input logistics line but only output logistics line, and the other is the output equipment unit, that is, the equipment unit with no output logistics line but only input logistics line. These two types of equipment units lack input or output logistics, so there are no constraints on the number and mass balance. Therefore, the following yield balance constraints and full grade balance constraints can be constructed. The yield balance constraints are shown in the following formula (7):
[0089] modified_RI × RY = 0 (7)
[0090] The full-grade balance constraint condition is shown in formula (8):
[0091] modified_RI × diag(RY) × RG = 0 (8)
[0092] Step S207: Solve the initial yield matrix and the initial grade matrix based on the yield balance constraint, the full grade balance constraint, and the target coupling function to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
[0093] In this step, after constructing the yield balance constraint conditions and the full grade balance constraint conditions, the target coupling function can be solved based on these two constraints to obtain the values of each yield variable in the initial yield matrix in the target coupling function, and the values of each grade variable in the initial grade matrix in the target coupling function, thereby obtaining the target yield matrix and the target grade matrix, and then realizing the adjustment of the actual yield values and actual grade values in the actual experimental data.
[0094] The method in this embodiment constructs a yield parameter matrix and a grade parameter matrix based on actual experimental data. Subsequently, a target coupling function including an initial yield matrix and an initial grade matrix can be constructed based on the yield parameter matrix and the grade parameter matrix. Then, based on the yield balance constraint condition and the full grade balance constraint condition, the initial yield matrix and the initial grade matrix in the target coupling function can be solved, thereby realizing multi-objective collaborative optimization and adjustment, that is, realizing the synchronous optimization and adjustment of the mineral processing multi-metal yield experimental data and the grade experimental data, and ensuring the accuracy of the final adjustment results of the experimental data.
[0095] Based on the above embodiment, another embodiment of the present application provides a method for adjusting experimental data for a multi-metallic mineral processing experiment. In this embodiment, to make the construction of the target coupling function more reasonable and accurate, before constructing the target coupling function, that is, before executing step S2053, the method further includes: pre-constructing a weight matrix based on the weight coefficients of each logistics line; and then constructing the target coupling function based on the weight matrix, the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix.
[0096] That is, for n logistics lines, we can first determine the weight coefficient w of each logistics line, and then construct an n×1 weight matrix RW based on the weight coefficient of each logistics line. Among them, the rows correspond to all logistics lines, and the columns represent the weight value of each logistics line, describing the credibility of all logistics lines in the mineral processing process. The weight matrix can be specifically expressed as follows (9):
[0097]
[0098] In this embodiment, after the weight matrix RW is constructed, the target coupling function can be constructed based on the weight matrix RW, the initial yield matrix RY, the initial grade matrix RG, the yield parameter matrix RY' and the grade parameter matrix RG'. The specific construction process is as follows:
[0099] Step 1: determining a second yield objective function based on the weight matrix, the initial yield matrix, and the yield parameter matrix;
[0100] In this step, the second yield objective function can be expressed as follows (10):
[0101] [RY-RY']T×diag(RW)×[RY-RY'](10)
[0102] Step 2: determining a second full-grade objective function including each metal component based on the weight matrix, the initial grade matrix, and the grade parameter matrix;
[0103] In this step, the grade objective function for the i-th component is first calculated. The second grade objective function for the i-th metal component is obtained from the initial grade matrix RG, the grade parameter matrix RG', and the weight matrix RW. Furthermore, the second overall grade objective function can be determined based on the second grade objective functions for each metal component.
[0104] Among them, the second grade objective function is shown in the following formula (11):
[0105] [RG-RG'].col(i)T×diag(RW)×[RG-RG'].col(i)(11)
[0106] The second full-grade objective function is shown in the following formula (12):
[0107] SUM([RG-RG'].col(i)T×diag(RW)×[RG-RG'].col(i))(12)
[0108] Step 3: Construct the target coupling function based on the second yield target function and the second overall grade target function.
[0109] In this step, after obtaining the second yield objective function and the second overall grade objective function, the target coupling function can be further determined, where the target coupling function is shown in the following formula (13):
[0110] [RY-RY']T×diag(RW)×[RY-RY']+SUM([RG-RG'].col(i)T×diag(RW)×[RG-RG'].col(i))(13)
[0111] In this embodiment, after constructing the target coupling function shown in formula (13), the initial yield matrix and the initial grade matrix in the target coupling function can be further solved in combination with the yield balance constraint condition and the full grade balance constraint condition, so as to obtain the values of each yield variable in the initial yield matrix in the target coupling function and the values of each grade variable in the initial grade matrix in the target coupling function, thereby obtaining the target yield matrix and the target grade matrix, and then realizing the adjustment of each actual yield value and actual grade value in the actual experimental data.
[0112] The method in this embodiment constructs a yield parameter matrix and a grade parameter matrix based on actual experimental data, and constructs a weight matrix for each logistics line. Subsequently, a target coupling function including an initial yield matrix and an initial grade matrix can be constructed based on the yield parameter matrix, the grade parameter matrix and the weight matrix, so that the construction of the target coupling function is reasonable and accurate. Then, based on the yield balance constraint condition and the full grade balance constraint condition, the initial yield matrix and the initial grade matrix in the target coupling function can be solved, thereby realizing multi-objective collaborative optimization and adjustment, that is, realizing the synchronous optimization and adjustment of the mineral processing multi-metal yield experimental data and the grade experimental data, and ensuring the accuracy of the final adjustment result of the experimental data.
[0113] Another embodiment of the present application provides an experimental data adjustment device for a multi-metal ore dressing experiment, such as Figure 2 Shown, including:
[0114] A first construction module 11 is used to construct a yield parameter matrix related to yield and a grade parameter matrix related to grade based on actual experimental data of each logistics line in the mineral processing experiment, wherein the actual experimental data includes actual yield values and actual grade values;
[0115] The second construction module 12 is used to construct an initial yield matrix to be adjusted and an initial grade matrix to be adjusted;
[0116] A third construction module 13 is configured to construct a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix;
[0117] The adjustment module 14 is configured to solve the initial yield matrix and the initial grade matrix based on pre-established constraints and the target coupling function to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
[0118] During the specific implementation of this embodiment, the first construction module is specifically used to: construct the yield parameter matrix based on the actual yield value of each logistics line in the mineral processing experiment; construct the grade parameter matrix based on the actual grade value of each metal component in each logistics line in the mineral processing experiment.
[0119] During the specific implementation of this embodiment, the second construction module is specifically used to: determine the initial yield variables corresponding to each logistics line in the mineral processing experiment, and construct the initial yield matrix based on each of the initial yield variables; determine the initial grade variables corresponding to each metal component in each logistics line based on each metal component in the mineral processing experiment, and construct the initial grade matrix based on each of the initial grade variables.
[0120] During the specific implementation of this embodiment, the third construction module is specifically used to: determine a first yield target function based on the initial yield matrix and the yield parameter matrix; determine a first full-grade target function including various metal components based on the initial grade matrix and the grade parameter matrix; and construct the target coupling function based on the first yield target function and the first full-grade target function.
[0121] During the specific implementation of this embodiment, the experimental data adjustment device of the multi-metal mineral processing experiment also includes: a constraint condition construction module, which is used to: construct a correlation matrix in advance based on the relationship between each equipment node and the logistics line in the mineral processing experiment; construct the constraint conditions based on the correlation matrix, and the constraint conditions include: yield balance constraint conditions and full grade balance constraint conditions.
[0122] In the specific implementation process of this embodiment, the experimental data adjustment device of the multi-metal beneficiation experiment further includes: a weight matrix construction module, the weight matrix construction module is used to: pre-construct a weight matrix based on the weight coefficient of each logistics line;
[0123] The third construction module is specifically used to construct the target coupling function based on the weight matrix, the initial yield matrix, the initial grade matrix, the yield parameter matrix and the grade parameter matrix.
[0124] During the specific implementation of this embodiment, the third construction module is specifically used to: determine the second yield objective function based on the weight matrix, the initial yield matrix and the yield parameter matrix; determine the second full-grade objective function including each metal component based on the weight matrix, the initial grade matrix and the grade parameter matrix; and construct the target coupling function based on the second yield objective function and the second full-grade objective function.
[0125] The experimental data adjustment device for the multi-metal mineral processing experiment in this embodiment constructs a yield parameter matrix and a grade parameter matrix based on actual experimental data. Subsequently, a target coupling function including an initial yield matrix and an initial grade matrix can be constructed based on the yield parameter matrix and the grade parameter matrix. Then, based on the constraint conditions, the initial yield matrix and the initial grade matrix in the target coupling function can be solved, thereby realizing multi-objective collaborative optimization and adjustment, that is, realizing the synchronous optimization and adjustment of the mineral processing multi-metal yield experimental data and the grade experimental data, thereby ensuring the accuracy of the final adjustment results of the experimental data.
[0126] Another embodiment of the present application provides a storage medium storing a computer program. When the computer program is executed by a processor, the following method steps are implemented:
[0127] Step 1: Based on the actual experimental data of each logistics line in the mineral processing experiment, construct a yield parameter matrix about yield and a grade parameter matrix about grade, wherein the actual experimental data includes actual yield values and actual grade values;
[0128] Step 2: Constructing the initial yield matrix to be adjusted and the initial grade matrix to be adjusted;
[0129] Step 3: constructing a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix;
[0130] Step 4: Based on the pre-established constraints and the target coupling function, the initial yield matrix and the initial grade matrix are solved to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
[0131] The specific implementation process of the above method steps can be found in the embodiment of the experimental data adjustment method for any multi-metal mineral processing experiment mentioned above, and this embodiment will not be repeated here.
[0132] The storage medium in the present application constructs a yield parameter matrix and a grade parameter matrix based on actual experimental data. Subsequently, a target coupling function including an initial yield matrix and an initial grade matrix can be constructed based on the yield parameter matrix and the grade parameter matrix. Then, based on the constraints, the initial yield matrix and the initial grade matrix in the target coupling function can be solved, thereby realizing multi-objective collaborative optimization and adjustment, that is, realizing the synchronous optimization and adjustment of the mineral processing multi-metal yield experimental data and the grade experimental data, and ensuring the accuracy of the final adjustment results of the experimental data.
[0133] Another embodiment of the present application provides an electronic device, such as Figure 3 As shown, it at least includes a memory 1 and a processor 2. The memory 1 stores a computer program. When the processor 2 executes the computer program on the memory 1, it implements the following method steps:
[0134] Step 1: Based on the actual experimental data of each logistics line in the mineral processing experiment, construct a yield parameter matrix about yield and a grade parameter matrix about grade, wherein the actual experimental data includes actual yield values and actual grade values;
[0135] Step 2: Constructing the initial yield matrix to be adjusted and the initial grade matrix to be adjusted;
[0136] Step 3: constructing a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix;
[0137] Step 4: Based on the pre-established constraints and the target coupling function, the initial yield matrix and the initial grade matrix are solved to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
[0138] The specific implementation process of the above method steps can be found in the embodiment of the experimental data adjustment method for any multi-metal mineral processing experiment mentioned above, and this embodiment will not be repeated here.
[0139] The electronic device in this embodiment constructs a yield parameter matrix and a grade parameter matrix based on actual experimental data. Subsequently, a target coupling function including an initial yield matrix and an initial grade matrix can be constructed based on the yield parameter matrix and the grade parameter matrix. Then, based on the constraints, the initial yield matrix and the initial grade matrix in the target coupling function can be solved, thereby realizing multi-objective collaborative optimization and adjustment, that is, realizing the synchronous optimization and adjustment of the mineral processing multi-metal yield experimental data and the grade experimental data, thereby ensuring the accuracy of the final adjustment results of the experimental data.
[0140] The above embodiments are merely exemplary embodiments of the present application and are not intended to limit the scope of the present application. The scope of protection of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and scope of protection of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the scope of protection of the present application.
Claims
1. A method for adjusting experimental data of a multi-metal beneficiation experiment, characterized in that: include: Based on the actual experimental data of each logistics line in the mineral processing experiment, a yield parameter matrix about yield and a grade parameter matrix about grade are constructed, wherein the actual experimental data include actual yield values and actual grade values; Constructing an initial yield matrix to be adjusted and an initial grade matrix to be adjusted; constructing a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix; Based on the pre-constructed constraint conditions and the target coupling function, the initial yield matrix and the initial grade matrix are solved to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
2. The method according to claim 1, wherein The method of constructing a yield parameter matrix for yield and a grade parameter matrix for grade based on the actual experimental data of each logistics line in the mineral processing experiment specifically includes: Based on the actual yield values of each logistics line in the mineral processing experiment, the yield parameter matrix is constructed; The grade parameter matrix is constructed based on the actual grade value of each metal component in each logistics line in the mineral processing experiment.
3. The method according to claim 1, wherein The construction of the initial yield matrix to be adjusted and the initial grade matrix to be adjusted specifically includes: Based on each logistics line in the mineral processing experiment, determining an initial yield variable corresponding to each logistics line, and constructing the initial yield matrix based on each of the initial yield variables; Based on each metal component in each logistics line in the mineral processing experiment, the initial grade variable corresponding to each metal component in each logistics line is determined, and the initial grade matrix is constructed based on each of the initial grade variables.
4. The method according to claim 1, wherein The constructing of a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix specifically includes: Determining a first yield objective function based on the initial yield matrix and the yield parameter matrix; Determining a first full-grade target function including each metal component based on the initial grade matrix and the grade parameter matrix; The target coupling function is constructed based on the first yield target function and the first full-grade target function.
5. The method according to any one of claims 1, wherein: Before solving the initial yield matrix and the initial grade matrix based on the pre-established constraint conditions and the target coupling function, the method further includes: Construct a correlation matrix in advance based on the relationship between each equipment node and logistics line in the mineral processing experiment; The constraint conditions are constructed based on the association matrix, and the constraint conditions include: a yield balance constraint condition and an overall grade balance constraint condition.
6. The method according to any one of claims 1 to 5, wherein: Before constructing the target coupling function, the method further includes: Based on the weight coefficients of each logistics line, a weight matrix is pre-built; The constructing of a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix specifically includes: The target coupling function is constructed based on the weight matrix, the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix.
7. The method according to claim 6, wherein The constructing of the target coupling function based on the weight matrix, the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix specifically includes: Determining a second yield objective function based on the weight matrix, the initial yield matrix, and the yield parameter matrix; Determining a second full-grade objective function including each metal component based on the weight matrix, the initial grade matrix, and the grade parameter matrix; The target coupling function is constructed based on the second yield target function and the second overall grade target function.
8. An experimental data adjustment device for multi-metal beneficiation experiments, characterized in that: include: A first construction module is used to construct a yield parameter matrix related to yield and a grade parameter matrix related to grade based on actual experimental data of each logistics line in the mineral processing experiment, wherein the actual experimental data includes actual yield values and actual grade values; The second construction module is used to construct an initial yield matrix to be adjusted and an initial grade matrix to be adjusted; a third construction module, configured to construct a target coupling function based on the initial yield matrix, the initial grade matrix, the yield parameter matrix, and the grade parameter matrix; An adjustment module is used to solve the initial yield matrix and the initial grade matrix based on pre-established constraints and the target coupling function to obtain an adjusted target yield matrix containing target yield values and a target grade matrix containing target grade values.
9. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the method for adjusting experimental data of the multi-metallic mineral processing experiment according to any one of claims 1 to 7 are implemented.
10. An electronic device, characterized in that: The method comprises at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the experimental data adjustment method of the multi-metal beneficiation experiment described in any one of claims 1 to 7 when executing the computer program on the memory.