A method for establishing a combined model of operating parameters of corn grain harvesters based on orthogonal experiments

Abstract

The present invention discloses a method for establishing an operating parameter combination model of a corn grain harvester based on an orthogonal experiment, comprising: step one, analyzing the entire operating process of the corn harvester, and preliminarily determining key operating factors that affect the operating quality of the corn harvester; step two, determining orthogonal experimental factors and levels; step three, formulating an orthogonal experimental plan; step four, carrying out a corn harvester harvesting experiment according to the orthogonal experimental plan determined in step three, and analyzing the experimental data; step five, performing experimental data processing and analysis; and step six, applying the optimized parameter combination to the actual operation of the harvester for verification. The present invention obtains an optimized operating parameter combination through this model, so that the harvester works under the optimized operating parameter combination, and can obtain better operating quality and operating efficiency, thereby providing a new solution for improving the operating quality and operating efficiency of the harvester. The operating parameter combination model can be used for the design and development of an intelligent control system for the harvester.

Description

Technical Field

[0001] The patent of this invention belongs to the technical field of corn harvesters, and specifically relates to a method for establishing a combination model of operating parameters of a corn grain harvester based on orthogonal experiments. Background Art

[0002] Corn is the world's most widely distributed food crop. It serves not only as a food source but also as a feed, oil, and bioenergy processing facility. It is a highly economically valuable crop with broad cultivation prospects. As corn plantings continue to expand, the demand for corn harvesters is also growing, requiring them to deliver superior harvesting quality and efficiency. Key performance indicators for evaluating corn harvester performance include impurity content, breakage rate, and kernel loss rate.

[0003] Improving the operating quality and efficiency of harvesters can reduce the loss of corn grains and profit losses caused by harvesters. The traditional method of improving the operating quality and efficiency of corn grain harvesters is to manually adjust the harvester parameters after the fact, and it relies on the operator's experience. It is inefficient and cannot be generally applied to most harvesting operating environments. It cannot make the harvester work under a better combination of operating parameters. Therefore, the present invention analyzes the impact of multiple factors on the operating quality and efficiency, and concludes that the operating quality and efficiency of corn grain harvesters are closely related to the threshing drum speed, load and concave plate gap. Through field experiments, the relationship curves between the engine speed and feed amount of the corn grain harvester; the threshing drum speed and feed amount; and the engine speed and threshing drum speed were obtained. A ternary quadratic regression orthogonal rotation center combination optimization test method was adopted, with feed amount, drum speed, and concave plate gap as influencing factors, and engine speed change rate, crushing rate, impurity rate, ground loss rate, and drum speed change rate as objective functions. The data obtained from the test were used to draw an operation quality contour map, through which a better range of operation parameter combinations can be found; Data Processing System V9.0.1 was used for data processing and analysis, and a regression model of the corn grain harvester operation quality index, efficiency index and operation parameter combination was obtained. The model of the present invention can obtain an optimized operation parameter combination, so that the harvester can work under the optimized operation parameter combination to obtain better operation quality and operation efficiency. The present invention provides a new solution for improving the operation quality and operation efficiency of the harvester, and the operation parameter combination model can be used for the design and development of the harvester intelligent control system. Summary of the Invention

[0004] The present invention aims to solve the problem of optimizing the operating parameters of a corn harvester, and provides a method for establishing a corn grain harvester operating parameter combination model based on orthogonal experiments. The corn grain harvester can achieve better operating quality and efficiency when operating under this parameter combination.

[0005] 1. A method for establishing a combined model of operating parameters of a corn grain harvester based on orthogonal experiment, characterized by the following steps:

[0006] 1) A three-variable quadratic regression orthogonal rotation center combination optimization test method was used to analyze the entire operation process of the corn harvester and preliminarily identify the key operating factors that affect the corn harvester's operating quality. These operating factors have a significant impact on the harvester's operating quality: feed rate, drum speed, and concave plate gap were selected as experimental factors, and engine speed change rate, crushing rate, impurity rate, ground loss rate, and drum speed change rate were selected as evaluation indicators of corn harvester operating quality;

[0007] 2) The three-dimensional quadratic universal rotation combination design method was selected to develop the test plan, determine the range of feed rate, drum speed and concave plate gap, as well as the representative values ​​of feed rate, drum speed and concave plate gap, and select the test factors and levels;

[0008] 3) Select an appropriate orthogonal test table based on the number of experimental factors and levels, and formulate an orthogonal test plan;

[0009] 4) Conducting a corn harvester harvesting test according to the orthogonal test plan determined in step 3), and recording the test results in an orthogonal test table;

[0010] 5) Plotting a scatter plot based on the data from step 4) to analyze the effects of the independent and coupled effects of the operating parameters on the corn harvester's operating quality; plotting contour maps of the breakage rate, impurity rate, grain loss rate, engine speed change rate, and drum speed change rate. Based on the target operating quality indicators, a suitable operating parameter combination can be found on the contour map.

[0011] 6) Data Processing System V9.0.1 was used to process and analyze the experimental data, and a mathematical regression model of the corn grain harvester operation quality index, efficiency index and operation parameter combination was established. The F test was used to test the significance of the influence of each factor in the regression model. A nonlinear optimization calculation method was used in combination with the MATLAB software optimization toolbox to optimize the operation parameter combination and obtain multiple groups of parameter combinations that met the requirements. The parameter combination obtained in step 5) was verified.

[0012] 7) Selecting multiple parameter combinations obtained in step 6) for actual operation of the harvester for verification, and comparing to obtain the optimal parameter combination among the multiple parameter combinations.

[0013] 2. The method for establishing a corn grain harvester operating parameter combination model based on orthogonal experiment is specifically as follows:

[0014] 7.1) Draw a scatter plot of the experimental data and analyze the scatter plot to obtain the relationship between engine speed and threshing drum operating parameters.

[0015] 7.2) Use the test data to create a contour map of the breakage rate, impurity rate, seed loss rate, engine speed change rate, and drum speed change rate. Based on the target quality indicators, the contour map can be used to identify the appropriate operating parameter combination.

[0016] 3. The method for establishing a corn grain harvester operating parameter combination model based on orthogonal experiment is specifically as follows:

[0017] 1) Data Processing System V9.0.1 was used to process and analyze the test data. The feed amount n1, drum speed n2, and concave plate gap n3 were used as test factors, and the engine speed change rate m1, crushing rate m2, impurity content m3, ground loss rate m4, and drum speed change rate m5 were used as response values. The regression equation was obtained:

[0018] m1=1.53+6.74n1+8.97n2+5.31n3+5.94n1 2 +7.90n2 2 +11.90n3 2 +36.56n1n2-11.69n1n3-12.44n2n3;

[0019] m2=4.18+0.40n1-0.19n2+0.36n3+0.28n1 2 -0.11n2 2 +0.05n3 2 -0.38n1n2-0.063n1n2+0.38n2n3;

[0020] m3=1.87-0.27n1++0.12n2+0.16n3+0.10n1 2 -0.28n2 2 -0.19n3 2 -0.19n1n2+0.03n1n3+0.03n2n3;

[0021] m4=10.11-2.17n1-0.85n2+2.04n3+2.83n1 2 -2.16n2 2 -2.66n3 2 +2.35n1n2-1.29n1n3-0.24n2n3;

[0022] m5=1.91+0.28n1-0.34n2+3.42n3+1.56n12 +2.23n2 2 +2.83n3 2 +6.56n1n2-3.71n1n3-3.37n2n3;

[0023] 2) MATLAB is used to optimize the calculation of the regression equation, combined with the actual operation parameter indicators, and the range of each operation parameter is obtained after optimization calculation.

[0024] 3) Analyze the relationship between the influencing factors (feed rate, concave plate gap, and drum speed) and the objective functions (crush rate, trash content, and drop loss rate) to optimize the operating parameter combination. Using Origin software, create scatter plots, box plots, and contour plots of feed rate, concave plate gap, and drum speed. Analyze the contour plots to determine the interplay between these multiple parameters and the optimal operating parameter range.

[0025] Specifically, the optimized parameter combination is used for actual operation of the harvester for verification, and each group of tests is repeated three times. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of the method for establishing the combined model of operating parameters of the corn grain harvester according to the present invention;

[0027] Figure 2 A graph showing the relationship between the engine speed and the threshing drum speed of the present invention;

[0028] Figure 3 It is the relationship curve diagram of the drum speed and feed amount of the present invention;

[0029] Figure 4 The graph of the relationship between the engine speed and the feed rate of the present invention is as follows;

[0030] Figure 5 is a contour map of the rate of change of the drum speed of the present invention;

[0031] Figure 6 is a contour map of the engine speed change rate of the present invention;

[0032] Figure 7 This is a contour map of the impurity content of the grains of the present invention;

[0033] Figure 8 is a contour map of the grain breakage rate of the present invention;

[0034] Figure 9 is a contour map of grain loss rate of the present invention;

[0035] Figure 10 It is a scatter diagram of feeding amount of the present invention;

[0036] Figure 11It is a feeding amount box plot of the present invention;

[0037] Figure 12 is a scatter plot of the drum speed of the present invention;

[0038] Figure 13 It is the drum speed box diagram of the present invention;

[0039] Figure 14 is a scatter plot of the concave plate gap of the present invention;

[0040] Figure 15 It is the box plot of the concave plate gap of the present invention;

[0041] Figure 16 This is a statistical diagram of parameter verification quality data of the present invention; DETAILED DESCRIPTION

[0042] See Figure 1 A method for establishing a combined model of operating parameters of a corn grain harvester based on orthogonal experiment is characterized by the following steps:

[0043] 1) Analyze the entire operation process of the corn harvester and preliminarily identify the key operating factors that affect the harvester's operating quality. These operating factors have a significant impact on the harvester's operating quality: feed rate, drum speed, and concave plate gap are selected as experimental factors, and engine speed change rate, crushing rate, impurity rate, ground loss rate, and drum speed change rate are selected as evaluation indicators of corn harvester operating quality;

[0044] 2) Select the ternary quadratic universal rotation combination design method to develop the experimental plan, determine the value range of each factor and the representative value of each factor, and select the experimental factors and levels;

[0045] 3) Select an appropriate orthogonal test table based on the number of experimental factors and levels, and formulate an orthogonal test plan;

[0046] 4) Conducting a corn harvester harvesting test according to the orthogonal test plan determined in step 3), and recording the test results in an orthogonal test table;

[0047] 5) Plotting a scatter plot based on the data from step 4) to analyze the effects of the independent and coupled effects of the operating parameters on the corn harvester's operating quality; plotting contour maps of the breakage rate, impurity rate, grain loss rate, engine speed change rate, and drum speed change rate. Based on the target operating quality indicators, a suitable operating parameter combination can be found on the contour map.

[0048] 6) Data Processing System V9.0.1 was used to process and analyze the experimental data. A mathematical regression model of the corn grain harvester's operating quality indicators, efficiency indicators, and operating parameter combinations was established. The F test was used to test the significance of the influence of each factor in the regression model. A nonlinear optimization calculation method was used in combination with the MATLAB software optimization toolbox to optimize the operating parameter combinations. Multiple groups of parameter combinations that met the requirements were obtained, and the parameter combinations obtained in step 5) were verified.

[0049] 7) Selecting multiple parameter combinations obtained in step 6) for actual operation of the harvester for verification, and comparing to obtain the optimal parameter combination among the multiple parameter combinations.

[0050] See Figure 2 、 Figure 3 and Figure 4 The experimental data was plotted and analyzed to reveal the relationship between engine speed and threshing drum operating parameters. As the concave gap increases, fluctuations in engine and drum speeds slow, even with increased feed rate. When adjusting feed rate, concave gap, and threshing drum speed to improve harvest quality, the concave gap is difficult to adjust in real time, so a preselected concave gap value is recommended before operation. The threshing drum speed is then adjusted, and finally the feed rate. A reduction of more than 10% in engine speed indicates a potential for clogging, requiring adjustment. A 20% reduction in engine speed leads to a rapid drop in both speed and output, resulting in severe clogging. When the engine speed falls below the minimum engine speed, the engine stalls, and the harvester stops.

[0051] See Figure 5 、 6 , 7, 8, 9, according to the contour map, you can find the appropriate combination of operating parameters on the map according to the operating quality of different indicators

[0052] See Figure 5 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of the roller speed change rate. First, the value of the roller speed change rate is determined, and then the horizontal coordinate of the value is the roller speed, the vertical coordinate of the value is the forward speed of the harvester, and the color grayscale of the value is the concave plate gap.

[0053] See Figure 6 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of the engine speed change rate. First, the roller speed change rate is determined, and then the horizontal coordinate of the value is the roller speed, the vertical coordinate of the value is the forward speed of the harvester, and the color grayscale of the value is the concave plate gap.

[0054] See Figure 7, the data recorded in the corn harvester harvesting test results are used to draw a contour map of the grain impurity content. First, the grain impurity content is determined, and then the horizontal coordinate of the value is the drum speed, the vertical coordinate of the value is the harvester forward speed, and the color grayscale of the value is the concave plate gap.

[0055] See Figure 8 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of the grain breakage rate. First, the grain breakage rate is determined, and then the horizontal coordinate of the value is the drum speed, the vertical coordinate of the value is the harvester forward speed, and the color grayscale of the value is the concave plate gap.

[0056] See Figure 9 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of grain loss rate. First, the grain loss rate is determined, and then the horizontal coordinate of the value is the drum speed, the vertical coordinate of the value is the harvester forward speed, and the color grayscale of the value is the concave plate gap.

[0057] See Figure 10 and Figure 11 MATLAB was used to optimize the regression equation and combine it with the actual operating parameter indicators. The breakage rate was less than 5%, the impurity rate was less than 2%, the dropped grain loss was less than 5%, and the critical indicators of engine speed and drum speed were 10% and 20% respectively. That is, when the speed change rate exceeded 10%, there would be a tendency of blockage, which required processing and adjustment; when the speed change rate exceeded 20%, the harvester would be seriously blocked. After optimization calculation, the range of each operating parameter was obtained. Figure 10 The feed amount solution scatter plot and box plot obtained by combining the regression model with the actual operation index optimization calculation are shown in the figure. Figure 10 It can be seen that the median of the solution range of the feed rate after the regression model optimization calculation is concentrated between 12kg / s and 13kg / s; Figure 11 The optimal feed rate range, calculated after optimizing the engine and drum speed change rates, is primarily around 12 kg / s, indicating a harvester forward speed of approximately 5 km / h. Furthermore, the scattered distribution of data outside the box line indicates that smaller system feed rates decrease the speed change rate, demonstrating more stable system operation. Feed rate has a significant impact on trash content. When the trash content is 2%, the resulting feed rate is concentrated below 12 kg / s. While ensuring that the trash content meets the target quality indicators, the optimal harvester operating speed can be selected between 3 and 5 km / h.

[0058] See Figure 12 and Figure 13 , Figure 12 The scatter plot and box-and-whisker plot of the drum speed n2 solution obtained after optimizing the regression model and actual operating indicators. Figure 12It can be seen that the solution set of the drum speed n2 after optimization calculation is above 360rad / min. Figure 13 The vast majority of solutions for the engine speed change rate m1 and the drum speed change rate m5 are concentrated at 360 rad / min. At 360 rad / min, the engine operates relatively smoothly, and the seed drop loss rate is also low. 360 rad / min can be selected as the initial parameter during operation, and can also be adjusted according to actual conditions. By appropriately increasing the speed, indicators such as the crushing rate can be reduced.

[0059] See Figure 14 and Figure 15 , Figure 14 The scatter plot and box-and-whisker plot of the concave plate gap n3 solution obtained by optimizing the regression model and the actual operation indicators are shown in the figure. Figure 14 、 Figure 15 It can be seen that the engine speed change rate m1 and the drum speed change rate m5 are at their optimal values ​​when the concave plate gap m3 is around 30mm; the change in the concave plate gap m3 has a relatively small impact on the impurity content n3; when the concave plate gap m3 is around 35mm, the crushing rate is the lowest. Therefore, the optimal operating parameters are a feed rate of 12kg / s, a drum speed of 360rad / min, and a concave plate gap of 30mm. Figure 5 / 6 found that, under the conditions that the engine speed change rate is less than 5%, the drum speed change rate is less than 10%, the crushing rate is less than 5%, the impurity rate is less than 2%, and the grain loss rate is less than 4%, the optimal operating parameter range is a feed rate of 9.8-12.25 kg / s, a forward speed of 4-5 km / h, a drum speed of 330-370 rad / min, and a concave plate gap m3 of 30 mm.

[0060] See Figure 16 The optimized parameter combination was validated in actual harvester operation. The harvesters were divided into two groups: Group 1: feed rates of 9.8 kg / s and 12.25 kg / s, a drum speed of 360 rad / min, and a concave gap of 30 mm; Group 2: feed rates of 12.25 kg / s, a drum speed of 360 rad / min, and a concave gap of 30 mm. The experiment was repeated three times for each group, as shown in Table 5. Comparison of the two data sets shows that the machine performed better when the feed rate was 12.25 kg / s, corresponding to a forward speed of 5 km / h, a drum speed of 360 rad / min, and a concave gap of 30 mm. The average crushing rate was 3.91% < 5%, the average trash content was 1.71% < 2%, and the dropped kernel loss was 3.1% < 5%.

[0061] Working principle of the present invention:

[0062] The present invention aims to solve the problem that the operating parameter range of the corn grain harvester is not in the optimal range, resulting in poor operating quality and low operating efficiency. By establishing a harvester operating parameter combination model, an optimized operating parameter combination can be obtained, so that the harvester can work under the optimized operating parameter combination to obtain better operating quality and operating efficiency.

[0063] See Figure 1 , the present invention is characterized by the following steps:

[0064] 1) Analyze the entire operation process of the corn harvester and preliminarily identify the key operating factors that affect the harvester's operating quality. These operating factors have a significant impact on the harvester's operating quality: feed rate, drum speed, and concave plate gap are selected as experimental factors, and engine speed change rate, crushing rate, impurity rate, ground loss rate, and drum speed change rate are selected as evaluation indicators of corn harvester operating quality;

[0065] 2) Select the ternary quadratic universal rotation combination design method to develop the experimental plan, determine the value range of each factor and the representative value of each factor, and select the experimental factors and levels;

[0066] 3) Select an appropriate orthogonal test table based on the number of experimental factors and levels, and formulate an orthogonal test plan;

[0067] 4) Conducting a corn harvester harvesting test according to the orthogonal test plan determined in step 3), and recording the test results in an orthogonal test table;

[0068] 5) Plotting a scatter plot based on the data from step 4) to analyze the effects of the independent and coupled effects of the operating parameters on the corn harvester's operating quality; plotting contour maps of the breakage rate, impurity rate, grain loss rate, engine speed change rate, and drum speed change rate. Based on the target operating quality indicators, a suitable operating parameter combination can be found on the contour map.

[0069] 6) Data Processing System V9.0.1 was used to process and analyze the experimental data, and a mathematical regression model of the corn grain harvester operation quality index, efficiency index and operation parameter combination was established. The F test was used to test the significance of the influence of each factor in the regression model. A nonlinear optimization calculation method was used in combination with the MATLAB software optimization toolbox to optimize the operation parameter combination and obtain multiple groups of parameter combinations that met the requirements. The parameter combination obtained in step 5) was verified.

[0070] 7) Selecting multiple parameter combinations obtained in step 6) for actual operation of the harvester for verification, and comparing to obtain the optimal parameter combination among the multiple parameter combinations.

[0071] See Figure 2、 Figure 3 and Figure 4 The experimental data was plotted as a scatter plot, and the relationship between engine speed and threshing drum operating parameters was analyzed. As the concave gap increased, fluctuations in engine and drum speeds decreased, even with increased feed rate. When adjusting feed rate, concave gap, and threshing drum speed to improve harvest quality, the concave gap is difficult to adjust in real time, so a preselected concave gap value is recommended before operation. The threshing drum speed is then adjusted, and finally the feed rate. If the engine speed decreases by more than 10%, it will tend to clog, requiring adjustment. When the harvester engine speed decreases by 20%, both the speed and output power drop rapidly, leading to severe clogs. When the engine speed drops below the minimum engine speed, the engine stalls, and the harvester stops.

[0072] See Figure 5 、 6 , 7, 8, 9, according to the contour map, you can find the appropriate combination of operating parameters on the map according to the operating quality of different indicators

[0073] See Figure 5 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of the roller speed change rate. First, the value of the roller speed change rate is determined, and then the horizontal coordinate of the value is the roller speed, the vertical coordinate of the value is the forward speed of the harvester, and the color grayscale of the value is the concave plate gap.

[0074] See Figure 6 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of the engine speed change rate. First, the roller speed change rate is determined, and then the horizontal coordinate of the value is the roller speed, the vertical coordinate of the value is the forward speed of the harvester, and the color grayscale of the value is the concave plate gap.

[0075] See Figure 7 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of the grain impurity content. First, the grain impurity content is determined, and then the horizontal coordinate of the value is the drum speed, the vertical coordinate of the value is the harvester forward speed, and the color grayscale of the value is the concave plate gap.

[0076] See Figure 8 , the data recorded in the corn harvester harvesting test results are used to draw a contour map of the grain breakage rate. First, the grain breakage rate is determined, and then the horizontal coordinate of the value is the drum speed, the vertical coordinate of the value is the harvester forward speed, and the color grayscale of the value is the concave plate gap.

[0077] See Figure 9, the data recorded in the corn harvester harvesting test results are used to draw a contour map of grain loss rate. First, the grain loss rate is determined, and then the horizontal coordinate of the value is the drum speed, the vertical coordinate of the value is the harvester forward speed, and the color grayscale of the value is the concave plate gap.

[0078] See Figure 10 and Figure 11 MATLAB was used to optimize the regression equation and combine it with the actual operating parameter indicators. The breakage rate was less than 5%, the impurity rate was less than 2%, the dropped grain loss was less than 5%, and the critical indicators of engine speed and drum speed were 10% and 20% respectively. That is, when the speed change rate exceeded 10%, there would be a tendency of blockage, which required processing and adjustment; when the speed change rate exceeded 20%, the harvester would be seriously blocked. After optimization calculation, the range of each operating parameter was obtained. Figure 10 The feed amount solution scatter plot and box plot obtained by combining the regression model with the actual operation index optimization calculation are shown in the figure. Figure 10 It can be seen that the median of the solution range of the feed rate after the regression model optimization calculation is concentrated between 12kg / s and 13kg / s; Figure 11 The optimal feed rate range, calculated after optimizing the engine and drum speed change rates, is primarily around 12 kg / s, indicating a harvester forward speed of approximately 5 km / h. Furthermore, the scattered distribution of data outside the box line indicates that smaller system feed rates decrease the speed change rate, demonstrating more stable system operation. Feed rate has a significant impact on trash content. When the trash content is 2%, the resulting feed rate is concentrated below 12 kg / s. While ensuring that the trash content meets the target quality indicators, the optimal harvester operating speed can be selected between 3 and 5 km / h.

[0079] See Figure 12 and Figure 13 , Figure 12 The scatter plot and box-and-whisker plot of the drum speed n2 solution obtained after optimizing the regression model and actual operating indicators. Figure 12 It can be seen that the solution set of the drum speed n2 after optimization calculation is above 360rad / min. Figure 13 The vast majority of solutions for the engine speed change rate m1 and the drum speed change rate m5 are concentrated at 360 rad / min. At 360 rad / min, the engine operates relatively smoothly, and the seed drop loss rate is also low. 360 rad / min can be selected as the initial parameter during operation, and can also be adjusted according to actual conditions. By appropriately increasing the speed, indicators such as the crushing rate can be reduced.

[0080] See Figure 14 and Figure 15 , Figure 14The scatter plot and box-and-whisker plot of the concave plate gap n3 solution obtained by optimizing the regression model and the actual operation indicators are shown in the figure. Figure 14 、 Figure 15 It can be seen that the engine speed change rate m1 and the drum speed change rate m5 are at their optimal values ​​when the concave plate gap m3 is around 30mm; the change in the concave plate gap m3 has a relatively small impact on the impurity content n3; when the concave plate gap m3 is around 35mm, the crushing rate is the lowest. Therefore, the optimal operating parameters are a feed rate of 12kg / s, a drum speed of 360rad / min, and a concave plate gap of 30mm. Figure 5 / 6 found that, under the conditions that the engine speed change rate is less than 5%, the drum speed change rate is less than 10%, the crushing rate is less than 5%, the impurity rate is less than 2%, and the grain loss rate is less than 4%, the optimal operating parameter range is a feed rate of 9.8-12.25 kg / s, a forward speed of 4-5 km / h, a drum speed of 330-370 rad / min, and a concave plate gap m3 of 30 mm.

[0081] See Figure 16 The optimized parameter combination was validated in actual harvester operation. The harvesters were divided into two groups: Group 1: feed rates of 9.8 kg / s and 12.25 kg / s, a drum speed of 360 rad / min, and a concave gap of 30 mm; Group 2: feed rates of 12.25 kg / s, a drum speed of 360 rad / min, and a concave gap of 30 mm. The experiment was repeated three times for each group, as shown in Table 5. Comparison of the two data sets shows that the machine performed better when the feed rate was 12.25 kg / s, corresponding to a forward speed of 5 km / h, a drum speed of 360 rad / min, and a concave gap of 30 mm. The average crushing rate was 3.91% < 5%, the average trash content was 1.71% < 2%, and the dropped kernel loss was 3.1% < 5%.

Claims

1. A method for establishing a combined model of operating parameters of a corn grain harvester based on orthogonal experiment, characterized in that The steps are as follows: 1) Analyze the entire operation process of the corn harvester and preliminarily identify the key operating factors that affect the harvester's operating quality. These operating factors have a significant impact on the harvester's operating quality: feed rate, drum speed, and concave plate gap are selected as experimental factors, and engine speed change rate, crushing rate, impurity rate, ground loss rate, and drum speed change rate are selected as evaluation indicators of corn harvester operating quality; 2) Select the ternary quadratic universal rotation combination design method to develop the experimental plan, determine the value range of each factor and the representative value of each factor, and select the experimental factors and levels; 3) Select an appropriate orthogonal test table based on the number of experimental factors and levels, and formulate an orthogonal test plan; 4) Conducting a corn harvester harvesting test according to the orthogonal test plan determined in step 3), and recording the test results in an orthogonal test table; 5) Based on the data from step 4), a scatter plot is drawn to analyze the effects of the independent and coupled effects of the operating parameters on the corn harvester's operating quality; contour plots of the breakage rate, impurity rate, grain loss rate, engine speed change rate, and drum speed change rate are drawn. Based on the target operating quality indicators, a suitable operating parameter combination can be found on the contour plots; 6) Data Processing System V9.0.1 was used to process and analyze the experimental data. A mathematical regression model was established for the corn grain harvester's operating quality indicators, efficiency indicators, and operating parameter combinations. The F test was used to test the significance of the influence of each factor in the regression model. A nonlinear optimization method was used in conjunction with the MATLAB software optimization toolbox to optimize the operating parameter combinations. Multiple sets of parameter combinations that met the requirements were obtained, and the parameter combinations obtained in step 5) were verified. 7) Selecting multiple parameter combinations obtained in step 6) for actual operation of the harvester for verification, and comparing to obtain the optimal parameter combination among the multiple parameter combinations.

2. The method for establishing a corn grain harvester operating parameter combination model based on orthogonal experiment according to claim 1 is characterized in that The step 5) is specifically as follows: 5.01) Plotting the experimental data into a scatter plot, and analyzing the scatter plot to obtain the relationship between the engine speed and the threshing drum operating parameters; 5.02) Use the test data to create a contour map of the breakage rate, impurity rate, seed loss rate, engine speed change rate, and drum speed change rate. Based on the target operating quality indicators, the appropriate operating parameter combination can be found on the contour map.

3. The method for establishing a corn grain harvester operating parameter combination model based on orthogonal experiment according to claim 1 is characterized in that The step 6) is specifically as follows: 6.01) Data Processing System V9.0.1 was used to process and analyze the test data. The test factors were feed rate n1, drum speed n2, and concave plate gap n3. The response values ​​were engine speed change rate m1, crushing rate m2, impurity content m3, ground loss rate m4, and drum speed change rate m5. The regression equation was obtained: ； ； ； ； ； 6.02) Use MATLAB to optimize the regression equation and combine it with the actual operating parameter indicators to obtain the range of each operating parameter after optimization calculation; 6.03) Analyze the relationship between the influencing factors (feed rate, concave gap, and drum speed) and the objective functions (crush rate, trash content, and drop loss rate) to optimize the operating parameter combination. Use Origin software to create scatter plots, box plots, and contour plots of feed rate, concave gap, and drum speed. Analyze the contour plots to determine the interplay of multiple parameters and the optimal operating parameter range.

4. The method for establishing a corn grain harvester operating parameter combination model based on orthogonal experiment according to claim 1 is characterized in that The step 7) is specifically as follows: 7.01) The optimized parameter combination was used in actual harvester operation for verification, with each test set repeated three times.

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