Copper-based solid waste batching optimization method and device, computer equipment and storage medium

Abstract

The embodiment of the invention discloses a copper-based solid waste batching optimization method and device, computer equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: taking a raw material ratio of the copper-based solid waste as a decision variable; acquiring batching index parameters according to the raw material parameters of the copper-based solid waste; based on an optimization target, establishing a copper-based solid waste batching optimization model according to the decision variables and the batching index parameters; and calculating the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain an optimal batching solution set of the copper-based solid waste. Thus, a real-time, accurate, diversified and better batching scheme is obtained through a multi-objective optimization algorithm, reasonable use of the multi-source copper-based solid waste material is achieved, the production cost of an enterprise is reduced, the production efficiency and the product quality are improved, the production smelting process is stabilized, and intelligent and efficient smelting of the copper-based solid waste is promoted.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method, device, computer equipment and storage medium for optimizing copper-based solid waste batching. Background Art

[0002] There are many constraints in the process of copper-based solid waste smelting, and the raw materials affect each other. It is difficult to find the best ratio through subjective experience. At present, the batching process of copper-based solid waste smelting is mainly calculated manually based on experience. This batching method has great artificial limitations and lacks certain scientificity. In addition, it lacks stability in manual operation and calculation, and is prone to errors. Summary of the invention

[0003] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a copper-based solid waste batching optimization method, device, computer equipment and storage medium for adding batching index parameters to the copper-based solid waste batching optimization model, and optimizing and solving the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain the best batching scheme.

[0004] The present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for optimizing copper-based solid waste batching, the method comprising:

[0006] The raw material ratio of copper-based solid waste is used as a decision variable;

[0007] Obtain batching index parameters according to raw material parameters of copper-based solid waste;

[0008] Based on the optimization goal, a copper-based solid waste batching optimization model is established according to the decision variables and the batching index parameters;

[0009] The copper-based solid waste batching optimization model is calculated based on a multi-objective optimization algorithm to obtain an optimal batching solution set for the copper-based solid waste.

[0010] In one embodiment, the raw material parameters include raw material taste, the batching index parameters include component constraints, and the batching index parameters are obtained according to the raw material parameters of the copper-based solid waste, including:

[0011] Calculate the standard amount of raw materials according to the taste of the raw materials;

[0012] The ingredient constraints are determined according to the standard batch quantities of the raw materials.

[0013] In one embodiment, the raw material parameters include raw material inventory parameters, the batching index parameters include inventory constraints, and the batching index parameters are obtained according to the raw material parameters of the copper-based solid waste, including:

[0014] Acquire current inventory data and safety inventory data according to the raw material inventory parameters;

[0015] Get raw material purchase interval data;

[0016] Acquire the normal consumption time data of raw materials according to the current inventory data, the safety inventory data and the raw material purchase interval time data;

[0017] The inventory constraint is determined according to the raw material purchase interval time data and the raw material normal consumption time data.

[0018] In one embodiment, the batching index parameter includes a slag shape constraint, and the method further includes:

[0019] Get the mass fraction of copper in matte;

[0020] Obtain the mass fractions of copper, iron and silicon dioxide in copper-based solid waste raw materials;

[0021] Obtaining the iron-silicon ratio in the slag according to the mass fraction of copper in the matte and the mass fractions of copper, iron and silicon dioxide in the copper-based solid waste raw material;

[0022] The slag type constraint is determined according to the iron-silicon ratio.

[0023] In one embodiment, the copper-based solid waste batching optimization model is calculated based on a multi-objective optimization algorithm to obtain an optimal batching solution set for the copper-based solid waste, including:

[0024] An initial population is generated by using a good point set method, and the initial population is used as the current population;

[0025] Calculating the objective function value of each individual in the current population, and normalizing each objective function value;

[0026] Selecting a target individual from the current population based on a binary tournament, and performing crossover and mutation operations on the target individual to obtain a descendant population;

[0027] Updating the offspring population based on the information entropy method to obtain a new offspring population;

[0028] Merging the parent population and the new offspring population to obtain a merged population;

[0029] Performing fast non-dominated sorting and crowding calculation on the merged population to determine a plurality of elite individuals in the merged population;

[0030] Determine whether each of the elite individuals meets the production requirements;

[0031] If not, the elite individuals are used as the new current population; and the step of calculating the objective function value of each individual in the current population is re-executed based on the new current population;

[0032] If so, the elite individuals are taken as the optimal batching solution set.

[0033] In one embodiment, the step of selecting a target individual from the current population based on a binary tournament includes:

[0034] Performing fast non-dominated sorting and congestion calculation on the current population to obtain a non-dominated level and multiple congestion degrees;

[0035] Based on the binary tournament, the target individual is selected according to the non-dominated level and each of the crowding degrees.

[0036] In one implementation manner, the updating of the offspring population based on the information entropy method to obtain a new offspring population includes:

[0037] Calculating the information entropy of multiple attributes corresponding to the copper-based solid waste batching optimization model to obtain multiple information entropies, and calculating the attribute weight of each attribute according to each information entropy;

[0038] Calculating the fit degree of each target individual according to the attribute weight and the normalized objective function value;

[0039] Find the worst individual in the current population according to each of the fitness degrees;

[0040] The worst individual is updated according to the individual information exchange mechanism to obtain the new offspring population.

[0041] In a second aspect, the present invention provides a copper-based solid waste batching optimization device, the device comprising:

[0042] A processing module for taking the raw material ratio of copper-based solid waste as a decision variable;

[0043] An acquisition module, used to obtain batching index parameters according to raw material parameters of copper-based solid waste;

[0044] Establishing a module for establishing a copper-based solid waste batching optimization model based on the optimization target, according to the decision variables and the batching index parameters;

[0045] The optimization module is used to calculate the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain the optimal batching solution set of the copper-based solid waste.

[0046] In a third aspect, the present invention proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the copper-based solid waste ingredient optimization method as described in the first aspect is implemented.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the copper-based solid waste batching optimization method as described in the first aspect.

[0048] The copper-based solid waste batching optimization method, device, computer equipment and storage medium disclosed in the present invention use the raw material ratio of copper-based solid waste as a decision variable; obtain batching index parameters according to the raw material parameters of copper-based solid waste; establish a copper-based solid waste batching optimization model based on the optimization target and the decision variables and the batching index parameters; calculate the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain the optimal batching solution set of the copper-based solid waste. In this way, a real-time, accurate, diverse and better batching scheme is obtained through a multi-objective optimization algorithm, which realizes the rational use of multi-source copper-based solid waste materials, reduces the production cost of the enterprise, is conducive to improving production efficiency and product quality, stabilizes the production and smelting process, and promotes the intelligent and efficient smelting of copper-based solid waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope of protection of the present invention. In each of the drawings, similar components are numbered similarly.

[0050] Figure 1 A schematic diagram of a process of optimizing the copper-based solid waste batching method proposed in this embodiment is shown;

[0051] Figure 2 Another schematic diagram of the process of optimizing the copper-based solid waste batching method proposed in this embodiment is shown;

[0052] Figure 3 Another schematic diagram of the process of optimizing the copper-based solid waste batching method proposed in this embodiment is shown;

[0053] Figure 4 A structural schematic diagram of the copper-based solid waste batching optimization device proposed in this embodiment is shown.

[0054] Description of reference numerals:

[0055] 400 - copper-based solid waste batching optimization device; 401 - processing module; 402 - acquisition module; 403 - establishment module; 404 - optimization module. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0057] The components of the embodiments of the present invention generally described and shown in the drawings herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0058] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present invention, are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0059] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0060] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present invention belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present invention.

[0061] Example 1

[0062] The disclosed embodiment provides a copper-based solid waste batching optimization method, which is used to add batching index parameters to a copper-based solid waste batching optimization model, and optimize and solve the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain an optimal batching solution.

[0063] See also Figure 1 The copper-based solid waste batching optimization method includes steps S101 to S104, and each step is described in detail below.

[0064] Step S101, taking the raw material ratio of copper-based solid waste as a decision variable.

[0065] In this embodiment, the raw material ratio (such as mass ratio, volume ratio, etc.) of copper-based solid waste is set as the decision variable of the optimization problem. For example, if the treatment process involves a variety of copper-containing waste materials (such as waste circuit boards, smelting slag, etc.) and other additives (such as reducing agents, flux, etc.), then the amount of each raw material added constitutes a decision variable set.

[0066] Step S102, obtaining batching index parameters according to raw material parameters of copper-based solid waste.

[0067] In this embodiment, each raw material has its own specific physical and chemical properties, such as copper content, impurity composition, particle size, density, etc. These parameters are crucial to determine the appropriate batching scheme. The batching index parameters are calculated or derived based on the raw material parameters to guide the constraints of the batching process for subsequent modeling.

[0068] Step S103, based on the optimization target, a copper-based solid waste batching optimization model is established according to the decision variables and the batching index parameters.

[0069] In this embodiment, the optimization objectives generally include improving the copper recovery rate of the target product, reducing production costs, reducing environmental pollution, etc. The copper-based solid waste batching optimization model is a multi-objective optimization model, which is a framework that integrates multiple optimization objectives into a mathematical model. In this model, the decision variables (raw material ratio) and the batching index parameters are associated through a series of equality or inequality constraints, and each optimization objective is quantified as a corresponding objective function.

[0070] Step S104, calculating the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain an optimal batching solution set for the copper-based solid waste.

[0071] In the present embodiment, since there is often no single optimal solution for multi-objective optimization problems, there is a solution set in which the solution reaches a balance between the various objectives. Therefore, it is necessary to adopt a special multi-objective optimization algorithm to solve, such as non-dominated sorting genetic algorithm II (NSGA-II). These algorithms gradually approach the optimal batching solution set through an iterative search process. The optimal batching solution set represents a possible solution after weighing between different optimization objectives. Therefore, the most suitable copper-based solid waste batching scheme can be selected from the optimal batching solution set according to actual conditions (such as market demand, production cost, environmental regulations, etc.), thereby solving the problems of low efficiency of existing batching methods, large artificial limitations, and failure to comprehensively consider the impact of various factors such as material sources, raw material inventory, production process, quality indicators, etc. on batching, which is conducive to improving product quality, reducing production costs, and stabilizing the production process.

[0072] In a specific embodiment, the raw material parameters include raw material taste, and step S102 includes: calculating the standard raw material dosage according to the raw material taste; and determining the ingredient constraint according to the standard raw material dosage.

[0073] In this embodiment, in the batching process of copper-based solid waste, in order to ensure the copper smelting production efficiency and product quality, the taste of various raw materials of copper-based solid waste is analyzed to obtain the standard batching amount of various raw materials, and then the component constraints are determined according to the standard batching amount of raw materials. For example, if the standard batching amount of raw material A is 50%, then the component content of raw material A in the optimal batching solution cannot be less than 50%. Among them, the raw material taste is the content or purity of the target component in the raw material.

[0074] In a specific embodiment, the raw material parameters include raw material inventory parameters, and step S102 includes: obtaining current inventory data and safety inventory data based on the raw material inventory parameters; obtaining raw material purchase interval data; obtaining normal raw material consumption time data based on the current inventory data, the safety inventory data and the raw material purchase interval data; and determining the inventory constraints based on the raw material purchase interval data and the normal raw material consumption time data.

[0075] In this embodiment, the inventory of each raw material involved in copper-based solid waste batching has a great constraint on the usage of the batching, so the inventory constraint can be established according to the raw material inventory parameters and the purchase time and consumption time of the raw materials. Among them, the current inventory data in the raw material inventory parameters can be the current inventory, and the safety inventory data in the raw material inventory parameters can be the safety inventory of the raw materials. The safety inventory is the minimum inventory set to prevent the production from being affected by the shortage of raw materials. If the current inventory drops below the safety inventory, the procurement process needs to be started; and the procurement time interval refers to the time required from the discovery of insufficient inventory to the replenishment of raw materials. If the raw materials are exhausted during this time period, the production line will be forced to stop production and wait for the arrival of new raw materials; the normal consumption time of raw materials is the length of time from the current time point that the production line can continue to batch without being affected by the shortage of raw materials. This time length should be long enough to ensure that new raw materials can arrive and replenish the inventory before the raw materials are exhausted.

[0076] For example, for the inventory constraints of various raw materials, assume that the total amount of ingredients required per day is W, and the current inventory of the i-th raw material is W i , its safety stock is W j , the purchase interval of this raw material is W t , then the normal consumption time of raw materials is W T Should satisfy W t ≤W T, thus ensuring that the production line has enough buffer time to wait for the arrival of new raw materials before the raw materials are exhausted, thereby ensuring the continuity and stability of the production line and avoiding the risk of production suspension due to raw material shortages. At the same time, the normal consumption time of raw materials W T With the current inventory of W i , the safety stock is W j and purchase interval W t Should satisfy This formula constitutes the inventory constraint.

[0077] In a specific embodiment, the batching index parameters include slag type constraints, and the method further includes: obtaining the mass fraction of copper in matte; obtaining the mass fractions of copper, iron and silicon dioxide in copper-based solid waste raw materials; obtaining the iron-silicon ratio in the slag based on the mass fraction of copper in the matte and the mass fractions of copper, iron and silicon dioxide in the copper-based solid waste raw materials; and determining the slag type constraints based on the iron-silicon ratio.

[0078] In this embodiment, the iron-silicon ratio in the slag can be calculated based on the composition of the copper-based solid waste raw material and the product matte through the principle of conservation of mass and the chemical reaction equation. Specifically, it is necessary to consider the transformation relationship between copper, iron and silicon during the smelting process and their distribution ratio in the slag and matte. Based on the calculated iron-silicon ratio, the slag type constraints of the copper-based solid waste ingredients can be determined. These constraints may include the range requirements of the physical properties of the slag, such as the melting point, viscosity, density, etc., and the content limit of certain harmful elements in the slag.

[0079] Among them, the expression of slag shape constraint is: In the formula, b is the slag type constraint, [Fe] and [SiO2] are the mass fractions of Fe and SiO2 in the slag, respectively, [Cu] is the mass fraction of Cu in matte, and Cu, Fe, and SiO2 are the mass fractions of Cu, Fe, and SiO2 in the copper-based solid waste raw materials.

[0080] See also Figure 2 In a specific embodiment, step S104 includes steps S1041 to S1049, and each step is described in detail below.

[0081] Step S1041, using the good point set method to generate an initial population, and using the initial population as the current population.

[0082] In this embodiment, the good point set method is a mathematical method for generating an initial population (i.e., an initial solution set) to improve the diversity and uniformity of the population. This method covers the solution space by a carefully designed point set, so that the initial population can better represent the entire solution space, thereby increasing the possibility of finding a global optimal solution. Among them, the good point set is determined according to the public notice period (1), formula (1): Where Pn (k) is the set of good points, r is a good point, is the decimal part and n is the number of points.

[0083] Step S1042, calculating the objective function value of each individual in the current population, and normalizing each objective function value.

[0084] In this embodiment, the objective function value of each individual in the current population is calculated and these values ​​are normalized to the same scale. Normalization is an important step in dealing with multi-objective optimization problems because it allows the algorithm to fairly compare the contributions of different objective functions. Among them, the objective function value is normalized according to formula (2), formula (2): In the formula, f i,k is the normalized value of the kth objective function of the ith individual; f i,max is the maximum value of the kth objective function of the ith individual; f i,min is the minimum value of the kth objective function of the ith individual.

[0085] Step S1043, selecting a target individual from the current population based on the binary tournament, and performing crossover and mutation operations on the target individual to obtain a descendant population.

[0086] In this embodiment, a binary tournament selection method is used to select individuals from the current population for crossover (gene recombination) and mutation (random change of genes) operations to generate a new offspring population. This is the core step in the genetic algorithm (GA) to simulate natural selection and genetic mechanisms. Genetic algorithms are used as a framework for multi-objective optimization algorithms such as NSGA-II. Crossover and mutation operations help explore new areas in the solution space and find better solutions.

[0087] Step S1044, updating the offspring population based on the information entropy method to obtain a new offspring population.

[0088] In this embodiment, the information entropy method is a method that uses information entropy theory to evaluate population diversity and guide population updates. It may increase the diversity and search ability of the population by evaluating the diversity of the offspring population and adjusting the individuals in the population accordingly. Maintaining the diversity of the population is an important issue in multi-objective optimization, because there are usually multiple Pareto optimal solutions to multi-objective optimization problems. The information entropy method helps to maintain this diversity during the search process.

[0089] Step S1045, merging the parent population and the new child population to obtain a merged population.

[0090] In this embodiment, the parent population and the new child population are merged, and then the merged population is subjected to a fast non-dominated sort (such as the method in NSGA-II) to determine the individuals on the Pareto frontier. At the same time, the crowding degree of each individual is calculated to evaluate its relative position on the Pareto frontier.

[0091] Step S1046, performing fast non-dominated sorting and crowding calculation on the merged population to determine a plurality of elite individuals in the merged population.

[0092] Step S1047, determining whether each of the elite individuals meets the production requirements.

[0093] Step S1048, taking the elite individuals as the new current population; and re-executing the step of calculating the objective function value of each individual in the current population based on the new current population.

[0094] Step S1049, taking the elite individuals as the optimal batching solution set.

[0095] In this embodiment, it is checked whether the currently found elite individuals meet the specific production requirements or optimization goals. If not, these optimal individuals are used as the new current population, and the optimization process is repeated in step S102; if they are satisfied, these optimal individuals are output as the final optimal batching solution set.

[0096] In a specific embodiment, step S1043 includes: performing fast non-dominated sorting and crowding calculation on the current population to obtain a non-dominated level and multiple crowdings; based on the binary tournament, selecting the target individual according to the non-dominated level and each crowding.

[0097] In this embodiment, the current population is fast non-dominated sorting and crowding calculation is performed, and in each iteration, the target individual is selected for genetic operation through binary tournament selection. When comparing two individuals, their non-dominated levels are first compared. If the levels are different, the individual with the lower level is selected as the target individual; if the levels are the same, their crowding is compared, and the individual with the higher crowding is selected as the target individual.

[0098] See also Figure 3 In a specific embodiment, step S1044 includes steps S301 to S303, and each step is described in detail below.

[0099] Step S301, calculating the information entropy of multiple attributes corresponding to the copper-based solid waste batching optimization model to obtain multiple information entropies, and calculating the attribute weight of each attribute according to each information entropy.

[0100] In this embodiment, based on the information entropy of each attribute, the weight of each attribute can be further calculated. The weight represents the relative importance of each attribute in the decision-making or optimization process. Generally, attributes with smaller information entropy are assigned higher weights because they contribute more to the final decision.

[0101] Specifically, according to formula (3): To calculate the information entropy of each attribute, where W k is the information entropy of the kth attribute; according to formula (4): Calculate the attribute weight of each attribute, where τ k is the weight of the kth attribute.

[0102] Step S302, calculating the degree of fit of each target individual according to the attribute weight and the normalized objective function value.

[0103] In this embodiment, the fit is an indicator that measures the degree of proximity between a target individual (i.e., a potential solution in a population) and an ideal solution or an optimization target. When calculating the fit, the normalized objective function value is first weighted according to the attribute weight (if the objective function is multidimensional, each dimension needs to be weighted). Finally, the fit is calculated by weighted sum or weighted product. The fit can be calculated according to formula (5).

[0104] Formula (5): In the formula, f i,k+ is the maximum value of all objective functions of target individual i after normalization, f i,k- is the minimum value of all objective functions of target individual i after normalization, C i is the fit of target individual i, C i The larger the value of is, the higher the comprehensive satisfaction of the attribute solution is.

[0105] Step S303, finding the worst individual in the current population according to each of the fits; updating the worst individual according to the individual information exchange mechanism to obtain the new offspring population.

[0106] In this embodiment, based on the calculated degree of fit, an individual with the lowest degree of fit (ie, the worst performance) can be found in the current population.

[0107] Furthermore, the worst individual is updated according to formula (6), thereby improving the fitness of the entire population. Formula (6): X = X w +r·(X b -X w ), where X is the updated individual, X b is the best individual in the current population; X Wis the worst individual in the current population; r is a random number between [0-1].

[0108] After the above update, the worst individual is replaced by a new individual to form a new offspring population. This population will serve as the starting point for the next round of iterations and continue the optimization process until a certain stopping condition is met (such as reaching a preset number of iterations, achieving satisfactory solution quality, etc.).

[0109] The copper-based solid waste batching optimization method proposed in this embodiment uses the raw material ratio of copper-based solid waste as a decision variable; obtains batching index parameters according to the raw material parameters of copper-based solid waste; establishes a copper-based solid waste batching optimization model based on the optimization target and the decision variables and the batching index parameters; calculates the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain the optimal batching solution set of the copper-based solid waste. In this way, a real-time, accurate, diverse and better batching scheme is obtained through a multi-objective optimization algorithm, which realizes the rational use of multi-source copper-based solid waste materials, reduces the production cost of enterprises, is conducive to improving production efficiency and product quality, stabilizes the production and smelting process, and promotes the intelligent and efficient smelting of copper-based solid waste.

[0110] Example 2

[0111] In addition, the present disclosure provides a copper-based solid waste batching optimization device 400, see Figure 4 , the device comprises:

[0112] Processing module 401, for taking the raw material ratio of copper-based solid waste as a decision variable;

[0113] An acquisition module 402 is used to acquire batching index parameters according to raw material parameters of copper-based solid waste;

[0114] Establishing module 403, for establishing a copper-based solid waste batching optimization model based on the optimization target, according to the decision variables and the batching index parameters;

[0115] The optimization module 404 is used to calculate the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain an optimal batching solution set for the copper-based solid waste.

[0116] Optionally, the raw material parameters include raw material taste, the ingredient index parameters include ingredient constraints, and the acquisition module 402 is used to calculate the standard ingredient quantity of the raw material according to the raw material taste; and determine the ingredient constraints according to the standard ingredient quantity of the raw material.

[0117] Optionally, the raw material parameters include raw material inventory parameters, and an acquisition module 402 is used to acquire current inventory data and safety inventory data based on the raw material inventory parameters; acquire raw material procurement interval data; acquire raw material normal consumption time data based on the current inventory data, the safety inventory data and the raw material procurement interval data; and determine the inventory constraints based on the raw material procurement interval data and the raw material normal consumption time data.

[0118] Optionally, the batching index parameters include slag type constraints, and an acquisition module 402 is used to obtain the mass fraction of copper in matte; obtain the mass fractions of copper, iron and silicon dioxide in copper-based solid waste raw materials; obtain the iron-silicon ratio in the slag based on the mass fraction of copper in the matte, and the mass fractions of copper, iron and silicon dioxide in the copper-based solid waste raw materials; determine the slag type constraints based on the iron-silicon ratio.

[0119] Optionally, the optimization module 404 is used to generate an initial population using a good point set method, and use the initial population as the current population; calculate the objective function value of each individual in the current population, and normalize each objective function value; select a target individual in the current population based on a binary tournament, and perform crossover and mutation operations on the target individual to obtain a child population; update the child population based on an information entropy method to obtain a new child population; merge the parent population and the new child population to obtain a merged population; perform fast non-dominated sorting and crowding calculation on the merged population to determine multiple elite individuals in the merged population; determine whether each of the elite individuals meets the production requirements; if not, use the elite individual as the new current population; and re-execute the step of calculating the objective function value of each individual in the current population based on the new current population; if so, use the elite individual as the optimal batching solution set.

[0120] Optionally, the optimization module 404 is used to perform fast non-dominated sorting and crowding calculation on the current population to obtain a non-dominated level and multiple crowding levels; based on the binary tournament, select the target individual according to the non-dominated level and each crowding level.

[0121] Optionally, the optimization module 404 is used to calculate the information entropy of multiple attributes corresponding to the copper-based solid waste ingredient optimization model to obtain multiple information entropies, and calculate the attribute weight of each attribute based on the information entropy; calculate the fit of each target individual based on the attribute weight and the normalized objective function value; find the worst individual in the current population based on each fit; update the worst individual according to the individual information exchange mechanism to obtain the new offspring population.

[0122] The device provided in the embodiment of the present disclosure can execute the steps of the copper-based solid waste batching optimization method provided in Example 1, which will not be described again to avoid repetition.

[0123] The copper-based solid waste batching optimization device proposed in this embodiment uses the raw material ratio of copper-based solid waste as a decision variable; obtains batching index parameters according to the raw material parameters of copper-based solid waste; establishes a copper-based solid waste batching optimization model based on the optimization target and the decision variables and the batching index parameters; calculates the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain the optimal batching solution set of the copper-based solid waste. In this way, a real-time, accurate, diverse and better batching scheme is obtained through a multi-objective optimization algorithm, which realizes the rational use of multi-source copper-based solid waste materials, reduces the production cost of enterprises, is conducive to improving production efficiency and product quality, stabilizes the production and smelting process, and promotes the intelligent and efficient smelting of copper-based solid waste.

[0124] Example 3

[0125] In addition, an embodiment of the present disclosure provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the copper-based solid waste ingredient optimization method described in Example 1 is implemented.

[0126] The equipment provided in the embodiment of the present disclosure can execute the steps of the copper-based solid waste batching optimization method provided in Example 1, which will not be described again to avoid repetition.

[0127] Example 4

[0128] The disclosed embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the copper-based solid waste batching optimization method described in Embodiment 1.

[0129] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0130] The computer-readable storage medium provided in this embodiment can implement the copper-based solid waste batching optimization method provided in Example 1, and will not be described again here to avoid repetition.

[0131] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limiting, and thus other examples of the exemplary embodiments may have different values.

[0132] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0133] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for optimizing copper-based solid waste ingredients, characterized in that: The method comprises: The raw material ratio of copper-based solid waste is used as a decision variable; Obtain batching index parameters according to raw material parameters of copper-based solid waste; Based on the optimization goal, a copper-based solid waste batching optimization model is established according to the decision variables and the batching index parameters; The copper-based solid waste batching optimization model is calculated based on a multi-objective optimization algorithm to obtain an optimal batching solution set for the copper-based solid waste.

2. The copper-based solid waste batching optimization method according to claim 1, characterized in that: The raw material parameters include raw material taste, the batching index parameters include component constraints, and the batching index parameters are obtained according to the raw material parameters of the copper-based solid waste, including: Calculate the standard amount of raw materials according to the taste of the raw materials; The ingredient constraints are determined according to the standard batch quantities of the raw materials.

3. The copper-based solid waste batching optimization method according to claim 1, characterized in that: The raw material parameters include raw material inventory parameters, the batching index parameters include inventory constraints, and the batching index parameters are obtained according to the raw material parameters of the copper-based solid waste, including: Acquire current inventory data and safety inventory data according to the raw material inventory parameters; Get raw material purchase interval data; Acquire the normal consumption time data of raw materials according to the current inventory data, the safety inventory data and the raw material purchase interval time data; The inventory constraint is determined according to the raw material purchase interval time data and the raw material normal consumption time data.

4. The copper-based solid waste batching optimization method according to claim 1, characterized in that: The batching index parameter includes a slag shape constraint, and the method further includes: Get the mass fraction of copper in matte; Obtain the mass fractions of copper, iron and silicon dioxide in copper-based solid waste raw materials; Obtaining the iron-silicon ratio in the slag according to the mass fraction of copper in the matte and the mass fractions of copper, iron and silicon dioxide in the copper-based solid waste raw material; The slag type constraint is determined according to the iron-silicon ratio.

5. The copper-based solid waste batching optimization method according to claim 1, characterized in that: The method of calculating the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain an optimal batching solution set for the copper-based solid waste includes: An initial population is generated by using a good point set method, and the initial population is used as the current population; Calculating the objective function value of each individual in the current population, and normalizing each objective function value; Selecting a target individual from the current population based on a binary tournament, and performing crossover and mutation operations on the target individual to obtain a descendant population; Updating the offspring population based on the information entropy method to obtain a new offspring population; Merging the parent population and the new offspring population to obtain a merged population; Performing fast non-dominated sorting and crowding calculation on the merged population to determine a plurality of elite individuals in the merged population; Determine whether each of the elite individuals meets the production requirements; If not, the elite individuals are used as the new current population; and the step of calculating the objective function value of each individual in the current population is re-executed based on the new current population; If so, the elite individuals are taken as the optimal batching solution set.

6. The copper-based solid waste batching optimization method according to claim 5, characterized in that: The method of selecting a target individual from the current population based on a binary tournament includes: Performing fast non-dominated sorting and congestion calculation on the current population to obtain a non-dominated level and multiple congestion degrees; Based on the binary tournament, the target individual is selected according to the non-dominated level and each of the crowding degrees.

7. The copper-based solid waste batching optimization method according to claim 5, characterized in that: The updating of the offspring population based on the information entropy method to obtain a new offspring population includes: Calculating the information entropy of multiple attributes corresponding to the copper-based solid waste batching optimization model to obtain multiple information entropies, and calculating the attribute weight of each attribute according to each information entropy; Calculating the fit degree of each target individual according to the attribute weight and the normalized objective function value; Find the worst individual in the current population according to each of the fitness degrees; The worst individual is updated according to the individual information exchange mechanism to obtain the new offspring population.

8. A copper-based solid waste batching optimization device, characterized in that: The device comprises: A processing module for taking the raw material ratio of copper-based solid waste as a decision variable; An acquisition module, used to obtain batching index parameters according to raw material parameters of copper-based solid waste; Establishing a module for establishing a copper-based solid waste batching optimization model based on the optimization target, according to the decision variables and the batching index parameters; The optimization module is used to calculate the copper-based solid waste batching optimization model based on a multi-objective optimization algorithm to obtain the optimal batching solution set of the copper-based solid waste.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the copper-based solid waste batching optimization method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the copper-based solid waste batching optimization method as described in any one of claims 1 to 7.

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