Method and device for calculating comprehensive water use efficiency of crops by multi-method comparison

Abstract

The present application provides a method and device for calculating the comprehensive water use efficiency of crops by multiple methods, which comprises: constructing a multi-dimensional calculation index system covering crop growth benefits, resource utilization efficiency and environmental benefits, obtaining original index data and standardizing to obtain a standardized index matrix; using multiple subjective and objective weighting methods to calculate subjective weight vectors and objective weight vectors respectively; combining the weight vectors, generating candidate weight combination schemes through multiple weight fusion strategies and calculating the comprehensive scores and ranking of each object; constructing a regional adaptability calculation function containing the indices of distinguishability, robustness and external consistency, comprehensively scoring each scheme and selecting the optimal calculation mode to output the final result of crop water use efficiency. This method improves the rationality of weight selection and the reliability of evaluation results, adapts to different regional characteristics, and the calculation results are more accurate and stable.

Description

Technical Field

[0001] This invention relates to the field of agricultural water-saving calculation and integrated decision-making technology, and in particular to a method and apparatus for calculating the comprehensive water use efficiency of crops by comparing multiple methods. Background Technology

[0002] Existing comprehensive calculation methods for crop water use efficiency are typically based on multi-index calculation models. The comprehensive result is obtained by determining the weights of each index and performing a weighted calculation. In existing technologies, index weights are often determined using either subjective or objective weighting methods.

[0003] When using subjective weighting methods, the weights mainly rely on expert experience and subjective judgment, which are easily affected by differences in personal cognition and subjective preferences, resulting in a certain degree of uncertainty in the calculation results. When using objective weighting methods, the weights are mainly determined by the dispersion or correlation of the indicator data. Although this reduces human intervention, it is more sensitive to data quality, sample structure and characteristics of the study area, which may lead to the problem that the weight allocation does not match the actual situation.

[0004] To balance subjective experience with data objectivity, some studies have attempted to combine subjective and objective weighting. However, existing combinations are mostly fixed or empirically set, lacking a systematic comparison and screening mechanism for different weighting combinations, making it difficult to determine which combination is more suitable for a specific research region. Furthermore, existing calculation methods typically employ a uniform weighting model, failing to test the regional adaptability and stability of the calculation results, leading to limited applicability of the calculation conclusions across different research areas.

[0005] Therefore, existing technologies still have shortcomings in terms of the flexibility of weight determination, the rationality of the integration of subjective and objective weighting, and the adaptability of the calculation model to the characteristics of the study area. It is necessary to propose a method for calculating crop water use efficiency that can compare different weighting combinations and select the optimal scheme.

[0006] Crop water use efficiency is a key calculation indicator in agricultural water conservation and efficiency improvement and optimal water resource allocation. In irrigated agricultural areas, the calculation objects are usually different water management schemes, fertilization systems and their combinations, and the calculation data often have characteristics such as multi-year, cross-scenario and strong fluctuations. Since crop water use efficiency not only involves crop output, but also relates to water consumption processes, input intensity and ecological and environmental effects, practical applications usually require the construction of a multi-indicator comprehensive calculation system, and the schemes are comprehensively scored and ranked according to the weight of each indicator to support water-saving irrigation management, scheme selection and regional water resource allocation decisions. Existing multi-indicator comprehensive calculation techniques generally require determining the indicator weights first, and then performing weighted calculations to obtain the comprehensive calculation results. In existing technologies, indicator weights are mostly determined by subjective weighting methods or objective weighting methods: subjective weighting relies on expert experience and preferences, which can easily introduce individual differences and lead to uncertain weights; objective weighting determines weights based on data dispersion or correlation, which can reduce human intervention, but is sensitive to sample structure, data quality and characteristics of the study area, and may lead to problems where the weight allocation is inconsistent with the actual goals of agricultural production. To balance subjective experience with objective data characteristics, existing studies have proposed a combined subjective and objective weighting approach. However, current combination methods mostly use fixed coefficients or empirical settings, typically providing only a single or limited set of combinations. They lack mechanisms for systematically comparing, quantitatively verifying, and automatically screening different subjective and objective weighting methods and their combinations. In multi-year or cross-regional applications, these shortcomings are further amplified. The same calculated data may produce significantly different comprehensive scores and rankings under different weighting methods or combinations of subjective and objective factors, thus affecting the stability and interpretability of the optimal solution conclusions. Furthermore, existing technologies typically do not verify the regional applicability of the calculation results, making it difficult to ensure that the comprehensive ranking aligns with the target orientation of the research region, thus limiting the general applicability of the calculation conclusions. Summary of the Invention

[0007] The present invention aims to at least partially solve one of the technical problems in the related art.

[0008] Therefore, the first objective of this invention is to propose a method for calculating the comprehensive water use efficiency of crops by comparing multiple methods.

[0009] Another objective of this invention is to provide a device for calculating the comprehensive water use efficiency of crops by comparing multiple methods.

[0010] The third objective of this invention is to provide a computer device.

[0011] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0012] To achieve the above objectives, a first aspect of the present invention proposes a method for calculating the comprehensive water use efficiency of crops through multi-method comparison, comprising:

[0013] S10, construct a multi-dimensional calculation index system covering crop growth benefits, resource utilization efficiency and environmental benefits, obtain the original index data of the calculation objects in the study area, and standardize the original index data according to the index attributes to obtain a standardized index matrix. S20, based on the standardized index matrix, multiple subjective weight vectors are calculated using various subjective weighting methods, and multiple objective weight vectors are calculated using various objective weighting methods. S30, combine the multiple subjective weight vectors with the multiple objective weight vectors, and use multiple weight fusion strategies to generate multiple candidate weight combination schemes, and calculate the comprehensive score and ranking result of each calculation object based on each candidate weight combination scheme; S40, construct a regional suitability calculation function that includes a discrimination index, a robustness index, and an external consistency index. Use the regional suitability calculation function to comprehensively score each candidate weight combination scheme, select the candidate weight combination scheme with the highest score as the optimal calculation mode, and output the final calculation result of crop water use efficiency based on the optimal calculation mode.

[0014] In one embodiment of the present invention, S10 includes: Obtain basic data related to crop water use efficiency within the study area, and construct a comprehensive calculation index system for crop water use efficiency that includes indicators of crop growth benefits, resource utilization efficiency, and environmental benefits. Among them, crop growth benefit indicators include crop yield and evapotranspiration, resource utilization efficiency indicators include irrigation water use efficiency and irrigation quota per unit area, and environmental benefit indicators include soil organic matter content and greenhouse gas emission intensity. Collect multi-year baseline data under different irrigation systems or fertilization measures within the study area to construct an original index numerical matrix; Based on the indicator attributes, positive and negative indicators are distinguished. Positive indicators are processed using a forward normalization method, while negative indicators are processed using a reverse normalization method, in order to eliminate the differences in the dimensions and orders of magnitude of different indicators and obtain a standardized indicator matrix.

[0015] In one embodiment of the present invention, the step of processing positive indicators using a positive normalization method and processing negative indicators using a negative normalization method includes: For positive indicators, use the formula:

[0016] For the study area The first sample Data for each indicator Dimensionless processing yields ; For contrarian indicators, use the formula:

[0017] For the study area The first sample Data for each indicator Dimensionless processing yields The processed standardized index matrix is ​​then used for subsequent weight calculations.

[0018] In one embodiment of the present invention, S20 includes: At least two subjective weighting methods are used to calculate the subjective weight vector based on the standardized index matrix. The subjective weighting methods include the analytic hierarchy process (AHP) and the Delphi method. The AHP calculates the subjective weight vector by constructing an expert judgment matrix and performing a consistency check. At the same time, at least two objective weighting methods are used to calculate the objective weight vector based on the standardized index matrix. The objective weighting methods include entropy weighting method, CRITIC method, standard deviation method and principal component analysis method. Among them, the entropy weight method calculates the information entropy of each indicator and determines the weight coefficient value based on the information entropy, while the CRITIC method calculates the information content of the indicator by calculating the standard deviation and correlation coefficient of the indicator and calculates the objective weight accordingly.

[0019] In one embodiment of the present invention, S30 includes: Different subjective weight vectors are paired with different objective weight vectors, and the subjective weights and objective weights are fused using the weighted average method, game theory combination weighting method or distance function method to generate a variety of candidate weight combination schemes. Under each candidate weight combination scheme, the comprehensive crop water use efficiency score of each calculation object is calculated using a weighted summation model to obtain the corresponding ranking results. The weighted average method uses the following formula:

[0020] Calculate the first The total weight of each factor is obtained by solving a system of equations that are consistent with the expression for the distance function and the difference between the allocation coefficients, using the distance function method to obtain the allocation coefficients and the combined weights.

[0021] In one embodiment of the present invention, the construction of a region fit calculation function including a discrimination index, a robustness index, and an external consistency index, and the use of the region fit calculation function to comprehensively score each candidate weight combination scheme, includes: Construct a region fit calculation function for different candidate weight combinations: , Where the coefficient satisfy ; Calculate the discrimination index The coefficient of variation of the comprehensive score sequence is used to measure the dispersion of the comprehensive calculation results. Calculate robustness indicators To measure the stability of the calculation results under data perturbation conditions, the Kendall or Spearman rank correlation coefficient is used to calculate the mean consistency between the perturbed sort and the original sort after multiple resampling or the addition of perturbation noise. Calculate the external consistency index To measure the consistency between the calculation results and external reference variables for yield and water consumption in the study area, the Spearman rank correlation coefficient between the composite score and the external reference variables was constructed.

[0022] In one embodiment of the present invention, selecting the candidate weight combination scheme with the highest score as the optimal calculation mode, and outputting the final calculation result of crop water use efficiency based on the optimal calculation mode, includes: Compare the regional suitability scores of all candidate schemes, and select the candidate weight combination scheme with the highest score as the optimal weight combination scheme for the study area. Based on the optimal weighted combination scheme, the final comprehensive crop water use efficiency calculation results and ranking are recalculated and output, providing a decision-making basis for water-saving irrigation management, water use efficiency improvement and optimal allocation of agricultural water resources in the study area.

[0023] To achieve the above objectives, a second aspect of the present invention provides an apparatus for calculating the comprehensive water use efficiency of crops using multiple methods, comprising: The indicator system construction module is used to construct a multi-dimensional calculation indicator system covering crop growth benefits, resource utilization efficiency and environmental benefits, obtain the original indicator data of the calculation objects in the study area, and standardize the original indicator data according to the indicator attributes to obtain a standardized indicator matrix. The weight calculation module is used to calculate multiple subjective weight vectors based on the standardized index matrix using multiple subjective weighting methods, and to calculate multiple objective weight vectors using multiple objective weighting methods. The weight combination and ranking module is used to combine the multiple subjective weight vectors with the multiple objective weight vectors, and to generate multiple candidate weight combination schemes using multiple weight fusion strategies. Based on each candidate weight combination scheme, the module calculates the comprehensive score and ranking result of each calculation object. The optimal mode selection module is used to construct a regional adaptability calculation function that includes a discrimination index, a robustness index, and an external consistency index. The regional adaptability calculation function is used to comprehensively score each candidate weight combination scheme, select the candidate weight combination scheme with the highest score as the optimal calculation mode, and output the final calculation result of crop water use efficiency based on the optimal calculation mode.

[0024] The present invention provides a method and apparatus for calculating the comprehensive water use efficiency of crops by comparing multiple methods. This method overcomes the limitations of a single weighting method and significantly improves the discrimination, robustness, and consistency with the actual objectives of the study area by quantitatively screening the optimal combination of subjective and objective methods.

[0025] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing the method described in the first aspect embodiment.

[0026] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described in the first aspect.

[0027] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0028] Figure 1 This is a flowchart of a method for calculating the comprehensive water use efficiency of crops by comparing multiple methods according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a method for calculating the comprehensive water use efficiency of crops by comparing multiple methods according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the process of scheme selection using the region adaptation function according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the combined weights of various indicators after calculation using the method according to an embodiment of the present invention. Figure 5 This is a schematic diagram of the combined weighting results according to an embodiment of the present invention; Figure 6 This is a structural diagram of an apparatus for calculating the comprehensive water use efficiency of crops using multiple methods according to an embodiment of the present invention; Figure 7 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0029] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0031] The following description, with reference to the accompanying drawings, describes a method and apparatus for calculating the comprehensive water use efficiency of crops using multiple methods in accordance with embodiments of the present invention.

[0032] Figure 1 This is a flowchart of a method for calculating the comprehensive water use efficiency of crops by comparing multiple methods according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S10, construct a multi-dimensional calculation index system covering crop growth benefits, resource utilization efficiency and environmental benefits, obtain the original index data of the calculation objects in the study area, and standardize the original index data according to the index attributes to obtain a standardized index matrix. S20, based on the standardized index matrix, multiple subjective weight vectors are calculated using various subjective weighting methods, and multiple objective weight vectors are calculated using various objective weighting methods. S30, combine the multiple subjective weight vectors with the multiple objective weight vectors, and use multiple weight fusion strategies to generate multiple candidate weight combination schemes, and calculate the comprehensive score and ranking result of each calculation object based on each candidate weight combination scheme; S40, construct a regional suitability calculation function that includes a discrimination index, a robustness index, and an external consistency index. Use the regional suitability calculation function to comprehensively score each candidate weight combination scheme, select the candidate weight combination scheme with the highest score as the optimal calculation mode, and output the final calculation result of crop water use efficiency based on the optimal calculation mode.

[0033] Furthermore, embodiments of the present invention propose another method for calculating the comprehensive water use efficiency of crops through multi-method comparison, such as... Figure 2 As shown, it includes: S1: Construct a comprehensive crop water use efficiency calculation index system, encompassing crop output / growth benefits, resource utilization efficiency, and environmental benefits. Specifically: crop growth benefit indicators should include at least: crop yield, evapotranspiration, and net photosynthetic rate; resource utilization efficiency indicators should include at least: irrigation water use efficiency, irrigation quota per unit area, water and fertilizer productivity, and nitrogen fertilizer use efficiency; environmental benefit indicators should include at least: changes in soil organic matter content, greenhouse gas emission intensity, global warming potential, and groundwater infiltration and recharge. Positive indicators should be processed to reflect positive trends, and negative indicators should be processed to reflect negative trends, thus unifying the calculation direction.

[0034] S2: Obtain multi-indicator data of the calculation objects in the study area and perform standardization processing. Collect multi-year basic data under different irrigation systems or fertilization measures within the study area to construct the original indicator numerical matrix. Based on the indicator attributes (positive or negative), use the range normalization method to perform dimensionless processing on the data, eliminating dimensional differences and obtaining the standardized indicator matrix. For methods requiring non-negative calculations, perform a small shift processing on the standardized data.

[0035] S3: Calculate the basic weight vector using multiple subjective and objective weighting methods. Subjective weighting: Use at least two subjective weighting methods (such as Analytic Hierarchy Process (AHP), order relation method, Delphi method, etc.) to construct a judgment matrix or order relation through expert scoring or experience judgment, calculate the subjective weight vector, and perform consistency checks. Objective weighting: Use at least two objective weighting methods (such as entropy weight method, CRITIC method, coefficient of variation method, principal component analysis, etc.) to calculate the objective weight vector based on the data dispersion, conflict, and information entropy of the standardized index matrix.

[0036] S4: Construct a candidate weighting scheme set with multiple combinations of subjective and objective weighting and calculate the comprehensive score. Combine different subjective weight vectors with objective weight vectors pairwise and employ various combination strategies to fuse them, generating a candidate weighting combination scheme set. Combination strategies include, but are not limited to: linear weighted average method, game theory combination weighting method, distance function method (minimizing the distance between subjective and objective weights), etc. Under each candidate weighting combination scheme, use a weighted summation model to calculate the comprehensive crop water use efficiency score for each computational object and obtain the corresponding ranking results.

[0037] S5: Construct a regional suitability calculation function and quantitatively screen candidate solutions, such as... Figure 3 As shown, for different candidate weight combinations, a region fit calculation function F is constructed, which includes three dimensions: discriminability, robustness, and external consistency, to comprehensively score each scheme:

[0038] Among them: Discrimination index (D): Used to measure the dispersion of the comprehensive calculation results, enhancing the ability to identify schemes. Calculated using the coefficient of variation or range normalization index of the comprehensive score sequence; a larger D value indicates better scheme discrimination. Robustness index (R): Used to measure the stability of the calculation results under data disturbance. Using Kendall or Spearman rank correlation coefficients, the mean consistency between the disturbed ranking and the original ranking is calculated by resampling the original data multiple times (e.g., by year or sample) or adding disturbance noise; a value closer to 1 indicates a more robust scheme. External consistency index (E): Used to measure the degree of agreement between the calculation results and the actual goals of the study area. External reference variables are selected (positive references such as yield, economic output; negative references such as evapotranspiration, irrigation quotas), and the Spearman rank correlation coefficient between the comprehensive score and the reference variables is calculated; a larger E value indicates that the scheme better meets the regional goal of "high yield—water conservation—low environmental cost". Coefficients (a,b,c): represent the weights of each indicator, satisfying a+b+c=1. Equal weighting (1 / 3) can be used, or the weights can be adaptively determined based on the information content (such as coefficient of variation or entropy value) of each index of D, R, and E in the candidate scheme set.

[0039] S6: Determine the optimal calculation model for the study area and output the decision support results. Compare the regional suitability scores (F) of all candidate schemes and select the candidate weight combination scheme with the highest score as the optimal weight combination scheme for the study area. Based on this optimal scheme, recalculate and output the final comprehensive crop water use efficiency calculation results and ranking, providing a scientific basis for water-saving irrigation management, water use efficiency improvement, and optimal allocation of agricultural water resources in the study area.

[0040] In step S1, an indicator system is constructed. The comprehensive calculation indicators of crop water use efficiency in the study area may include, but are not limited to, the following categories: crop growth benefit indicators: yield, evapotranspiration; resource consumption indicators: irrigation water use efficiency, irrigation quota per unit area; environmental indicators: soil organic matter content, greenhouse gas emission intensity; when there are negative indicators, they can be homogenized to meet the unified direction of "the larger the better" or "the smaller the better".

[0041] In step S2, relevant data within the study area are acquired and dimensionless processing is performed based on the indicator attributes (positive indicators and negative indicators). This represents the data for the j-th indicator of the i-th sample in the study area.

[0042] Positive indicators (the higher the value, the better):

[0043] Contrarian indicator (the smaller the value, the better):

[0044] In step S3, subjective and objective weighting methods are used to assign weights. Subjective weighting methods include, but are not limited to, the Analytic Hierarchy Process (AHP) and the Delphi method; objective weighting methods include, but are not limited to, the entropy weighting method, the CRITIC method, the standard deviation method, and the principal component analysis method.

[0045] In step S4, different subjective and objective weighting results are combined, such as analytic hierarchy process (AHP) + entropy weighting, or AHP + CRITIC method. Then, subjective and objective weights are combined using various strategies, such as weighted averaging, game theory, and distance function methods, to generate candidate weights. (Weights of index j under strategy k), calculate the score of comprehensive crop water use efficiency for each candidate weight combination in the study area:

[0046] Furthermore, to screen the optimal combination scheme for the study area, this invention constructs a region fit calculation function: in: , a, b, and c are coefficients that satisfy a + b + c = 1; they can be adaptively determined based on the discriminative information content of the candidate scheme set. Furthermore, the coefficients a, b, and c can be determined adaptively: their proportions are determined based on the information content or dispersion of each indicator (D, R, E) in the candidate weight combination scheme set; in one embodiment, the coefficient of variation, range, or entropy value of each indicator in the candidate scheme set can be normalized and used as a, b, and c; or an equal-weighted approach can be used, taking a = b = c = 1 / 3.

[0047] The discrimination index D represents the degree of dispersion of the comprehensive scores of different computational objects, thereby enhancing the ability to distinguish between different schemes. In one embodiment, D can be calculated using the coefficient of variation of the comprehensive score sequence. The input is the comprehensive score sequence of each computational object under the candidate scheme. The output is the coefficient of variation of the sequence. .

[0048] formula: , n represents the number of samples; , Can be taken To prevent the mean from being close to 0, which could cause problems with the denominator; The larger the value, the more "dispersed" the overall score given by the scheme, and the stronger its comparative identification ability.

[0049] This represents a robustness index used to measure the stability of the calculation results to perturbations. The input is the original sample set and the perturbation strategy (such as Bootstrap resampling or noise perturbation). The output is the average rank correlation coefficient between the ranking results under multiple perturbations and the original ranking. In one embodiment, the standardized index matrix is ​​repeatedly resampled (including but not limited to resampling by year, resampling by sample, or adding perturbation noise). The ranking results are obtained under each perturbation, and Kendall resampling is repeated T times. The ranking is calculated for each resampled sample and its consistency with the original ranking is measured, resulting in:

[0050] The closer the value is to 1, the more stable the ranking is. High robustness means that the combined weighting scheme is suitable for the fluctuations and uncertainties of the data in the study area (it is not sensitive to yearly disturbances), and the conclusions are more reliable. This represents the external consistency index, used to characterize the consistency between the overall calculated ranking and the target external reference variables of the study area; the input is a pre-defined set of external reference variables for the study area. The output is a weighted sum of the overall score and the Spearman correlation coefficients of each external reference variable. The external reference variables include at least positive and negative reference variables. Positive reference variables include, but are not limited to, output and economic output, while negative reference variables include, but are not limited to, evapotranspiration and irrigation quota per unit area. In one embodiment, E can be constructed using the Spearman correlation coefficients between the overall score and the external reference variables, and positive and negative consistency can be weighted.

[0051] For example, it is positively correlated with output and negatively correlated with water consumption; set external reference variables (which can be selected according to the study area): positive external reference: output Y, economic output E, etc. (expected value is positively correlated with score); negative external reference: evapotranspiration ET, irrigation quota per unit area I, etc. (expected value is negatively correlated with score).

[0052]

[0053] Represents the Spearman correlation coefficient, when High and Low (or even negative), then The larger the value, the better the plan aligns with the study area's comprehensive goal of "high yield and water conservation." The reference values ​​are shown in Table 1: Table 1

[0054] Furthermore, the optimal computational model for the study area is determined and the computational results are output. The candidate weight combination scheme with the highest regional adaptation calculation score is selected as the optimal calculation mode for the study area. Based on the optimal calculation mode, the comprehensive calculation results and ranking of crop water use efficiency are calculated and output, providing a decision-making basis for water-saving irrigation management, crop water use efficiency improvement and agricultural water resource optimization in the study area.

[0055] This invention addresses the problems of existing comprehensive crop water use efficiency calculations, such as the reliance on a single method for determining weights, difficulty in balancing subjective experience with objective data characteristics, and insufficient adaptability of the calculation results to the study area. It proposes a multi-method comparative calculation system based on a combination of subjective and objective weighting. This system constructs a candidate set of combined weights using various subjective and objective weighting methods and introduces a region-appropriate calculation function to compare and filter different combinations, thereby selecting the weight combination scheme most suitable for the characteristics of the study area and forming a comprehensive crop water use efficiency calculation model.

[0056] This invention can improve the consistency between the comprehensive calculation results and the actual characteristics of crop yield and water consumption in the study area while ensuring the distinguishability and stability of the calculation results, thereby providing a scientific basis for water-saving irrigation management, water use efficiency improvement and optimal allocation of agricultural water resources.

[0057] In some embodiments, the following indicators can be used to construct the calculation system in step S1, as shown in Table 2: Table 2

[0058] Using the aforementioned indicators, step S3 employs subjective weighting methods including the Analytic Hierarchy Process (AHP) and the order relation method, and objective weighting methods including the entropy weight method and the CRITIC method, as an example for illustration. Step S3, calculating subjective weights using the AHP includes the following steps: constructing an expert judgment matrix and scaling it pairwise; calculating the judgment matrix weights using the arithmetic mean method; performing a consistency check; and calculating the subjective weights. The following example uses the criterion layer, with the expert judgment matrix as follows:

[0059] in, This indicates the scale corresponding to crop growth benefits. This represents the scale corresponding to resource utilization efficiency. This indicates the scale corresponding to the environmental benefit criteria.

[0060] Then, the judgment matrix is ​​normalized. n is the order of the matrix; the weight vector is obtained by summing the processed matrix row by row and normalizing it. After completion, a consistency check is performed, and a one-time index is calculated. , Where n is the matrix order, It is the largest eigenvalue of A; =0, indicating complete consistency; Approaching 0, it exhibits satisfactory consistency; The larger the value, the more severe the inconsistency. Define the consistency ratio:

[0061] In the formula: RI is the random consistency index, which is generally obtained by looking up a table, as shown in Table 3: Table 3

[0062] From the table, we can find RI = 0.52. If the inconsistency of A is considered to be within the acceptable range, a one-time test is passed; otherwise, the judgment matrix A is reconstructed. Based on the expert scoring results, the criterion-level index judgment matrix is ​​obtained, and then the subjective weight values ​​are obtained, as shown in Table 4: Table 4

[0063] Furthermore, the steps for calculating subjective weights using the ordinal relationship method are as follows: using expert consultation or decision-makers' experience to rank the indicators within the same level according to their importance, a strict ordinal relationship is formed.

[0064] The criterion layer is ordered as follows: A>B>C The index layers are ordered as follows: Level A: A1>A2>A3; Layer B: B1>B2>B3>B4; Layer C: C1>C2>C3>C4>C5; Given the order X1>X2>X3>...>Xn, give the ratios of adjacent elements. First calculate the last weight: Then, proceed backward step by step: The adjacent importance ratio used in this example; Criterion layer: r AB =w A / w B =1.2, r BC =w B / w C =1.1; Group A: r 12 =1.10, r 23 =1.05; Group B: r 12 =1.10, r 23 =1.05, r 34 =1.05; Group C: r 12 =1.10, r 23 =1.05, r34 =1.05, r 45 =1.05.

[0065] The subjective weight values ​​are obtained; in one embodiment, the various indicators of the indicator layer are aggregated and constructed. Using actual data as an example, the data of each indicator is normalized to obtain Table 5: Table 5

[0066] Furthermore, the steps for calculating the objective weights using the entropy weight method are as follows: Because the entropy weight method requires the data to be non-negative, the data after dimensionless processing in step S2 needs to be non-negatively shifted, i.e., all data are added with 0.001 or 0.0001; calculate the proportion of the i-th sample value under the j-th indicator. Where n represents the number of samples and m represents the number of indicators; calculate the information entropy of each indicator. , ; Calculate the weighting coefficient value The obtained weight values ​​will be used for subsequent comprehensive score calculation; the objective weight values ​​calculated by the entropy weight method are obtained.

[0067] Furthermore, the steps for calculating the objective weights using the CRITIC method are as follows: The variability of an indicator is represented by calculating the standard deviation: ; In the formula This represents the standard deviation of the j-th indicator; The correlation coefficient is used to represent the conflict between indicators: In the formula This represents the correlation coefficient between indexes i and j. The amount of information in the calculated indicators: ; Calculate objective weights:

[0068] Standard deviation of the normalized data Calculate the correlation coefficients between each indicator to obtain the weight values ​​of each indicator: The correlation coefficients between the indicators are shown in Table 6. Table 6

[0069] Furthermore, the steps for calculating the objective weights using the coefficient of variation method are as follows: For the standardized data, the mean and standard deviation of each indicator are calculated; based on the mean and standard deviation of each indicator, the coefficient of variation of each indicator is calculated to characterize the relative dispersion of the corresponding indicator in the sample data; the coefficients of variation of each indicator are normalized to obtain the indicator weights; the weight calculation results of the above weighting methods are shown in Table 7 and... Figure 4 As shown: Table 7

[0070] Furthermore, the weight combination strategy in step S4 will be illustrated using the weighted average and distance function methods as examples: Weighted average formula: ;in, The total weight of the i-th factor Let i be the objective weight of the i-th indicator. Let be the subjective weight of the i-th indicator, and 'a' be the coefficient of the combined weight, which is 0.5.

[0071] Distance function method: Distance function: , Let i be the objective weight of the i-th indicator. Let be the subjective weight of the i-th indicator. Let the combined weight be... The combined weight is a linear weighted sum of the two values, expressed as: a and b are the allocation coefficients for the two weights.

[0072] To ensure that the differences between the different distance coefficients and allocation coefficients among the weights are consistent, and to make the distance function in the above formula consistent with the expression for the difference in allocation coefficients, the expression is as follows: By introducing the constraint a+b=1, solving the system of equations yields the allocation coefficients a and b, as well as the combined weights. By combining the above methods, 12 schemes were obtained, as shown in Table 8: Table 8

[0073] The calculated combined weights of different schemes are shown in Table 9:

[0074] Table 9 The comprehensive water use efficiency of crops is shown in Table 10 and Figure 5 As shown: The data in the table shows that Scheme 10 is the most suitable for calculating integrated crop water use in this study area.

[0075] Table 10

[0076] The embodiments of the present invention also have the following technical effects: under the same data-driven approach, subjective experience and objective data characteristics are considered simultaneously. By constructing a set of candidate weighted combination schemes and introducing a regional suitability calculation function, quantitative comparison and automatic screening of different weighted combination schemes are achieved. This improves the distinguishability, robustness and regional target consistency of the calculation results of crop integrated water use efficiency, reduces the impact of weighting method differences on the scheme selection conclusion, and enhances the reliability of the calculation conclusions in the study area for application and promotion.

[0077] To achieve the above embodiments, such as Figure 6 As shown, this embodiment also provides a device 10 for calculating the comprehensive water use efficiency of crops using multiple methods, including: The indicator system construction module 100 is used to construct a multi-dimensional calculation indicator system covering crop growth benefits, resource utilization efficiency and environmental benefits, obtain the original indicator data of the calculation objects in the study area, and perform standardization processing on the original indicator data according to the indicator attributes to obtain a standardized indicator matrix. The weight calculation module 200 is used to calculate multiple subjective weight vectors based on the standardized index matrix using multiple subjective weighting methods and to calculate multiple objective weight vectors using multiple objective weighting methods. The weight combination and ranking module 300 is used to combine the multiple subjective weight vectors with the multiple objective weight vectors, and generate multiple candidate weight combination schemes by using multiple weight fusion strategies, and calculate the comprehensive score and ranking result of each calculation object based on each candidate weight combination scheme; The optimal mode selection module 400 is used to construct a regional adaptability calculation function that includes a discrimination index, a robustness index, and an external consistency index. The regional adaptability calculation function is used to comprehensively score each candidate weight combination scheme, select the candidate weight combination scheme with the highest score as the optimal calculation mode, and output the final calculation result of crop water use efficiency based on the optimal calculation mode.

[0078] The present invention provides a method and apparatus for calculating the comprehensive water use efficiency of crops by comparing multiple methods. This method overcomes the limitations of a single weighting method and significantly improves the discrimination, robustness, and consistency with the actual objectives of the study area by quantitatively screening the optimal combination of subjective and objective methods.

[0079] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 7 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads the executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the method for calculating the comprehensive water use efficiency of crops by comparing multiple methods as described above.

[0080] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for calculating the comprehensive water use efficiency of crops through multi-method comparison as described in the foregoing embodiments.

[0081] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0082] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method for calculating the comprehensive water use efficiency of crops through multi-method comparison, characterized in that, include: S10, construct a multi-dimensional calculation index system covering crop growth benefits, resource utilization efficiency and environmental benefits, obtain the original index data of the calculation objects in the study area, and standardize the original index data according to the index attributes to obtain a standardized index matrix. S20, based on the standardized index matrix, multiple subjective weight vectors are calculated using various subjective weighting methods, and multiple objective weight vectors are calculated using various objective weighting methods. S30, combine the multiple subjective weight vectors with the multiple objective weight vectors, and use multiple weight fusion strategies to generate multiple candidate weight combination schemes, and calculate the comprehensive score and ranking result of each calculation object based on each candidate weight combination scheme; S40, construct a regional suitability calculation function that includes a discrimination index, a robustness index, and an external consistency index. Use the regional suitability calculation function to comprehensively score each candidate weight combination scheme, select the candidate weight combination scheme with the highest score as the optimal calculation mode, and output the final calculation result of crop water use efficiency based on the optimal calculation mode.

2. The method as described in claim 1, characterized in that, S10 includes: Obtain basic data related to crop water use efficiency within the study area, and construct a comprehensive calculation index system for crop water use efficiency that includes indicators of crop growth benefits, resource utilization efficiency, and environmental benefits. Among them, crop growth benefit indicators include crop yield and evapotranspiration, resource utilization efficiency indicators include irrigation water use efficiency and irrigation quota per unit area, and environmental benefit indicators include soil organic matter content and greenhouse gas emission intensity. Collect multi-year baseline data under different irrigation systems or fertilization measures within the study area to construct an original index numerical matrix; Based on the indicator attributes, positive and negative indicators are distinguished. Positive indicators are processed using a forward normalization method, while negative indicators are processed using a reverse normalization method, in order to eliminate the differences in the dimensions and orders of magnitude of different indicators and obtain a standardized indicator matrix.

3. The method as described in claim 2, characterized in that, The process of processing positive indicators using a positive normalization method and negative indicators using a negative normalization method includes: For positive indicators, use the formula: For the study area The first sample Data for each indicator Dimensionless processing yields ; For contrarian indicators, use the formula: For the study area The first sample Data for each indicator Dimensionless processing yields The processed standardized index matrix is ​​then used for subsequent weight calculations.

4. The method as described in claim 1, characterized in that, S20 includes: At least two subjective weighting methods are used to calculate the subjective weight vector based on the standardized index matrix. The subjective weighting methods include the analytic hierarchy process (AHP) and the Delphi method. The AHP calculates the subjective weight vector by constructing an expert judgment matrix and performing a consistency check. At the same time, at least two objective weighting methods are used to calculate the objective weight vector based on the standardized index matrix. The objective weighting methods include entropy weighting method, CRITIC method, standard deviation method and principal component analysis method. Among them, the entropy weight method calculates the information entropy of each indicator and determines the weight coefficient value based on the information entropy, while the CRITIC method calculates the information content of the indicator by calculating the standard deviation and correlation coefficient of the indicator and calculates the objective weight accordingly.

5. The method as described in claim 1, characterized in that, S30 includes: Different subjective weight vectors are paired with different objective weight vectors, and the subjective weights and objective weights are fused using the weighted average method, game theory combination weighting method or distance function method to generate a variety of candidate weight combination schemes. Under each candidate weight combination scheme, the comprehensive crop water use efficiency score of each calculation object is calculated using a weighted summation model to obtain the corresponding ranking results. The weighted average method uses the following formula: Calculate the first The total weight of each factor is obtained by solving a system of equations that are consistent with the expression for the distance function and the difference between the allocation coefficients, using the distance function method to obtain the allocation coefficients and the combined weights.

6. The method as described in claim 1, characterized in that, The construction of a region fit calculation function includes a discrimination index, a robustness index, and an external consistency index. This function is then used to comprehensively score each candidate weight combination scheme, including: Construct a region fit calculation function for different candidate weight combinations: ， Where the coefficient satisfy ; Calculate the discrimination index The coefficient of variation of the comprehensive score sequence is used to measure the dispersion of the comprehensive calculation results. Calculate robustness indicators To measure the stability of the calculation results under data perturbation conditions, the Kendall or Spearman rank correlation coefficient is used to calculate the mean consistency between the perturbed sort and the original sort after multiple resampling or the addition of perturbation noise. Calculate the external consistency index To measure the consistency between the calculation results and external reference variables for yield and water consumption in the study area, the Spearman rank correlation coefficient between the composite score and the external reference variables was constructed.

7. The method as described in claim 1, characterized in that, The process of selecting the candidate weight combination scheme with the highest score as the optimal calculation mode, and outputting the final calculation result of crop water use efficiency based on the optimal calculation mode, includes: Compare the regional suitability scores of all candidate schemes, and select the candidate weight combination scheme with the highest score as the optimal weight combination scheme for the study area. Based on the optimal weighted combination scheme, the final comprehensive crop water use efficiency calculation results and ranking are recalculated and output, providing a decision-making basis for water-saving irrigation management, water use efficiency improvement and optimal allocation of agricultural water resources in the study area.

8. A device for calculating the comprehensive water use efficiency of crops by comparing multiple methods, characterized in that, include: The indicator system construction module is used to construct a multi-dimensional calculation indicator system covering crop growth benefits, resource utilization efficiency and environmental benefits, obtain the original indicator data of the calculation objects in the study area, and standardize the original indicator data according to the indicator attributes to obtain a standardized indicator matrix. The weight calculation module is used to calculate multiple subjective weight vectors based on the standardized index matrix using multiple subjective weighting methods, and to calculate multiple objective weight vectors using multiple objective weighting methods. The weight combination and ranking module is used to combine the multiple subjective weight vectors with the multiple objective weight vectors, and to generate multiple candidate weight combination schemes using multiple weight fusion strategies. Based on each candidate weight combination scheme, the module calculates the comprehensive score and ranking result of each calculation object. The optimal mode selection module is used to construct a regional adaptability calculation function that includes a discrimination index, a robustness index, and an external consistency index. The regional adaptability calculation function is used to comprehensively score each candidate weight combination scheme, select the candidate weight combination scheme with the highest score as the optimal calculation mode, and output the final calculation result of crop water use efficiency based on the optimal calculation mode.

9. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement a method for calculating the comprehensive water use efficiency of crops by comparison of multiple methods as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a method for calculating the comprehensive water use efficiency of crops using multiple methods as described in any one of claims 1-7.

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