Sprinkling machine structure parameter optimization method based on response surface method

Abstract

The invention aims to solve the problems of poor utilization rate of water resources and great influence of equipment parameters and operation conditions in the operation of the sprinkling machine. According to the research, spray irrigation intensity and uniformity are taken as key evaluation indexes, the spray irrigation intensity determines soil infiltration and runoff risks, the uniformity affects space-time consistency of crop moisture acquisition, and four influence factors including spray nozzle spacing, spray nozzle height, spray nozzle pressure and running speed are selected; the optimal hydraulic performance interval range is selected on the basis of a single-factor test result, a mathematical model under the multi-factor coupling effect is established through a response surface method, the influence rule of the mathematical model on the hydraulic performance is explored, multi-target optimization analysis is carried out, the optimal parameter combination of structural parameters of the sprinkling machine is obtained, and the hydraulic performance of the sprinkling machine is improved to the maximum extent. Researches prove that the response surface method can effectively analyze a multi-parameter coupling mechanism, an optimal sprinkler structure combination parameter is obtained, a theoretical basis is provided for sprinkler design and field operation, and the method has engineering popularization value.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of agricultural water-saving irrigation technology, and particularly relates to a sprinkler structure parameter optimization method based on a response surface method. TECHNICAL BACKGROUND

[0002] The contradiction between global water resource shortage and food safety is increasingly prominent, and developing efficient water-saving irrigation technology has become an important direction for sustainable development of agriculture. Sprinkler irrigation technology is widely used in dry farming areas due to its high water-saving rate and strong adaptability, however, the hydraulic performance and sprinkling operation quality of the sprinkler are directly affected by the equipment parameters and operation conditions, among which the sprinkler structure parameters, such as sprinkler spacing, sprinkler height, sprinkler pressure and running speed, have important influences, and the sprinkling intensity determines the soil infiltration and runoff risk, and the uniformity affects the spatiotemporal consistency of water acquisition of crops, but the existing researches mostly focus on isolated analysis of a single parameter (such as pressure or height), and lack of systematic analysis of the interaction of multiple factors, resulting in problems such as single parameter configuration in actual application. Therefore, establishing a multi-parameter collaborative optimization model has important theoretical and application value for improving the comprehensive performance of the sprinkler irrigation system. In summary, the sprinkler spacing, height and pressure and running speed of the sprinkler will all affect the hydraulic performance of the sprinkler, and the best working parameters of the sprinkler need to be selected, and a multi-parameter collaborative optimization model is established, so that the hydraulic performance of the sprinkler reaches the best state, which has important theoretical and application value for improving the comprehensive performance of the sprinkler irrigation system. SUMMARY

[0003] To solve the technical problem that the research on the structure parameters of the sprinkler is too single, the application provides a sprinkler structure parameter optimization method based on a response surface method. In the research, a translation type sprinkler is taken as the object, the influences of the sprinkler spacing, sprinkler height, sprinkler pressure and running speed on the hydraulic performance are deeply researched, the best working parameters of the sprinkler in the field are obtained based on the multi-objective optimization analysis of the response surface method, and the irrigation performance of the sprinkler is improved. The application mainly relates to a sprinkler hydraulic performance model, and is mainly applied to the field of agricultural water-saving irrigation technology.

[0004] The application achieves the above technical object through the following technical means.

[0005] This study presents a method for optimizing the structural parameters of a sprinkler irrigation machine based on response surface methodology. Irrigation intensity and uniformity are the key evaluation indicators. Irrigation intensity determines soil infiltration and runoff risk, while uniformity affects the spatiotemporal consistency of crop water acquisition. Four influencing factors—sprinkler spacing *s*, sprinkler height *h*, sprinkler pressure *p*, and operating speed *v*—are selected. A mathematical model under the coupled effects of these factors is established using response surface methodology to explore their influence on sprinkler irrigation performance, and multi-objective optimization analysis is conducted. A four-factor, three-level experimental scheme is designed and constructed using the Box-Behnken response surface methodology. Based on the experimental results, a second-order regression model is established. Analysis of variance (ANOVA) shows that the model has good significance and can be used to predict the changing trends of irrigation intensity and uniformity. The optimal parameter combination is obtained through multi-objective optimization.

[0006] To achieve the above objectives, the present invention includes the following steps:

[0007] Step 1: A method for optimizing the structural parameters of a sprinkler irrigation machine based on response surface methodology. This method uses irrigation intensity and uniformity as key evaluation indicators to determine the response parameters of a lightweight, small-scale, translational sprinkler irrigation machine. The response parameters include irrigation intensity and irrigation uniformity. Irrigation intensity refers to the depth of water sprayed onto the ground per unit time. Before the test, an empty rain gauge container is weighed, and then the water volume in the rain gauge is collected through a spraying test. The formula for calculating irrigation intensity is: Where: I is the sprinkler intensity, mm / h; ΔM is the volume of water in the rain gauge at the measuring point, cm³. 3 A represents the area of ​​the opening at the top of the rain gauge, in cm². 2 t represents the irrigation time, in seconds. The uniformity of sprinkler irrigation is often evaluated using the Christensen uniformity coefficient (CU), which is calculated using the following formula: Where: Cu represents sprinkler uniformity, %; x i Let the water depth for the i-th rain gauge be in mm. is the average water depth for sprinkler irrigation using rain gauges, in mm; n is the number of rain gauges.

[0008] Step 2: Determine the structural parameters to be optimized for the lightweight, small-scale translational sprinkler irrigation machine: four influencing factors—sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed. Based on the sprinkler irrigation machine's design parameters and actual test conditions, the selected test parameters for sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed are all within the normal adjustment range of the sprinkler irrigation machine, meeting the requirements of sprinkler irrigation tests. Furthermore, the selection of key parameters in the sprinkler irrigation system performance optimization test follows the principle of multi-dimensional scientific demonstration, determining the test range and gradient of each parameter through a combination of theoretical analysis and practical experience.

[0009] Step 3: The aforementioned single-factor influence analysis on hydraulic performance is conducted. Based on the single-factor experimental results, the optimal hydraulic performance range is selected. The key feature is the use of the single-factor controlled variable method to determine the experimental scheme, obtain the influence law of each factor on hydraulic performance, and determine the range of optimal conditions for each factor in irrigation intensity and uniformity. Based on the comprehensive single-factor experimental results, response surface methodology is used to design parameters within the range of optimal conditions for each factor in irrigation intensity and uniformity.

[0010] Step 4: The optimal range of the single-factor experiment is input into the response surface methodology software Design-Expert. The Box-Behnken surface response method is used to analyze the experimental results. To gain a deeper understanding of the impact of each factor and its interaction on hydraulic performance and to determine the optimal operating parameters, based on the optimal level range obtained from the single-factor experiment, four factors are selected as independent variables: nozzle spacing s, nozzle height h, nozzle pressure p, and operating speed v. Sprinkler intensity and sprinkler uniformity are selected as dependent variables. An efficient four-factor, three-level experimental design method is designed using the Box-Behnken experimental method. This method is suitable for analyzing the nonlinear relationships between factors and determining the significance level of each factor.

[0011] Step 5: The experimental data are subjected to multiple regression fitting analysis. Based on the analysis results, an optimized calculation model between the structural parameters and response values ​​of the lightweight translational sprinkler is obtained. The response surface model of sprinkler intensity and sprinkler uniformity is obtained. The calculation model of sprinkler intensity and sprinkler uniformity can be better simulated and calculated based on the response variables such as nozzle spacing s, nozzle height h, nozzle pressure p, and running speed v.

[0012] Step 6: The aforementioned analysis of variance was performed on the hydraulic performance of the sprinkler irrigation machine. To evaluate the accuracy of the model and the significance of the influence of each factor on the irrigation intensity and uniformity, the goodness-of-fit R-value was used. 2 Two methods were used: the F-test and the significance test. With a significance level of α = 0.05, a factor is considered to have a significant impact on the hydraulic response value when the p-value is less than 0.05, and a factor has a relatively small impact on the hydraulic response value when the p-value is greater than 0.05.

[0013] Step 7: The response surface methodology (RSM) software Design-Expert is used to analyze the hydraulic performance using response surfaces and contour plots. Through analysis of the RSM and contour plots, the conditions and specific trends of interaction between influencing factors are determined. When the slope of the RSM curve is large, the interaction profile is elliptical, indicating a significant interaction between the two factors. When the slope of the RSM curve is relatively gentle, the interaction curve is circular, indicating that the interaction between the two factors does not significantly affect the hydraulic performance of the experiment.

[0014] Step 8: Based on the experimental data and response surface methodology, the optimal combination parameters for different sprinkler structures are obtained to achieve the best hydraulic performance. Design-Expert software is used to process and analyze the model, determining the values ​​of sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed that maximize irrigation intensity and uniformity. This yields the optimal conditions and predicted values ​​from the model. The reliability of the model is verified by validating the optimal combination of factors. The experiment is repeated three times, and the experimental results are compared with the model predictions. If the prediction error between the experimental results and the model predictions is small, and the predicted values ​​are not significantly different from the actual measured values, indicating a high degree of agreement, the model prediction is reliable, and the obtained combination parameters of sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed represent the optimal combination parameters for the optimized sprinkler irrigation machine.

[0015] By employing the above technical solutions, the beneficial effects of the present invention are as follows:

[0016] The present invention discloses a method for optimizing the structural parameters of a sprinkler irrigation machine based on response surface methodology. This method selects the optimal operating parameters for a lightweight, small-scale translational sprinkler irrigation machine and establishes a multi-parameter collaborative optimization model, thereby achieving the best hydraulic performance of the sprinkler irrigation machine. This provides a theoretical basis for sprinkler irrigation machine design and field operations. Furthermore, the proposed optimization model can be adapted to different soil infiltration conditions, which has significant theoretical and application value for improving the overall performance of sprinkler irrigation systems and also has engineering promotion value.

[0017] To make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the lightweight translational sprinkler irrigation machine described in this invention;

[0020] Figure 2 This is a schematic diagram of the test site layout described in this invention;

[0021] Figure 3 This is a diagram illustrating the effect of different nozzle spacings on hydraulic performance as described in this invention.

[0022] Figure 4 This is a diagram illustrating the effect of different nozzle heights on hydraulic performance as described in this invention.

[0023] Figure 5 This is a diagram illustrating the effect of different nozzle pressures on hydraulic performance as described in this invention.

[0024] Figure 6 This is a diagram illustrating the effect of different operating speeds on hydraulic performance as described in this invention;

[0025] Figure 7 The response surface curve and contour plot of the nozzle spacing versus nozzle pressure described in this invention are shown.

[0026] Figure 8 The response surface curve and contour plot of the nozzle height versus nozzle pressure described in this invention are shown.

[0027] Figure 9 The response surface graph and contour plot of the nozzle pressure and operating speed described in this invention;

[0028] Figure 10 The response surface curves and contour plots of the nozzle spacing and operating speed described in this invention are shown.

[0029] Figure 11 The above are response surface curves and contour plots of the nozzle height and operating speed described in this invention.

[0030] Figure 12 The response surface graph and contour plot of the nozzle pressure and operating speed described in this invention;

[0031] In the picture:

[0032] 1. Tracked chassis; 2. Water supply pipe; 3. Booster pump; 4. Support structure; 5. Lifting structure; 6. Truss; 7. Sprinkler head; 8. Horizontal sprinkler; 9. Rain gauge. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] The specific implementation steps of the sprinkler irrigation machine structural parameter optimization method based on response surface methodology of the present invention are as follows:

[0035] Example: Combining Figures 1-12As shown, this embodiment discloses a method for optimizing the structural parameters of a sprinkler irrigation machine based on the response surface methodology. The method uses irrigation intensity and uniformity as key evaluation indicators to determine the response parameters of a lightweight, small-scale translational sprinkler irrigation machine. The response parameters include irrigation intensity and irrigation uniformity. Irrigation intensity refers to the depth of water sprayed onto the ground per unit time. Before the test, an empty rain gauge container is weighed, and then the water volume in the rain gauge is collected through a spraying test. The formula for calculating irrigation intensity is: Where: I is the sprinkler intensity, mm / h; ΔM is the volume of water in the rain gauge at the measuring point, cm³. 3 A represents the area of ​​the opening at the top of the rain gauge, in cm². 2 t represents the irrigation time, in seconds. The uniformity of sprinkler irrigation is often evaluated using the Christensen uniformity coefficient (CU), which is calculated using the following formula: Where: Cu represents sprinkler uniformity, %; x i Let the water depth for the i-th rain gauge be in mm. denoted as , where is the average water depth per rain gauge, in mm; 'n' represents the number of rain gauges. A schematic diagram of a lightweight, horizontally moving sprinkler irrigation system is shown below. Figure 1 As shown in the diagram, the experimental site layout is as follows: Figure 2 As shown.

[0036] The structural parameters requiring optimization for the lightweight, small-scale, translational sprinkler irrigation machine are determined by four influencing factors: sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed. Based on the sprinkler irrigation machine's design parameters and actual test conditions, the selected test parameters for sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed are all within the normal adjustment range of the sprinkler irrigation machine, meeting the requirements of the sprinkler irrigation test. Furthermore, the selection of key parameters in the sprinkler irrigation system performance optimization test follows the principle of multi-dimensional scientific demonstration, determining the test range and gradient of each parameter through a combination of theoretical analysis and practical experience. The single-factor test parameters are shown in Table 1.

[0037] Table 1 Single factor experimental parameter table

[0038]

[0039] The single-factor analysis of the hydraulic performance was conducted to obtain the effects and patterns of four factors—sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed—on irrigation intensity and uniformity. The effects and patterns of these four factors on irrigation intensity and uniformity are as follows: Figures 3-6 As shown.

[0040] Depend on Figure 3It can be seen that the sprinkler irrigation intensity is the highest when the sprinkler spacing is 1.0m, reaching 15.1mm / h; the sprinkler irrigation intensity is the lowest when the sprinkler spacing is 1.4m, reaching 8.8mm / h. The sprinkler irrigation intensity shows a trend of first increasing and then decreasing with increasing sprinkler spacing; the sprinkler irrigation uniformity also shows a trend of first increasing and then decreasing with increasing sprinkler spacing. Figure 4 It can be seen that the sprinkler irrigation intensity is the highest at a sprinkler head height of 1.2m, reaching 12.9 mm / h, and the lowest at a sprinkler spacing of 1.6m, reaching 10.5 mm / h. The sprinkler irrigation intensity shows a trend of first increasing and then decreasing with increasing sprinkler height; similarly, the sprinkler irrigation uniformity shows a trend of first increasing and then decreasing with increasing sprinkler spacing. Figure 5 It can be seen that the irrigation intensity is highest at a nozzle pressure of 300 kPa, reaching 17.9 mm / h, and lowest at a nozzle pressure of 100 kPa, reaching 6.3 mm / h. The irrigation intensity increases continuously with increasing nozzle pressure, and exhibits a uniform increasing trend with a consistent nozzle pressure. The irrigation uniformity, however, initially increases and then decreases with increasing nozzle pressure. Figure 6 It can be seen that the irrigation intensity is the highest at an operating speed of 30 m / h, with an irrigation intensity of 16.9 mm / h, and the irrigation intensity is the lowest at an operating speed of 150 m / h, with an irrigation intensity of 7.8 mm / h. The irrigation intensity decreases continuously with the increase of operating speed, and the rate of decrease shows a trend of being faster at first and then slower as the operating speed increases uniformly. The irrigation uniformity decreases continuously with the increase of operating speed, and the irrigation uniformity shows a trend of decreasing uniformly as the operating speed increases uniformly.

[0041] Based on the results of the single-factor experiments, the optimal hydraulic performance range was selected. The single-factor controlled variable method was used to determine the experimental scheme, obtaining the influence law of each factor on hydraulic performance and the optimal range of each factor under the conditions of sprinkler intensity and uniformity. Combining the results of the single-factor experiments, response surface methodology was used to design parameters based on the optimal range of each factor under the conditions of sprinkler intensity and uniformity. The sprinkler spacing s was selected as 0.8–1.2 m, the sprinkler height h as 1.0–1.4 m, the sprinkler pressure p as 200–300 kPa, and the operating speed v as 30–90 m / h for the experiments. The parameter design ranges for the response surface methodology are shown in Table 2.

[0042] Table 2 Scope of Response Surface Methodology Parameter Design

[0043]

[0044] The optimal range results selected from the single-factor experiments were input into the response surface methodology software Design-Expert. The Box-Behnken surface response method was used to analyze the experimental results. To gain a deeper understanding of the impact of each factor and their interactions on hydraulic performance and to determine the optimal operating parameters, based on the optimal level range obtained from the single-factor experiments, four factors—sprinkler spacing s, sprinkler height h, sprinkler pressure p, and operating speed v—were selected as independent variables, and irrigation intensity and irrigation uniformity as dependent variables. An efficient four-factor, three-level experimental design method was designed using the Box-Behnken experimental method. This method is suitable for analyzing the nonlinear relationships between factors and determining the interaction between them, as well as the significance levels of each factor. The hydraulic performance test results are shown in Table 3.

[0045] Table 3 Hydraulic performance test results

[0046]

[0047]

[0048] The aforementioned multivariate regression fitting analysis of the experimental data yields an optimized calculation model between the structural parameters and response values ​​of the lightweight translational sprinkler irrigation machine. This results in a response surface model for irrigation intensity and uniformity. Based on the response variables—sprinkler spacing s, sprinkler height h, sprinkler pressure p, and running speed v—the calculation model for irrigation intensity and uniformity can be better simulated and calculated.

[0049] The surface model of the sprinkler intensity response is shown in Equation (1).

[0050] I=-144.6+153.3S+161.6H-0.12P+0.06V+0.16SH+0.03SP+0.015SV

[0051] -0.01HP+0.015HV+0.00015PV-82.9S 2 -67.9H 2 +0.0003P 2 -0.002V 2 (1) Where: S—nozzle spacing, m; H—nozzle height, m; P—nozzle pressure, kPa; V—operating speed, m / h.

[0052] The response surface model of the sprinkler uniformity is shown in Equation (2).

[0053] Cu=-552.3+373.4S+428.8H+1.4P+0.89V+12.66SH+0.045SP-0.035SV

[0054] -0.01HP-0.07HV-0.0003PV-192S 2 -184.5H 2 +-0.003P 2 -0.006V 2 (2) Where: S—nozzle spacing, m; H—nozzle height, m; P—nozzle pressure, kPa; V—operating speed, m / h.

[0055] The aforementioned analysis of variance on the hydraulic performance of the sprinkler irrigation machine was conducted to evaluate the accuracy of the model and the significance of the influence of each factor on the irrigation intensity and uniformity. The goodness-of-fit R-value was used. 2 Two methods were used: the F-test and the significance test. The significance level was set at α = 0.05. When the p-value of a factor is less than 0.05, it can be considered that this factor has a significant impact on the hydraulic response value; when the p-value is greater than 0.05, the impact of this factor on the hydraulic performance response value is relatively small. The variance analysis of sprinkler irrigation intensity is shown in Table 4, and the variance analysis of sprinkler irrigation uniformity is shown in Table 5.

[0056] Table 4. Variance Analysis of Sprinkler Irrigation Intensity

[0057]

[0058]

[0059] Table 5. Variance analysis table of sprinkler uniformity

[0060]

[0061] Table 4 shows the significance of the relationship between each factor and sprinkler irrigation intensity. The p-value of this model is less than 0.0001, indicating that the model has high significance. 2 =0.9899, ​​indicating that the change in response value caused by the selected variable is 98.99%; R 2 Adj =0.9797, indicating that the established model can explain 97.97% of the variation, demonstrating its feasibility and suitability for analyzing and predicting sprinkler irrigation intensity. Its response variables are: linear terms—sprinkler spacing s, sprinkler height h, sprinkler pressure p, and operating speed v; and quadratic term—the square of the sprinkler spacing s. 2 The square of the nozzle height h 2 Nozzle pressure p 2 Operating speed v2 These are significant influencing factors. Based on the significance of the P-value and the difference in the F-value, the influence of each factor on the sprinkler irrigation intensity, from largest to smallest, is P (sprinkler pressure) > V (operating speed) > S (sprinkler spacing) > H (sprinkler height).

[0062] Table 5 shows the significance of the relationship between each factor and sprinkler irrigation uniformity. The p-value of this model is <0.0001, indicating that the model has high significance. 2 = 0.9860, indicating that the change in response value caused by the selected variable is 98.60%; R 2 Adj =0.9720, indicating that the established model can explain 97.20% of the variation, demonstrating its feasibility and suitability for analyzing and predicting sprinkler irrigation uniformity. Its response variables are: linear terms—sprinkler spacing s, sprinkler height h, sprinkler pressure p, and operating speed v; and quadratic term—the square of the sprinkler spacing s. 2 The square of the nozzle height h 2 Nozzle pressure p 2 Operating speed v 2 These are significant influencing factors. Based on the significance of the P-value and the difference in the F-value, the influence of each factor on the uniformity of sprinkler irrigation, from largest to smallest, is S (sprinkler spacing) > H (sprinkler height) > V (operating speed) > P (sprinkler pressure).

[0063] The aforementioned method utilizes the response surface methodology Design-Expert to analyze hydraulic performance using response surfaces and contour plots. Through analysis of these data, the conditions and specific trends of interaction between influencing factors are determined. When the slope of the response surface curve is large, the interaction profile is elliptical, indicating a significant interaction between the two factors. Conversely, when the slope of the response surface curve is relatively gentle, the interaction curve is circular, indicating that the interaction between the two factors does not significantly affect the experimental hydraulic performance.

[0064] Analysis of the sprinkler irrigation intensity response surface and contour plot reveals the conditions for the interaction among influencing factors. Figure 8 It can be seen that as the nozzle spacing and nozzle pressure increase, the irrigation intensity of the sprinkler first increases and then decreases. When the nozzle spacing is 0.9-1.1m and the nozzle pressure is 300kPa, there is an optimal range for irrigation intensity. The nozzle pressure has a significant impact on the irrigation intensity. The contour plots of nozzle spacing and nozzle pressure are close to ellipses, indicating a significant interaction effect. According to... Figure 9It can be seen that as the nozzle height and nozzle pressure increase, the irrigation intensity of the sprinkler first increases and then decreases. When the nozzle height is 1.1-1.3m and the nozzle pressure is 300kPa, there is an optimal range for irrigation intensity. The nozzle pressure has a significant impact on the irrigation intensity. The contour plot of the interaction between nozzle height and nozzle pressure is close to an ellipse, indicating a significant interaction effect. According to... Figure 10 It can be seen that the irrigation intensity of the sprinkler increases with increasing nozzle pressure and decreasing operating speed. The irrigation intensity reaches its maximum when the nozzle pressure is 300 kPa and the operating speed is 30 m / h, indicating that nozzle pressure has a significant impact on irrigation intensity. The contour plot of the interaction between nozzle pressure and operating speed is close to an ellipse, indicating a significant interaction. The contour plots of the interactions between nozzle spacing and nozzle speed, nozzle height and nozzle speed, and nozzle spacing and nozzle height are close to circles, indicating that the interaction effects are not significant.

[0065] Analysis of the response surface and contour plot of sprinkler irrigation uniformity reveals the conditions for the interaction between influencing factors. Figure 12 It can be seen that as the nozzle spacing and operating speed increase, the irrigation uniformity of the sprinkler first increases and then decreases. When the nozzle spacing is 0.9-1.1m and the operating speed is 30-60m / h, there is an optimal range for irrigation uniformity, indicating that the nozzle spacing has a significant impact on irrigation uniformity. The contour plot of the interaction between nozzle spacing and operating speed is close to an ellipse, indicating a significant interaction. Similarly, as the nozzle height and operating speed increase, the irrigation uniformity of the sprinkler first increases and then decreases. When the nozzle height is 1.1-1.3m and the operating speed is 30-60m / h, there is an optimal range for irrigation uniformity, indicating that the nozzle height has a significant impact on irrigation uniformity. The contour plot of the interaction between nozzle height and operating speed is close to an ellipse, indicating a significant interaction. As sprinkler pressure and operating speed increase, the uniformity of irrigation first increases and then decreases. An optimal range for uniformity is found when sprinkler pressure is 230-280 kPa and operating speed is 30-60 m / h, indicating that sprinkler pressure has a significant impact on uniformity. The contour plot of the interaction between sprinkler pressure and operating speed is close to an ellipse, indicating a significant interaction. Contour plots of the interactions between sprinkler spacing and sprinkler pressure, sprinkler height and sprinkler pressure, and sprinkler spacing and sprinkler height are close to circles, indicating that the interactions are not significant.

[0066] The model was processed and analyzed to obtain the values ​​of sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed that maximized irrigation intensity and uniformity. The optimal conditions and predicted values ​​obtained using Design-Expert software are shown in Table 6.

[0067] Table 6 shows the optimal hydraulic performance results.

[0068]

[0069] The optimal conditions predicted by the model are as follows: sprinkler spacing of 1.01 m, sprinkler height of 1.18 m, sprinkler pressure of 271.12 kPa, and operating speed of 47.59 m / h. Under these conditions, the predicted irrigation intensity is 19.66 mm / h, and the predicted irrigation uniformity is 88.37%.

[0070] To facilitate experimental verification, and considering the actual working conditions of the sprinkler machine, the optimal combination of working parameters was modified to: sprinkler spacing of 1m, sprinkler height of 1.2m, sprinkler pressure of 270kPa, and operating speed of 45m / h. The model calculated that the irrigation intensity at this time was 19.70mm / h, and the irrigation uniformity was 88.03%. To verify the reliability of the model, the optimal combination of factors was tested and the experiment was repeated three times. The experimental results are compared with the model predictions in Table 7.

[0071] Table 7 Comparison of Experimental Results and Model Predictions

[0072] Table 7Comparison Table ofExperimental Results and Model Prediction

[0073]

[0074] As shown in Table 7, the prediction errors of the test results and model prediction results for irrigation intensity and irrigation uniformity are all within 6%. The predicted values ​​are not much different from the actual measured values, and the degree of agreement is high, indicating that the model prediction effect is reliable. This shows that the obtained combination parameters of sprinkler spacing, sprinkler height, sprinkler pressure and running speed are the optimal combination parameters of the optimized sprinkler irrigation machine.

[0075] It should be understood that although this specification is described according to various embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0076] The detailed descriptions listed above are merely specific illustrations of feasible embodiments of the present invention and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the structural parameters of a sprinkler irrigation machine based on response surface methodology, characterized in that, This method includes: using irrigation intensity and uniformity as key evaluation indicators, where irrigation intensity determines soil infiltration and runoff risk, and uniformity affects the spatiotemporal consistency of crop water acquisition; selecting four influencing factors—sprinkler spacing s, sprinkler height h, sprinkler pressure p, and operating speed v—to conduct single-factor impact analysis on hydraulic performance; selecting the optimal hydraulic performance range based on the single-factor experimental results; establishing a mathematical model under multi-factor coupling effects using response surface methodology to explore the influence law on hydraulic performance; and conducting multi-objective optimization analysis; designing and constructing a four-factor, three-level experimental scheme using response surface methodology; establishing a second-order regression model based on the experimental results; and showing good significance in variance analysis, indicating that the model can be used to predict the changing trends of irrigation intensity and uniformity. Response surface and contour plot analysis of hydraulic performance is also performed, and an optimal sprinkler machine structural combination parameter is obtained through multi-objective optimization.

2. The method for optimizing the structural parameters of a sprinkler irrigation machine based on the response surface methodology according to claim 1, characterized in that, The key evaluation indicators are irrigation intensity and uniformity. The following measures are taken: determining the response parameters of the lightweight translational irrigation machine, including irrigation intensity and irrigation uniformity. Irrigation intensity refers to the depth of water sprayed on the ground per unit time. Before the test, the empty rain gauge container is weighed, and then the water volume of the rain gauge is collected through the spray test. The formula for calculating sprinkler irrigation intensity is: Where: I is the sprinkler intensity, mm / h; ΔM is the volume of water in the rain gauge at the measuring point, cm³. 3 A represents the area of ​​the opening at the top of the rain gauge, in cm². 2 t represents the irrigation time, in seconds. The uniformity of sprinkler irrigation is often evaluated using the Christensen uniformity coefficient (CU), which is calculated using the following formula: Where: Cu represents sprinkler uniformity, %; x i Let the water depth for the i-th rain gauge be in mm. is the average water depth for sprinkler irrigation using rain gauges, in mm; n is the number of rain gauges.

3. The method for optimizing the structural parameters of a sprinkler irrigation machine based on the response surface methodology according to claim 1, characterized in that, Four influencing factors were selected: sprinkler spacing s, sprinkler height h, sprinkler pressure p, and operating speed v. This included determining the structural parameters that need to be optimized for the lightweight, small-scale, horizontally moving sprinkler irrigation machine: sprinkler spacing s, sprinkler height h, sprinkler pressure p, and operating speed v. Based on the design parameters of the sprinkler irrigation machine and the actual test conditions, the selected test parameters for sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed are all within the normal adjustment range of the sprinkler irrigation machine, meeting the requirements of the sprinkler irrigation test. Furthermore, the selection of key parameters in the performance optimization test of the sprinkler irrigation system follows the principle of multi-dimensional scientific demonstration, and the test range and gradient of each parameter are determined by combining theoretical analysis with practical experience.

4. The method for optimizing the structural parameters of a sprinkler irrigation machine based on response surface methodology according to claim 1, characterized in that, The influence of single factors on hydraulic performance was analyzed, and the optimal hydraulic performance range was selected based on the results of single-factor experiments. This included: determining the experimental scheme using the single-factor control variable method, obtaining the influence law of single factors on hydraulic performance, and obtaining the range of each factor under the optimal conditions of sprinkler intensity and sprinkler uniformity; and using response surface methodology to design parameters based on the combined results of single-factor experiments and the range of each factor under the optimal conditions of sprinkler intensity and sprinkler uniformity.

5. The method for optimizing the structural parameters of a sprinkler irrigation machine based on the response surface methodology according to claim 1, characterized in that, The optimal range of the single-factor experiment was input into the response surface methodology software, and the experimental results were analyzed using the surface response method. This included: to gain a deeper understanding of the impact of each factor and its interactions on hydraulic performance and to determine the optimal operating parameters, based on the optimal level range obtained from the single-factor experiment, four factors—sprinkler spacing s, sprinkler height h, sprinkler pressure p, and operating speed v—were selected as independent variables, and irrigation intensity and irrigation uniformity as dependent variables. An efficient four-factor, three-level experimental design method was designed using the surface response method, applicable to nonlinear relationships among factors. The interaction between factors was analyzed, and the significance level of each factor was determined.

6. The method for optimizing the structural parameters of a sprinkler irrigation machine based on response surface methodology according to claim 1, characterized in that, Multiple regression fitting analysis was performed on the experimental data. Based on the analysis results, an optimized calculation model between the structural parameters and response values ​​of the lightweight translational sprinkler was obtained. This included obtaining the response surface model of sprinkler intensity and sprinkler uniformity. The calculation model of sprinkler intensity and sprinkler uniformity can be better simulated and calculated based on the response variables of nozzle spacing s, nozzle height h, nozzle pressure p, and running speed v.

7. The method for optimizing the structural parameters of a sprinkler irrigation machine based on the response surface methodology according to claim 1, characterized in that, An analysis of variance was conducted on the hydraulic performance of the sprinkler irrigation machine, including: to evaluate the accuracy of the model and the significance of the influence of each factor on the irrigation intensity and uniformity, the goodness-of-fit R-value was used. 2 Two methods are used: the F-test and the significance test. A certain significance level is set. When the p-value of a factor is less than this level, it can be considered that the factor has a significant impact on the hydraulic performance response value. When the p-value is greater than this level, the factor has a smaller impact on the hydraulic performance response value.

8. The method for optimizing the structural parameters of a sprinkler irrigation machine based on response surface methodology according to claim 1, characterized in that, Response surface methodology (RSM) software was used to analyze the hydraulic performance using response surfaces and contour plots. This included: analyzing the response surfaces and contour plots to determine the conditions and specific trends of interaction between influencing factors; when the slope of the response surface curve is large, the interaction profile is elliptical, indicating a significant interaction between the two factors; when the slope of the response surface curve is relatively gentle, the interaction curve is circular, indicating that the interaction between the two factors will not have a significant impact on the hydraulic performance of the experiment.

9. The method for optimizing the structural parameters of a sprinkler irrigation machine based on the response surface methodology according to claim 1, characterized in that, Based on experimental data and a response surface optimization model, the combined parameters of different structures for optimal sprinkler hydraulic performance can be obtained. This includes: using surface response software to process and analyze the model to obtain the values ​​of sprinkler spacing, sprinkler height, sprinkler pressure, and operating speed when the irrigation intensity and uniformity are maximized, thus obtaining the optimal conditions and predicted values ​​predicted by the model; and verifying the reliability of the model by validating the optimal combination of factors. The experiment was repeated three times, and the experimental results were compared with the model predictions. If the prediction error between the experimental results and the model prediction results is small, and the predicted values ​​are not significantly different from the actual measured values, indicating a high degree of agreement, it shows that the model prediction effect is reliable.

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