Compressed air usage amount prediction system for whole vehicle factory

Abstract

The invention relates to the technical field of automobile manufacturing, and discloses a compressed air usage amount prediction system for a whole automobile factory, which comprises the steps of opening Excel software, selecting a data tab, clicking a data analysis button, selecting a regression analysis tool in a popped data analysis dialog box, and clicking a determination button. According to the method, regression analysis is carried out by utilizing Excel, the compressed air usage amount of the whole vehicle factory can be predicted more accurately, reliable data support is provided for energy management, energy waste and production interruption risks caused by inaccurate prediction are reduced, in addition, accurate prediction is beneficial to reasonable planning of compressed air supply, and the method is suitable for popularization and application. According to the method, the equipment investment and operation cost increase caused by excessive supply can be avoided, and meanwhile, the production loss caused by insufficient supply can be reduced, so that the overall operation cost is reduced, and the whole vehicle factory can more effectively adjust the production plan and the energy distribution strategy by timely mastering the change trend of the compressed air usage amount.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile manufacturing, and specifically relates to a prediction system for the consumption of compressed air in a whole vehicle factory. Background Art

[0002] In the production and operation of a whole vehicle factory, accurately predicting the consumption of compressed air is of great significance for optimizing energy management, reducing costs, and ensuring the smooth progress of production. Traditional methods for predicting the consumption of compressed air often rely on empirical estimates or simple mathematical models, with limited accuracy and reliability.

[0003] With the development of data analysis technology, regression analysis, as a powerful statistical tool, provides the possibility for more accurate prediction of the consumption of compressed air. However, for some staff in whole vehicle factories, professional statistical software may have problems such as a high usage threshold and complex operations. Excel, as a widely used office software with data analysis functions, has not been fully explored and standardized in the application of predicting the consumption of compressed air in whole vehicle factories. Therefore, developing a prediction technology for the consumption of compressed air in whole vehicle factories based on Excel, which can not only utilize the convenience of Excel but also provide accurate and reliable prediction results, has important practical needs for improving the energy management level of whole vehicle factories. Summary of the Invention

[0004] Technical Solution The present invention provides the following technical solution: including the following steps: S1. Open the Excel software, select the "Data" tab, click the "Data Analysis" button, select the "Regression" analysis tool in the pop-up "Data Analysis" dialog box, and click the "OK" button.

[0005] S2. In the "Regression" dialog box, input the data ranges of the independent variable and the dependent variable, select the storage location of the output result, and then click the "OK" button.

[0006] S3. The Excel software will automatically generate the regression analysis results, including regression statistics tables, analysis of variance tables, regression parameter tables, residual output results, and probability output results, etc.

[0007] S4. Interpret and analyze the regression analysis results, and further calculations and processing can be carried out as needed.

[0008] Further, in the regression statistical table, the correlation coefficient (Multiple) represents the strength of the linear relationship between the independent variable and the dependent variable, i.e., R = 0.989416. The value corresponding to R Square is the determination coefficient, or goodness of fit, which is the square of the correlation coefficient, i.e., R 2 = 0.989416 2 = 0.978944. Adjusted corresponds to the adjusted determination coefficient, and the calculation formula is .

[0009] Further, in the analysis of variance table, the degrees of freedom (df) represents the degrees of freedom, the sum of squares of errors (SS) represents the sum of squares of errors, the mean square (MS) represents the mean square, the F value represents the F statistic, and the P value represents the P value.

[0010] Further, in the regression parameter table, the regression coefficient (Coefficients) represents the parameters of the regression model, the standard error (Standard Error) represents the standard deviation of the regression coefficient, the t statistic (t Stat) represents the t statistic of the regression coefficient, the P value (P value) represents the P value of the regression coefficient, and the confidence interval (95% Confidence Interval) represents the confidence interval of the regression coefficient.

[0011] Further, in the residual output result, the observation number (i) represents the number of the observation, the predicted value of the dependent variable (Predicted Value) represents the predicted value of the dependent variable, the residual (Residuals) represents the residual of the dependent variable, and the standardized residual (Standardized Residuals) represents the standardized value of the residual.

[0012] Further, in the probability output result, the percentile (Percentile) represents the percentile ranking, and the original dependent variable (Original Dependent Variable) represents the dependent variable of the original data.

[0013] Furthermore, it includes: 1. A data input module for inputting data of independent variables and dependent variables; 2. A data analysis module for invoking the regression analysis tool of Excel and performing regression analysis on the input data; 3. A result output module for outputting the results of regression analysis, including regression statistical tables, analysis of variance tables, regression parameter tables, residual output results, probability output results, etc.; 4. A result interpretation module for interpreting and analyzing the results of regression analysis and performing further calculations and processing as needed.

[0014] Furthermore, the data input module stores a computer program which, when executed by a processor, can perform the following operations: First, open the Excel software, select the "Data" tab in the software interface, and then click the "Data Analysis" button. In the pop-up "Data Analysis" dialog box, accurately select the "Regression" analysis tool and click the "OK" button. Next, in the subsequent "Regression" dialog box, accurately enter the data ranges of independent variables and dependent variables, carefully select the storage location of the output results, and then click the "OK" button. After that, the Excel software will quickly and automatically generate the results of regression analysis, which include regression statistical tables, analysis of variance tables, regression parameter tables, residual output results, probability output results, etc. Finally, conduct in-depth interpretation and detailed analysis of the generated regression analysis results, and perform further accurate calculations and proper processing according to actual needs.

[0015] (III) Beneficial effects Compared with the prior art, the present invention provides a prediction system for the compressed air usage of a vehicle manufacturing plant, having the following beneficial effects: 1. The prediction system for the compressed air usage of a vehicle manufacturing plant can more accurately predict the compressed air usage of the vehicle manufacturing plant by using Excel for regression analysis, providing reliable data support for energy management, reducing the risk of energy waste and production interruption caused by inaccurate prediction. In addition, accurate prediction helps to reasonably plan the supply of compressed air, avoiding the increase in equipment investment and operating costs caused by over-supply, and also reducing production losses caused by insufficient supply, thereby reducing the overall operating cost.

[0016] 2. The prediction system for the compressed air usage in vehicle manufacturing plants can enable vehicle manufacturing plants to more effectively adjust production plans and energy distribution strategies, improve energy utilization efficiency, and achieve the goals of energy conservation and emission reduction by timely grasping the changing trend of compressed air usage. Based on the widely used Excel software, it does not require complex professional statistical software and programming skills, reduces the technical usage threshold, facilitates the quick start-up and application of the staff in vehicle manufacturing plants, can quickly generate prediction results and analysis reports, provides timely decision-making basis for management, helps to quickly respond to market changes and production demands, and enhances the competitiveness of the enterprise. The chart function provided by Excel can present the results of regression analysis in an intuitive form, facilitating the staff to understand and interpret data and discover potential problems and rules. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the regression statistics of the present invention; Figure 2 It is a schematic diagram of the analysis of variance of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Specific Embodiment 1, please refer to Figure 1 , including the following steps: S2. Open the Excel software, select the "Data" tab, click the "Data Analysis" button, select the "Regression" analysis tool in the popped-up "Data Analysis" dialog box, and click the "OK" button; 2. In the "Regression" dialog box, input the data ranges of the independent variable and the dependent variable, select the storage location of the output result, and then click the "OK" button; S3. The Excel software will automatically generate the regression analysis results, including the regression statistics table, the analysis of variance table, the regression parameter table, the residual output result, and the probability output result, etc.; S4. Interpret and analyze the regression analysis results, and further calculations and processing can be performed as needed. In the regression statistics table, the correlation coefficient (Multiple) represents the strength of the linear relationship between the independent variable and the dependent variable, that is, R = 0.989416, and the value corresponding to R Square is the determination coefficient, or the goodness of fit, which is the square of the correlation coefficient, that is, there is R 2 = 0.989416 2= 0.978944. The "Adjusted" corresponds to the adjusted determination coefficient, and its calculation formula is: , in the analysis of variance table, the degree of freedom (df) represents the degree of freedom, the sum of squares of errors (SS) represents the sum of squares of errors, the mean square (MS) represents the mean square, the F value represents the F statistic, the P value represents the P value. In the regression parameter table, the regression coefficient (Coefficients) represents the parameters of the regression model, the standard error (Standard Error) represents the standard deviation of the regression coefficient, the t statistic (t Stat) represents the t statistic of the regression coefficient, the P value (P value) represents the P value of the regression coefficient, and the confidence interval (95% Confidence Interval) represents the confidence interval of the regression coefficient. In the residual output result, the observation number (i) represents the number of the observation, the predicted value of the dependent variable (Predicted Value) represents the predicted value of the dependent variable, the residual (Residuals) represents the residual of the dependent variable, and the standardized residual (Standardized Residuals) represents the standardized value of the residual. In the probability output result, the percentile (Percentile) represents the percentile ranking, and the original data dependent variable (Original Dependent Variable) represents the dependent variable of the original data, including: 1. A data input module for inputting the data of the independent variable and the dependent variable; 2. A data analysis module for calling the regression analysis tool of Excel and performing regression analysis on the input data; 3. A result output module for outputting the results of the regression analysis, including the regression statistical table, the analysis of variance table, the regression parameter table, the residual output result, and the probability output result, etc.; 4. A result interpretation module for interpreting and analyzing the results of the regression analysis, and further calculations and processing can be performed as needed. It stores a computer program, and when the computer program is executed by a processor, the following operations can be realized: First, open the Excel software, select the "Data" tab in the software interface, and then click the "Data Analysis" button. In the pop-up "Data Analysis" dialog box, accurately select the "Regression" analysis tool and click the "OK" button. Then, in the subsequent "Regression" dialog box, accurately enter the data range of the independent variable and the dependent variable, carefully select the storage location of the output result, and then click the "OK" button. After that, the Excel software will quickly and automatically generate the results of the regression analysis, which include the regression statistical table, the analysis of variance table, the regression parameter table, the residual output result, and the probability output result, etc. Finally, conduct in-depth interpretation and detailed analysis of the generated regression analysis results, and further accurate calculations and proper processing can be performed according to actual needs.

[0020] Working principle: Data preparation: First, collect data related to the compressed air usage of the vehicle factory over a period of time, such as the running time of different equipment on the production line, production process parameters, ambient temperature, etc. as independent variables, and the corresponding compressed air usage as the dependent variable.

[0021] Excel operation: Open the Excel software, select the "Data" tab, click the "Data Analysis" button, select the "Regression" analysis tool in the pop-up "Data Analysis" dialog box, and click the "OK" button.

[0022] In the "Regression" dialog box, accurately enter the data ranges of the prepared independent and dependent variables, carefully select the storage location of the output results, and then click the "OK" button.

[0023] Result generation and analysis: The Excel software will automatically generate regression analysis results including a regression statistics table, an analysis of variance table, a regression parameter table, a residual output result, and a probability output result, etc.

[0024] Regression statistics table: Understand the strength of the linear relationship between the independent and dependent variables through the correlation coefficient, determine the goodness of fit of the regression model using the coefficient of determination, evaluate the adjusted goodness of fit using the adjusted coefficient of determination, measure the dispersion degree of the predicted values of the dependent variable using the standard error, and reflect the number of observations using the number of samples.

[0025] Analysis of variance table: Judge the degrees of freedom of the data based on the degrees of freedom, understand the error situation through the sum of squared errors, evaluate the average error using the mean square error, judge the linear relationship using the F value, and judge the significance of the model using the P value.

[0026] Regression parameter table: Analyze the regression coefficients to determine the parameters of the model, evaluate the precision of the parameters with the help of the standard error, test the significance of the model parameters using the t statistic and P value, and judge the possible range of the parameters through the confidence interval.

[0027] Residual output result: Observe the observation number, obtain the residuals by comparing the predicted values and actual values of the dependent variable, and analyze the standardized residuals to judge the goodness of fit of the model.

[0028] Probability output result: Further analyze the data distribution through the percentage rank and the original data dependent variable.

[0029] Result interpretation and processing: Conduct a comprehensive and in-depth interpretation and analysis of the generated regression analysis results. If the model fitting effect is not ideal, the independent variables can be reselected, the data range can be adjusted, or other data analysis methods can be adopted. According to actual needs, further calculations and processing can be carried out on the results, such as predicting the compressed air usage in the next period of time, providing data support for the energy management decision-making of the vehicle factory.

[0030] In addition, this technology also includes a system composed of the following modules: Data input module: Used to conveniently and accurately input the data of independent variables and dependent variables.

[0031] Data analysis module: Efficiently call the regression analysis tool of Excel to perform accurate regression analysis on the input data.

[0032] Result output module: Clearly and completely output the results of regression analysis.

[0033] Result interpretation module: Professionally interpret and analyze the regression analysis results, and conduct more in-depth calculations and processing according to actual needs.

[0034] Meanwhile, a computer-readable storage medium storing relevant computer programs can, when the program is executed by a processor, realize the predictive analysis of the compressed air usage of the vehicle factory according to the above steps.

[0035] It should be noted that the data corresponding to Multiple is the correlation coefficient, that is, R = 0.989416.

[0036] The value corresponding to R Square is the determination coefficient, or goodness of fit. It is the square of the correlation coefficient, that is, R2 = 0.9894162 = 0.978944.

[0037] The value corresponding to Adjusted is the adjusted determination coefficient, and the calculation formula is

[0038] In the formula, n is the number of samples, m is the number of variables, and R2 is the determination coefficient. For this example, n = 10, m = 1, R2 = 0.978944. Substituting into the above formula, we get

[0039] The so-called standard error corresponds to the standard error, and the calculation formula is

[0040] Here, SSe is the sum of squared residuals, which can be read from the following analysis of variance table. That is, SSe = 16.10676. Substituting it into the above formula, we get

[0041] The observed value in the last row corresponds to the number of samples, that is, n = 10 Figure 2 In the first column, df corresponds to the degrees of freedom. In the first row is the degrees of freedom of regression dfr, which is equal to the number of variables, that is, dfr = m. The second row is the degrees of freedom of residuals dfe, which is equal to the number of samples minus the number of variables minus 1, that is, dfe = n - m - 1. The third row is the total degrees of freedom dft, which is equal to the number of samples minus 1, that is, dft = n - 1. For this example, m = 1, n = 10. Therefore, dfr = 1, dfe = n - m - 1 = 8, dft = n - 1 = 9.

[0042] In the second column, SS corresponds to the sum of squared errors, or variation. In the first row is the sum of squared regression, or regression variation SSr, that is,

[0043] It represents the total deviation of the predicted value of the dependent variable from its mean.

[0044] The second row is the sum of squared residuals (also called the sum of squared errors) or residual variation SSe, that is,

[0045] It represents the total deviation of the dependent variable from its predicted value. The larger this value, the worse the fitting effect. The standard error of y above is given by SSe.

[0046] The third row is the total sum of squares or total variation SSt, that is,

[0047] It represents the total deviation of the dependent variable from its mean. It is easy to verify that 748.8542 + 16.10676 = 764.961, that is,

[0048] And the coefficient of determination is the proportion of the sum of squared regression in the total sum of squares, that is,

[0049] Obviously, the larger this value, the better the fitting effect.

[0050] The fourth column MS corresponds to the mean square error, which is the quotient obtained by dividing the sum of squared errors by the corresponding degrees of freedom. The first row is the regression mean square MSr, that is

[0051] The second row is the residual mean square MSe, that is

[0052] Obviously, the smaller this value is, the better the fitting effect.

[0053] The fourth column corresponds to the F value, which is used for the determination of the linear relationship. For simple linear regression, the calculation formula of the F value is

[0054]

[0055] In the formula, R 2 = 0.978944, dfe = 10 - 1 - 1 = 8, therefore

[0056] The fifth column Significance F corresponds to the critical value Fα at the significance level, which is actually equal to the P value, that is, the probability of rejecting a true null hypothesis. The so-called "probability of rejecting a true null hypothesis" is the probability that the model is false. Obviously, 1 - P is the probability that the model is true. It can be seen that the smaller the P value is, the better. For this example, P = 0.0000000542 < 0.0001, so the confidence level reaches more than 99.99%.

[0057] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for predicting compressed air usage in a vehicle plant, characterized in that: The prediction technique comprises the following steps: S1. Open the Excel software, select the "Data" tab, click the "Data Analysis" button, select the "Regression" analysis tool in the pop-up "Data Analysis" dialog box, and click the "OK" button. 2.S2. In the "Regression" dialog box, enter the data range of the independent variable and the dependent variable, select the location to store the output results, and then click the "OK" button. 3.S3. Excel software will automatically generate regression analysis results, including regression statistics table, variance analysis table, regression parameter table, residual output results and probability output results S4. Interpret and analyze the regression analysis results, and perform further calculations and processing as needed.

4. The system for predicting compressed air usage in a vehicle plant according to claim 1, characterized in that: In the regression statistics table, the correlation coefficient (Multiple) indicates the strength of the linear relationship between the independent variable and the dependent variable, that is, R = 0.989416. The value corresponding to R Square is the determination coefficient, or goodness of fit, which is the square of the correlation coefficient, that is, R 2 =0.989416 2 =0.978944, Adjusted corresponds to the adjusted determination coefficient, and the calculation formula is:

5. The system for predicting compressed air usage in a vehicle plant according to claim 1, characterized in that: In the ANOVA table, df means degrees of freedom, SS means the sum of squares, MS means the mean square, F value means F statistic, and P value means P value.

6. The system for predicting compressed air usage in a vehicle plant according to claim 1, characterized in that: In the regression parameter table, the regression coefficient (Coefficients) represents the parameters of the regression model, the standard error (Standard Error) represents the standard deviation of the regression coefficient, the t statistic (t Stat) represents the t statistic of the regression coefficient, the P value (Pvalue) represents the P value of the regression coefficient, and the confidence interval (95% Confidence Interval) represents the confidence interval of the regression coefficient.

7. The system for predicting compressed air usage in a vehicle plant according to claim 1, characterized in that: In the residual output results, the observation number (i) indicates the number of the observation, the predicted value of the dependent variable (Predicted Value) indicates the predicted value of the dependent variable, the residual (Residuals) indicates the residual of the dependent variable, and the standardized residual (StandardizedResiduals) indicates the standardized value of the residual.

8. The system for predicting compressed air usage in a vehicle plant according to claim 1, characterized in that: In the probability output results, Percentile indicates the percentile rank, and OriginalDependentVariable indicates the original data dependent variable.

9. The system for predicting compressed air usage in a vehicle plant according to claim 1, characterized in that: include:

1. Data input module, used to input data of independent variables and dependent variables; 2. Data analysis module, used to call Excel's regression analysis tool and perform regression analysis on the input data; 3. Result output module, used to output the results of regression analysis, including regression statistics table, variance analysis table, regression parameter table, residual output results and probability output results; 4. The result interpretation module is used to interpret and analyze the regression analysis results, and further calculations and processing can be performed as needed.

10. The system for predicting compressed air usage in a vehicle plant according to claim 1, characterized in that: The data input module stores a computer program, and when the computer program is executed by the processor, the following operations can be performed: first, open the Excel software, select the "Data" tab in the software interface, and then click the "Data Analysis" button, accurately select the "Regression" analysis tool in the "Data Analysis" dialog box that pops up, and click the "OK" button. Then, in the "Regression" dialog box that appears, accurately enter the data range of the independent variable and the dependent variable, carefully select the storage location of the output result, and then click the "OK" button. After that, the Excel software will quickly and automatically generate regression analysis results, which cover regression statistics tables, variance analysis tables, regression parameter tables, residual output results, and probability output results. Finally, the generated regression analysis results are deeply explained and carefully analyzed, and further accurate calculations and proper processing can be performed according to actual needs.

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