CN111444477B - Glass insulator formula automatic generation method based on gradient lifting regression model - Google Patents
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Abstract
The invention provides a glass insulator formula automatic generation method based on a gradient lifting regression model, which adopts a big data analysis method combined with a machine-learned gradient lifting regression model to process historical data of a glass insulator, trains the gradient lifting regression model through the processed historical data, and then generates a required glass insulator formula only by inputting the purity of each pure object in each raw material in the required glass insulator formula into the trained gradient lifting regression model, thereby realizing the automatic generation of the glass insulator formula. Meanwhile, the yield of the glass insulator can be gradually improved in continuous iteration of experimental data and actual production.
Description
Technical Field
The invention relates to the technical field of glass insulators, in particular to a glass insulator formula automatic generation method based on a gradient lifting regression model.
Background
The insulator is widely applied to the power transmission line because of excellent electrical insulation performance and stable mechanical performance. In practical application, most of the glass insulators are used, the glass insulators have the characteristics of high mechanical strength, excellent insulating performance and thermal stability, and the chemical composition of the glass insulators is the key for determining the performance of the glass insulators.
At present, the formula generation of the glass insulator is mainly realized manually, the method is time-consuming and labor-consuming, is easy to make mistakes, and consumes a lot of unnecessary human resources. Deviation exists in each formula calculation, the yield of products is greatly fluctuated, and the automatic generation technology of the glass insulator formula is in a blank state.
Disclosure of Invention
In view of the above situation, the present invention provides an automatic generation method for a glass insulator formula based on a gradient lifting regression model, so as to solve the problems of time consuming, labor consuming and error prone in the manual generation of the glass insulator formula.
The technical scheme of the invention is as follows:
a glass insulator formula automatic generation method based on a gradient lifting regression model comprises the following steps:
extracting historical formula data of the glass insulator, wherein the historical formula data of the glass insulator comprises raw material purity data and batching quality data put into actual production, the raw material purity data is the purity percentage of each batching where each purified object is in the batching, and the batching quality data put into actual production corresponds to the raw material purity data one by one;
preprocessing the historical formula data of the glass insulator, merging the raw material purity data in the preprocessed historical formula data of the glass insulator and the batching quality data put into practical production according to the corresponding relation of time to obtain a data set, splitting the data set into a training set and a testing set, and training a gradient lifting regression model through the training set and the testing set;
and in the trained gradient lifting regression model, taking the purity of each pure object in each raw material in the required glass insulator formula as an input to obtain the quality of each raw material in the required glass insulator formula, and then normalizing the quality of each raw material to obtain the required glass insulator formula.
According to the automatic generation method of the glass insulator formula based on the gradient lifting regression model, firstly, historical data of a glass insulator are processed by adopting a big data analysis method and combining a machine-learned gradient lifting regression model, the gradient lifting regression model is trained through the processed historical data, and then the required glass insulator formula can be generated only by inputting the purity of each pure object in each raw material in the required glass insulator formula into the trained gradient lifting regression model, so that the automatic generation of the glass insulator formula is realized. Meanwhile, the yield of the glass insulator can be gradually improved in continuous iteration of experimental data and actual production.
In addition, the automatic generation method of the glass insulator formula based on the gradient lifting regression model, provided by the invention, also has the following technical characteristics:
further, the evaluation parameters of the gradient boost regression model include: decision coefficient, correction decision coefficient, mean absolute error, mean square error, root mean square error.
Further, in the step of preprocessing the historical formula data of the glass insulator, the following preprocessing is respectively carried out on the raw material purity data and the batching quality data put into practical production:
and (4) filling the average value of the missing data, and deleting the repeated redundant data.
Further, the data sets are differentiated according to product types.
Further, the ratio of the training set to the test set is 8:2.
Further, when the data set is divided into a training set and a test set, the data set is divided in a cross validation mode, and the number of cross times is set to 10.
Further, in the trained gradient boost regression model, the purity of each pure substance in each raw material in the required glass insulator formula is used as an input, and the quality of each raw material in the required glass insulator formula is obtained.
Drawings
FIG. 1 is a schematic diagram of the classification of four raw materials and the purity of each of the different raw materials.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Several embodiments of the invention are presented in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
The embodiment of the invention provides a glass insulator formula automatic generation method based on a gradient lifting regression model, which comprises the steps of S1-S3.
S1, historical formula data of the glass insulator are extracted, wherein the historical formula data of the glass insulator comprise raw material purity data and batching quality data put into actual production, the raw material purity data are purity percentages of respective batches of all purities in the batching, and the batching quality data put into actual production correspond to the raw material purity data one to one.
In the embodiment, after analyzing the ingredient formula of the glass insulator, the purity percentages of the ingredients of fourteen pure substances in the four raw materials are extracted, and the extracted purity percentages are taken as raw material purity data, as shown in fig. 1, the raw materials are divided into 4 types of mixtures, namely quartz sand (1), potassium feldspar (2), dolomite (3) and limestone (4), and the pure substances in the four raw materials are respectively silicon dioxide _1, ferric oxide _1 and aluminum oxide _1 in the quartz sand (1); silicon dioxide _2, ferric oxide _2, aluminum oxide _2, potassium oxide _2 and sodium oxide _2 in the potassium feldspar (2); calcium oxide _3, ferric oxide _3, magnesium oxide _3 and aluminum oxide _3 in the dolomite (3); calcium oxide _4 and ferric oxide _4 in the limestone (4). It should be noted that the raw materials should also be soda ash, potassium carbonate, barium carbonate and mirabilite, and since these four raw materials are all industrial grade purities, the purity is 100% by default, and the purity analysis is not needed.
In addition, according to the time sequence, the quality data of the ingredients put into practical production are extracted and are in one-to-one correspondence with the purity data of the raw materials, and the data are historical formula data of the glass insulator.
S2, preprocessing the historical formula data of the glass insulator, combining the raw material purity data in the preprocessed historical formula data of the glass insulator with the batching quality data put into practical production according to the corresponding relation of time to obtain a data set, splitting the data set into a training set and a testing set, and training a gradient lifting regression model through the training set and the testing set.
In the step of preprocessing the historical formula data of the glass insulator, the following preprocessing is respectively carried out on the raw material purity data and the batching quality data put into practical production:
and (4) filling the average value of the missing data, and deleting the repeated redundant data.
The data set is used for training and testing a formula prediction regression model, and is obtained by analyzing and sorting historical production data of the glass insulator. Including historical purity data for each item of ingredient, as well as historical recipe data under the purity data. The data sets are differentiated according to product type. In this embodiment, the data sets are divided into 9 types (named as T8F, T, T12F, T, T16G, T H, T30F, T30P, T, respectively) according to the existing product models, and the data set corresponding to each product model contains one thousand to several thousand pieces of data.
When splitting the data set into a training set and a test set, the ratio of the training set to the test set is 8:2. Specifically, the data set is divided by adopting a cross validation mode, and the number of cross is set to be 10.
The data information of the training set comprises the mass of 8 raw materials (adding up to 500kg of the mass of a pair of formula raw materials), the purity of 14 purified materials in four mixture raw materials of quartz sand, potassium feldspar, dolomite and limestone, and the time for the pair of formula raw materials to enter a factory for production.
In this embodiment, a Gradient Boost Regression (GBR) model is selected as a calculation model, and the accuracy of the trained GBR model is evaluated, wherein the evaluated parameters are R ^2 (decision coefficient), R ^2 (corrected decision coefficient), MSE (mean square error), RMSE (root mean square error), and MAE (mean absolute error). As shown in formulas (1), (2), (3), (4) and (5).
Wherein, the formula (1) represents a determination coefficient, the greater the goodness of fit, the higher the interpretation degree of the independent variable on the dependent variable, and the higher the percentage of the variation caused by the independent variable to the total variation. The denser the observation points are near the regression line. Equation (2) is a correction decision coefficient, and is a complement to equation (1), where n is the number of samples and p is the number of features. The decision coefficient R can be used for evaluating the quality of the regression equation, but the R is increased along with the increase of the number of independent variables. The correction decision coefficient of equation (2) is introduced to solve the problem that the decision coefficient R becomes larger when the independent variable is large. The MSE in the formula (3) is Mean Squared Error, which is an expected value of the square of the difference between a parameter estimation value and a parameter true value; the MSE can evaluate the change degree of the data, and the smaller the value of the MSE is, the better the accuracy of the prediction model for describing the experimental data is. The RMSE root mean square error of equation (4) is the arithmetic square root of the mean square error. The MAE in the formula (5) is the Mean Absolute Error, and the Mean Absolute Error is an average value of Absolute errors, and can better reflect the actual situation of predicted value errors. The experimental results in table 1 were obtained based on historical data for a glass insulator model number T12F. The experimental result shows that all evaluation parameters of the gradient lifting regression model are excellent in performance.
TABLE 1
| Gradient lifting regression model | Quartz sand | Potassium feldspar | Dolomite | Limestone | Soda ash | Potassium carbonate | Barium carbonate | Natrii sulfas |
| Cross validation results | 0.9976 | 0.9973 | 0.9984 | 0.9966 | 0.9987 | 0.9929 | 0.9992 | 0.9848 |
| Determining coefficients | 0.9989 | 0.9991 | 0.9992 | 0.9987 | 0.9758 | 0.9925 | 0.9923 | 0.9931 |
| Correction decision coefficient | 0.9977 | 0.9983 | 0.9984 | 0.9974 | 0.9511 | 0.9848 | 0.9874 | 0.986 |
| Mean absolute error of MAE | 0.0199 | 0.0209 | 0.00111 | 0.0134 | 0.0021 | 0.0124 | 0.0137 | 0.0005 |
| Mean square error of MSE | 0.0196 | 0.0184 | 0.0063 | 0.0091 | 0.0013 | 0.0047 | 0.0007 | 0.00002 |
| RMSE root mean square error | 0.1399 | 0.1355 | 0.0796 | 0.0952 | 0.0356 | 0.0685 | 0.0264 | 0.004 |
And S3, in the trained gradient lifting regression model, taking the purity of each pure object in each raw material in the required glass insulator formula as an input to obtain the quality of each raw material in the required glass insulator formula, and then normalizing the quality of each raw material to obtain the required glass insulator formula.
The purity of each kind of purified substances in the raw materials is used as input, wherein the purity of the purified substances in fourteen raw materials of silica _1, ferric oxide _1, aluminum oxide _1, silica _2, ferric oxide _2, aluminum oxide _2, potassium oxide _2, sodium oxide _2, calcium oxide _3, ferric oxide _3, magnesium oxide _3, aluminum oxide _3, calcium oxide _4 and ferric oxide _4 in the four raw materials of quartz sand (1), potash feldspar (2), dolomite (3) and limestone (4) is included. The other four raw material data (soda ash, potassium carbonate, barium carbonate and mirabilite) are pure substances by default. Since the regression model can only yield one value at a time, the model needs to be run eight times, each time to predict the quality of one material. And finally, the mass of each auxiliary material is fixed, and after the mass of the eight raw materials is predicted, the eight raw materials are normalized to obtain the required glass insulator formula.
For example, the purity of each pure type of the input raw material is divided into:
the purity of the silica _1 is 98.47, the purity of the iron trioxide _1 is 0.15, the purity of the alumina _1 is 0.77, the purity of the silica _2 is 66.94, the purity of the iron trioxide _2 is 0.73, the purity of the alumina _2 is 17.12, the purity of the potassium oxide _2 is 9.86, the purity of the sodium oxide _2 is 1.36, the purity of the calcium oxide _3 is 31.99, the purity of the iron oxide _3 is 0.2, the purity of the magnesium oxide _3 is 19.95, the purity of the aluminum oxide _3 is 0.01, the purity of the calcium oxide _4 is 55.57, and the purity of the iron trioxide _4 is 0.02.
And inputting the data into a trained gradient lifting regression model to finally obtain the formula of the glass insulator. The error of the obtained recipe data compared to the actual recipe data is shown in table 1.
In summary, according to the method for automatically generating a glass insulator formula based on the gradient lifting regression model provided by this embodiment, firstly, the historical data of the glass insulator is processed by using a big data analysis method in combination with the GBR gradient lifting regression model learned by the machine, the gradient lifting regression model is trained by using the processed historical data, and then, the required glass insulator formula can be generated only by inputting the purity of each pure object in each raw material in the required glass insulator formula into the trained gradient lifting regression model, so that the automatic generation of the glass insulator formula is realized. Meanwhile, the yield of the glass insulator can be gradually improved in continuous iteration of experimental data and actual production.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention. Therefore, the protection scope of the present patent should be subject to the appended claims.
Claims (7)
1. A glass insulator formula automatic generation method based on a gradient lifting regression model is characterized by comprising the following steps:
extracting historical formula data of the glass insulator, wherein the historical formula data of the glass insulator comprises raw material purity data and batching quality data put into actual production, the raw material purity data is the purity percentage of each batching where each purified object is in the batching, and the batching quality data put into actual production corresponds to the raw material purity data one by one;
preprocessing the historical formula data of the glass insulator, combining raw material purity data in the preprocessed historical formula data of the glass insulator and batching quality data put into practical production together according to a corresponding relation of time to obtain a data set, splitting the data set into a training set and a testing set, and training a gradient lifting regression model through the training set and the testing set;
in the trained gradient lifting regression model, the purity of each pure object in each raw material in the required glass insulator formula is used as input to obtain the quality of each raw material in the required glass insulator formula, the quality of one raw material is obtained by operating the gradient lifting regression model once, and then the quality of each raw material is normalized to obtain the required glass insulator formula.
2. The method for automatically generating a glass insulator formula based on a gradient lifting regression model according to claim 1, wherein the evaluation parameters of the gradient lifting regression model comprise: decision coefficient, correction decision coefficient, mean absolute error, mean square error, root mean square error.
3. The automatic generation method of a glass insulator formula based on a gradient lifting regression model according to claim 1, characterized in that in the step of preprocessing the historical formula data of the glass insulator, the following preprocessing is performed on the raw material purity data and the batching quality data which have been put into actual production respectively:
and (4) filling the average value of the missing data, and deleting the repeated redundant data.
4. The method of claim 1, wherein the data sets are differentiated according to product type.
5. The method of claim 4, wherein the ratio of the training set to the test set is 8:2.
6. The method for automatically generating a glass insulator formula based on a gradient lifting regression model according to claim 5, wherein when the data set is divided into a training set and a testing set, the data set is divided in a cross validation mode, and the number of crossing times is set to 10.
7. The method according to claim 1, wherein in the step of obtaining the quality of each raw material in the glass insulator formulation, the quality of each raw material is obtained by running the gradient lifting regression model once, in the trained gradient lifting regression model, using the purity of each pure substance in each raw material in the glass insulator formulation as an input.
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