Boiler multi-region CFD-AI modeling method and system based on federated learning

Abstract

The invention discloses a boiler multi-region CFD-AI modeling method and system based on federated learning, and belongs to the technical field of boiler monitoring, and the modeling method comprises the following specific steps: S1, simulating the heat transfer and fluid behaviors of a boiler, building multi-region CFD grid division, and collecting and preprocessing the dynamic operation data of each region from the actual operation of the boiler; s2, extracting features of the preprocessed dynamic operation data, constructing a boiler multi-region CFD-AI model according to the extracted features, and globally optimizing the boiler multi-region CFD-AI model; s3, dynamically adjusting the structure and parameters of the boiler multi-region CFD-AI model after global optimization, and enhancing the key region decision of the boiler multi-region CFD-AI model; according to the method, the efficiency of model training can be improved, a large number of communication resources are saved, the privacy disclosure risk is reduced, the cross-regional adaptability of the model is promoted, the performance of the model under the complex working condition is remarkably improved, the multi-objective optimization problem can be effectively solved, and the performance of the boiler in different regions and working conditions can be more accurately predicted and controlled.

Description

Technical Field

[0001] The present invention relates to the field of boiler monitoring technology, and in particular to a boiler multi-region CFD-AI modeling method and system based on federated learning. Background Art

[0002] With the continuous development of industrial automation and intelligence, boiler systems play a vital role in the power, chemical, metallurgical and other industries. The operating efficiency, stability and emission level of the boiler directly affect the optimization of the production process and energy conservation. In order to improve the safety and economy of boiler operation, accurate dynamic modeling and prediction are necessary. Traditional boiler modeling methods rely on the accurate description of physical models and control systems, but they often fail to meet high-precision requirements in the face of complex nonlinear and multivariable real environments. In recent years, models combining computational fluid dynamics (CFD) and artificial intelligence (AI) have gradually become a research hotspot, especially CFD-AI models can effectively improve the dynamic prediction capabilities of boilers. However, the training of CFD-AI models usually requires a large amount of high-quality data, and the collection of these data often faces challenges such as privacy protection and uneven data distribution; therefore, it is particularly important to invent a multi-region CFD-AI modeling method for boilers based on federated learning.

[0003] The existing boiler multi-zone CFD-AI modeling method has poor model training efficiency, wastes a lot of communication resources, has a high risk of privacy leakage, and has poor cross-regional adaptability of the model; in addition, the existing boiler multi-zone CFD-AI modeling method performs poorly under complex working conditions and cannot effectively handle multi-objective optimization problems. Summary of the invention

[0004] The purpose of the present invention is to solve the defects in the prior art and propose a boiler multi-zone CFD-AI modeling method and system based on federated learning.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: The boiler multi-zone CFD-AI modeling method based on federated learning includes the following steps: S1. Simulate the heat transfer and fluid behavior of the boiler, establish multi-region CFD meshing, and collect and pre-process the dynamic operation data of each region from the actual operation of the boiler; S2. extracting the features of the preprocessed dynamic operation data, constructing a boiler multi-region CFD-AI model based on the extracted features, and globally optimizing the boiler multi-region CFD-AI model; S3, dynamically adjust the structure and parameters of the globally optimized boiler multi-region CFD-AI model to enhance the key regional decision-making of the boiler multi-region CFD-AI model; S4. Evaluate the deviation between the prediction results of the boiler multi-zone CFD-AI model and the actual data, and adjust the decision path and model structure to improve the fitting effect of high-dimensional features; S5. Repeatedly train and update the boiler multi-zone CFD-AI model, perform real-time predictions through the final model, and regularly collect operating data for online updates and optimization.

[0006] A further improvement of the present invention is that the specific steps of preprocessing the dynamic operation data of each area in S1 are as follows: 1.1: Collect dynamic operation data from the actual operation of the boiler, use a high-pass filter to smooth the noise in each group of dynamic operation data, and fill in the missing values ​​in the dynamic operation data through the cubic spline interpolation method; 1.2: Use the quartile algorithm to divide the dynamic running data after missing value filling into 4 parts in ascending order, each accounting for 25% of the entire data set, and the lower quartile is recorded as , the median is recorded as , the upper quartile is recorded as , calculate the difference between the upper quartile and the lower quartile and record it as the interquartile range ,according to as well as Get the inner limit of outliers [ , ] and remove the data beyond this range; 1.3: Combine the boiler mechanism model and CFD software to simulate the combustion, heat transfer and fluid behavior of the boiler, generate simulation data consistent with the actual operating status, align the pre-processed actual dynamic operation data with the CFD simulation data, and integrate each group of dynamic operation data through partition matching.

[0007] A further improvement of the present invention is that the specific steps of constructing the boiler multi-region CFD-AI model in S2 are as follows: 2.1: According to the boiler CFD grid division, the integrated dynamic operation data is partitioned by spatial region, and then each group of training data sets after partitioning is normalized. The feature information corresponding to each group of dynamic operation data is obtained through the feature extraction formula, and the dynamic operation data of each region and its corresponding feature information are divided into training set, verification set and test set; 2.2: Convert the training set, validation set, and test set into time series format, establish a boiler multi-zone CFD-AI model based on the model architecture of the input layer, LSTM layer, fully connected layer, and output layer, and then use the random initialization method to initialize the weight parameters and bias parameters of the boiler multi-zone CFD-AI model; 2.3: Input the training set data into the boiler multi-zone CFD-AI model. The CFD-AI model calculates the hidden state and output value of the LSTM layer through the forward propagation algorithm, and recursively updates the hidden state and memory unit step by step to obtain the final output layer prediction value. The model prediction value is compared with the true value, and the corresponding loss value is calculated through the MSE loss function. 2.4: Through the chain rule, the loss value is gradually propagated from the output layer to the input layer, the gradient of the loss value to the parameters of each layer of the model is calculated, and the model parameters are updated using the gradient descent algorithm. After each round of training, the performance indicators of the boiler multi-region CFD-AI model are evaluated through the validation set data, and the current evaluation results are compared with the previous round of evaluation results. If there is fitting or overfitting, the training is terminated in advance. Otherwise, the hyperparameters of the model are adjusted and the model is retrained; 2.5: Repeat forward propagation, loss calculation, back propagation and parameter update until the loss value of the boiler multi-zone CFD-AI model converges to the preset range or reaches the maximum number of iterations. Then use the test set to evaluate the performance of the final model to verify its accuracy and robustness on unseen data, and store the trained model parameters and structure in the central server.

[0008] A further improvement of the present invention is that the specific steps of the global optimization boiler multi-zone CFD-AI model in S2 are as follows: 3.1: The central server sends the boiler multi-zone CFD-AI model to each client as the client's initial local model, and uses the trained model parameters and structure to initialize the local model of each client; 3.2: Each client uses each set of dynamic running data collected locally to train the corresponding local model, and uses the back propagation algorithm to calculate the gradient and update the model parameters with the goal of minimizing the loss function. After completing multiple rounds of local training, each client transmits the final model parameters updated by local training to the central server; 3.3: The central server calculates the model parameters of each client through the weighted average method and aggregates them into a global model. Then, the global model is updated through mean iteration. After the global model is updated, the central server broadcasts the new global model to all clients and updates the parameter information of each client's local model. 3.4: Each client continues to train locally based on the new global model and performs the next round of mean iteration. When the change in the global model parameter update of the central server is less than the preset threshold, the change in the loss function for multiple consecutive rounds converges to the preset threshold, or the maximum number of training rounds is reached, the training ends; 3.5: Use the optimized global model to evaluate the test set data again to verify its performance on unseen data, replace the original boiler multi-zone CFD-AI model parameters and structure with the global model parameters and structure, and deploy the updated boiler multi-zone CFD-AI model parameters to the actual boiler monitoring platform.

[0009] A further improvement of the present invention is that the specific steps of dynamically adjusting the structure and parameters of the globally optimized boiler multi-region CFD-AI model in S3 are as follows: 4.1: Collect the structure and parameter information of the boiler multi-zone CFD-AI model, randomly generate multiple groups of optimization body positions to represent each group of solutions in the model space, and the position of each optimization body represents a potential model structure and parameter combination. Substitute the parameters of each optimization body into the globally optimized boiler multi-zone CFD-AI model, use the validation set to evaluate its performance, and use the result of the MSE loss function as the fitness of the optimization body; 4.2: At the beginning of the iteration, select the three groups of optimized bodies with the highest fitness, and define them as the Alpha body with the best fitness, the Beta body with the second best fitness, and the Gamma body with the third best fitness, and record the positions of the three groups of optimized bodies; 4.3: By updating the positions of the remaining optimized bodies according to the position of the Alpha body, a global search is performed. According to the positions of the Beta body and the Gamma body, the positions of the remaining optimized bodies except the Alpha body are adjusted to perform a local search. After the position of each optimized body is updated, it is detected whether the position of each optimized body meets the preset model structure and parameter boundary value. If it exceeds the preset boundary value, it is corrected to the boundary value, and then the fitness calculation, optimization body selection and position update are repeated until the fitness value change of the Alpha body converges to the preset range; 4.4: After the iteration, the model structure and parameter combination corresponding to the optimization body with the highest fitness, that is, the Alpha body, is taken as the optimal combination, and it is used as the optimized boiler multi-zone CFD-AI model parameters for final deployment.

[0010] A further improvement of the present invention is that the specific steps of enhancing the key area decision of the boiler multi-area CFD-AI model in S3 are as follows: 5.1: The different states of each area of ​​the boiler are taken as nodes, and the transfer relationship between each state is generated according to the control system and environmental conditions of the boiler. The current state of each area of ​​the boiler predicted by the boiler multi-area CFD-AI model is taken as the root node, and according to the transfer relationship of the state, the corresponding state of each area of ​​the boiler is taken as the child node, and its state transfer relationship is taken as the edge. The corresponding search tree is constructed based on its root node and child nodes, and the number of visits and cumulative returns of each group of nodes are initialized; 5.2: Starting from the root node, use the UCT formula to calculate the upper confidence value of each child node, select the child node with the highest upper confidence value along the search tree to build the current optimal path until reaching the leaf node. If the current leaf node is not fully expanded, randomly select an unexplored action from the action space of the current node, use the state transfer function to calculate the new state, and add the new state as a new node to the search tree, initialize the number of visits to the node and the accumulated reward; 5.3: Starting from the currently expanded new node, the state transfer is repeated according to the multiple transfer paths generated by the boiler multi-zone CFD-AI model to generate a complete path until the preset maximum number of steps is reached and then the simulation stops. After the simulation is completed, the cumulative reward of the simulation path is calculated by the set reward function, and the cumulative reward obtained by the simulation is traced back to all nodes on the path, and the number of visits to these nodes is updated until the root node is traced back and then stopped; 5.4: After the cumulative rewards and visit times of each node are updated, repeat the selection, expansion, simulation and backtracking until the cumulative reward changes of each path converge to the preset range. Then, starting from the root node, the child nodes with the most visits are selected in turn as the best decisions for the key areas. Based on the decisions obtained through the search, the optimization direction and parameter adjustment strategy of the boiler multi-region CFD-AI model in the boiler's high nonlinear area are determined.

[0011] The boiler multi-zone CFD-AI modeling system based on federated learning includes: Data collection and preprocessing module, used to simulate the heat transfer and fluid behavior of the boiler, establish multi-region CFD grid division, and collect and preprocess the dynamic operation data of each region from the actual operation of the boiler; The model building module is used to extract the features of the pre-processed dynamic operation data, build a boiler multi-zone CFD-AI model based on the extracted features, and globally optimize the boiler multi-zone CFD-AI model; Model dynamic adjustment module, used to dynamically adjust the structure and parameters of the globally optimized boiler multi-zone CFD-AI model, and enhance the key area decision of the boiler multi-zone CFD-AI model; An evaluation module is used to evaluate the deviation between the prediction results of the boiler multi-zone CFD-AI model and the actual data, and adjust the decision path and model structure to improve the fitting effect of high-dimensional features; The model real-time prediction module is used to repeatedly train and update the boiler multi-zone CFD-AI model, perform real-time predictions through the final model, and regularly collect operating data for online updates and optimization.

[0012] A further improvement of the present invention is that, in the data collection preprocessing module, the specific steps of preprocessing the dynamic operation data of each area are as follows: 1.1: Collect dynamic operation data from the actual operation of the boiler, use a high-pass filter to smooth the noise in each group of dynamic operation data, and fill in the missing values ​​in the dynamic operation data through the cubic spline interpolation method; 1.2: Use the quartile algorithm to divide the dynamic running data after missing value filling into 4 parts in ascending order, each accounting for 25% of the entire data set, and the lower quartile is recorded as , the median is recorded as , the upper quartile is recorded as , calculate the difference between the upper quartile and the lower quartile and record it as the interquartile range ,according to as well as Get the inner limit of outliers [ , ] and remove the data beyond this range; 1.3: Combine the boiler mechanism model and CFD software to simulate the combustion, heat transfer and fluid behavior of the boiler, generate simulation data consistent with the actual operating status, align the pre-processed actual dynamic operation data with the CFD simulation data, and integrate each group of dynamic operation data through partition matching.

[0013] A further improvement of the present invention is that, in the model building module, the specific steps of building a boiler multi-region CFD-AI model are as follows: 2.1: According to the boiler CFD grid division, the integrated dynamic operation data is partitioned by spatial region, and then each group of training data sets after partitioning is normalized. The feature information corresponding to each group of dynamic operation data is obtained through the feature extraction formula, and the dynamic operation data of each region and its corresponding feature information are divided into training set, verification set and test set; 2.2: Convert the training set, validation set, and test set into time series format, establish a boiler multi-zone CFD-AI model based on the model architecture of the input layer, LSTM layer, fully connected layer, and output layer, and then use the random initialization method to initialize the weight parameters and bias parameters of the boiler multi-zone CFD-AI model; 2.3: Input the training set data into the boiler multi-zone CFD-AI model. The CFD-AI model calculates the hidden state and output value of the LSTM layer through the forward propagation algorithm, and recursively updates the hidden state and memory unit step by step to obtain the final output layer prediction value. The model prediction value is compared with the true value, and the corresponding loss value is calculated through the MSE loss function. 2.4: Through the chain rule, the loss value is gradually propagated from the output layer to the input layer, the gradient of the loss value to the parameters of each layer of the model is calculated, and the model parameters are updated using the gradient descent algorithm. After each round of training, the performance indicators of the boiler multi-region CFD-AI model are evaluated through the validation set data, and the current evaluation results are compared with the previous round of evaluation results. If there is fitting or overfitting, the training is terminated in advance. Otherwise, the hyperparameters of the model are adjusted and the model is retrained; 2.5: Repeat forward propagation, loss calculation, back propagation and parameter update until the loss value of the boiler multi-zone CFD-AI model converges to the preset range or reaches the maximum number of iterations. Then use the test set to evaluate the performance of the final model to verify its accuracy and robustness on unseen data, and store the trained model parameters and structure in the central server.

[0014] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the boiler multi-zone CFD-AI modeling method based on federated learning.

[0015] Compared with the prior art, the present invention has at least the following beneficial technical effects: The boiler multi-zone CFD-AI modeling method and system based on federated learning provided by the present invention sends the boiler multi-zone CFD-AI model to each client through a central server as the initial local model of the client, and uses the model parameters and structure after training to initialize the local model of each client. Each client uses each group of dynamic operation data collected locally to train the corresponding local model, and takes minimizing the loss function as the goal, uses the back propagation algorithm to calculate the gradient and update the model parameters. After completing multiple rounds of local training, each client transmits the final model parameters updated by the local training to the central server. The central server calculates the model parameters of each client by weighted averaging method, aggregates them into a global model, and then updates the global model by mean iteration. After the global model is updated, the central server The new global model is broadcast to all clients, and the parameter information of the local model of each client is updated. Each client continues to train locally according to the new global model and performs the next round of mean iteration. When the change in the global model parameter update of the central server is less than the preset threshold, and the loss function change for multiple consecutive rounds converges to the preset threshold or reaches the maximum number of training rounds, the training is terminated, and the optimized global model is used to evaluate the test set data again to verify its performance on unseen data. The original boiler multi-zone CFD-AI model parameters and structure are replaced with the global model parameters and structure, and the updated boiler multi-zone CFD-AI model parameters are deployed on the actual boiler monitoring platform. This can improve the efficiency of model training, save a lot of communication resources, reduce the risk of privacy leakage, and promote the cross-regional adaptability of the model.

[0016] The boiler multi-zone CFD-AI modeling method based on federated learning of the present invention collects the structure and parameter information of the boiler multi-zone CFD-AI model, randomly generates the positions of multiple groups of optimization bodies to represent each group of solutions in the model space, the position of each optimization body represents a potential model structure and parameter combination, substitutes the parameters of each optimization body into the globally optimized boiler multi-zone CFD-AI model, uses the validation set to evaluate its performance, and uses the result of the MSE loss function as the fitness of the optimization body. At the beginning of the iteration, the three groups of optimization bodies with the highest fitness are selected, and global and local searches are performed based on the selected three groups of optimal bodies. After the position of each optimization body is updated, the detection Whether the position of each optimization body meets the preset model structure and parameter boundary values, if it exceeds the preset boundary values, it will be corrected to the boundary values, and then the fitness calculation, optimization body selection and position update are repeated until the fitness value change of the optimization body with the highest fitness converges to the preset range. After the iteration, the model structure and parameter combination corresponding to the optimization body with the highest fitness is taken as the optimal combination, and it is used as the optimized boiler multi-zone CFD-AI model parameter and used for final deployment, which significantly improves the performance of the model under complex working conditions, can effectively handle multi-objective optimization problems, and can more accurately predict and control the performance of the boiler in different areas and working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the boiler multi-zone CFD-AI modeling method based on federated learning proposed in the present invention.

[0019] Figure 2 This is a structural block diagram of the boiler multi-zone CFD-AI modeling system based on federated learning proposed in the present invention. DETAILED DESCRIPTION

[0020] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0021] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0022] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0023] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0024] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0025] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0026] Example 1 Reference Figure 1 , a boiler multi-zone CFD-AI modeling method based on federated learning, the specific steps of the modeling method are as follows: Simulate the heat transfer and fluid behavior of the boiler, establish multi-zone CFD meshing, and collect and pre-process the dynamic operation data of each zone from the actual operation of the boiler.

[0027] Specifically, dynamic operation data are collected from the actual operation of the boiler, and the noise in each group of dynamic operation data is smoothed by using a high-pass filter. The missing values ​​in the dynamic operation data are filled by the cubic spline interpolation method. The dynamic operation data after missing value filling are divided into 4 parts in ascending order using the quartile algorithm, each accounting for 25% of the entire data set, and the lower quartile is recorded as , the median is recorded as , the upper quartile is recorded as , calculate the difference between the upper quartile and the lower quartile and record it as the interquartile range ,according to as well as Get the inner limit of outliers [ , ] and eliminate the data beyond this range. Combine the boiler mechanism model and CFD software to simulate the combustion, heat transfer and fluid behavior of the boiler, generate simulation data consistent with the actual operating status, align the preprocessed actual dynamic operation data with the CFD simulation data, and integrate each group of dynamic operation data through partition matching.

[0028] The features of the preprocessed dynamic operation data are extracted, a boiler multi-zone CFD-AI model is constructed based on the extracted features, and the boiler multi-zone CFD-AI model is globally optimized.

[0029] Specifically, according to the boiler CFD grid division, the integrated dynamic operation data are partitioned according to the spatial region, and then the partitioned groups of training data sets are normalized, and the feature information corresponding to each group of dynamic operation data is obtained through the feature extraction formula, and the dynamic operation data of each region and its corresponding feature information are divided into training set, verification set and test set, and the training set, verification set and test set are converted into time series format. According to the model architecture of the input layer, LSTM layer, fully connected layer and output layer, a boiler multi-region CFD-AI model is established, and then the random initialization method is used to initialize the weight parameters and bias parameters of the boiler multi-region CFD-AI model, and the training set data is input into the boiler multi-region CFD-AI model. The CFD-AI model calculates the hidden state and output value of the LSTM layer through the forward propagation algorithm, and recursively updates the hidden state and memory unit step by step to obtain the final output Output the layer prediction value, compare the model prediction value with the true value, calculate the corresponding loss value through the MSE loss function, and gradually propagate the loss value from the output layer to the input layer through the chain rule. Calculate the gradient of the loss value to the parameters of each layer of the model, and use the gradient descent algorithm to update the model parameters. After each round of training, evaluate the performance indicators of the boiler multi-zone CFD-AI model through the validation set data, and compare the current evaluation results with the evaluation results of the previous round. If there is fitting or overfitting, terminate the training in advance. Otherwise, adjust the hyperparameters of the model, retrain the model, repeat forward propagation, loss calculation, back propagation and parameter update until the loss value of the boiler multi-zone CFD-AI model converges to the preset range or reaches the maximum number of iterations. Then use the test set to evaluate the performance of the final model to verify its accuracy and robustness on unseen data, and store the trained model parameters and structure in the central server.

[0030] Specifically, the central server sends the boiler multi-zone CFD-AI model to each client as the client's initial local model, and uses the model parameters and structure after training to initialize the local model of each client. Each client uses the local dynamic operation data collected to train the corresponding local model, and uses the back propagation algorithm to calculate the gradient and update the model parameters with the goal of minimizing the loss function. After completing multiple rounds of local training, each client transmits the final model parameters updated by local training to the central server. The central server calculates the model parameters of each client by weighted averaging and aggregates them into a global model. The global model is then updated by mean iteration. The global model update is completed. After that, the central server broadcasts the new global model to all clients and updates the parameter information of each client's local model. Each client continues to train locally according to the new global model and performs the next round of mean iteration. When the change in the global model parameter update of the central server is less than the preset threshold and the loss function change for multiple consecutive rounds converges to the preset threshold or reaches the maximum number of training rounds, the training is terminated and the test set data is evaluated again using the optimized global model to verify its performance on unseen data. The original boiler multi-zone CFD-AI model parameters and structure are replaced with the global model parameters and structure, and the updated boiler multi-zone CFD-AI model parameters are deployed on the actual boiler monitoring platform.

[0031] Example 2 Reference Figure 1 , a boiler multi-zone CFD-AI modeling method based on federated learning, the specific steps of the modeling method are as follows: Dynamically adjust the structure and parameters of the globally optimized boiler multi-zone CFD-AI model to enhance the key area decision-making of the boiler multi-zone CFD-AI model.

[0032] Specifically, the structure and parameter information of the boiler multi-zone CFD-AI model is collected, and the positions of multiple groups of optimization bodies are randomly generated to represent each group of solutions in the model space. The position of each optimization body represents a potential model structure and parameter combination. The parameters of each optimization body are substituted into the globally optimized boiler multi-zone CFD-AI model, and its performance is evaluated using a validation set. The result of the MSE loss function is used as the fitness of the optimization body. At the beginning of the iteration, the three groups of optimization bodies with the highest fitness are selected and defined as the Alpha body with the best fitness, the Beta body with the second best fitness, and the Gamma body with the third best fitness. At the same time, the positions of the three groups of optimization bodies are recorded, and the positions of the remaining optimization bodies are updated according to the position of the Alpha body. The positions of the optimization bodies except the Alpha body are adjusted according to the positions of the Beta body and the Gamma body for local search. After the positions of the optimization bodies are updated, it is detected whether the positions of the optimization bodies meet the preset model structure and parameter boundary values. If they exceed the preset boundary values, they are corrected to the boundary values. Then, the fitness calculation, optimization body selection and position update are repeated until the fitness value change of the Alpha body converges to the preset range. After the iteration, the optimization body with the highest fitness, that is, the model structure and parameter combination corresponding to the Alpha body is taken as the optimal combination, and it is used as the optimized boiler multi-zone CFD-AI model parameters for final deployment.

[0033] Specifically, the different states of each area of ​​the boiler are taken as nodes, and the transfer relationship between each state is generated according to the control system and environmental conditions of the boiler. The current state of each area of ​​the boiler predicted by the boiler multi-area CFD-AI model is taken as the root node, and according to the transfer relationship of the state, the corresponding state of each area of ​​the boiler is taken as a child node, and its state transfer relationship is taken as an edge. The corresponding search tree is constructed based on its root node and child nodes, and the number of visits and cumulative returns of each group of nodes are initialized. Starting from the root node, the UCT formula is used to calculate the upper confidence value of each child node, and the child node with the highest upper confidence value is selected along the search tree to construct the current optimal path until the leaf node is reached. If the current leaf node is not fully expanded, an unexplored action is randomly selected from the action space of the current node, and the new state is calculated using the state transfer function. The new state is added to the search tree as a new node to initialize the node. The number of visits and cumulative rewards of each node are calculated. Starting from the currently expanded new node, the state transfer is repeated according to the multiple transfer paths generated by the boiler multi-region CFD-AI model to generate a complete path until the preset maximum number of steps is reached and then stopped. After the simulation is completed, the cumulative reward of the simulated path is calculated by the set reward function, and the cumulative reward obtained by simulation is traced back to all nodes on the path, and the number of visits to these nodes is updated until it is traced back to the root node and then stopped. After the cumulative rewards and the number of visits to each node are updated, the selection, expansion, simulation and backtracking are repeated until the cumulative reward changes of each path converge to the preset range. Then, starting from the root node, the child nodes with the most visits are selected in turn as the best decisions for the key areas, and based on the decisions obtained by the search, the optimization direction and parameter adjustment strategy of the boiler multi-region CFD-AI model in the high nonlinear area of ​​the boiler are determined.

[0034] Evaluate the deviation between the prediction results of the boiler multi-zone CFD-AI model and the actual data, and adjust the decision path and model structure to improve the fitting effect of high-dimensional features.

[0035] The boiler multi-zone CFD-AI model is repeatedly trained and updated, and real-time predictions are made through the final model. Operation data is regularly collected for online updates and optimization.

[0036] Example 2 like Figure 2 As shown, the boiler multi-region CFD-AI modeling system based on federated learning provided by the present invention includes: Data collection and preprocessing module, used to simulate the heat transfer and fluid behavior of the boiler, establish multi-region CFD grid division, and collect and preprocess the dynamic operation data of each region from the actual operation of the boiler; The model building module is used to extract the features of the pre-processed dynamic operation data, build a boiler multi-zone CFD-AI model based on the extracted features, and globally optimize the boiler multi-zone CFD-AI model; Model dynamic adjustment module, used to dynamically adjust the structure and parameters of the globally optimized boiler multi-zone CFD-AI model, and enhance the key area decision of the boiler multi-zone CFD-AI model; An evaluation module is used to evaluate the deviation between the prediction results of the boiler multi-zone CFD-AI model and the actual data, and adjust the decision path and model structure to improve the fitting effect of high-dimensional features; The model real-time prediction module is used to repeatedly train and update the boiler multi-zone CFD-AI model, perform real-time predictions through the final model, and regularly collect operating data for online updates and optimization.

[0037] Example 3 The present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the boiler multi-zone CFD-AI modeling method based on federated learning.

[0038] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0039] The present application is described with reference to the flowcharts and / or block diagrams of the methods, systems and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0040] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0041] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0042] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0043] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A multi-region CFD-AI modeling method for boilers based on federated learning, characterized in that: The steps include: S1. Simulate the heat transfer and fluid behavior of the boiler, establish multi-region CFD meshing, and collect and pre-process the dynamic operation data of each region from the actual operation of the boiler; S2. extracting the features of the preprocessed dynamic operation data, constructing a boiler multi-region CFD-AI model based on the extracted features, and globally optimizing the boiler multi-region CFD-AI model; S3, dynamically adjust the structure and parameters of the globally optimized boiler multi-region CFD-AI model to enhance the key regional decision-making of the boiler multi-region CFD-AI model; S4. Evaluate the deviation between the prediction results of the boiler multi-zone CFD-AI model and the actual data, and adjust the decision path and model structure to improve the fitting effect of high-dimensional features; S5. Repeatedly train and update the boiler multi-zone CFD-AI model, perform real-time predictions through the final model, and regularly collect operating data for online updates and optimization.

2. The boiler multi-zone CFD-AI modeling method based on federated learning according to claim 1 is characterized in that: The specific steps of preprocessing the dynamic operation data of each region in S1 are as follows: 1.1: Collect dynamic operation data from the actual operation of the boiler, use a high-pass filter to smooth the noise in each group of dynamic operation data, and fill in the missing values ​​in the dynamic operation data through the cubic spline interpolation method; 1.2: Use the quartile algorithm to divide the dynamic running data after missing value filling into 4 parts in ascending order, each accounting for 25% of the entire data set, and the lower quartile is recorded as , the median is recorded as , the upper quartile is recorded as , calculate the difference between the upper quartile and the lower quartile and record it as the interquartile range ,according to as well as Get the inner limit of outliers [ , ] and remove the data beyond this range; 1.3: Combine the boiler mechanism model and CFD software to simulate the combustion, heat transfer and fluid behavior of the boiler, generate simulation data consistent with the actual operating status, align the pre-processed actual dynamic operation data with the CFD simulation data, and integrate each group of dynamic operation data through partition matching.

3. The boiler multi-zone CFD-AI modeling method based on federated learning according to claim 2 is characterized in that: The specific steps for constructing the boiler multi-zone CFD-AI model described in S2 are as follows: 2.1: According to the boiler CFD grid division, the integrated dynamic operation data is partitioned by spatial region, and then each group of training data sets after partitioning is normalized. The feature information corresponding to each group of dynamic operation data is obtained through the feature extraction formula, and the dynamic operation data of each region and its corresponding feature information are divided into training set, verification set and test set; 2.2: Convert the training set, validation set, and test set into time series format, establish a boiler multi-zone CFD-AI model based on the model architecture of the input layer, LSTM layer, fully connected layer, and output layer, and then use the random initialization method to initialize the weight parameters and bias parameters of the boiler multi-zone CFD-AI model; 2.3: Input the training set data into the boiler multi-zone CFD-AI model. The CFD-AI model calculates the hidden state and output value of the LSTM layer through the forward propagation algorithm, and recursively updates the hidden state and memory unit step by step to obtain the final output layer prediction value. The model prediction value is compared with the true value, and the corresponding loss value is calculated through the MSE loss function. 2.4: Through the chain rule, the loss value is gradually propagated from the output layer to the input layer, the gradient of the loss value to the parameters of each layer of the model is calculated, and the model parameters are updated using the gradient descent algorithm. After each round of training, the performance indicators of the boiler multi-region CFD-AI model are evaluated through the validation set data, and the current evaluation results are compared with the previous round of evaluation results. If there is fitting or overfitting, the training is terminated in advance. Otherwise, the hyperparameters of the model are adjusted and the model is retrained; 2.5: Repeat forward propagation, loss calculation, back propagation and parameter update until the loss value of the boiler multi-zone CFD-AI model converges to the preset range or reaches the maximum number of iterations. Then use the test set to evaluate the performance of the final model to verify its accuracy and robustness on unseen data, and store the trained model parameters and structure in the central server.

4. The boiler multi-zone CFD-AI modeling method based on federated learning according to claim 3 is characterized in that: The specific steps of the global optimization boiler multi-zone CFD-AI model described in S2 are as follows: 3.1: The central server sends the boiler multi-zone CFD-AI model to each client as the client's initial local model, and uses the trained model parameters and structure to initialize the local model of each client; 3.2: Each client uses each set of dynamic running data collected locally to train the corresponding local model, and uses the back propagation algorithm to calculate the gradient and update the model parameters with the goal of minimizing the loss function. After completing multiple rounds of local training, each client transmits the final model parameters updated by local training to the central server; 3.3: The central server calculates the model parameters of each client through the weighted average method and aggregates them into a global model. Then, the global model is updated through mean iteration. After the global model is updated, the central server broadcasts the new global model to all clients and updates the parameter information of each client's local model. 3.4: Each client continues to train locally based on the new global model and performs the next round of mean iteration. When the change in the global model parameter update of the central server is less than the preset threshold, the change in the loss function for multiple consecutive rounds converges to the preset threshold, or the maximum number of training rounds is reached, the training ends; 3.5: Use the optimized global model to evaluate the test set data again to verify its performance on unseen data, replace the original boiler multi-zone CFD-AI model parameters and structure with the global model parameters and structure, and deploy the updated boiler multi-zone CFD-AI model parameters to the actual boiler monitoring platform.

5. The boiler multi-zone CFD-AI modeling method based on federated learning according to claim 4 is characterized in that: The specific steps of dynamically adjusting the structure and parameters of the globally optimized boiler multi-zone CFD-AI model described in S3 are as follows: 4.1: Collect the structure and parameter information of the boiler multi-zone CFD-AI model, randomly generate multiple groups of optimization body positions to represent each group of solutions in the model space, and the position of each optimization body represents a potential model structure and parameter combination. Substitute the parameters of each optimization body into the globally optimized boiler multi-zone CFD-AI model, use the validation set to evaluate its performance, and use the result of the MSE loss function as the fitness of the optimization body; 4.2: At the beginning of the iteration, select the three groups of optimized bodies with the highest fitness, and define them as the Alpha body with the best fitness, the Beta body with the second best fitness, and the Gamma body with the third best fitness, and record the positions of the three groups of optimized bodies; 4.3: By updating the positions of the remaining optimized bodies according to the position of the Alpha body, a global search is performed. According to the positions of the Beta body and the Gamma body, the positions of the remaining optimized bodies except the Alpha body are adjusted to perform a local search. After the position of each optimized body is updated, it is detected whether the position of each optimized body meets the preset model structure and parameter boundary value. If it exceeds the preset boundary value, it is corrected to the boundary value, and then the fitness calculation, optimization body selection and position update are repeated until the fitness value change of the Alpha body converges to the preset range; 4.4: After the iteration, the model structure and parameter combination corresponding to the optimization body with the highest fitness, that is, the Alpha body, is taken as the optimal combination, and it is used as the optimized boiler multi-zone CFD-AI model parameters for final deployment.

6. The boiler multi-zone CFD-AI modeling method based on federated learning according to claim 1 is characterized in that: The specific steps for enhancing the key area decision of the boiler multi-area CFD-AI model described in S3 are as follows: 5.1: The different states of each area of ​​the boiler are taken as nodes, and the transfer relationship between each state is generated according to the control system and environmental conditions of the boiler. The current state of each area of ​​the boiler predicted by the boiler multi-area CFD-AI model is taken as the root node, and according to the transfer relationship of the state, the corresponding state of each area of ​​the boiler is taken as the child node, and its state transfer relationship is taken as the edge. The corresponding search tree is constructed based on its root node and child nodes, and the number of visits and cumulative returns of each group of nodes are initialized; 5.2: Starting from the root node, use the UCT formula to calculate the upper confidence value of each child node, select the child node with the highest upper confidence value along the search tree to build the current optimal path until reaching the leaf node. If the current leaf node is not fully expanded, randomly select an unexplored action from the action space of the current node, use the state transfer function to calculate the new state, and add the new state as a new node to the search tree, initialize the number of visits to the node and the accumulated reward; 5.3: Starting from the currently expanded new node, the state transfer is repeated according to the multiple transfer paths generated by the boiler multi-zone CFD-AI model to generate a complete path until the preset maximum number of steps is reached and then the simulation stops. After the simulation is completed, the cumulative reward of the simulation path is calculated by the set reward function, and the cumulative reward obtained by the simulation is traced back to all nodes on the path, and the number of visits to these nodes is updated until the root node is traced back and then stopped; 5.4: After the cumulative rewards and visit times of each node are updated, repeat the selection, expansion, simulation and backtracking until the cumulative reward changes of each path converge to the preset range. Then, starting from the root node, the child nodes with the most visits are selected in turn as the best decisions for the key areas. Based on the decisions obtained through the search, the optimization direction and parameter adjustment strategy of the boiler multi-region CFD-AI model in the boiler's high nonlinear area are determined.

7. The boiler multi-zone CFD-AI modeling system based on federated learning is characterized by: include: Data collection and preprocessing module, used to simulate the heat transfer and fluid behavior of the boiler, establish multi-region CFD grid division, and collect and preprocess the dynamic operation data of each region from the actual operation of the boiler; The model building module is used to extract the features of the pre-processed dynamic operation data, build a boiler multi-zone CFD-AI model based on the extracted features, and globally optimize the boiler multi-zone CFD-AI model; Model dynamic adjustment module, used to dynamically adjust the structure and parameters of the globally optimized boiler multi-zone CFD-AI model, and enhance the key area decision of the boiler multi-zone CFD-AI model; An evaluation module is used to evaluate the deviation between the prediction results of the boiler multi-zone CFD-AI model and the actual data, and adjust the decision path and model structure to improve the fitting effect of high-dimensional features; The model real-time prediction module is used to repeatedly train and update the boiler multi-zone CFD-AI model, perform real-time predictions through the final model, and regularly collect operating data for online updates and optimization.

8. The boiler multi-zone CFD-AI modeling system based on federated learning according to claim 7 is characterized in that: In the data collection preprocessing module, the specific steps of preprocessing the dynamic operation data of each area are as follows: 1.1: Collect dynamic operation data from the actual operation of the boiler, use a high-pass filter to smooth the noise in each group of dynamic operation data, and fill in the missing values ​​in the dynamic operation data through the cubic spline interpolation method; 1.2: Use the quartile algorithm to divide the dynamic running data after missing value filling into 4 parts in ascending order, each accounting for 25% of the entire data set, and the lower quartile is recorded as , the median is recorded as , the upper quartile is recorded as , calculate the difference between the upper quartile and the lower quartile and record it as the interquartile range ,according to as well as Get the inner limit of outliers [ , ] and remove the data beyond this range; 1.3: Combine the boiler mechanism model and CFD software to simulate the combustion, heat transfer and fluid behavior of the boiler, generate simulation data consistent with the actual operating status, align the pre-processed actual dynamic operation data with the CFD simulation data, and integrate each group of dynamic operation data through partition matching.

9. The boiler multi-zone CFD-AI modeling system based on federated learning according to claim 7 is characterized in that: In the model building module, the specific steps of building a boiler multi-zone CFD-AI model are as follows: 2.1: According to the boiler CFD grid division, the integrated dynamic operation data is partitioned by spatial region, and then each group of training data sets after partitioning is normalized. The feature information corresponding to each group of dynamic operation data is obtained through the feature extraction formula, and the dynamic operation data of each region and its corresponding feature information are divided into training set, verification set and test set; 2.2: Convert the training set, validation set, and test set into time series format, establish a boiler multi-zone CFD-AI model based on the model architecture of the input layer, LSTM layer, fully connected layer, and output layer, and then use the random initialization method to initialize the weight parameters and bias parameters of the boiler multi-zone CFD-AI model; 2.3: Input the training set data into the boiler multi-zone CFD-AI model. The CFD-AI model calculates the hidden state and output value of the LSTM layer through the forward propagation algorithm, and recursively updates the hidden state and memory unit step by step to obtain the final output layer prediction value. The model prediction value is compared with the true value, and the corresponding loss value is calculated through the MSE loss function. 2.4: Through the chain rule, the loss value is gradually propagated from the output layer to the input layer, the gradient of the loss value to the parameters of each layer of the model is calculated, and the model parameters are updated using the gradient descent algorithm. After each round of training, the performance indicators of the boiler multi-region CFD-AI model are evaluated through the validation set data, and the current evaluation results are compared with the previous round of evaluation results. If there is fitting or overfitting, the training is terminated in advance. Otherwise, the hyperparameters of the model are adjusted and the model is retrained; 2.5: Repeat forward propagation, loss calculation, back propagation and parameter update until the loss value of the boiler multi-zone CFD-AI model converges to the preset range or reaches the maximum number of iterations. Then use the test set to evaluate the performance of the final model to verify its accuracy and robustness on unseen data, and store the trained model parameters and structure in the central server.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the federated learning-based boiler multi-zone CFD-AI modeling method described in any one of claims 1 to 6.

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