An intelligent optimization method, system and medium for operation of a waste combustion plant

Abstract

The application provides an intelligent optimization method, system and medium for operation of a waste incineration device. The method comprises: obtaining a preset number of historical input variable data and historical output variable data of the waste incineration device, then classifying the historical input variable data and the corresponding historical output variable data to obtain a training data set and a test data set, obtaining key parameters of a gradient boosting tree and training to obtain an updated gradient boosting tree model, calculating an input variable importance index according to the updated gradient boosting tree model and obtaining an optimized input variable by threshold comparison, processing to obtain a heat efficiency prediction model through a multilayer BP neural network and a particle swarm optimization algorithm, and obtaining optimal input variable feature data after the heat efficiency prediction model is processed by a reinforcement learning algorithm; so that the gradient boosting tree model, the multilayer BP neural network and the particle swarm optimization algorithm are used to realize intelligent optimization of the waste incineration device and reduce the power generation cost of the waste incineration device.

Description

Technical Field

[0001] The present application relates to the field of waste combustion, and more specifically, to an intelligent optimization method, system and medium for the operation of waste combustion equipment. Background Art

[0002] With the acceleration of urbanization, garbage production is increasing, and garbage incineration has been widely used as an effective way to dispose of garbage. However, there are many problems in the operation of current garbage incineration equipment. For example, the operation of garbage furnaces mainly relies on personal experience, which leads to insufficient garbage combustion and low effective conversion rate. Moreover, traditional garbage incineration equipment operation control methods are often based on fixed parameters or simple feedback mechanisms, which are difficult to adapt to the dynamic changes in garbage characteristics and cannot effectively adjust key parameters such as temperature, air supply, and combustion time during the combustion process in real time and accurately, thereby affecting the efficiency and environmental protection effect of garbage combustion. In addition, there are many parameters in the combustion process of garbage incineration equipment, and how to optimize and control the parameters in the combustion process is also one of the difficulties. In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0003] The purpose of this application is to provide an intelligent optimization method, system and medium for the operation of waste combustion equipment. This application first analyzes the parameters in the combustion process through a gradient boosting tree model to screen out important parameters, thereby reducing the number of parameters to be optimized; in addition, the intelligent optimization of the waste combustion equipment is achieved through multi-layer BP neural network and particle swarm optimization algorithm technology, and further optimizes the parameters in the combustion process through reinforcement learning, thereby obtaining the highest boiler thermal efficiency and reducing the power generation cost of the waste combustion equipment.

[0004] The present application also provides an intelligent optimization method for the operation of a garbage incineration device, comprising the following steps:

[0005] Obtaining a preset amount of historical operating data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a correspondence between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set;

[0006] Obtain the key parameters of the preset gradient boosting tree, and train the gradient boosting tree according to the training data set, test data set and key parameters to obtain an updated gradient boosting tree model;

[0007] Obtaining an input variable importance index according to the updated gradient boosting tree model calculation, and comparing the input variable importance index with a preset optimized input variable threshold to obtain an optimized input variable;

[0008] According to the optimization input variable corresponding to the optimization input variable data and the output variable data, an initial thermal efficiency prediction model is established by combining a preset multi-layer BP neural network, and the thermal efficiency prediction model is obtained by processing through a preset particle swarm optimization algorithm.

[0009] The thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimization input variable.

[0010] Optionally, in the intelligent optimization method for the operation of the waste incineration equipment, the historical operation data of the waste incineration equipment in the preset quantity, including the historical input variable data and the historical output variable data, are obtained, the historical input variable data and the corresponding historical output variable data are established in a corresponding relationship and classified to obtain a training data set and a test data set, and specifically, the method comprises the following steps:

[0011] The historical input variable data and the corresponding historical output variable data are established in a corresponding relationship, and are grouped according to a preset proportion to obtain the training data set and the test data set.

[0012] The training data set comprises training historical input variable data and corresponding training historical output variable data.

[0013] The test data set comprises test historical input variable data and corresponding test historical output variable data.

[0014] Optionally, in the intelligent optimization method for the operation of the waste incineration equipment, the key parameters of the preset gradient boosting tree are obtained, and an updated gradient boosting tree model is obtained by training the gradient boosting tree according to the training data set, the test data set and the key parameters, and specifically, the method comprises the following steps:

[0015] The key parameters of the preset gradient boosting tree are obtained, including the number of decision trees, learning rate data and maximum depth data of the decision tree.

[0016] The average value of the training historical output variable data is obtained and marked as a model initial value.

[0017] According to the model initial value, the training historical input variable data and the corresponding training historical output variable data, the number of decision trees is iterated, and the test historical input variable data and the corresponding test historical output variable data are tested to obtain the updated gradient boosting tree model.

[0018] Optionally, in the intelligent optimization method for the operation of the waste incineration equipment, the input variable importance index is calculated according to the updated gradient boosting tree model, the input variable importance index is compared with a preset optimization input variable threshold to obtain the optimization input variable, and specifically, the method comprises the following steps:

[0019] The input variable importance index of each input variable is obtained through gradient boosting tree model processing;

[0020] Calculate the sum of the importance indexes of the input variables in each input variable and record it as the sum of importance;

[0021] The input variable importance index of each input variable is divided by the total importance to obtain the input variable importance ratio data of each input variable;

[0022] Sort the important proportion data of the input variables from large to small;

[0023] Select input variables with importance greater than the preset threshold and mark them as optimized input variables.

[0024] Optionally, in the intelligent optimization method for the operation of a waste incineration device described in the present application, the optimized input variable data and output variable data corresponding to the optimized input variables are combined with a preset multi-layer BP neural network to establish an initial thermal efficiency prediction model, and the thermal efficiency prediction model is obtained by processing with a preset particle swarm optimization algorithm, specifically including:

[0025] An initial thermal efficiency prediction model is established by presetting a multi-layer BP neural network and combining optimized input variable data and output variable data;

[0026] Obtain the predicted value of the output variable and the actual value of the output variable, calculate the mean square error based on the predicted value of the output variable and the actual value of the output variable, and mark it as the fitness value;

[0027] The initial thermal efficiency prediction model is optimized in a preset manner according to the fitness value to obtain a thermal efficiency prediction model.

[0028] Optionally, in the intelligent optimization method for the operation of the waste incineration equipment described in the present application, the thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables, specifically including:

[0029] S51, initializing the input variables to be optimized;

[0030] S52. The intelligent agent obtains a predicted output variable according to the input variable to be optimized;

[0031] S53, obtaining the output variable reward value of each output variable data and the sum of the output variable reward values ​​respectively through a preset reward function; the intelligent agent of the reinforcement learning is the thermal efficiency prediction model;

[0032] S54. Adjust the optimized input variable data according to the sum of the output variable reward values, and then re-execute steps S51-S53 until the optimal input variable characteristic data is obtained.

[0033] In a second aspect, the present application provides an intelligent optimization system for the operation of a waste incineration device, the system comprising: a memory and a processor, the memory including a program for an intelligent optimization method for the operation of the waste incineration device, the program for the intelligent optimization method for the operation of the waste incineration device, when executed by the processor, implementing the following steps:

[0034] Obtaining a preset amount of historical operating data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a correspondence between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set;

[0035] Obtain the key parameters of the preset gradient boosting tree, and train the gradient boosting tree according to the training data set, test data set and key parameters to obtain an updated gradient boosting tree model;

[0036] Obtaining an input variable importance index according to the updated gradient boosting tree model calculation, and comparing the input variable importance index with a preset optimized input variable threshold to obtain an optimized input variable;

[0037] An initial thermal efficiency prediction model is established based on the optimized input variable data and output variable data corresponding to the optimized input variables in combination with a preset multi-layer BP neural network, and the thermal efficiency prediction model is obtained through processing with a preset particle swarm optimization algorithm;

[0038] The thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables.

[0039] The method of obtaining a preset amount of historical operation data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a corresponding relationship between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set, specifically includes:

[0040] Establish a corresponding relationship between historical input variable data and corresponding historical output variable data, and group them according to a preset ratio to obtain a training data set and a test data set;

[0041] The training data set includes training history input variable data and corresponding training history output variable data;

[0042] The test data set includes test history input variable data and corresponding test history output variable data.

[0043] The step of obtaining the key parameters of the preset gradient boosting tree and training the gradient boosting tree according to the training data set, the test data set, and the key parameters to obtain an updated gradient boosting tree model specifically includes:

[0044] Get the key parameters of the preset gradient boosting tree, including the number of decision trees, learning rate data, and maximum depth of the decision tree;

[0045] Get the average value of the training history output variable data and mark it as the model initial value;

[0046] Iterate according to the model initial value, training history input variable data and corresponding training history output variable data combined with the number of decision trees, and obtain the updated gradient boosting tree model after testing the test history input variable data and the corresponding test history output variable data.

[0047] The step of calculating the input variable importance index according to the updated gradient boosting tree model and comparing the input variable importance index with a preset optimized input variable threshold to obtain the optimized input variable specifically includes:

[0048] The input variable importance index of each input variable is obtained through gradient boosting tree model processing;

[0049] Calculate the sum of the importance indexes of the input variables in each input variable and record it as the sum of importance;

[0050] The input variable importance index of each input variable is divided by the total importance to obtain the input variable importance ratio data of each input variable;

[0051] Sort the important proportion data of the input variables from large to small;

[0052] Select input variables with importance greater than the preset threshold and mark them as optimized input variables.

[0053] The step of calculating the input variable importance index according to the updated gradient boosting tree model and comparing the input variable importance index with a preset optimized input variable threshold to obtain the optimized input variable specifically includes:

[0054] The input variable importance index of each input variable is obtained through gradient boosting tree model processing;

[0055] Calculate the sum of the importance indexes of the input variables in each input variable and record it as the sum of importance;

[0056] The input variable importance index of each input variable is divided by the total importance to obtain the input variable importance ratio data of each input variable;

[0057] Sort the important proportion data of the input variables from large to small;

[0058] Select input variables with importance greater than the preset threshold and mark them as optimized input variables.

[0059] The method of establishing an initial thermal efficiency prediction model based on the optimized input variable data and output variable data corresponding to the optimized input variables in combination with a preset multi-layer BP neural network, and obtaining the thermal efficiency prediction model through a preset particle swarm optimization algorithm specifically includes:

[0060] An initial thermal efficiency prediction model is established by presetting a multi-layer BP neural network and combining optimized input variable data and output variable data;

[0061] Obtain the predicted value of the output variable and the actual value of the output variable, calculate the mean square error based on the predicted value of the output variable and the actual value of the output variable, and mark it as the fitness value;

[0062] The initial thermal efficiency prediction model is optimized in a preset manner according to the fitness value to obtain a thermal efficiency prediction model.

[0063] The thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables, specifically including:

[0064] Initializing the input variables to be optimized;

[0065] The intelligent agent obtains the predicted output variable according to the input variable to be optimized;

[0066] The output variable reward value and the sum of the output variable reward values ​​of each output variable data are obtained respectively by processing with a preset reward function; the intelligent agent of the reinforcement learning is the thermal efficiency prediction model;

[0067] The optimized input variable data is adjusted according to the sum of the output variable reward values, and then the above steps are executed again until the sum of the reward values ​​no longer changes, at which time the optimal input variable characteristic data is obtained.

[0068] Optionally, in the intelligent optimization system for the operation of the waste incineration equipment described in the present application, obtaining a preset amount of historical operation data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a correspondence between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set, specifically includes:

[0069] Establish a corresponding relationship between historical input variable data and corresponding historical output variable data, and group them according to a preset ratio to obtain a training data set and a test data set;

[0070] The training data set includes training history input variable data and corresponding training history output variable data;

[0071] The test data set includes test history input variable data and corresponding test history output variable data.

[0072] Optionally, in the intelligent optimization system for the operation of the waste incineration equipment described in the present application, obtaining key parameters of a preset gradient boosting tree, and training the gradient boosting tree according to the training data set, the test data set, and the key parameters to obtain an updated gradient boosting tree model specifically includes:

[0073] Get the key parameters of the preset gradient boosting tree, including the number of decision trees, learning rate data, and maximum depth of the decision tree;

[0074] Get the average value of the training history output variable data and mark it as the model initial value;

[0075] Iterate according to the model initial value, training history input variable data and corresponding training history output variable data combined with the number of decision trees, and obtain the updated gradient boosting tree model after testing the test history input variable data and the corresponding test history output variable data.

[0076] In a third aspect, the present application also provides a readable storage medium, which stores a program for an intelligent optimization method for the operation of a waste incineration device. When the program for the intelligent optimization method for the operation of a waste incineration device is executed by a processor, the steps of an intelligent optimization method for the operation of a waste incineration device as described in any one of the above items are implemented.

[0077] As can be seen from the above, the present application provides an intelligent optimization method, system, and medium for the operation of a waste incineration device. The method obtains a preset number of historical input variable data and historical output variable data of the waste incineration device, then classifies the historical input variable data and the corresponding historical output variable data to obtain a training data set and a test data set, obtains key parameters of a gradient boosting tree and trains to obtain an updated gradient boosting tree model, calculates the input variable importance index based on the updated gradient boosting tree model, and obtains optimized input variables through threshold comparison, obtains a thermal efficiency prediction model through multi-layer BP neural network and particle swarm optimization algorithm processing, and obtains the optimal input variable feature data after the thermal efficiency prediction model is processed by a reinforcement learning algorithm; thereby, the intelligent optimization of the waste incineration device is achieved through the gradient boosting tree model, the multi-layer BP neural network, and the particle swarm optimization algorithm technology, and the power generation cost of the waste incineration device is reduced.

[0078] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0080] Figure 1 A flowchart of an intelligent optimization method for the operation of a garbage incineration device provided in an embodiment of the present application;

[0081] Figure 2 A flowchart of obtaining a training data set and a test data set for an intelligent optimization method for the operation of a garbage incineration device provided in an embodiment of the present application;

[0082] Figure 3 A flowchart of obtaining and updating a gradient boosting tree model for an intelligent optimization method for operation of a garbage incineration device provided in an embodiment of the present application;

[0083] Figure 4 A flowchart of optimizing input variables of an intelligent optimization method for operation of a garbage incineration device provided in an embodiment of the present application;

[0084] Figure 5 A schematic diagram illustrating the importance of input parameters of the incinerator according to an embodiment of the present application;

[0085] Figure 6 Schematic diagram of the number of iterations of reinforcement learning in an embodiment of the present application;

[0086] Figure 7 Schematic diagram of the fitness values ​​of particles in an embodiment of the present application. DETAILED DESCRIPTION

[0087] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0088] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0089] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for intelligently optimizing the operation of a waste incineration device in some embodiments of the present application. The method is used in a terminal device, such as a computer or mobile phone. The method comprises the following steps:

[0090] S11. Obtain a preset amount of historical operating data of the waste incineration equipment, including historical input variable data and historical output variable data, establish a correspondence between the historical input variable data and the corresponding historical output variable data, and classify them to obtain a training data set and a test data set;

[0091] S12, obtaining key parameters of a preset gradient boosting tree, and training the gradient boosting tree according to the training data set, the test data set, and the key parameters to obtain an updated gradient boosting tree model;

[0092] S13, calculating an input variable importance index according to the updated gradient boosting tree model, and comparing the input variable importance index with a preset optimized input variable threshold to obtain an optimized input variable;

[0093] S14, establishing an initial thermal efficiency prediction model based on the optimized input variable data and output variable data corresponding to the optimized input variable in combination with a preset multi-layer BP neural network, and obtaining the thermal efficiency prediction model through a preset particle swarm optimization algorithm;

[0094] S15. Processing the thermal efficiency prediction model through a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables.

[0095] It should be noted that the garbage combustion equipment operation is a relatively complex process, so there are many factors involved, and a neural network model needs to be used in the data processing process. First, the preset number of historical operation data of the garbage combustion equipment is obtained, including historical input variable data and historical output variable data. The historical input variable data has multiple, the historical output variable data also has multiple, and the historical input variable data and the historical output variable data have a corresponding relationship. In order to better utilize the historical data, the training data set and the test data set are classified to obtain the key parameters of the preset gradient boosting tree. After training, the updated gradient boosting tree model is obtained. In the input variable, there are some factors that have no obvious influence, so the input variable importance index needs to be calculated by the updated gradient boosting tree model. Then, the optimized input variable is obtained by comparing the input variable importance index threshold. The optimized input variable has multiple influencing factors, but the number of influencing factors is less than the number of influencing factors in the input variable data. Then, the thermal efficiency prediction model is obtained by processing the preset multilayer BP neural network and the preset particle swarm optimization algorithm. According to the preset reinforcement learning algorithm, the optimal input variable feature data is obtained by processing the thermal efficiency prediction model, and then the adjustment target of the input variable is determined.

[0096] Please refer to Figure 2 , Figure 2 is a flowchart of a garbage combustion equipment operation intelligent optimization method provided by the embodiment of the application. According to the embodiment of the application, the preset number of historical operation data of the garbage combustion equipment is obtained, including historical input variable data and historical output variable data. The historical input variable data and the corresponding historical output variable data are established in a corresponding relationship and classified to obtain a training data set and a test data set. Specifically, it includes:

[0097] S21, the historical input variable data and the corresponding historical output variable data are established in a corresponding relationship, and are grouped according to a preset proportion to obtain a training data set and a test data set;

[0098] S22, the training data set includes training historical input variable data and corresponding training historical output variable data;

[0099] S23, the test data set includes test historical input variable data and corresponding test historical output variable data.

[0100] It should be noted that the historical input variable data is the independent variable, and the historical output variable data is the dependent variable, so there is a corresponding relationship between the two. In this embodiment, the historical input variable data includes historical feeder speed data, historical main steam flow data, historical main steam temperature data, historical main steam pressure data, historical furnace temperature data, historical feed water temperature data, historical feed water flow data, historical primary air temperature data, historical induced draft fan frequency data, historical secondary air volume data, historical exhaust gas temperature data, historical primary air volume data of the first grate, historical primary air volume data of the second grate, and historical primary air volume data of the third grate. , historical primary air volume data of the 4th section grate, historical primary air volume data of the 5th section grate, historical operating speed data of the 1st section grate, historical operating speed data of the 2nd section grate, historical operating speed data of the 3rd section grate, historical operating speed data of the 4th section grate and historical operating speed data of the 5th section grate; historical output variable data include historical flue gas oxygen content data, historical furnace negative pressure data, historical furnace outlet temperature data and historical unit steam volume data; the preset ratio can be set according to user needs. In this embodiment, the ratio of the corresponding data number in the training data set to the corresponding data number in the test data set is 7:3.

[0101] The combustion parameters (i.e., incinerator combustion parameters) in the combustion process of this embodiment have 21 input variables, and the output has 4 variables (i.e., flue gas oxygen content, furnace negative pressure, furnace outlet temperature, and unit steam volume). This embodiment expects to adjust the incinerator combustion parameters so that the unit steam volume data is maximized, while the flue gas oxygen content, furnace negative pressure, and furnace outlet temperature are maintained within a reasonable range. However, there are 21 incinerator combustion parameters. If all parameters are used, the training time of subsequent neural networks, reinforcement learning, etc. will be lengthened, that is, the amount of calculation will be large. This implementation expects to reduce the incinerator parameters to reduce the amount of calculation.

[0102] Please refer to Figure 3 , Figure 3 This is a flow chart of obtaining an updated gradient boosting tree model for an intelligent optimization method for waste incineration equipment operation provided in an embodiment of the present application. According to an embodiment of the present invention, obtaining key parameters of a preset gradient boosting tree and training the gradient boosting tree based on a training dataset, a test dataset, and the key parameters to obtain an updated gradient boosting tree model specifically includes:

[0103] S31. Obtain key parameters of the preset gradient boosting tree, including the number of decision trees, learning rate data, and maximum depth data of the decision tree;

[0104] S32. Obtain the average value of the training history output variable data and mark it as the model initial value;

[0105] S33. Iterate according to the model initial value, the training history input variable data and the corresponding training history output variable data in combination with the number of decision trees, and obtain an updated gradient boosting tree model through testing the test history input variable data and the corresponding test history output variable data.

[0106] It should be noted that the number of decision trees refers to the number of decision trees to be generated, that is, the number of iterations of the entire training process; the learning rate data can control how much influence each newly generated decision tree can have on the part that was not predicted accurately before (residual); the maximum depth data of the decision tree, how deep the decision tree can grow at most, that is, the maximum depth of the decision tree; the average value of the training history output variable data is to obtain the average value of the historical flue gas oxygen content data, historical furnace negative pressure data, historical furnace outlet temperature data and historical unit steam volume data in the training data set respectively; in the iterative process, the residual needs to be calculated, and the actual target value is subtracted from the value predicted by the gradient boosting tree, and the difference is the residual to be processed in this round; at the same time, a new decision tree will be trained, and a new decision tree will be trained based on the historical input variable data or the training history input variable data, the corresponding residual and the maximum depth data of the decision tree, and then the new prediction value is obtained by adding the predicted value of the gradient boosting tree model to the learning rate data and multiplying it by the predicted value of the new decision tree. After the number of decision trees is cycled, the updated gradient boosting tree model is obtained.

[0107] Please refer to Figure 4 and Figure 5 , Figure 4 This is a flowchart of obtaining optimized input variables for an intelligent optimization method for the operation of a waste incineration device provided in an embodiment of the present application. The method calculates and obtains an input variable importance index based on the updated gradient boosting tree model, and compares the input variable importance index with a preset optimization input variable threshold to obtain the optimized input variable, specifically including:

[0108] S41, obtaining an input variable importance index of each input variable through a gradient boosting tree model;

[0109] S42. Calculate the sum of the importance indexes of the input variables in each input variable and record it as the total importance;

[0110] S43, dividing the input variable importance index of each input variable by the total importance to obtain input variable importance ratio data of each input variable;

[0111] S44, sorting the important proportion data of the input variables from large to small;

[0112] S45. Select input variables with importance greater than a preset threshold and mark them as optimized input variables.

[0113] It should be noted that in the present embodiment, the initial values of the importance indexes of the input variables are all set to 0, then the gradient boosting tree model is updated by traversing in a preset manner, the contribution of each input variable to reducing the error, i.e. the importance index of each input variable corresponding to the input variable, is calculated, then the input variable importance proportion data is calculated, and the threshold is compared to obtain the optimized input variable, i.e. the input variable that has a greater impact on the prediction result.

[0114] Reference Figure 5 In the present embodiment, the importance percentage of the 21 parameters of the incinerator is set to a preset importance threshold, and the input variable greater than the preset importance threshold is marked as the optimized input variable. Reference Figure 5 In the present embodiment, the above-mentioned 21 variables are reduced to 13 variables, i.e. left unit 1 primary air flow (Nm3 / h), left unit 2 primary air flow (Nm3 / h), left unit 3 primary air flow (Nm3 / h), left unit 4 primary air flow (Nm3 / h), left unit 5 primary air flow (Nm3 / h), left primary air fan suction pipe temperature measurement (℃), secondary air fan frequency (%), feeder speed mm / s, unit conveying speed (mm / s), 2 unit conveying speed (mm / s), 3 unit conveying speed (mm / s), 4 unit conveying speed (mm / s), 5 unit conveying speed (mm / s).

[0115] This embodiment first analyzes the parameters of the combustion process using a gradient boosting tree model to screen out important parameters, thereby reducing the number of parameters to be optimized. This, in turn, reduces the number of input parameters for the subsequent multi-layer BP neural network, thereby reducing the amount of training. Furthermore, in subsequent reinforcement learning, the reduction in parameters to be optimized also helps reduce the selection space of reinforcement learning activities and the number of training times. This embodiment obtains important parameters through the gradient boosting tree model, which, on the one hand, reduces the number of parameters to be optimized. On the other hand, even with the reduced number of parameters to be optimized, the combustion process of the combustion equipment can be well predicted using the BP neural network. Due to the nonlinear relationship between the parameters to be optimized (i.e., the parameters of the above-mentioned incinerator) and the output variables (i.e., flue gas oxygen content, furnace negative pressure, furnace outlet temperature and unit steam volume) during the combustion process (if dimensionality reduction is not performed, there are too many parameters to be optimized, the required calculation amount is large, and it will also lead to falling into a local optimal solution during optimization), this embodiment first establishes the contribution of the parameters to be optimized to the output variables in the combustion process through the gradient boosting tree model, thereby screening the parameters to be optimized with a larger contribution, thereby reducing the number of input variables of the subsequent BP neural network model and the selection space of the action of reinforcement learning, which can greatly reduce the amount of training. On the other hand, it can also reduce the reinforcement learning action selection space caused by the parameters to be optimized with a small contribution, and the local optimal solution caused by the learning of the parameters to be optimized with a small contribution.

[0116] According to an embodiment of the present invention, the initial thermal efficiency prediction model is established based on the optimized input variable data and output variable data corresponding to the optimized input variables in combination with a preset multi-layer BP neural network, and the thermal efficiency prediction model is obtained by processing using a preset particle swarm optimization algorithm, specifically including:

[0117] An initial thermal efficiency prediction model is established by presetting a multi-layer BP neural network and combining optimized input variable data and output variable data;

[0118] Obtain the predicted value of the output variable and the actual value of the output variable, calculate the mean square error based on the predicted value of the output variable and the actual value of the output variable, and mark it as the fitness value;

[0119] The initial thermal efficiency prediction model is optimized in a preset manner according to the fitness value to obtain a thermal efficiency prediction model.

[0120] It should be noted that the BP neural network is a feedforward neural network widely used in the field of machine learning. It uses a backpropagation algorithm to adjust the connection weights between neurons to minimize the error between the predicted output and the actual output. The multi-layer BP neural network includes an input layer, a hidden layer (which can be one or more layers), and an output layer. The input layer receives external data, which is then transformed by a complex nonlinear transformation in the hidden layer, ultimately obtaining a prediction result in the output layer. In this embodiment, the input layer refers to the data corresponding to the optimized input variables, and the output layer refers to the output variable data corresponding to the output variables. The number of neurons and layers in the hidden layer can be adjusted according to actual conditions. Through continuous trial and optimization, the network structure that is most suitable for modeling the operating data of the waste incineration equipment is found. The particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. In this embodiment, the fitness value is obtained by calculating the mean squared error. The mean squared error (MSE) is an indicator that measures the "mean squared error." It is used to evaluate the performance of the prediction model. It calculates the average value of the square of the difference (error) between the predicted value and the true value. The smaller the fitness value, the better the corresponding neural network parameters. The preset method refers to the PSO update mechanism and particle optimal value update method commonly used in the particle swarm optimization algorithm. The thermal efficiency prediction model is obtained after the mean square error reaches the set accuracy target.

[0121] refer to Figure 7 , Figure 7 is the fitness value of the particle in this embodiment, that is, after 200 iterations, the fitness value of the particle drops to the minimum value, and the BP neural parameter is optimal at this time.

[0122] According to an embodiment of the present invention, the thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables, specifically including:

[0123] S51, initializing the input variables to be optimized;

[0124] The input variables to be optimized in this embodiment are the above-mentioned 13 incinerator combustion parameters.

[0125] S52. The intelligent agent obtains a predicted output variable according to the input variable to be optimized;

[0126] The intelligent agent of this embodiment is the thermal efficiency prediction model;

[0127] S53, obtaining the output variable reward value and the sum of the output variable reward values ​​of each output variable data respectively through a preset reward function; the intelligent agent of the reinforcement learning is the thermal efficiency prediction model;

[0128] S54. Adjust the optimized input variable data according to the sum of the output variable reward values, and then re-execute steps S51-S53 until the optimal input variable characteristic data is obtained.

[0129] It should be noted that the requirements for output variables are generally relatively fixed. In this embodiment, the standard data for the output variables include standard data for flue gas oxygen content (data variation range), standard data for furnace negative pressure (data variation range), standard data for furnace outlet temperature (data variation range), and standard data for unit steam volume (data variation range). The standard data for flue gas oxygen content is [3, 8], the standard data for furnace negative pressure is [20, 70], and the standard data for furnace outlet temperature is [950, 1150]. Unit steam volume refers to the amount of steam generated after burning a unit weight of garbage, and the standard data for unit steam volume is [1.5, 3.5]. When the flue gas oxygen content data, furnace negative pressure data, and furnace outlet temperature data are within the standard data range, the reward value is -2, indicating that the current state is acceptable but there is still room for improvement. Otherwise, the reward value is 0, indicating that the current state deviates significantly from the ideal range and needs to be adjusted. The reward value for unit steam volume data is calculated using a preset method based on its average value and standard deviation, as detailed below.

[0130] The reward values ​​corresponding to the flue gas oxygen content data, furnace negative pressure data, furnace outlet temperature data and unit steam volume data are added together to obtain the total output variable reward value. When the total reward value no longer changes, it can be considered that the optimal solution has been obtained; by continuously trying different input variable data combinations, the BP neural network algorithm gradually learns the strategy that can maximize the reward value, that is, the optimal input variable characteristic data corresponding to the optimal input variable.

[0131] refer to Figure 6 In this embodiment, if the total reward value does not change after 60 iterations, the iteration can be terminated at the 80th time, thereby obtaining the optimal input variable feature data corresponding to the termination of the iteration.

[0132] This embodiment needs to optimize to maximize the unit steam volume. Therefore, this embodiment uses the first three output variables to calculate the reward value, and the reward value range is as follows:

[0133] 1. Oxygen content of boiler outlet flue gas (%) Variation range: 3-8

[0134] 2. The first channel flue gas outlet pressure (furnace outlet negative pressure (Pa)) changes in the range of: 20-70

[0135] 3. Average temperature of the upper and middle parts of the furnace (furnace temperature) (℃) Variation range: 950-1150 If the value of the output variable is within the given upper and lower bounds: then reward = -2, otherwise reward = 0;

[0136] The fourth variable (steam production) is maximized as the optimization goal:

[0137] According to professional heat balance calculation: the maximum steam output is 3.5 and the minimum is 1.5; the reward values ​​are as follows:

[0138] Reward consists of four parts: reward 1, reward 2, reward 3, reward 4

[0139] Reward_tota l=reward 1+reward 2+reward 3+reward 4

[0140] Reward4 = -norm(Z4 - 3.5); Z4 is the predicted value of the fourth variable (steam production), that is, the predicted value output by the agent based on the input parameters;

[0141] When Z4<(1.5-2.0871) / 0.1574, Reward_tota l=-10; when Z4=(3-2.0871) / 0.1574, Reward_tota l=0.

[0142] (Z4-2.0871) / 0.1574mean=2.0871,std=0.1574

[0143] All calculations are done using normalized values: If the predicted value of the fourth item is Z4, then enter (Z4-2.0871) / 0.1574

[0144] goa l State=(3-2.0871) / 0.1574=5.8;

[0145] reward4=-norm((Z4-2.0871) / 0.1574-goal State);

[0146] Minimum reward4: -norm((1.5-2.0871) / 0.1574-goal State) = -norm(-3.72-5.8) = -9.53

[0147] Maximum value of reward4: -norm((3-2.0871) / 0.1574-goal State)=0

[0148] furnace exit flue gas oxygen content reward 1:

[0149] Reward 1 = -10 when Z1 (normalized value) > Z1 max (normalized value) or Z1 (normalized value) < Z1 min (normalized value); wherein Z1 is the predicted value of the furnace exit flue gas oxygen content;

[0150] Reward 1 = 0 when Z1 min (normalized value) < Z1 (normalized value) < Z1 max (normalized value).

[0151] reward 2:

[0152] Reward 2 = -10 when Z2 (normalized value) > Z2 max (normalized value) or Z2 < Z2 min (normalized value);

[0153] Reward 2 = 0 when Z2 min (normalized value) < Z2 (normalized value) < Z2 max (normalized value). Wherein Z2 is the predicted value of the first channel flue gas outlet pressure;

[0154] reward 3:

[0155] Reward 3 = -10 when Z3 (normalized value) > Z3 max (normalized value) or Z3 < Z3 min (normalized value);

[0156] Reward 3 = 0 when Z3 min (normalized value) < Z3 (normalized value) < Z3 max (normalized value). Wherein Z3 is the predicted value of the furnace temperature;

[0157] Specifically, the reward values Reward 1, Reward 2, Reward 3 and Reward 4 corresponding to the furnace exit flue gas oxygen content, the furnace exit negative pressure, the furnace temperature and the steam production output by the agent are calculated respectively, and the reward values Reward 1, Reward 2, Reward 3 and Reward 4 are summed as the total reward value Reward total.

[0158] Specifically, Reward4 is calculated as follows: when Z4<(1.5-2.0871) / 0.1574, Reward_tota l=-10; when Z4=(3-2.0871) / 0.1574, Reward_tota l=0; otherwise, goa l State=(3-2.0871) / 0.1574=5.8; reward4=-norm((Z4-2.0871) / 0.1574-goa l State).

[0159] This embodiment considers steam production as a factor in calculating the reward value, while also considering other output variables (i.e., oxygen content in flue gas at the furnace outlet, negative pressure at the furnace outlet, and furnace temperature) as the total reward value. This ensures that the incinerator operates normally while maximizing its output, reducing the risk of the incinerator not operating normally due to reinforcement learning when maximizing the reward value for steam production. Furthermore, this embodiment directly sets the total reward value for steam production to 0 when the predicted steam production is no longer within the preset range, indicating that optimization is required.

[0160] By the method of this embodiment, the final optimized variables are:

[0161] (1) Input variable optimization value:

[0162] 1. Left unit 1 primary air flow (Nm3 / h) = 770.35 Reasonable change range: 500-----1800

[0163] 2. Left unit 2 primary air flow (Nm3 / h) = 540.8 Reasonable change range: 500-----1500

[0164] 3. Left unit 3 primary air flow (Nm3 / h) = 7160.24 Reasonable change range: 3000------10000

[0165] 4. Left unit 4 primary air flow (Nm3 / h) = 4322.86 Reasonable change range: 2000------10000

[0166] 5. Left unit 5 primary air flow (Nm3 / h) = 2556.49 Reasonable change range: 2000------5000

[0167] 6. Temperature measurement of the left primary fan suction pipe (℃) = 170.69 Reasonable variation range: 150----230

[0168] 7. Secondary fan frequency (%) = 47.59 Reasonable variation range: 30------86

[0169] 8. Feeder speed mm / s=0.74 Reasonable variation range: 0.3----0.8

[0170] 9.1 Unit transmission speed (mm / s) = 0.75 Reasonable variation range: 0.5----2

[0171] 10.2 Unit transmission speed (mm / s) = 1.56 Reasonable variation range: 0.7----2.5

[0172] 11.3 Unit transmission speed (mm / s) = 1.38 Reasonable variation range: 0.3----1.5

[0173] 12.4 Unit transmission speed (mm / s) = 1.17 Reasonable variation range: 0.3----1.2

[0174] 13.5 Unit transmission speed (mm / s) = 0.43 Reasonable variation range: 0.35----1.3

[0175] (2) Optimal value of output variable:

[0176] 1. Oxygen content of boiler outlet flue gas (%) = 6.92 Reasonable variation range: 3-8

[0177] 2. The first channel flue gas outlet pressure (furnace outlet negative pressure, Pa) = 68.36 Reasonable change range: 20-70

[0178] 3. Average temperature of the upper middle part of the furnace (furnace temperature) (℃) = 1086.86 Reasonable variation range: 950----1150

[0179] 4. Gas production per unit of garbage = 2.41

[0180] The current average value in DCS data is 2.01, an increase of about 20%.

[0181] The present invention also discloses an intelligent optimization system for the operation of a garbage combustion device, comprising a memory and a processor. The memory stores a program for an intelligent optimization method for the operation of the garbage combustion device. When the program is executed by the processor, the following steps are implemented:

[0182] Obtaining a preset amount of historical operating data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a correspondence between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set;

[0183] Obtain the key parameters of the preset gradient boosting tree, and train the gradient boosting tree according to the training data set, test data set and key parameters to obtain an updated gradient boosting tree model;

[0184] Obtaining an input variable importance index according to the updated gradient boosting tree model calculation, and comparing the input variable importance index with a preset optimized input variable threshold to obtain an optimized input variable;

[0185] An initial thermal efficiency prediction model is established based on the optimized input variable data and output variable data corresponding to the optimized input variables in combination with a preset multi-layer BP neural network, and the thermal efficiency prediction model is obtained through processing with a preset particle swarm optimization algorithm;

[0186] The thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables.

[0187] It should be noted that the operation of the waste incineration equipment is a relatively complex process, so it involves many factors. A neural network model is needed in the data processing process. First, a preset amount of historical operation data of the waste incineration equipment must be obtained, including historical input variable data and historical output variable data. There are multiple historical input variable data and multiple historical output variable data. There is a corresponding relationship between the historical input data and the historical output variable data. In order to better utilize the historical data, classification is performed to obtain a training data set and a test data set, and the key parameters of the preset gradient boosting tree are obtained. After training, an updated gradient boosting tree model is obtained. Some factors in the input variables may have an insignificant effect. Therefore, it is necessary to calculate the input variable importance index through the updated gradient boosting tree model, and then obtain the optimized input variable based on the input variable importance index threshold comparison. The optimized input variable has multiple influencing factors, but the number of influencing factors is less than the number of influencing factors in the input variable data. Then, the thermal efficiency prediction model is obtained through the preset multi-layer BP neural network and the preset particle swarm optimization algorithm. The thermal efficiency prediction model is processed according to the preset reinforcement learning algorithm to obtain the optimal input variable feature data, thereby clarifying the adjustment target of the input variable.

[0188] According to an embodiment of the present invention, obtaining a preset amount of historical operating data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a correspondence between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set, specifically includes:

[0189] Establish a corresponding relationship between historical input variable data and corresponding historical output variable data, and group them according to a preset ratio to obtain a training data set and a test data set;

[0190] The training data set includes training history input variable data and corresponding training history output variable data;

[0191] The test data set includes test history input variable data and corresponding test history output variable data.

[0192] It should be noted that the historical input variable data is the independent variable, and the historical output variable data is the dependent variable, so there is a corresponding relationship between the two. In this embodiment, the historical input variable data includes historical feeder speed data, historical main steam flow data, historical main steam temperature data, historical main steam pressure data, historical furnace temperature data, historical feed water temperature data, historical feed water flow data, historical primary air temperature data, historical induced draft fan frequency data, historical secondary air volume data, historical exhaust gas temperature data, historical primary air volume data of the first grate, historical primary air volume data of the second grate, and historical primary air volume data of the third grate. , historical primary air volume data of the 4th section grate, historical primary air volume data of the 5th section grate, historical operating speed data of the 1st section grate, historical operating speed data of the 2nd section grate, historical operating speed data of the 3rd section grate, historical operating speed data of the 4th section grate and historical operating speed data of the 5th section grate; historical output variable data include historical flue gas oxygen content data, historical furnace negative pressure data, historical furnace outlet temperature data and historical unit steam volume data; the preset ratio can be set according to user needs. In this embodiment, the ratio of the corresponding data number in the training data set to the corresponding data number in the test data set is 7:3.

[0193] According to an embodiment of the present invention, obtaining key parameters of a preset gradient boosting tree and training the gradient boosting tree according to the training data set, the test data set, and the key parameters to obtain an updated gradient boosting tree model specifically includes:

[0194] Get the key parameters of the preset gradient boosting tree, including the number of decision trees, learning rate data, and maximum depth of the decision tree;

[0195] Get the average value of the training history output variable data and mark it as the model initial value;

[0196] Iterate according to the model initial value, training history input variable data and corresponding training history output variable data combined with the number of decision trees, and obtain the updated gradient boosting tree model after testing the test history input variable data and the corresponding test history output variable data.

[0197] It should be noted that the number of decision trees refers to the number of decision trees to be generated, that is, the number of iterations in the entire training process; the learning rate data can control the influence of each newly generated decision tree on the part (residual error) that has not been predicted accurately before; the maximum depth of the decision tree data, the maximum depth of the decision tree, that is, the maximum depth of the decision tree; the average value of the training historical output variable data is the average value of the historical flue gas oxygen content data, the historical furnace negative pressure data, the historical furnace outlet temperature data and the historical unit steam volume data in the training data set respectively; in the iteration process, the residual error needs to be calculated, that is, the actual target value is subtracted from the value predicted by the gradient boosting tree, and the difference value is the residual error to be processed in this round; at the same time, a new decision tree is trained, and a new decision tree is trained according to the historical input variable data or the training historical input variable data, the corresponding residual error and the maximum depth of the decision tree, and then the new predicted value is obtained by adding the gradient boosting tree model prediction value and the learning rate data multiplied by the new decision tree prediction value. After the cycle of the number of decision trees, the updated gradient boosting tree model is obtained.

[0198] The input variable importance index is calculated according to the updated gradient boosting tree model, and the optimized input variable is obtained by comparing the input variable importance index with the preset optimization input variable threshold. Specifically, it comprises:

[0199] The input variable importance index of each input variable is obtained by processing the gradient boosting tree model;

[0200] The sum of the input variable importance indexes of each input variable is calculated and recorded as the importance total;

[0201] The input variable importance proportion data of each input variable is obtained by dividing the input variable importance index of each input variable by the importance total;

[0202] The input variable importance proportion data is sorted in descending order;

[0203] Select the input variable greater than the preset importance threshold and mark it as the optimized input variable.

[0204] It should be noted that in this embodiment, the initial value of each input variable importance index is set to 0, and then the updated gradient boosting tree model is traversed in a preset manner to calculate the contribution of each input variable to error reduction, that is, the input variable importance index corresponding to each input variable. Then, the input variable importance proportion data is calculated and compared with the threshold to obtain the optimized input variable, that is, the input variable that has a greater impact on the prediction result.

[0205] According to the embodiment of the present application, the initial thermal efficiency prediction model is established by combining the optimization input variable data and the output variable data corresponding to the optimization input variable according to a preset multi-layer BP neural network, and the thermal efficiency prediction model is obtained by processing through a preset particle swarm optimization algorithm, specifically including:

[0206] The initial thermal efficiency prediction model is established by combining the optimization input variable data and the output variable data through a preset multi-layer BP neural network;

[0207] The output variable predicted value and the output variable actual value are obtained, the mean square error is calculated according to the output variable predicted value and the output variable actual value, and the fitness value is marked;

[0208] The initial thermal efficiency prediction model is optimized and processed to obtain the thermal efficiency prediction model according to the fitness value through a preset mode.

[0209] It should be noted that the BP neural network is a kind of feedforward neural network widely used in the field of machine learning, which adjusts the connection weight between neurons through back propagation algorithm to minimize the error between predicted output and actual output. The multi-layer BP neural network includes input layer, hidden layer (which can be one or more layers) and output layer. The input layer receives external data, which is transformed by the hidden layer in a complex nonlinear way, and finally the prediction result is obtained in the output layer; in this embodiment, the input layer refers to the data corresponding to the optimization input variable, and the output layer refers to the output variable data corresponding to the output variable; the number of neurons and the number of layers of the hidden layer can be adjusted according to the actual situation, and through continuous trial and optimization, the network structure most suitable for modeling the running data of the waste incineration equipment is found; the particle swarm optimization algorithm is a kind of optimization algorithm based on swarm intelligence, in this embodiment, the fitness value is obtained by calculating the mean square error, and the mean square error (MSE) is a kind of index for measuring the average error square. It is used to evaluate the performance of the prediction model, and the average value of the square of the difference (error) between the predicted value and the true value is calculated, the smaller the fitness value, the better the corresponding neural network parameters; the preset mode refers to the PSO update mechanism and particle best value update mode commonly used in particle swarm optimization algorithm, and the thermal efficiency prediction model is obtained after the mean square error reaches the set accuracy target.

[0210] According to the embodiment of the present application, the thermal efficiency prediction model is processed through a preset reinforcement learning algorithm to obtain the optimal input variable feature data corresponding to the optimization input variable, specifically including:

[0211] The output variable standard data and the output variable real-time data are obtained;

[0212] The output variable reward value and the sum of the output variable reward values of each output variable data are obtained by processing through a preset reward function;

[0213] The thermal efficiency prediction model adjusts the optimized input variable data corresponding to the output variable reward value and the sum of the output variable reward values ​​in a preset manner to obtain the optimal input variable characteristic data.

[0214] It should be noted that the requirements for output variables are generally relatively fixed. In this embodiment, the standard data of the output variables include standard data of flue gas oxygen content, standard data of furnace negative pressure, standard data of furnace outlet temperature and standard data of unit steam volume. The standard data of flue gas oxygen content is [3, 8], the standard data of furnace negative pressure is [20, 70], and the standard data of furnace outlet temperature is [950, 1150]. The unit steam volume data refers to the steam volume data generated after the combustion of unit weight of garbage. The standard data of unit steam volume is [1.5, 3.5]. When the flue gas oxygen content data, furnace negative pressure data and furnace outlet temperature data are within the standard data range, the reward value is [1.5, 3.5]. If the reward value is -2, it means that the current state is acceptable but there is still room for improvement. Otherwise, the reward value is 0, which means that the current state deviates greatly from the ideal range and needs to be adjusted. The reward value of the unit steam volume data is calculated in a preset way according to the reward values ​​of the flue gas oxygen content data, furnace negative pressure data and furnace outlet temperature data. The reward values ​​corresponding to the flue gas oxygen content data, furnace negative pressure data, furnace outlet temperature data and unit steam volume data are added together to obtain the total reward value of the output variable. By continuously trying different combinations of input variable data, the BP neural network algorithm gradually learns the strategy that can maximize the reward value, that is, the optimal input variable characteristic data corresponding to the optimal input variable.

[0215] The third aspect of the present invention provides a readable storage medium, which includes a program for an intelligent optimization method for the operation of a waste incineration device. When the program for the intelligent optimization method for the operation of a waste incineration device is executed by a processor, the steps of the intelligent optimization method for the operation of a waste incineration device as described in any one of the above items are implemented.

[0216] The present invention discloses an intelligent optimization method, system and medium for the operation of a waste combustion device. The method obtains a preset number of historical input variable data and historical output variable data of the waste combustion device, and then classifies the historical input variable data and the corresponding historical output variable data to obtain a training data set and a test data set, obtains key parameters of a gradient boosting tree and trains to obtain an updated gradient boosting tree model, calculates the input variable importance index based on the updated gradient boosting tree model and obtains optimized input variables through threshold comparison, obtains a thermal efficiency prediction model through multi-layer BP neural network and particle swarm optimization algorithm processing, and obtains optimal input variable feature data after the thermal efficiency prediction model is processed by a reinforcement learning algorithm; thereby, the intelligent optimization of the waste combustion device is achieved through the gradient boosting tree model, the multi-layer BP neural network and the particle swarm optimization algorithm technology, and the power generation cost of the waste combustion device is reduced.

[0217] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0218] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0219] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0220] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0221] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. An intelligent optimization method for the operation of a garbage incineration device, characterized in that: include: Obtaining a preset amount of historical operating data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a correspondence between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set; Obtain the key parameters of the preset gradient boosting tree, and train the gradient boosting tree according to the training data set, test data set and key parameters to obtain an updated gradient boosting tree model; Obtaining an input variable importance index according to the updated gradient boosting tree model calculation, and comparing the input variable importance index with a preset optimized input variable threshold to obtain an optimized input variable; An initial thermal efficiency prediction model is established based on the optimized input variable data and output variable data corresponding to the optimized input variables in combination with a preset multi-layer BP neural network, and the thermal efficiency prediction model is obtained through processing with a preset particle swarm optimization algorithm; The thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables.

2. The intelligent optimization method for the operation of a waste incineration device according to claim 1, characterized in that: The method of obtaining a preset amount of historical operation data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a corresponding relationship between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set, specifically includes: Establish a corresponding relationship between historical input variable data and corresponding historical output variable data, and group them according to a preset ratio to obtain a training data set and a test data set; The training data set includes training history input variable data and corresponding training history output variable data; The test data set includes test history input variable data and corresponding test history output variable data.

3. The intelligent optimization method for the operation of a garbage incineration plant according to claim 2, characterized in that: The step of obtaining the key parameters of the preset gradient boosting tree and training the gradient boosting tree according to the training data set, the test data set, and the key parameters to obtain an updated gradient boosting tree model specifically includes: Get the key parameters of the preset gradient boosting tree, including the number of decision trees, learning rate data, and maximum depth of the decision tree; Get the average value of the training history output variable data and mark it as the model initial value; Iterate according to the model initial value, training history input variable data and corresponding training history output variable data combined with the number of decision trees, and obtain the updated gradient boosting tree model after testing the test history input variable data and the corresponding test history output variable data.

4. The intelligent optimization method for the operation of a garbage incineration plant according to claim 3 is characterized in that: The step of calculating the input variable importance index according to the updated gradient boosting tree model and comparing the input variable importance index with a preset optimized input variable threshold to obtain the optimized input variable specifically includes: The input variable importance index of each input variable is obtained through gradient boosting tree model processing; Calculate the sum of the importance indexes of the input variables in each input variable and record it as the sum of importance; The input variable importance index of each input variable is divided by the total importance to obtain the input variable importance ratio data of each input variable; Sort the important proportion data of the input variables from large to small; Select input variables with importance greater than the preset threshold and mark them as optimized input variables.

5. The intelligent optimization method for the operation of a garbage incineration plant according to claim 4, characterized in that: The method of establishing an initial thermal efficiency prediction model based on the optimized input variable data and output variable data corresponding to the optimized input variables in combination with a preset multi-layer BP neural network, and obtaining the thermal efficiency prediction model through a preset particle swarm optimization algorithm specifically includes: An initial thermal efficiency prediction model is established by presetting a multi-layer BP neural network and combining optimized input variable data and output variable data; Obtain the predicted value of the output variable and the actual value of the output variable, calculate the mean square error based on the predicted value of the output variable and the actual value of the output variable, and mark it as the fitness value; The initial thermal efficiency prediction model is optimized in a preset manner according to the fitness value to obtain a thermal efficiency prediction model.

6. The intelligent optimization method for the operation of a garbage incineration plant according to claim 5, characterized in that: The thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables, specifically including: S51, initializing the input variables to be optimized; S52. The intelligent agent obtains a predicted output variable according to the input variable to be optimized; S53, obtaining the output variable reward value and the sum of the output variable reward values ​​of each output variable data respectively through a preset reward function; the intelligent agent of the reinforcement learning is the thermal efficiency prediction model; S54. Adjust the optimized input variable data according to the sum of the output variable reward values, and then re-execute steps S51-S53 until the sum of the reward values ​​no longer changes, at which point the optimal input variable characteristic data is obtained.

7. An intelligent optimization system for the operation of garbage incineration equipment, characterized in that: The system comprises a memory and a processor, wherein the memory comprises a program for an intelligent optimization method for the operation of a garbage combustion device, and when the program is executed by the processor, the following steps are implemented: Obtaining a preset amount of historical operating data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a correspondence between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set; Obtain the key parameters of the preset gradient boosting tree, and train the gradient boosting tree according to the training data set, test data set and key parameters to obtain an updated gradient boosting tree model; Obtaining an input variable importance index according to the updated gradient boosting tree model calculation, and comparing the input variable importance index with a preset optimized input variable threshold to obtain an optimized input variable; An initial thermal efficiency prediction model is established based on the optimized input variable data and output variable data corresponding to the optimized input variables in combination with a preset multi-layer BP neural network, and the thermal efficiency prediction model is obtained through processing with a preset particle swarm optimization algorithm; The thermal efficiency prediction model is processed by a preset reinforcement learning algorithm to obtain optimal input variable characteristic data corresponding to the optimized input variables.

8. The intelligent optimization system for the operation of garbage incineration equipment according to claim 7 is characterized in that: The method of obtaining a preset amount of historical operation data of the waste incineration equipment, including historical input variable data and historical output variable data, establishing a corresponding relationship between the historical input variable data and the corresponding historical output variable data, and classifying them to obtain a training data set and a test data set, specifically includes: Establish a corresponding relationship between historical input variable data and corresponding historical output variable data, and group them according to a preset ratio to obtain a training data set and a test data set; The training data set includes training history input variable data and corresponding training history output variable data; The test data set includes test history input variable data and corresponding test history output variable data.

9. The intelligent optimization system for operation of garbage incineration equipment according to claim 8, characterized in that: The step of obtaining the key parameters of the preset gradient boosting tree and training the gradient boosting tree according to the training data set, the test data set, and the key parameters to obtain an updated gradient boosting tree model specifically includes: Get the key parameters of the preset gradient boosting tree, including the number of decision trees, learning rate data, and maximum depth of the decision tree; Get the average value of the training history output variable data and mark it as the model initial value; Iterate according to the model initial value, training history input variable data and corresponding training history output variable data combined with the number of decision trees, and obtain the updated gradient boosting tree model after testing the test history input variable data and the corresponding test history output variable data.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a program for an intelligent optimization method for the operation of a waste incineration device. When the program for the intelligent optimization method for the operation of a waste incineration device is executed by a processor, the steps of the intelligent optimization method for the operation of a waste incineration device as described in any one of claims 1 to 6 are implemented.

Images