An AHP-EWM-RSR evaluation method suitable for micro-jet mist regulation and optimization

Abstract

This invention discloses an AHP-EWM-RSR evaluation method suitable for optimizing micro-spraying and misting control. Micro-spraying and misting parameters are set as core control variables, and key growth and physiological indicators such as crop height, photosynthetic parameters, chlorophyll fluorescence parameters, and yield are used as feedback signals. The analytic hierarchy process (AHP) and entropy weighting method are used to obtain subjective and objective weights respectively. The combined weights are calculated using the scalar multiplication synthesis normalization method, and the AHP-EWM-RSR model is constructed using the rank-sum ratio method. The frequency distribution is obtained by ranking, and the data are sorted according to RSR values. Linear regression analysis is performed with probit as the independent variable and RSR as the dependent variable. The evaluated objects are ranked and categorized according to the RSR estimates obtained from the regression equation. This method has significant advantages, as it can compensate for the objectivity bias inherent in single weighting and accurately determine the optimal combination of micro-spraying height and duration in field production management, improving water use efficiency and high-temperature stress resistance, and achieving efficient and reliable field high-temperature stress resistance management.

Description

Technical Field

[0001] This invention belongs to the field of agricultural production technology, and in particular relates to an evaluation method for micro-spraying mist control at different spatiotemporal scales, specifically an AHP-EWM-RSR evaluation method suitable for optimizing micro-spraying mist control. Background Technology

[0002] In recent years, global warming has led to frequent high-temperature events, seriously threatening the normal growth of crops. Micro-spraying, as an irrigation technology that combines efficient water conservation and microclimate regulation, has been widely used in agriculture. However, existing micro-spraying systems primarily aim at irrigation, and their operating parameters are usually determined based on traditional irrigation experience. They lack effective strategies for addressing high-temperature stress, and existing micro-spraying is not directly linked to crop growth and physiological responses, making it difficult to confirm the actual effectiveness of this measure in alleviating inherent physiological stress in crops. Therefore, to address high-temperature stress, establishing irrigation strategies that accurately respond to crop physiological states requires a comprehensive evaluation system based on crop growth and physiological indicators. However, existing comprehensive evaluation models often rely on single indicator weighting methods, leading to subjective and arbitrary weight allocation or unreasonable weight coefficients. Furthermore, they fail to fully consider the correlation between indicators, cannot achieve a balance between subjective and objective information, and have poor robustness in evaluation results, making it impossible to accurately determine the optimal solution. Therefore, it is crucial to establish a method that accurately responds to crop growth and physiological states and optimizes micro-spraying parameters based on a robust comprehensive evaluation model, thereby providing efficient and reliable high-temperature resistance strategies for field production management. Summary of the Invention

[0003] This invention addresses the problems of existing micro-spraying and misting technologies relying on experience and lacking precise control in high-temperature management. It proposes a parameter optimization method for micro-spraying and misting based on feedback from crop growth physiological indicators. This method uses the setting height and spraying duration of micro-spraying and misting as core control variables, and key growth physiological indicators such as crop height, photosynthetic parameters, and chlorophyll fluorescence parameters as feedback signals. It achieves synergistic parameter optimization and precise decision-making by constructing an AHP-EWM-RSR comprehensive evaluation model. Based on the AHP and EWM models, it integrates subjective and objective information within a unified framework, and applies the scalar multiplication synthesis normalization method to calculate the combined weights. This fully considers the correlation between indicators while reducing the subjective arbitrariness or objective bias of single weighting methods, effectively reducing uncertainty, enhancing explanatory power, and improving the acceptability of the weighting results, making the weight allocation more scientific. Furthermore, it combines this with the RSR (rank-sum ratio) method to rank and classify the treatment effects of each parameter combination, thereby identifying the optimal irrigation management scheme and providing differentiated control strategies. This effectively alleviates crop high-temperature stress and achieves precise field management from "experience-driven" to "data model-driven."

[0004] To achieve the above objectives, the technical solution adopted by this invention is: an AHP-EWM-RSR evaluation method suitable for micro-spraying mist control optimization, comprising the following steps: S1, divide the field crops into multiple independent test blocks, and configure a set of different micro-spraying height and misting duration parameters for each test block, and carry out micro-spraying and misting treatment simultaneously; S2, select several representative leaves in each test block for live marking, and measure the physiological indicators and crop growth of the marked leaves synchronously at a fixed period; measure the yield and quality indicators of each test block at maturity; measure the temperature and humidity of the crop canopy after each micro-spraying and misting. S3. Based on the values ​​measured in step S2, subjective weights are assigned using the analytic hierarchy process (AHP) to obtain the subjective weights. S4. Based on the values ​​measured in S2, objective weights are assigned using the entropy weighting method to obtain objective weights. S5. Based on the subjective and objective weights obtained from S3 and S4, the combined weights are derived by applying the scalar multiplication synthesis normalization method. S6. Construct an AHP-EWM-RSR comprehensive evaluation model to derive the optimal work plan and provide hierarchical management suggestions.

[0005] Furthermore, in step S2, the method for selecting several representative leaves in each test block is as follows: in each of the four directions of the crop, at least one leaf that is growing normally, free from pests and diseases, and receives uniform light is selected for live marking.

[0006] Furthermore, in step S2, the fixed period is 15 days.

[0007] Furthermore, in step S2, the physiological indicators include relative chlorophyll content (SPAD), photosynthetic parameters, and chlorophyll fluorescence parameters; the growth indicators include crop growth; and the quality indicators include sugar content, firmness, and fruit shape index.

[0008] Furthermore, the photosynthetic parameters include the photosynthetic rate P. n transpiration rate T r Instantaneous water use efficiency (IWUE); the chlorophyll fluorescence parameters include actual photosynthetic rate Y(II) of photosystem II, photosynthetic electron transfer rate (ETR), and maximum photochemical quantum yield F of photosystem II. v / F m Photochemical quenching coefficient qP, non-photochemical quenching coefficient NPQ.

[0009] Furthermore, in step S3, the calculation formula for subjective weighting using the analytic hierarchy process is as follows: In the formula, A is the element judgment matrix; n represents the order of the judgment matrix; a ij For indicator a in the evaluation system i For a j Importance level; T indicates transpose of the vector; W i W is the eigenvector; A λ is the weight vector; max The largest eigenvalue of the feature judgment matrix; (AW) A ) i It is a column vector AW A The i-th element; CI is the consistency index; CR is the randomness-consistency ratio.

[0010] Furthermore, in step S4, the calculation formula for objective weighting using the entropy weight method is as follows: In the formula, X min X represents the minimum value of each indicator. max X represents the maximum value of each indicator; nj Z represents the nth value of the j-th evaluation index; ij This is the result after normalization; p ij The weight of the i-th value of the j-th indicator in the sum of all values ​​of that indicator; k is an adjustment coefficient used to ensure the entropy value e j The physical meaning of e; j Let d be the entropy value of the j-th index; j Information entropy redundancy; W j E The weights are the indicator weights.

[0011] Furthermore, in step S5, the formula for calculating the combined weights is as follows, obtained by applying the scalar multiplication composition normalization method: In the formula, W A This is the weight vector; Let be the subjective weight value of the j-th indicator; W represents the objective weight value of the j-th indicator. j For combined weights.

[0012] Furthermore, the calculation formula for constructing the AHP-EWM-RSR comprehensive evaluation model in step S6 is as follows: In the formula, X min X represents the minimum value of each indicator. max Z represents the maximum value of each indicator. ij The result is after normalization; X is the data matrix; R ij W is the rank of the j-th index of the i-th object; j Represents portfolio weights; RSR i RSR value when weights are equal; WRSR i RSR values ​​for different weights; RSR i The larger the value, the better the evaluation object.

[0013] The beneficial effects of this invention are: by using crop growth physiological indicators as real-time feedback signals, an AHP-EWM-RSR comprehensive evaluation model is constructed, which realizes the synergistic optimization of micro-spraying height and duration parameters, significantly improves the accuracy and adaptability of micro-spraying mist control under high temperature conditions, and effectively solves the problems of existing micro-spraying mist control relying on experience and being inaccurate. Attached Figure Description

[0014] Figure 1 This is a flowchart of the evaluation method described in this invention.

[0015] Figure 2 This is the AHP weighting model of the present invention. Detailed Implementation

[0016] The present invention will be further described in detail below with reference to the embodiments, but this should not be construed as limiting the invention in any way. Example 1

[0017] An AHP-EWM-RSR evaluation method suitable for micro-spraying mist control optimization includes the following steps: S1 divides the field crops into multiple independent test blocks, and configures a set of different combinations of micro-spraying height and misting duration parameters for each test block, and performs micro-spraying and misting treatment simultaneously.

[0018] S2, select a number of representative leaves in each test block for live marking, and simultaneously measure the physiological indicators and crop growth of the marked leaves at a fixed cycle of 15 days; measure the yield and quality indicators of each test block at maturity; measure the temperature and humidity of the crop canopy after each micro-spraying and misting.

[0019] Specifically, the method for selecting a number of representative leaves in each experimental block is as follows: at least one leaf from each of the four directions of the crop that is growing normally, free from pests and diseases, and receives even light is selected for live marking.

[0020] The physiological indicators include relative chlorophyll content (SPAD), photosynthetic parameters, and chlorophyll fluorescence parameters. Among these, photosynthetic parameters include photosynthetic rate (P0.05). n μmol CO2 m -2 s -1 ), transpiration rate (T) r mmol H2O m -2 s -1 Instantaneous water use efficiency (IWUE, mg CO2 g) -1 H2O); chlorophyll fluorescence parameters include actual photosynthetic rate Y(II) of photosystem II, photosynthetic electron transfer rate ETR, maximum photochemical quantum yield Fv / Fm of photosystem II, photochemical quenching coefficient qP, and non-photochemical quenching NPQ.

[0021] S3. Based on the values ​​measured in S2, the subjective weights are obtained using the Analytic Hierarchy Process (AHP). The AHP weighting calculation formula is as follows: (1) (2) (3) (4) (5) (6) In the formula, A is the element judgment matrix; n represents the order of the judgment matrix; a ij For indicator a in the evaluation system i For a j Importance level; T indicates transpose of the vector; W i W is the eigenvector; A λ is the weight vector; max The largest eigenvalue of the feature judgment matrix; (AW) A ) i It is a column vector AW A The i-th element; CI is the consistency index; CR is the randomness-consistency ratio.

[0022] S4, based on the values ​​measured in S2, uses the Entropy Weight Method (EWM) to perform objective weighting, deriving the objective weights. The EWM weighting calculation formula is as follows: (7) (8) (9) (10) (11) (12) (13) In the formula, X min X represents the minimum value of each indicator. max X represents the maximum value of each indicator; nj Z represents the nth value of the j-th evaluation index; ij This is the result after normalization; p ij The weight of the i-th value of the j-th indicator in the sum of all values ​​of that indicator; k is an adjustment coefficient used to ensure the entropy value e j The physical meaning, and k = 1 / ln(n), where n is the number of values ​​in a certain index; e j Let d be the entropy value of the j-th index; j Information entropy redundancy; W j E The weights are the indicator weights.

[0023] S5, based on the subjective and objective weights calculated in S3 and S4, uses the scalar multiplication and normalization method to calculate the combined weight, as shown in the following formula: (14) (15) (16) (17) In the formula, W A This is the weight vector; Let be the subjective weight value of the j-th indicator; W represents the objective weight value of the j-th indicator. j For combined weights.

[0024] S6, based on AHP and EWM, constructs an AHP-EWM-RSR integrated evaluation model to derive the optimal work plan and provide hierarchical management suggestions. The calculation formula is as follows: (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) In the formula, X min X represents the minimum value of each indicator. max Z represents the maximum value of each indicator. ij The result is after normalization; X is the data matrix; R ij W is the rank of the j-th index of the i-th object; j Represents portfolio weights; RSR i RSR value when weights are equal; WRSR i RSR values ​​for different weights; RSR i The larger the value, the better the evaluation object.

[0025] Based on the practical significance of each indicator in agricultural production, benefit-oriented indicators (the higher the value, the better the evaluation effect) include plant growth, relative chlorophyll content (SPAD), and photosynthetic rate (P). n Instantaneous water use efficiency (IWUE), actual photosynthetic rate of photosystem II (Y(II)), photosynthetic electron transfer rate (ETR), photochemical quenching coefficient (qP), and maximum photochemical quantum yield (F) of photosystem II. v / F m transpiration rate T rSoluble solids content, fruit shape index, and yield are all indicators. Higher values ​​of these indicators indicate more vigorous crop growth, stronger photosynthetic capacity, better cooling effect, better quality, or higher yield. They should be normalized using formulas (9) and (20). Cost-related indicators (lower values ​​indicate better evaluation results) include non-photochemical quenching (NPQ). Excessively high values ​​indicate severe light energy excess and stress on the photosynthetic apparatus. Therefore, they should be normalized using formulas (10) and (21). As for hardness indicators, their type depends on the specific crop's quality evaluation standards. For fruit and vegetable crops, considering moderate taste, hardness should be used as a cost-related indicator. For cereal crops, higher hardness indicates fuller grains and better processing quality. Therefore, hardness is a benefit-related indicator. In practical applications, the type of hardness indicator should be determined according to the crop type, and the corresponding normalization formula should be used.

[0026] Description of the measuring instruments in this embodiment: SPAD was measured using a SPAD-502Plus chlorophyll meter; P was measured using an SC-3051D photosynthesis meter. n T r IWUE; crop growth was measured using a standard measuring tape; Y(II), ETR, Fv / Fm, qP, and NPQ were measured using a MINI-PAM-II ultra-portable modulated chlorophyll fluorometer; yield was measured using an electronic scale; hardness was measured using a GY-4 hardness tester; soluble solids content was measured using a PAL-1 portable digital display saccharimeter; and temperature and humidity in the middle and outer parts of the crop canopy were measured using an AS817 handheld thermo-hygrometer. Example 2

[0027] This embodiment uses apple saplings as an example to provide a specific implementation plan for the AHP-EWM-RSR evaluation method suitable for micro-spraying and misting control optimization, which includes the following steps: S1. Divide the apple saplings of the same variety and fruit age in the test field into multiple independent test blocks. Configure a set of different micro-spraying height and misting duration parameters for each test block and repeat the test.

[0028] S2a. The relative chlorophyll content (SPAD) of apple saplings under different treatments in S1 was determined using a SPAD-502Plus chlorophyll meter.

[0029] S2b, The photosynthetic rate (P) of apple saplings under different treatments in S1 was measured using an SC-3051D photosynthesis meter. n μmol CO2 m -2 s -1 ), transpiration rate (T) r mmol H2O m -2 s -1Instantaneous water use efficiency (IWUE, mg CO2 g) -1 Determination of H2O.

[0030] S2c. The height of apple saplings under different treatments in S1 was measured using a standard measuring tape. The difference between the two measurements of the plant height is the plant growth.

[0031] In S2d, the actual photosynthetic rate (Y(II)), photosynthetic electron transfer rate (ETR), maximum photochemical quantum yield (Fv / Fm), photochemical quenching coefficient (qP), and non-photochemical quenching (NPQ) of apple saplings under different treatments in S1 were determined using a MINI-PAM-II ultra-portable modulated chlorophyll fluorometer.

[0032] In S2e, at the fruit ripening stage, yield (g) and firmness (kg / cm²) of apple saplings under different treatments in S1 were measured using an electronic scale, a GY-4 fruit firmness tester, and a PAL-1 portable digital refractometer. -2 The content of soluble solids (°Brix) was determined.

[0033] S2f: Each time micro-spraying and misting is carried out, the temperature and humidity in the middle of the canopy of apple saplings under different treatments in S1 are measured using an AS817 handheld thermometer and hygrometer, and the temperature and humidity of the environment are measured simultaneously.

[0034] S3. Subjective weighting is performed on the data measured in steps S2a, S2b, S2c, S2d, and S2e using AHP, employing the following formula: (1) (2) (3) (4) (5) (6) S4. The data measured in steps S2a, S2b, S2c, S2d, and S2e are objectively weighted using EWM, using the following formula: (7) (8) (9) (10) (11) (12) (13) S5. Apply the scalar multiplication and normalization method to the subjective and objective weights obtained in steps S3 and S4 to calculate the combined weights, using the following formula: (14) (15) (16) (17) After normalizing the data measured in steps S2a, S2b, S2c, S2d, and S2e, the AHP-EWM-RSR comprehensive evaluation model is constructed using the data calculated in step S5, employing the following formula: (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) (26) The following steps are used to verify the above method.

[0035] S7. Analyze and calculate the data from step S2f to obtain the temperature and humidity change ratios under different treatments. To verify the beneficial effects of this method in alleviating crop high-temperature stress and optimizing the growth microenvironment, the height variables are set as H1 (upper canopy), H2 (middle canopy), and H3 (lower canopy), and the duration variables are T1 (2.0h), T2 (1.5h), T3 (1.0h), and T4 (0.5h). The variable settings are only for the exemplary experimental design used to verify this method. The value of treatment T4 is the ratio of the temperature and humidity change in the apple tree canopy to the external environmental value 0.5 hours after the start of micro-spraying. The value of treatment T3 is the ratio of the temperature and humidity change in the apple tree canopy to the change in the external environment during the period from 0.5 hours to 1 hour after micro-spraying. The values ​​of treatments T2 and T1 are the same. The temperature and humidity change ratio data of the four key time nodes are plotted in a table, as shown in Table 1 below: S8. Perform AHP weighting on the data from steps S2a, S2b, S2c, S2d, and S2e, construct a judgment matrix, perform a consistency check, obtain the subjective weights, and plot the results in the table below: S9. Perform EWM weighting on the data from steps S2a, S2b, S2c, S2d, and S2e. After normalization, calculate the entropy value and information entropy redundancy to obtain the objective weights. The resulting table is shown below: S10. The subjective and objective weights calculated in steps S3 and S4 are weighted, and the combined weights are calculated using the scalar multiplication and normalization method. The resulting table is shown below: S11. After normalizing the data measured in steps S2a, S2b, S2c, S2d, and S2e, the AHP-EWM-RSR comprehensive evaluation model is constructed by combining it with the data calculated in step S5. A matrix is ​​constructed, and to compensate for the loss of quantitative information of the original index values ​​during rank-based normalization, non-integer RSR is used to calculate the RSR value and probability unit. Then, the linear regression equation is calculated, and the data is sorted and categorized. The resulting table is shown below: S12. Based on the data tables drawn in steps S8, S9, S10 and S11, perform multi-dimensional parameter comparison and quantitative evaluation to form a systematic comprehensive evaluation conclusion on the scheme.

[0036] The top five weights obtained from AHP are: production volume, IWUE, soluble solids content, hardness, and P. n Among the factors, yield has the largest weight, followed by IWUE. In EWM, plant growth has the largest weight at 11.157%, followed by SPAD, while Y(II) has the smallest weight at 3.624%. The final combined weight ranking, calculated using the scalar multiplication and normalization method combining subjective and objective weights, is: yield, IWUE, P. n Hardness, soluble solids content, T r Plant growth, SPAD, Fv / Fm, Y(II), fruit shape index, NPQ, qP, ETR.

[0037] An AHP-EWM-RSR comprehensive evaluation model was constructed. The rank values ​​were calculated, and the RSR frequency distribution was obtained by ranking the output. The RSR ranking was H2T2>H2T3>H1T3>H2T1>H1T2>H2T4, with the H2T2 treatment ranking first (RSR value of 0.950) and the H2T3 treatment ranking second (RSR value of 0.831). Linear regression analysis was performed with probit values ​​as the independent variable and RSR as the dependent variable. R0 2 =0.943. Based on the RSR estimates obtained from the regression equation, the evaluation objects were ranked. Treatments H2T2 and H2T3 belonged to the optimal category, H3T4 was in the acceptable category, and the other treatments were in the good category. According to step S7, compared with the appropriate amount of fogging treatment T2, the large amount of fogging treatment T1 did not show a significant cooling and humidifying effect. This may be because the field temperature and humidity approached the threshold after 1.5 hours of fogging. This indicates that in apple tree cultivation and management, setting the fogging location in the middle of the canopy and setting the fogging duration to 1.5 hours on sunny days can optimize apple irrigation management, effectively improve the microclimate, significantly enhance the photosynthetic characteristics of apple trees, and achieve high and stable apple yields. However, when water resources become a limiting factor, treatment H2T3 can serve as an ideal alternative for efficient water resource utilization. Setting the fogging location in the middle of the crop canopy and appropriately shortening the fogging duration can also achieve better overall results.

[0038] This invention proposes an AHP-EWM-RSR integrated evaluation method suitable for optimizing micro-spraying and misting control. Through an "indicator monitoring-combination weighting-comprehensive evaluation" system, combined with crop growth physiological indicators, and based on AHP and EWM models, it integrates subjective and objective information within a unified framework. The combined weights are calculated using the scalar multiplication synthesis normalization method, effectively reducing uncertainty, enhancing explanatory power, and improving the acceptability of the weighting results, making the weight allocation more scientific. Based on this, a comprehensive evaluation model is constructed using RSR to rank and categorize the evaluation results, determining the optimal combination of micro-spraying height and duration in field irrigation management. This improves water use efficiency and stress resistance, promoting the transformation of field irrigation from experience-based to model-driven precision control, thereby achieving efficient and reliable field high-temperature stress resistance management.

[0039] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the specific implementation of the present invention with reference to the above embodiments. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention are within the protection scope of the pending claims.

Claims

1. An AHP-EWM-RSR evaluation method suitable for micro-spraying mist control optimization, characterized in that, Includes the following steps: S1, divide the field crops into multiple independent test blocks, and configure a set of different micro-spraying height and misting duration parameters for each test block, and carry out micro-spraying and misting treatment simultaneously; S2, select several representative leaves in each test block for live marking, and measure the physiological indicators and crop growth of the marked leaves synchronously at a fixed period; measure the yield and quality indicators of each test block at maturity; measure the temperature and humidity of the crop canopy after each micro-spraying and misting. S3. Based on the values ​​measured in step S2, subjective weights are assigned using the analytic hierarchy process (AHP) to obtain the subjective weights. S4. Based on the values ​​measured in S2, objective weights are assigned using the entropy weighting method to obtain objective weights. S5. Based on the subjective and objective weights obtained from S3 and S4, the combined weights are derived by applying the scalar multiplication synthesis normalization method. S6. Construct an AHP-EWM-RSR comprehensive evaluation model to derive the optimal work plan and provide hierarchical management suggestions.

2. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 1, characterized in that, In step S2, the method for selecting several representative leaves in each test block is as follows: in each of the four directions of the crop, at least one leaf that is growing normally, free from pests and diseases, and receives uniform light is selected for live marking.

3. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 1, characterized in that, In step S2, the fixed period is 15 days.

4. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 1, characterized in that, In step S2, the physiological indicators include relative chlorophyll content (SPAD), photosynthetic parameters, and chlorophyll fluorescence parameters; the growth indicators include crop growth; and the quality indicators include sugar content, firmness, and fruit shape index.

5. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 4, characterized in that, The photosynthetic parameters include the photosynthetic rate P. n transpiration rate T r Instantaneous water use efficiency (IWUE); the chlorophyll fluorescence parameters include actual photosynthetic rate Y(II) of photosystem II, photosynthetic electron transfer rate (ETR), and maximum photochemical quantum yield F of photosystem II. v / F m Photochemical quenching coefficient qP, non-photochemical quenching coefficient NPQ.

6. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 1, characterized in that, In step S3, the calculation formula for subjective weighting using the analytic hierarchy process is as follows: In the formula, A is the element judgment matrix; n represents the order of the judgment matrix; a ij For indicator a in the evaluation system i For a j Importance level; T represents the transpose of the vector; W i W is the eigenvector; A λ is the weight vector; max The largest eigenvalue of the feature judgment matrix; (AW) A ) i It is a column vector AW A The i-th element; CI stands for consistency index; CR stands for randomness consistency ratio.

7. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 1, characterized in that, In step S4, the calculation formula for objective weighting using the entropy weight method is as follows: In the formula, X min X represents the minimum value of each indicator; max X represents the maximum value of each indicator; nj Z represents the nth value of the j-th evaluation index; ij This is the result after normalization; p ij The weight of the i-th value of the j-th indicator in the sum of all values ​​of that indicator; k is an adjustment coefficient used to ensure the entropy value e j The physical meaning of e; j d is the entropy value of the j-th index; j Information entropy redundancy; W j E The weights are the indicator weights.

8. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 1, characterized in that, In step S5, the formula for calculating the combined weights is as follows, obtained by applying the scalar multiplication composition normalization method: In the formula, W A This is the weight vector; Let be the subjective weight value of the j-th indicator; W represents the objective weight value of the j-th indicator. j For combined weights.

9. The AHP-EWM-RSR evaluation method for micro-spraying mist control optimization according to claim 1, characterized in that, The calculation formula for constructing the AHP-EWM-RSR comprehensive evaluation model in step S6 is as follows: In the formula, X min X represents the minimum value of each indicator; max Z represents the maximum value of each indicator. ij The result is after normalization; X is the data matrix; R ij W is the rank of the j-th index of the i-th object; j Represents portfolio weights; RSR i RSR value when weights are equal; WRSR i RSR values ​​for different weights; RSR i The larger the value, the better the evaluation object.

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