Oxidation condition optimization method and device for extracting zinc oxide from zinc-containing waste residues

Abstract

The invention relates to an artificial intelligence technology, and discloses an oxidation condition optimization method and device for extracting zinc oxide from zinc-containing waste residue, and the method comprises the following steps: obtaining oxidation experiment data of extracting zinc oxide from zinc-containing waste residue, extracting key parameters and experiment efficiency from the oxidation experiment data, and constructing a state space according to the key parameters; performing parameter influence quantization on the key parameters by using experimental efficiency to obtain a quantization result, and constructing an action space according to the quantization result; constructing a reward function by using the state space and the action space, and generating an oxidation condition adaptive model by using the reward function, the state space and the action space; and deploying the oxidation condition self-adaptive model to a preset actual zinc oxide extraction process, and performing self-adaptive adjustment of oxidation parameters in the actual zinc oxide extraction process by using the oxidation condition self-adaptive model to obtain an optimal oxidation effect. The problem that an existing zinc oxide extraction method is low in zinc oxide extraction efficiency can be solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and device for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag. Background Art

[0002] As a multifunctional inorganic material, zinc oxide plays a vital role in many industrial fields such as rubber manufacturing, paint, ceramic production and pharmaceutical products. With the increase of global industrial activities, the demand for high-quality zinc oxide is also growing. In order to meet this demand and realize the recycling of resources, the technology of extracting zinc oxide from various zinc-containing waste residues has emerged, which not only helps to reduce environmental pollution, but also effectively recycles valuable resources.

[0003] However, zinc-containing waste residues come from various sources, and their chemical composition and physical properties vary, such as zinc content, impurity types and proportions, which significantly affect the efficiency of the oxidation process and the quality of the product. Traditional extraction methods often use fixed oxidation conditions, which are not effective when dealing with different types of waste residues because they fail to fully consider the differences in waste residue characteristics, resulting in low zinc oxide extraction rates and purity, high energy consumption, and limited economic benefits. In summary, the existing zinc oxide extraction methods still have the problem of low zinc oxide extraction efficiency. Summary of the invention

[0004] The present invention provides a method, a device and a computer-readable storage medium for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag, the main purpose of which is to solve the problem that the existing zinc oxide extraction method still has low zinc oxide extraction efficiency.

[0005] To achieve the above object, the present invention provides a method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag, comprising:

[0006] Obtaining oxidation experimental data for extracting zinc oxide from zinc-containing waste slag, extracting key parameters and experimental efficiency from the oxidation experimental data, and constructing a state space according to the key parameters;

[0007] quantifying the parameter influence of the key parameters by using the experimental efficiency to obtain a quantification result, and constructing an action space according to the quantification result;

[0008] constructing a reward function using the state space and the action space, and generating an oxidation condition adaptive model using the reward function, the state space, and the action space;

[0009] The oxidation condition adaptive model is deployed in a preset actual zinc oxide extraction process, and the oxidation condition adaptive model is used to adaptively adjust the oxidation parameters of the actual zinc oxide extraction process to obtain the optimal oxidation effect.

[0010] Optionally, constructing a state space according to the key parameters includes:

[0011] generating a parameter range according to the key parameters;

[0012] generating a state vector based on the parameter range and the key parameter;

[0013] Generating a conversion rule for the state vector to obtain a transfer function;

[0014] A state space is generated according to the transfer function and the state vector.

[0015] Optionally, the using the experimental efficiency to quantify the parameter influence on the key parameter to obtain a quantified result includes:

[0016] Performing sensitivity analysis on the key parameters using the experimental efficiency to obtain sensitivity indicators;

[0017] Taking the sensitivity index as the influence coefficient corresponding to the key parameter, normalizing the influence coefficient to obtain a weight coefficient;

[0018] The weight coefficients are aggregated to obtain quantitative results.

[0019] Optionally, the utilizing the experimental efficiency to perform sensitivity analysis on the key parameters to obtain a sensitivity index includes:

[0020] constructing an efficiency function according to the experimental efficiency;

[0021] Calculating average parameters of the key parameters;

[0022] Using the efficiency function and the average parameter to perform single parameter change to obtain a change result;

[0023] The sensitivity index of the key parameter is calculated using the change result.

[0024] Optionally, constructing an action space according to the quantization result includes:

[0025] generating an action list according to the key parameters and the quantification results;

[0026] Performing action mapping between the action list and the states in the state space to obtain an action space set;

[0027] Action encoding is performed on the action space set to obtain an action space.

[0028] Optionally, constructing a reward function using the state space and the action space includes:

[0029] Quantitatively represent each state in the state space to obtain a quantitative index;

[0030] Performing an influence measurement on each action in the action space to obtain an action influence degree;

[0031] A reward function is generated according to the quantitative indicator and the action impact.

[0032] Optionally, the generating the oxidation condition adaptive model by using the reward function, the state space and the action space includes:

[0033] generating an action selection model according to the state space and the action space;

[0034] Creating a revisit buffer corresponding to the action selection model;

[0035] Performing network replication on the network structure and weights of the action selection model to obtain a target value calculation model;

[0036] Calculating the target value in the revisit buffer area using the target value calculation model;

[0037] The target value is used to update the network weights of the action selection model to obtain an oxidation condition adaptive model.

[0038] Optionally, extracting key parameters and experimental efficiency from the oxidation experimental data includes:

[0039] Calculating the covariance matrix of the oxidation experimental data;

[0040] Performing eigendecomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0041] Filtering out the principal component from the eigenvector according to the size of the eigenvalue;

[0042] Projecting the principal components onto the experimental data to obtain key data;

[0043] The experimental efficiency corresponding to the key data is extracted from the oxidation experimental data.

[0044] Optionally, the step of using the oxidation condition adaptive model to adaptively adjust oxidation parameters in an actual zinc oxide extraction process includes:

[0045] Acquiring key oxidation parameters obtained through monitoring, and inputting the key oxidation parameters into the oxidation condition adaptive model;

[0046] Using the oxidation condition adaptive model to output the current optimal oxidation parameters to adjust key parameters;

[0047] A parameter adjustment strategy is generated based on the oxidation parameter adjustment of key parameters.

[0048] In order to solve the above problems, the present invention also provides an oxidation condition optimization device for extracting zinc oxide from zinc-containing waste slag, the device comprising:

[0049] A data extraction module is used to obtain oxidation experimental data of extracting zinc oxide from zinc-containing waste slag, extract key parameters and experimental efficiency from the oxidation experimental data, and construct a state space according to the key parameters;

[0050] An action space generation module, used to quantify the parameter influence of the key parameters by using the experimental efficiency, obtain a quantification result, and construct an action space according to the quantification result;

[0051] An oxidation model construction module, used to construct a reward function using the state space and the action space, and generate an oxidation condition adaptive model using the reward function, the state space and the action space;

[0052] The oxidation strategy generation module is used to deploy the oxidation condition adaptive model to a preset actual zinc oxide extraction process, and use the oxidation condition adaptive model to adaptively adjust the oxidation parameters of the actual zinc oxide extraction process to obtain the optimal oxidation effect.

[0053] The present invention identifies the key parameters that affect the extraction efficiency of zinc oxide by analyzing the oxidation experimental data, and constructs a state space using the key parameters, so that it can respond under actual changing conditions, avoiding the limitations of traditional fixed parameter control; by quantifying the sensitivity results of the key parameters, the influence of different parameter combinations on the extraction of zinc oxide can be understood, so as to explore different operating conditions to find the best parameter combination to achieve the highest extraction efficiency; by constructing a reward function and generating an oxidation condition adaptive model, the best decision can be made under various conditions, and even in the face of new or unforeseen situations, the parameters can be automatically adjusted to achieve or approach the optimal oxidation effect; by using an adaptive model to dynamically adjust the oxidation parameters, the changes in the production environment can be responded to in real time, and the efficiency of zinc oxide extraction can be improved. Therefore, the oxidation condition optimization method and device for extracting zinc oxide from zinc-containing waste slag proposed by the present invention can solve the problem that the existing zinc oxide extraction method still has low zinc oxide extraction efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic flow chart of a method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag provided in one embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a sensitivity analysis process provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of a process for constructing an adaptive model of oxidation conditions provided in one embodiment of the present invention;

[0057] Figure 4 A functional module diagram of an oxidation condition optimization device for extracting zinc oxide from zinc-containing waste slag provided in one embodiment of the present invention.

[0058] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0060] Reference Figure 1 FIG. 1 is a flow chart of a method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag provided in one embodiment of the present invention. In this embodiment, the method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag includes:

[0061] S1. Obtain oxidation experimental data for extracting zinc oxide from zinc-containing waste slag, extract key parameters and experimental efficiency from the oxidation experimental data, and construct a state space according to the key parameters.

[0062] In the embodiment of the present invention, the oxidation experimental data refers to the measured values ​​and observation results in the reaction process of extracting zinc oxide from zinc-containing waste slag, including data such as temperature, pressure, and zinc concentration in the initial waste slag.

[0063] In the embodiment of the present invention, by analyzing the experimental efficiency, it is possible to identify which parameter combinations are most conducive to improving the yield and purity of zinc oxide, thereby identifying the key parameters that have the greatest impact on the extraction of zinc oxide.

[0064] In the embodiment of the present invention, extracting key parameters and experimental efficiency from the oxidation experimental data includes:

[0065] Calculating the covariance matrix of the oxidation experimental data;

[0066] Performing eigendecomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0067] Filtering out the principal component from the eigenvector according to the size of the eigenvalue;

[0068] Projecting the principal components onto the experimental data to obtain key data;

[0069] The experimental efficiency corresponding to the key data is extracted from the oxidation experimental data.

[0070] In an embodiment of the present invention, by calculating the average value corresponding to each parameter in the oxidation experiment data, covariance calculation is performed on any two parameters to obtain covariance values, and the covariance values ​​are placed in the same matrix to obtain a covariance matrix, wherein the elements on the diagonal are the variances of each variable, and the off-diagonal elements are the covariances between the variables.

[0071] In an embodiment of the present invention, a characteristic equation is constructed based on the covariance matrix, and the characteristic equation is solved as a polynomial equation to obtain the roots of the characteristic equation, that is, the eigenvalues; the characteristic equation is solved based on the eigenvalues ​​to obtain a non-zero vector, and the non-zero vector is normalized so that the non-zero vector is a unit vector to obtain the eigenvector.

[0072] In the embodiment of the present invention, the eigenvectors are sorted in descending order according to the size of the eigenvalues ​​to obtain a sorted sequence, and each eigenvalue is divided by the sum of all eigenvalues ​​to obtain the variance ratio explained by each eigenvalue. Then, the variance ratios are accumulated until a predetermined threshold is reached, and the number of eigenvalues ​​required to reach the threshold is screened out, and the eigenvector corresponding to the eigenvalue is the principal component.

[0073] In an embodiment of the present invention, the screened principal components are used to form a new matrix to obtain a principal component matrix, wherein each column is a principal component, and the principal component matrix is ​​transposed so that the rows represent the principal components and the columns represent the dimensions of the original data. The data in the oxidation experimental data is used to construct an original data matrix (each row is an observation value and each column is a variable), and the original data matrix is ​​multiplied by the transposed principal component matrix to obtain key data.

[0074] In the embodiment of the present invention, constructing the state space according to the key parameters includes:

[0075] generating a parameter range according to the key parameters;

[0076] generating a state vector based on the parameter range and the key parameter;

[0077] Generating a conversion rule for the state vector to obtain a transfer function;

[0078] A state space is generated according to the transfer function and the state vector.

[0079] In an embodiment of the present invention, the parameter values ​​of the key parameters are divided based on the parameter range, and a number of discrete intervals are divided, each interval represents a discrete parameter level, and a discrete parameter is obtained, and all elements in two or more sets are paired to form a state vector. For example, the temperature range of 100°C to 300°C is divided into 10 equally spaced intervals, and each interval represents a discrete temperature level, and then the temperature level and other parameter levels are paired in pairs to obtain a state vector, and the state vector can be (200°C, 2 atmospheres, 5% zinc concentration).

[0080] In an embodiment of the present invention, a kinetic equation of the actual change of the state during the zinc oxide extraction process is established based on the state vector, and a function is constructed based on the kinetic equation, which is the transfer function, wherein the function accepts the current state and parameter changes as input, and outputs the probability corresponding to the state at the next moment.

[0081] In the embodiment of the present invention, for each state vector in the state set, a transfer function is applied to calculate the next possible state, and a matrix is ​​created, in which the rows correspond to the current state and the columns represent the probability or possibility of the next possible state. Each state can be transferred to multiple states with different probabilities, and the final state space includes all possible states and the transfer relations between states.

[0082] In the embodiments of the present invention, the experimental data can be used to accurately identify which parameters have a significant impact on the extraction efficiency of zinc oxide, which helps to find the best combination of process conditions, thereby improving the extraction rate of zinc oxide.

[0083] S2. quantify the parameter influence of the key parameters by using the experimental efficiency to obtain a quantification result, and construct an action space according to the quantification result.

[0084] In the embodiments of the present invention, by quantifying the influence of parameters, it is possible to identify which parameters have the most significant influence on the zinc oxide extraction efficiency, understand the influence degree of each parameter, and improve the efficiency of optimizing the zinc oxide oxidation conditions.

[0085] In the embodiment of the present invention, the step of using the experimental efficiency to quantify the parameter influence on the key parameter to obtain a quantified result includes:

[0086] Performing sensitivity analysis on the key parameters using the experimental efficiency to obtain sensitivity indicators;

[0087] Taking the sensitivity index as the influence coefficient corresponding to the key parameter, normalizing the influence coefficient to obtain a weight coefficient;

[0088] The weight coefficients are aggregated to obtain quantitative results.

[0089] In the embodiment of the present invention, the average value and standard deviation of all influence coefficients are calculated, and then the difference between each influence coefficient and the average value is divided by the standard deviation to obtain the weight coefficient.

[0090] In the embodiment of the present invention, all weight coefficients are organized into a structured format, such as a table or a matrix. Each row represents a key parameter, and the columns include parameter name, sensitivity index, weight coefficient, etc.

[0091] Ginseng Figure 2 As shown, the sensitivity analysis of the key parameters using the experimental efficiency is performed to obtain sensitivity indicators, including:

[0092] S21, constructing an efficiency function according to the experimental efficiency;

[0093] S22, calculating the average parameter of the key parameter;

[0094] S23, using the efficiency function and the average parameter to perform a single parameter change to obtain a change result;

[0095] S24. Calculate the sensitivity index of the key parameter using the change result.

[0096] In detail, based on experimental data or theoretical models, a function is constructed that describes how the experimental efficiency changes with changes in key parameters.

[0097] Specifically, for each key parameter, the average value of all experimental data points is calculated, and the average value is taken as the average parameter of the parameter.

[0098] In detail, a key parameter is selected, other parameters are kept unchanged, and based on the average parameter, the average parameter is increased or decreased by a preset ratio based on the parameter range corresponding to the key parameter, and the experimental efficiency changes under different parameter values ​​are calculated.

[0099] In detail, the sensitivity index can be calculated using the following formula:

[0100]

[0101] Among them, LSC i It refers to the sensitivity index corresponding to the key parameter ι, ΔE is the change in experimental efficiency, and Δx i is the change of the key parameter i.

[0102] In the embodiment of the present invention, constructing the action space according to the quantization result includes:

[0103] generating an action list according to the key parameters and the quantification results;

[0104] Performing action mapping between the action list and the states in the state space to obtain an action space set;

[0105] Action encoding is performed on the action space set to obtain an action space.

[0106] In an embodiment of the present invention, based on key parameters and their quantification results, a series of adjustment ranges reflecting the key parameters are defined, and a set of possible actions are defined for each key parameter, wherein the action adjustment amplitude is small for a quantification result with a large impact, and the action adjustment amplitude is large for a quantification result with a small impact. For example, if temperature is one of the key parameters and the weight coefficient of temperature is 0.5, the adjustment range of temperature is defined as increasing by 1%, remaining unchanged, decreasing by 1%, etc., then possible actions may include "increasing temperature by 1%", "keeping temperature unchanged", "decreasing temperature by 1%", and if pressure is one of the key parameters and the weight coefficient of temperature is 0.03, possible actions may include "increasing pressure by 10%", "keeping pressure unchanged", "decreasing pressure by 10%".

[0107] In an embodiment of the present invention, for each state vector in the state space, all possible actions are listed, and the state space is generated according to the actions and the state vector. For example, for the state vector (200°C, 1.5atm), possible actions include: A1: "increase temperature by 5%", A2: "keep temperature unchanged", A3: "reduce temperature by 5%".

[0108] In the embodiment of the present invention, each action in the action list is encoded, and numbers or other identifiers can be used to represent different actions. For example, if there are three actions in the action list: "Increase temperature by 5%", "Keep temperature unchanged", and "Lower temperature by 5%", the actions can be represented by 1, 2, and 3 respectively.

[0109] In the embodiment of the present invention, through quantitative analysis, the specific influence of each parameter on the zinc oxide extraction efficiency can be clearly understood, so that different parameters can be quickly and accurately adjusted respectively to achieve the best extraction efficiency.

[0110] S3. Construct a reward function using the state space and the action space, and generate an oxidation condition adaptive model using the reward function, the state space and the action space.

[0111] In the embodiment of the present invention, by constructing an oxidation condition adaptive model, it is possible to quickly find highly efficient oxidation conditions, avoid invalid parameter combinations, and achieve efficient optimization of oxidation conditions.

[0112] In the embodiment of the present invention, constructing a reward function using the state space and the action space includes:

[0113] Quantitatively represent each state in the state space to obtain a quantitative index;

[0114] Performing an influence measurement on each action in the action space to obtain an action influence degree;

[0115] A reward function is generated according to the quantitative indicator and the action impact.

[0116] In the embodiment of the present invention, each parameter value in the state space is multiplied by its corresponding weight coefficient by weighted summation, and then summed to obtain a comprehensive quantitative index, and the value in the state vector is converted into a quantitative index that can reflect the zinc oxide extraction efficiency.

[0117] In the embodiment of the present invention, this is achieved by comparing the quantitative index difference before and after the action is executed, and the action impact is evaluated based on the quantitative index difference.

[0118] In an embodiment of the present invention, a preliminary reward function is generated based on the quantitative index of the state and the influence of the action. The preliminary reward function accepts the quantitative index of the state and the influence of the action as input, outputs a reward value, and the preliminary reward function is adjusted using the oxidation experiment data to obtain a reward function.

[0119] Ginseng Figure 3 As shown, the use of the reward function, the state space, and the action space to generate an oxidation condition adaptive model includes:

[0120] S31, generating an action selection model according to the state space and the action space;

[0121] S32, creating a revisit buffer corresponding to the action selection model;

[0122] S33, performing network replication on the network structure and weights of the action selection model to obtain a target value calculation model;

[0123] S34, calculating the target value in the revisit buffer area using the target value calculation model;

[0124] S35. Using the target value, update the network weight of the action selection model to obtain an oxidation condition adaptive model.

[0125] In an embodiment of the present invention, an initial neural network is generated according to the state space and the action space, and the weight parameters in the initial neural network are randomly initialized to obtain a neural network. Then, a preset loss function is used to calculate the difference between the output value of the neural network and the target value, and the gradient of the difference relative to the corresponding weight of the neural network is used to update the weight of the neural network, and finally an action selection model is obtained. Among them, the initial neural network includes an input layer, an output layer, and an intermediate layer. The input layer receives a quantitative representation of the state space, the output layer corresponds to each action in the action space, and the intermediate layer may include multiple hidden layers for feature extraction and abstraction; the target value is the expected return value calculated based on the reward function according to the current state and action.

[0126] In the embodiment of the present invention, a corresponding four-tuple (state, action, reward, next state) is generated according to the result output by the action selection model, and the four-tuple data is stored to obtain a revisit buffer area.

[0127] In an embodiment of the present invention, a network structure identical to the action selection model is created to obtain a target value calculation model, wherein the structure of the network is the same as the action selection model, but its weights are fixed. The target network is used to calculate the target value, and the action selection model is used to predict the target value. The weights of the two networks are periodically synchronized during the training process.

[0128] In the embodiment of the present invention, the weight of the action selection model is updated using the difference between the target value and the target value predicted by the action selection model. The difference between the predicted target value and the target value is calculated, and the difference is quantified using a loss function, and the gradient of the loss relative to the network weight is calculated by a back propagation algorithm. After multiple iterations of training, the action selection model can adaptively select the optimal action according to the current state, thereby forming an oxidation condition adaptive model.

[0129] In the embodiment of the present invention, the oxidation condition adaptive model can quickly find the optimal oxidation condition through the guidance of the reward function, thereby being able to maintain high-efficiency oxidation conditions in different environments.

[0130] S4. Deploy the oxidation condition adaptive model to a preset actual zinc oxide extraction process, and use the oxidation condition adaptive model to adaptively adjust oxidation parameters of the actual zinc oxide extraction process to obtain an optimal oxidation effect.

[0131] In the embodiment of the present invention, the oxidation condition adaptive model can monitor and adjust the key parameters in the oxidation process in real time. By applying the oxidation condition adaptive model to the actual zinc oxide extraction process, it can be ensured in real time that the reaction is carried out under the optimal oxidation conditions, thereby improving the yield and purity of zinc oxide.

[0132] In the embodiment of the present invention, the method of using the oxidation condition adaptive model to adaptively adjust the oxidation parameters of the actual zinc oxide extraction process includes:

[0133] Acquiring key oxidation parameters obtained through monitoring, and inputting the key oxidation parameters into the oxidation condition adaptive model;

[0134] Using the oxidation condition adaptive model to output the current optimal oxidation parameters to adjust key parameters;

[0135] A parameter adjustment strategy is generated based on the oxidation parameter adjustment of key parameters.

[0136] In an embodiment of the present invention, during the actual zinc oxide extraction process, key oxidation parameters such as temperature, pressure, and other key parameters are monitored in real time, and the key parameters are input into the oxidation condition adaptive model. The model will output the current optimal oxidation parameter scheme and how to adjust the key parameters to achieve the optimal oxidation conditions. After the parameters are adjusted, the oxidation effect is monitored again, and the data is fed back to the oxidation condition adaptive model to form a closed-loop control system to achieve the optimal oxidation effect.

[0137] In the embodiment of the present invention, the oxidation condition adaptive model can automatically adjust the oxidation conditions according to changes in raw material composition and environmental conditions, thereby achieving precise control of the oxidation process, thereby improving the extraction efficiency and purity of zinc oxide.

[0138] like Figure 4 , is a functional module diagram of an oxidation condition optimization device for extracting zinc oxide from zinc-containing waste slag provided by one embodiment of the present invention.

[0139] The oxidation condition optimization device 100 for extracting zinc oxide from zinc-containing waste slag of the present invention can be installed in an electronic device. According to the functions to be implemented, the oxidation condition optimization device 100 for extracting zinc oxide from zinc-containing waste slag can include a data extraction module 101, an action space generation module 102, an oxidation model construction module 103 and an oxidation strategy generation module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0140] In this embodiment, the functions of each module / unit are as follows:

[0141] The data extraction module 101 is used to obtain oxidation experimental data of extracting zinc oxide from zinc-containing waste slag, extract key parameters and experimental efficiency from the oxidation experimental data, and construct a state space according to the key parameters;

[0142] The action space generation module 102 is used to quantify the parameter influence of the key parameters by using the experimental efficiency to obtain a quantification result, and construct an action space according to the quantification result;

[0143] The oxidation model construction module 103 is used to construct a reward function using the state space and the action space, and generate an oxidation condition adaptive model using the reward function, the state space and the action space;

[0144] The oxidation strategy generation module 104 is used to deploy the oxidation condition adaptive model to a preset actual zinc oxide extraction process, and use the oxidation condition adaptive model to adaptively adjust oxidation parameters of the actual zinc oxide extraction process to obtain an optimal oxidation effect.

[0145] In detail, each module described in the oxidation condition optimization device 100 for extracting zinc oxide from zinc-containing waste slag in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The same technical means as the method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag described in the invention can produce the same technical effects, which will not be repeated here.

[0146] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0147] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag, It is characterized in that include: Obtaining oxidation experimental data for extracting zinc oxide from zinc-containing waste slag, extracting key parameters and experimental efficiency from the oxidation experimental data, and constructing a state space according to the key parameters; quantifying the parameter influence of the key parameters by using the experimental efficiency to obtain a quantification result, and constructing an action space according to the quantification result; constructing a reward function using the state space and the action space, and generating an oxidation condition adaptive model using the reward function, the state space, and the action space; The oxidation condition adaptive model is deployed in a preset actual zinc oxide extraction process, and the oxidation condition adaptive model is used to adaptively adjust the oxidation parameters of the actual zinc oxide extraction process to obtain the optimal oxidation effect.

2. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to claim 1, It is characterized in that The step of constructing a state space according to the key parameters includes: generating a parameter range according to the key parameters; generating a state vector based on the parameter range and the key parameter; Generating a conversion rule for the state vector to obtain a transfer function; A state space is generated according to the transfer function and the state vector.

3. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to claim 2, It is characterized in that The step of utilizing the experimental efficiency to quantify the parameter influence on the key parameter to obtain a quantified result includes: Performing sensitivity analysis on the key parameters using the experimental efficiency to obtain sensitivity indicators; Taking the sensitivity index as the influence coefficient corresponding to the key parameter, normalizing the influence coefficient to obtain a weight coefficient; The weight coefficients are aggregated to obtain quantitative results.

4. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to claim 3, It is characterized in that The sensitivity analysis of the key parameters using the experimental efficiency to obtain sensitivity indicators includes: constructing an efficiency function according to the experimental efficiency; Calculating average parameters of the key parameters; Using the efficiency function and the average parameter to perform single parameter change to obtain a change result; The sensitivity index of the key parameter is calculated using the change result.

5. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to claim 1, It is characterized in that The step of constructing an action space according to the quantization result includes: generating an action list according to the key parameters and the quantification results; Performing action mapping between the action list and the states in the state space to obtain an action space set; Action encoding is performed on the action space set to obtain an action space.

6. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to any one of claims 1 to 5, It is characterized in that The using the state space and the action space to construct a reward function includes: Quantitatively represent each state in the state space to obtain a quantitative index; Performing an influence measurement on each action in the action space to obtain an action influence degree; A reward function is generated according to the quantitative indicator and the action impact.

7. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to claim 1, It is characterized in that The step of generating an oxidation condition adaptive model using the reward function, the state space, and the action space includes: generating an action selection model according to the state space and the action space; Creating a revisit buffer corresponding to the action selection model; Performing network replication on the network structure and weights of the action selection model to obtain a target value calculation model; Calculating the target value in the revisit buffer area using the target value calculation model; The target value is used to update the network weights of the action selection model to obtain an oxidation condition adaptive model.

8. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to claim 1, It is characterized in that The step of extracting key parameters and experimental efficiency from the oxidation experimental data includes: Calculating the covariance matrix of the oxidation experimental data; Performing eigendecomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors; Filtering out the principal component from the eigenvector according to the size of the eigenvalue; Projecting the principal components onto the experimental data to obtain key data; The experimental efficiency corresponding to the key data is extracted from the oxidation experimental data.

9. The method for optimizing oxidation conditions for extracting zinc oxide from zinc-containing waste slag according to claim 1, It is characterized in that The method of using the oxidation condition adaptive model to adaptively adjust the oxidation parameters of the actual zinc oxide extraction process includes: Acquiring key oxidation parameters obtained through monitoring, and inputting the key oxidation parameters into the oxidation condition adaptive model; Using the oxidation condition adaptive model to output the current optimal oxidation parameters to adjust key parameters; A parameter adjustment strategy is generated based on the oxidation parameter adjustment key parameters.

10. An oxidation condition optimization device for extracting zinc oxide from zinc-containing waste slag, It is characterized in that The device comprises: A data extraction module, used to obtain oxidation experimental data of extracting zinc oxide from zinc-containing waste slag, extract key parameters and experimental efficiency from the oxidation experimental data, and construct a state space according to the key parameters; An action space generation module, used to quantify the parameter influence of the key parameters by using the experimental efficiency, obtain a quantification result, and construct an action space according to the quantification result; An oxidation model construction module, used to construct a reward function using the state space and the action space, and generate an oxidation condition adaptive model using the reward function, the state space and the action space; The oxidation strategy generation module is used to deploy the oxidation condition adaptive model to a preset actual zinc oxide extraction process, and use the oxidation condition adaptive model to adaptively adjust the oxidation parameters of the actual zinc oxide extraction process to obtain the optimal oxidation effect.

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