CN115049093A - Yield stress prediction method and system based on ensemble learning algorithm - Google Patents
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Abstract
The invention discloses a yield stress prediction method and a system based on an ensemble learning algorithm, wherein the method comprises the following steps: the paste yield stress prediction method based on the Stacking ensemble learning algorithm is based on a large amount of experimental data, a Stacking model fusion algorithm is adopted to integrate various regression models (DT, SVM, KNN, RF and the like) to construct a paste yield stress prediction model, and the experimental data are subjected to pretreatment such as elimination of abnormal values and dimensionless treatment to obtain a training set by utilizing the influence of various factors such as waste stone/tailing ratio, cement quantity, mass concentration and the like in the paste, so that the yield stress Stacking ensemble model is trained, and the prediction efficiency and the prediction accuracy are improved.
Description
Technical Field
The invention relates to the field of mine filling, in particular to a yield stress prediction method and system based on an integrated learning algorithm.
Background
The mining industry is the national industry life line and is an important foundation for developing national economy and guaranteeing national safety. Although mineral resources in China are rich, the method has the characteristics of more lean ores, less large ore deposits, difficult development and utilization and the like. In recent years, a great deal of mineral resources are developed, and shallow mineral resources are gradually exhausted, and the mining of the mineral resources in deep areas is gradually shifted. A series of problems such as mine safety accidents, mining area ecological environment damage, large amount of solid waste stacking and the like are developed. Therefore, higher requirements are put forward on the aspects of safe production, environmental protection, resource utilization and the like in mineral resource exploitation, the development of circular economy is emphasized, and green exploitation becomes the inevitable trend of the development of the mining industry. The paste filling method becomes the development direction of the future mining industry due to the outstanding characteristics of safety, economy, environmental protection, high efficiency and the like.
In the paste filling process flow, conveying is used as the last core process, and the paste slurry conveying quality directly determines the filling effect. In order to ensure the filling effect, paste slurry cannot bleed and separate, and proper flatness needs to be maintained. The yield stress of the paste body is used as a key parameter of rheological property, and is an important way for judging the conveying quality of paste body slurry. At present, the yield stress of the paste is mainly detected by a paddle rheometer operating method, and then a slump yield stress theory is introduced for inspection and correction. The method needs to carry out a plurality of groups of experiments, has relatively complex operation and needs a large amount of time, and is not suitable for actual production scenes.
However, in the prior art, a prediction model of a Stacking ensemble learning algorithm relies on a large amount of original data for learning, and with the increase of massive sample training, the technical problems that the method for measuring the yield stress of the paste body in the early stage is complex and time-consuming and has relatively low prediction accuracy are solved.
Disclosure of Invention
The invention aims to provide a yield stress prediction method and system based on an ensemble learning algorithm, which are used for solving the technical problems that a prediction model of a Stacking ensemble learning algorithm in the prior art relies on a large amount of original data for learning, and the yield stress of a paste body is measured in the early stage in a complex and time-consuming manner and has relatively low prediction accuracy along with the increase of massive sample training.
In view of the above problems, the present invention provides a method and a system for predicting yield stress based on an ensemble learning algorithm.
In a first aspect, the present invention provides a yield stress prediction method based on an ensemble learning algorithm, which is implemented by a yield stress prediction system based on an ensemble learning algorithm, wherein the method includes: building a yield stress prediction model, wherein the yield stress prediction model comprises a first layer base learner model and a second layer meta-learner model; acquiring an original experiment data set of a target object based on big data, wherein the original experiment data set comprises a multi-feature set of the target object; preprocessing the original experimental data set to obtain a first training data set; inputting the first training data set into the first layer base learner model for model training to obtain an initial yield stress prediction value set of the target object; inputting the initial yield stress prediction value set into the second layer element learner model for correction training to obtain a yield stress model prediction value of the target object; training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target object; inputting the multi-feature experimental data set of the first target object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first target object to be predicted, and performing relative error analysis on the first predicted yield stress result.
In another aspect, the present invention further provides an ensemble learning algorithm based yield stress prediction system, for implementing the ensemble learning algorithm based yield stress prediction method according to the first aspect, wherein the system includes: the system comprises a first building unit and a second building unit, wherein the first building unit is used for building a yield stress prediction model, and the yield stress prediction model comprises a first layer basis learner model and a second layer meta learner model; the device comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring an original experiment data set of a target object based on big data, and the original experiment data set comprises a multi-feature set of the target object; the first processing unit is used for preprocessing the original experiment data set to obtain a first training data set; a first input unit, configured to input the first training data set to the first layer-based learner model for model training, so as to obtain an initial set of yield stress prediction values of the target object; the second input unit is used for inputting the initial yield stress prediction value set into the second layer element learner model for correction training to obtain a yield stress model prediction value of the target object; a first training unit for training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target object; and the third input unit is used for inputting the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first object to be predicted, and performing relative error analysis on the first predicted yield stress result.
In a third aspect, the present invention further provides a yield stress prediction system based on an ensemble learning algorithm, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to the first aspect when executing the program.
In a fourth aspect, an electronic device, comprising a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first aspect above by calling.
In a fifth aspect, a computer program product comprises a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the first aspect described above.
One or more technical schemes provided by the invention at least have the following technical effects or advantages:
the paste yield stress prediction method based on the Stacking ensemble learning algorithm is characterized in that a paste yield stress prediction model is constructed by integrating multiple regression models (DT, SVM, KNN, RF and the like) through the Stacking model fusion algorithm on the basis of a large amount of experimental data, and the experimental data are preprocessed by removing abnormal data, dimensionless data and the like to obtain a training set by utilizing the influence of multiple factors such as waste stone/tailing ratio, cement quantity, mass concentration and the like in the paste, so that the yield stress Stacking ensemble model is trained, and the prediction efficiency and the prediction accuracy are improved. The method can greatly improve the accuracy and convenience of paste yield stress prediction.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can be obtained according to the provided drawings without inventive effort.
FIG. 1 is a schematic flow chart of a yield stress prediction method based on an ensemble learning algorithm according to the present invention;
FIG. 2 is a schematic flow chart of the initial yield stress prediction value set obtained in the yield stress prediction method based on the ensemble learning algorithm according to the present invention;
FIG. 3 is a schematic flow chart of the yield stress model prediction value obtained in the yield stress prediction method based on the ensemble learning algorithm of the present invention;
FIG. 4 is a schematic structural diagram of a yield stress prediction system based on an ensemble learning algorithm according to the present invention;
fig. 5 is a schematic structural diagram of an exemplary electronic device of the present invention.
Description of reference numerals:
the system comprises a first building unit 11, a first acquisition unit 12, a first processing unit 13, a first input unit 14, a second input unit 15, a first training unit 16, a third input unit 17, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304 and a bus interface 305.
Detailed Description
The invention provides a yield stress prediction method and system based on an ensemble learning algorithm, and solves the technical problems that a prediction model of a Stacking ensemble learning algorithm relies on a large amount of original data for learning, and the yield stress of a paste body is measured in the early stage in a complex and time-consuming manner and relatively low in prediction accuracy along with the increase of massive sample training. The paste yield stress prediction method based on the Stacking ensemble learning algorithm is based on a large amount of experimental data, a Stacking model fusion algorithm is adopted to integrate multiple regression models to construct a paste yield stress prediction model, abnormal data are removed from the experimental data and dimensionless preprocessing is carried out to obtain a training set by utilizing the influence of multiple factors in the paste, so that the yield stress Stacking ensemble model is trained, the accuracy and convenience of paste yield stress prediction are improved, and the technical effect of improving the production efficiency is further achieved.
In the technical scheme of the invention, the data acquisition, storage, use, processing and the like all conform to relevant regulations of national laws and regulations.
In the following, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention. It should be further noted that, for the convenience of description, only some but not all of the elements associated with the present invention are shown in the drawings.
The invention provides a yield stress prediction method based on an ensemble learning algorithm, which is applied to a yield stress prediction system based on the ensemble learning algorithm, wherein the method comprises the following steps: building a yield stress prediction model, wherein the yield stress prediction model comprises a first layer base learner model and a second layer meta-learner model; acquiring an original experiment data set of a target object based on big data, wherein the original experiment data set comprises a multi-feature set of the target object; preprocessing the original experiment data set to obtain a first training data set; inputting the first training data set into the first layer base learner model for model training to obtain an initial yield stress prediction value set of the target object; inputting the initial yield stress prediction value set into the second layer element learner model for correction training to obtain a yield stress model prediction value of the target; training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target object; inputting the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first object to be predicted, and performing relative error analysis on the first predicted yield stress result.
Having described the general principles of the invention, reference will now be made in detail to various non-limiting embodiments of the invention, examples of which are illustrated in the accompanying drawings.
Example one
Referring to fig. 1, the present invention provides a yield stress prediction method based on an ensemble learning algorithm, wherein the method is applied to a yield stress prediction system based on the ensemble learning algorithm, and the method specifically includes the following steps:
step S100: building a yield stress prediction model, wherein the yield stress prediction model comprises a first layer base learner model and a second layer meta-learner model;
step S200: acquiring an original experimental data set of a target object based on big data, wherein the original experimental data set comprises a multi-feature set of the target object;
specifically, in the paste filling process flow, the conveying is used as the last core process, and the paste slurry conveying quality directly determines the filling effect. In order to ensure the filling effect, paste slurry cannot bleed and separate, and proper flatness needs to be maintained. The yield stress of the paste is taken as a key parameter of rheological property, and is an important way for judging the conveying quality of paste slurry. At present, the yield stress of the paste is mainly detected by a paddle rheometer operation method, and then a slump yield stress theory is introduced for inspection and correction. The method needs to carry out a plurality of groups of experiments, has relatively complex operation and needs a large amount of time, and is not suitable for actual production scenes. In order to solve the problems, the yield stress prediction method based on the ensemble learning algorithm is provided, a paste yield stress prediction model is established based on the Stacking ensemble learning method, the paste yield stress can be predicted only by inputting test set data, and the production efficiency is greatly improved.
Specifically, the yield stress prediction model is implemented based on a Stacking ensemble learning method, wherein a method used when individual learners are combined together is called a combination strategy, in the Stacking method, the individual learners are called primary learners, a learner used for combination is called a secondary learner or a meta-learner (meta-learner), and data used for training by the secondary learner is called a secondary training set. The secondary training set is obtained with the primary learner on the training set. If it is desired to predict the output of a datum, it is only necessary to predict the datum with the primary learner and then predict the predicted result with the secondary learner. Thus, the yield stress prediction model includes a first layer base learner model, i.e., corresponding to a primary learner, and a second layer meta learner model, i.e., corresponding to a secondary learner.
Furthermore, based on big data, an original experimental data set of the target object is acquired, namely, the waste rock/tailing ratio, the cement amount and the mass concentration data of the paste with different characteristics of the same type, the corresponding paste yield stress data and the like are acquired, and based on the original experimental data, a yield stress prediction model of the paste can be trained.
Step S300: preprocessing the original experiment data set to obtain a first training data set;
further, step S300 includes:
step S310: obtaining a first characteristic data set according to the original experiment data set;
step S320: performing centralized processing on the first characteristic data set to obtain a second characteristic data set;
step S330: obtaining a first covariance matrix of the second feature data set;
step S340: calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
step S350: and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.
Specifically, after the original experiment data set of the target object is acquired, because the waste stone/tailing ratio, the cement amount and the mass concentration of the paste all affect the yield stress of the paste, a large number of experiments are needed to acquire the waste stone/tailing ratio, the cement amount, the mass concentration and other data of the paste with the same type and different characteristics and corresponding yield stress values as a training set, and the training set is preprocessed through manual elimination of repeated and abnormal data, completion of missing data, dimensionless data and the like for use in a follow-up training prediction model.
When the original experiment data set is preprocessed, multiple characteristics of the paste can be obtained according to the original experiment data set, the extracted characteristic data is subjected to numerical processing, a characteristic data set matrix is constructed, and the first characteristic data set is obtained. And then carrying out centralization processing on each feature data in the first feature data set, firstly solving an average value of each feature in the first feature data set, then subtracting the average value of each feature from each feature for all samples, and then obtaining a new feature value, wherein the second feature data set is formed by the new feature values, and is a data matrix. By the covariance formula:
and operating the second characteristic data set to obtain a first covariance matrix of the second characteristic data set. Wherein the content of the first and second substances,
characteristic data in the second characteristic data set;
is the average value of the characteristic data; and M is the total amount of sample data in the second characteristic data set. Then, through matrix operation, the eigenvalue and the eigenvector of the first covariance matrix are solved, and each eigenvalue corresponds to one eigenvector. And selecting the largest first K characteristic values and the characteristic vectors corresponding to the maximum first K characteristic values from the obtained first characteristic vectors, and projecting the original characteristics in the first characteristic data set onto the selected characteristic vectors to obtain the first characteristic data set after dimension reduction, so as to realize the pretreatment of the original experimental data set.
Step S400: inputting the first training data set into the first layer base learner model for model training to obtain an initial yield stress prediction value set of the target object;
further, as shown in fig. 2, step S400 includes:
step S410: the first layer of base learner model comprises a decision tree model, a support vector machine model and a proximity algorithm model;
step S420: inputting the first training data set into the decision tree model to obtain a first model prediction result;
step S430: inputting the first training data set into the support vector machine model to obtain a second model prediction result;
step S440: inputting the first training data set into the proximity algorithm model to obtain a third model prediction result;
step S450: and performing data fusion on the first model prediction result, the second model prediction result and the third model prediction result to obtain the initial yield stress prediction value set.
Specifically, the paste yield stress prediction model based on the Stacking ensemble learning algorithm is obtained by a plurality of regression model sets and is divided into a first layer basis learner and a second layer meta learner. Wherein the first layer base learner model comprises a Decision Tree (DT), a Support Vector Machine (SVM), and a proximity algorithm (KNN).
First, Decision Tree (DT) is often used for data analysis, and has the characteristics of high efficiency, simplicity, strong interpretability, and the like. Each decision tree model is usually composed of a single root node, a plurality of internal nodes and leaf nodes, and data analysis is realized through a tree structure of a plurality of judgment nodes in the structure tree model. The decision tree divides samples into different nodes mainly by constructing a series of attribute tests, so that the 'similarity' between the samples in the same node is higher and higher, and the purposes of learning and prediction are further achieved. And inputting the first training data set into the decision tree model to obtain a first model prediction result, wherein the first model prediction result comprises first yield stress prediction results of different pastes under a multi-feature set.
Secondly, a basic idea of a Support Vector Machine (SVM for short) is to solve a hyperplane which can be correctly divided and trained and has the largest geometric interval in a feature space. In the sample space, the hyperplane may be defined by W T x + b is described as 0, where W is a normal vector and b is a position term, determining the sum of the directions of the hyperplaneDistance between the hyperplane and the origin. The training sample point closest to the hyperplane is called a Support Vector (Support Vector), the sum of the distances from 2 heterogeneous Support vectors to the hyperplane is called an interval, and the learning strategy of the SVM is to find a certain hyperplane and maximize the interval. And inputting the first training data set into the support vector machine model to obtain a second model prediction result, wherein the second model prediction result reflects the maximum interval from two heterogeneous support vectors to a hyperplane under a certain hyperplane, and further represents the second yield stress prediction results of different pastes under a multi-feature set.
Thirdly, the Neighbor algorithm (K-Nearest Neighbor, KNN for short) is a supervised learning algorithm for solving the classification and regression problems. The adjacent algorithm uses the average value of the characteristics of the training samples to be predicted by establishing a vector space model, selecting K training samples and using an averaging method. The basic flow is that firstly, a training set and a test set are divided, then, the Euclidean distance between sample data and a prediction sample is calculated, finally, the Euclidean distance is listed from small to large, the training data in the first K numbers are removed, and the average value of the training data is calculated, namely the final prediction value. The KNN algorithm has the advantages of simplicity and good generalization capability. By inputting the first training data set to the proximity algorithm model, a third model prediction result can be obtained, wherein the third model prediction result represents a third yield stress prediction result of different pastes under a multi-feature set.
Since a Decision Tree (DT), a Support Vector Machine (SVM) and a proximity algorithm (KNN) are all used for carrying out classification prediction on the first training data set, the first model prediction result, the second model prediction result and the third model prediction result can be subjected to data fusion to obtain the initial yield stress prediction value set, and primary yield stress prediction on the paste with the same type and different characteristics is realized.
Step S500: inputting the initial yield stress prediction value set into the second layer element learner model for correction training to obtain a yield stress model prediction value of the target;
further, as shown in fig. 3, step S500 includes:
step S510: embedding the second layer element learner model into a random forest regression model;
step S520: dividing the initial yield stress prediction value set into a first sub training set, a second sub training set and an Nth sub training set;
step S530: obtaining a first prediction result based on the first sub-training set, obtaining a second prediction result based on the second sub-training set, and so on, obtaining an Nth prediction result based on the Nth sub-training set;
step S540: and carrying out average calculation on the weights of the first prediction result, the second prediction result and the Nth prediction result to obtain the yield stress model prediction value.
Specifically, after the initial paste yield stress predicted value is obtained based on the first layer base learner model, the initial paste yield stress predicted value needs to be corrected to obtain a final paste yield stress predicted value, so that the initial yield stress predicted value set can be input to the second layer element learner model for correction training, and the second layer element learner model is realized based on a random forest regression model.
Specifically, a Random Forest (RF) is an extended variant of parallel ensemble learning Bagging. By establishing a plurality of unrelated decision trees, the combined decision trees can be more accurately and stably predicted and are used for solving the problems of discrimination, classification and regression. The initial yield stress prediction value set is divided into a first sub-training set, a second sub-training set and an Nth sub-training set, wherein each sub-training set represents a decision tree, and different decision characteristics exist among the decision trees, so that comprehensive and accurate decision is ensured to be made on training data.
The training data can be trained for the first time through the first sub-training set, the first prediction result is a first paste yield stress prediction value obtained based on the training of the first sub-decision tree, and by analogy, the second prediction result can be obtained through the second sub-training set until the Nth prediction result is obtained, the Nth prediction result represents an Nth paste yield stress prediction value obtained based on the training of the Nth sub-decision tree, the average value of the proportion of a single paste yield stress prediction value in the N paste yield stress prediction values is calculated, the yield stress model prediction value is obtained, the yield stress model prediction value is a result obtained through the average value calculation, and the prediction result of the original data is more accurate and stable based on a random forest regression model.
Step S600: training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target object;
step S700: inputting the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first object to be predicted, and performing relative error analysis on the first predicted yield stress result.
Further, step S700 includes:
step S710: based on a relative error calculation formula
And carrying out relative error analysis on the first predicted yield stress result, wherein delta is an actual relative error, delta is an absolute error, and L is a true value.
Specifically, after the yield stress model predicted values of the pastes with the same type and different characteristics are obtained, the predicted values can be compared with actual measured values of the pastes to perform error analysis. The method comprises the steps of establishing a paste yield stress prediction model based on Stacking ensemble learning, taking a training set as input, taking corresponding paste yield stress data as expected output, and training a paste yield stress prediction model based on a Stacking ensemble learning algorithm based on an actual yield stress measured value of the paste, so that the finally trained paste yield stress prediction model can accurately and stably predict the yield stress of the paste to be tested.
And then inputting the multi-feature experimental data set of the first target object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first target object to be predicted, namely inputting the barren rock/tailing ratio, the cement quantity and the mass concentration data of the paste to be predicted into the paste yield stress prediction model based on the Stacking ensemble learning algorithm, and predicting the yield stress result of the paste. Exemplary paste parameters to be predicted are shown in the following table:
furthermore, the estimation accuracy evaluation of the paste yield stress Stacking ensemble learning algorithm model is analyzed by a relative error delta method, and the expression is as follows:
and carrying out relative error analysis on the first predicted yield stress result, wherein delta is an actual relative error, delta is an absolute error, and L is a true value. In order to verify the prediction accuracy, the prediction result is analyzed by a relative error analysis method, in 35 groups of test data, the relative errors of the predicted value and the actual measured value of the paste yield stress model do not exceed +/-30%, the relative errors of the predicted value and the experimental measured value of the paste yield stress model exceeding 65% are less than +/-10%, and the analysis result is shown in the following table:
therefore, the Stacking ensemble learning algorithm is adopted to fuse a plurality of prediction models, parameters in each model are optimized through a Bayesian optimization method, single model prediction errors are avoided, advantages of each model are brought into play, the overall model performance is optimized, prediction accuracy is improved, good economic benefits are brought to enterprises, and the method is suitable for popularization and application in the mine filling field.
Further, after obtaining the first dimension reduction data set, step S350 includes:
step S351: obtaining the first training data set according to the first dimension reduction data set;
step S352: based on a K-fold cross-validation method, dividing the first training data set into K subsets A (A) with the same size 1 ,A 2 ,…,A K }；
Step S353: (ii) converting said A to { A ═ A 1 ,A 2 ,…,A K Every subset in the test set is divided into K times to be used as a test set B C The other subset is used as training set B X Will train set B X Inputting the first-layer base learner model for training to obtain the A ═ A 1 ,A 2 ,…,A K Each sample in the set of test results.
Specifically, a preprocessed training set is used as input, corresponding paste yield stress data is used as expected output, a paste yield stress prediction model based on a Stacking ensemble learning algorithm is trained, and specifically, an original data set can be divided into K subsets with the same size by adopting K-fold cross validation: a ═ A 1 ,A 2 ,…,A K And then changing a to { a } 1 ,A 2 ,…,A K Every subset in the test set is divided into K times to be used as a test set B C And the other subsets are used as training sets B X Training set B X Inputting the learning machine to train, and obtaining the A ═ A 1 ,A 2 ,…,A K And (4) collecting test results of each sample. The first layer base learner model carries out K-fold cross validation on the Kth test set B C Predicting the result of each sample. After K-fold cross validation, the output result of the base learner forms a new data set which is used as the input data of the second-layer meta-learner. Namely, the output result of the first layer base learner model is used as a data set input by a second layer of the Stacking formation model, and the second layer meta learner model adopts a Support Vector Machine (SVM) regression model to carry out induction learning, so that the characteristics and advantages of each model in the base learner can be fully exerted, and the prediction error of each model in the base learner can be avoided.
Further, before inputting the multi-feature experimental data set of the first target object to be predicted into the trained yield stress prediction model, step S600 includes:
step S610: judging whether the yield stress prediction model is initialized or not;
step S620: if the yield stress prediction model is initialized, obtaining each model parameter set in the yield stress prediction model;
step S630: and automatically adjusting the parameter sets of the models based on a Bayesian optimization algorithm.
Specifically, because each model in the basis learner and the meta learner has more parameters, the parameters affect the precision of the model together, and the algorithm cannot be optimized by manually adjusting the parameters, a Bayesian optimization method is introduced to automatically adjust the parameters before the multi-feature experimental data set of the first target object to be predicted is input into the trained yield stress prediction model.
Specifically, whether the yield stress prediction model is initialized or not is judged, if the yield stress prediction model is initialized, each model parameter set in the yield stress prediction model can be obtained, and then each model parameter set is automatically adjusted based on a Bayesian optimization algorithm. Giving y a prior probability and the maximum iteration number N; randomly initializing n0 points and obtaining n0 point results; updating the prior probability n-n 0 by using initialized n0 points; when N < ═ N: calculating an acquisition function an (x) according to a current posterior probability p (y | { (x1, f (x1)), (xn, f (xn)) }); b) selecting the point that maximizes an (x) as xn + 1; c) a new point xn +1, brought to f (xn + 1); d) updating n to n + 1; returning to the currently evaluated data a point x such that f (x) is maximal; or x is chosen such that the posterior probability mean of f is maximal.
In summary, the yield stress prediction method based on the ensemble learning algorithm provided by the invention has the following technical effects:
1. the paste yield stress prediction method based on the Stacking ensemble learning algorithm is characterized in that a paste yield stress prediction model is constructed by integrating multiple regression models (DT, SVM, KNN, RF and the like) through the Stacking model fusion algorithm on the basis of a large amount of experimental data, and the experimental data are preprocessed by removing abnormal data, dimensionless data and the like to obtain a training set by utilizing the influence of multiple factors such as waste stone/tailing ratio, cement quantity, mass concentration and the like in the paste, so that the yield stress Stacking ensemble model is trained, and the prediction efficiency and the prediction accuracy are improved. The method can greatly improve the accuracy and convenience of paste yield stress prediction.
2. Parameters in each model are optimized through a Bayesian optimization method, prediction errors of a single model are avoided, advantages of each model are played, overall model performance is optimized, and prediction accuracy is improved.
3. The Stacking model fusion method abandons the conventional method of fusion by adopting a plurality of algorithms with higher similarity, and through experimental comparison, the algorithm fusion with high difference and strong learning capability is adopted for optimization, so that the prediction effect of the Stacking model fusion can reach the optimum.
Example two
Based on the same inventive concept as the yield stress prediction method based on the ensemble learning algorithm in the foregoing embodiment, the present invention further provides a yield stress prediction system based on the ensemble learning algorithm, please refer to fig. 4, where the system includes:
the first building unit 11 is used for building a yield stress prediction model, wherein the yield stress prediction model comprises a first layer basis learner model and a second layer meta learner model;
the first acquisition unit 12 is configured to acquire an original experimental data set of a target object based on big data, where the original experimental data set includes a multi-feature set of the target object;
the first processing unit 13, where the first processing unit 13 is configured to pre-process the original experimental data set to obtain a first training data set;
a first input unit 14, where the first input unit 14 is configured to input the first training data set to the first layer-based learner model for model training, and obtain an initial set of yield stress prediction values of the object;
a second input unit 15, where the second input unit 15 is configured to input the initial yield stress prediction value set into the second layer learner model for modification training, so as to obtain a yield stress model prediction value of the target object;
a first training unit 16, wherein the first training unit 16 is used for training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target object;
a third input unit 17, where the third input unit 17 is configured to input the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model, perform prediction training to obtain a first predicted yield stress result of the first object to be predicted, and perform relative error analysis on the first predicted yield stress result.
Further, the system further comprises:
a first embedding unit for embedding the first layer-based learner model into a decision tree model, a support vector machine model, and a proximity algorithm model;
a fourth input unit, configured to input the first training data set to the decision tree model to obtain a first model prediction result;
a fifth input unit for inputting the first training data set to the support vector machine model to obtain a second model prediction result;
a sixth input unit, configured to input the first training data set to the proximity algorithm model, to obtain a third model prediction result;
and the first fusion unit is used for carrying out data fusion on the first model prediction result, the second model prediction result and the third model prediction result to obtain the initial yield stress prediction value set.
Further, the system further comprises:
a second embedding unit, configured to embed the second hierarchical element learner model into a random forest regression model;
the first dividing unit is used for dividing the initial yield stress prediction value set into a first sub training set, a second sub training set and an Nth sub training set;
a first obtaining unit, configured to obtain a first prediction result based on the first sub-training set, obtain a second prediction result based on the second sub-training set, and so on, and obtain an nth prediction result based on the nth sub-training set;
the first calculation unit is used for carrying out average calculation on the weights of the first prediction result, the second prediction result and the Nth prediction result to obtain the yield stress model prediction value.
Further, the system further comprises:
a second obtaining unit, configured to obtain a first feature data set according to the original experiment data set;
the second processing unit is used for carrying out centralized processing on the first characteristic data set to obtain a second characteristic data set;
a third obtaining unit configured to obtain a first covariance matrix of the second feature data set;
a fourth obtaining unit, configured to perform operation on the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
a fifth obtaining unit, configured to project the first feature data set to the first feature vector, and obtain a first dimension-reduced data set, where the first dimension-reduced data set is a feature data set obtained after dimension reduction of the first feature data set.
Further, the system further comprises:
a sixth obtaining unit, configured to obtain the first training data set according to the first dimension reduction data set;
first of allA dividing unit, configured to divide the first training data set into K equal subsets a { a ═ a ] with the same size based on a K-fold cross validation method 1 ,A 2 ,…,A K }；
A seventh obtaining unit for setting the A ═ A 1 ,A 2 ,…,A K Every subset in the test set is divided into K times to be used as a test set B C The other subset is used as training set B X Will train set B X Inputting the first-layer base learner model for training to obtain the A ═ A 1 ,A 2 ,…,A K And (4) collecting test results of each sample.
Further, the system further comprises:
a first analysis unit for calculating a formula based on a relative error
Performing relative error analysis on the first predicted yield stress result; where δ is the actual relative error, Δ is the absolute error, and L is the true value.
Further, the system further comprises:
a first judgment unit configured to judge whether the yield stress prediction model is initialized;
an eighth obtaining unit, configured to obtain each model parameter set in the yield stress prediction model if the yield stress prediction model is initialized;
and the first adjusting unit is used for automatically adjusting each model parameter set based on a Bayesian optimization algorithm.
In the present specification, the embodiments are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the yield stress prediction method based on the ensemble learning algorithm in the first embodiment of fig. 1 and the specific example are also applicable to the yield stress prediction system based on the ensemble learning algorithm in the present embodiment. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Exemplary electronic device
The electronic device of the present invention is described below with reference to fig. 5.
Fig. 5 illustrates a schematic structural diagram of an electronic device according to the present invention.
Based on the inventive concept of a method for predicting yield stress based on ensemble learning algorithm as described in the previous embodiments, the present invention further provides a system for predicting yield stress based on ensemble learning algorithm, on which a computer program is stored, which, when being executed by a processor, implements the steps of any one of the methods for predicting yield stress based on ensemble learning algorithm as described above.
Where in fig. 5 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The invention provides a yield stress prediction method based on an ensemble learning algorithm, which is applied to a yield stress prediction system based on the ensemble learning algorithm, wherein the method comprises the following steps: building a yield stress prediction model, wherein the yield stress prediction model comprises a first layer base learner model and a second layer meta-learner model; acquiring an original experiment data set of a target object based on big data, wherein the original experiment data set comprises a multi-feature set of the target object; preprocessing the original experimental data set to obtain a first training data set; inputting the first training data set into the first layer base learner model for model training to obtain an initial yield stress prediction value set of the target object; inputting the initial yield stress prediction value set into the second layer element learner model for correction training to obtain a yield stress model prediction value of the target object; training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target object; inputting the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first object to be predicted, and performing relative error analysis on the first predicted yield stress result. The technical problems that a prediction model of a Stacking ensemble learning algorithm relies on a large amount of original data to learn, and the yield stress of the paste is measured in the early stage in a complex and time-consuming manner and relatively low in prediction accuracy along with the increase of massive sample training are solved. The paste yield stress prediction method based on the Stacking ensemble learning algorithm is based on a large amount of experimental data, a Stacking model fusion algorithm is adopted to integrate various regression models to construct a paste yield stress prediction model, and the experimental data are preprocessed by removing abnormal values, dimensionless values and the like to obtain a training set by utilizing the influence of various factors in the paste to train the yield stress Stacking ensemble model, so that the technical effects of improving the accuracy and convenience of paste yield stress prediction and further improving the production efficiency are achieved.
The invention also provides an electronic device, which comprises a processor and a memory;
the memory is used for storing;
the processor is configured to execute the method according to any one of the first embodiment through calling.
The invention also provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any of the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus or computer program product. Accordingly, the present invention may take the form of an entirely software embodiment, an entirely hardware embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention is in the form of a computer program product that may be embodied on one or more computer-usable storage media having computer-usable program code embodied therewith. And such computer-usable storage media include, but are not limited to: various media capable of storing program codes, such as a usb disk, a portable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk Memory, a Compact Disc Read-Only Memory (CD-ROM), and an optical Memory.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, it is intended that the present invention also include such modifications and variations.
Claims (10)
1. A yield stress prediction method based on an ensemble learning algorithm is characterized by comprising the following steps:
building a yield stress prediction model, wherein the yield stress prediction model comprises a first layer base learner model and a second layer element learner model;
acquiring an original experimental data set of a target object based on big data, wherein the original experimental data set comprises a multi-feature set of the target object;
preprocessing the original experimental data set to obtain a first training data set;
inputting the first training data set into the first layer-based learner model for model training to obtain an initial yield stress prediction value set of the target object;
inputting the initial yield stress prediction value set into the second layer element learner model for correction training to obtain a yield stress model prediction value of the target;
training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target;
inputting the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first object to be predicted, and performing relative error analysis on the first predicted yield stress result.
2. The method of claim 1, wherein said inputting said first training data set to said first layer base learner model for model training comprises:
embedding a decision tree model, a support vector machine model and a proximity algorithm model into the first layer of base learner model;
inputting the first training data set into the decision tree model to obtain a first model prediction result;
inputting the first training data set into the support vector machine model to obtain a second model prediction result;
inputting the first training data set into the proximity algorithm model to obtain a third model prediction result;
and performing data fusion on the first model prediction result, the second model prediction result and the third model prediction result to obtain the initial yield stress prediction value set.
3. The method of claim 1, wherein said inputting said set of initial yield stress predictor values into said second layer meta-learner model for correction training comprises:
embedding the second layer element learner model into a random forest regression model;
dividing the initial yield stress prediction value set into a first sub training set, a second sub training set and an Nth sub training set;
obtaining a first prediction result based on the first sub-training set, obtaining a second prediction result based on the second sub-training set, and so on, obtaining an Nth prediction result based on the Nth sub-training set;
and carrying out average calculation on the weights of the first prediction result, the second prediction result and the Nth prediction result to obtain the predicted value of the yield stress model.
4. The method of claim 1, wherein said preprocessing said raw experimental data set comprises:
obtaining a first characteristic data set according to the original experiment data set;
performing centralized processing on the first characteristic data set to obtain a second characteristic data set;
obtaining a first covariance matrix of the second feature data set;
calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.
5. The method of claim 4, wherein the method comprises:
obtaining the first training data set according to the first dimension reduction data set;
based on a K-fold cross-validation method, dividing the first training data set into K subsets A (A) with the same size 1 ,A 2 ,…,A K }；
Changing said A to { A ═ A 1 ,A 2 ,…,A K Every subset in the test set is divided into K times to be used as a test set B C And the other subsets are used as training sets B X Will train set B X Inputting the first-layer base learner model for training to obtain the A ═ A 1 ,A 2 ,…,A K And (4) collecting test results of each sample.
6. The method of claim 1, wherein said performing a relative error analysis on said first predicted yield stress result comprises:
based on a relative error calculation formula
Performing relative error analysis on the first predicted yield stress result;
where δ is the actual relative error, Δ is the absolute error, and L is the true value.
7. The method of claim 1, wherein inputting the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model previously comprises:
judging whether the yield stress prediction model is initialized or not;
if the yield stress prediction model is initialized, obtaining each model parameter set in the yield stress prediction model;
and automatically adjusting the parameter sets of the models based on a Bayesian optimization algorithm.
8. A yield stress prediction system based on an ensemble learning algorithm, the system comprising:
the system comprises a first building unit and a second building unit, wherein the first building unit is used for building a yield stress prediction model, and the yield stress prediction model comprises a first layer basis learner model and a second layer meta learner model;
the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for acquiring and obtaining an original experiment data set of a target object based on big data, and the original experiment data set comprises a multi-feature set of the target object;
the first processing unit is used for preprocessing the original experiment data set to obtain a first training data set;
a first input unit, configured to input the first training data set to the first layer-based learner model for model training, so as to obtain an initial set of yield stress prediction values of the target object;
the second input unit is used for inputting the initial yield stress prediction value set into the second layer element learner model for correction training to obtain a yield stress model prediction value of the target object;
a first training unit for training the yield stress prediction model based on the yield stress model predicted value and the yield stress actual measured value of the target object;
and the third input unit is used for inputting the multi-feature experimental data set of the first object to be predicted into the trained yield stress prediction model, performing prediction training to obtain a first predicted yield stress result of the first object to be predicted, and performing relative error analysis on the first predicted yield stress result.
9. An electronic device comprising a processor and a memory;
the memory is used for storing;
the processor is used for executing the method of any one of claims 1-7 through calling.
10. A computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method according to any one of claims 1 to 7.
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