Multi-evaluation index and multi-weighting method coupled crop efficient water utilization model

Abstract

The invention discloses a multi-evaluation index and multi-weighting method coupled crop efficient water consumption model. The method comprises the following steps: acquiring related data in a research area; inputting the related data into a crop model for simulation to obtain various index parameters influencing the research results of different planting scenes in the research area; constructing sample data, optimizing the crop model, and outputting various optimized parameter indexes; establishing a crop efficient water consumption evaluation model by using an analytic hierarchy process; carrying out subjective weight and objective weight calculation analysis on the index layer and the criterion layer; and obtaining an evaluation result of the crop efficient water consumption model. The crop model and the random forest algorithm are utilized to simulate and optimize various indexes and factors influencing crop efficient water consumption, the crop efficient water consumption model is established in combination with the analytic hierarchy process, and a more scientific crop efficient water consumption evaluation model is obtained by coupling subjective and objective weight distribution. And the implementation of water-saving and water-retaining measures is promoted according to the efficient water consumption model.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural water-saving and water use, and in particular to a crop efficient water use model coupled with multiple evaluation indicators and multiple weighting methods. Background Art

[0002] With the continuous growth of my country's population and the continuous development of industry and agriculture, water shortage has become an increasingly serious problem. Agriculture is a major water user in my country, and agricultural water safety is an important part of my country's water security. However, in recent years, the amount of water available for agricultural production has continued to decrease. The contradiction between water supply and demand has become an important factor restricting agricultural production and rural life. It is necessary to propose efficient water use for agricultural crops to achieve water saving and conservation.

[0003] Crop efficient water use refers to the fact that crops actively adapt to the external environment through their own genetic potential, and through a series of physiological and ecological control measures, reduce extravagant transpiration water consumption and ineffective water loss, improve the utilization rate of water resources, and thus achieve the purpose of water conservation and water conservation. A large number of studies have shown that crop water use efficiency is affected by water-saving irrigation technology, planting structure and conditions, and climatic factors. However, there is currently no unified standard for the evaluation theory and related methods of crop efficient water use. Although there are many existing studies on crop efficient water use evaluation, most of the existing studies are based on a single direction to evaluate agricultural water use as a whole. There are relatively few comprehensive evaluation studies that combine multiple evaluation indicators and use multiple methods for coupling and weighting. It is impossible to provide a scientific evaluation model or system that combines multiple influencing factors, objective evaluation, and subjective evaluation. Summary of the invention

[0004] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0005] To this end, an embodiment of the present invention proposes a crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods, including the following steps:

[0006] S1. Obtain relevant data in the study area, including meteorological data, soil data, water resources data, and irrigation and fertilization system in the study area;

[0007] S2. According to the planting scenarios of different crops and the relevant data obtained in the corresponding scenarios, they are input into the crop model for simulation to obtain various indicator parameters that affect the research results of different planting scenarios in the study area;

[0008] S3, using the various parameter indicators obtained in S2 as predictive variables, constructing sample data, and inputting them into the random forest algorithm to compensate for the deviation caused by unclear information or insufficient knowledge reserve in the crop model, optimize the crop model, and output the optimized various parameter indicators;

[0009] S4. Use the analytic hierarchy process to establish a crop water efficiency evaluation model, taking the crop water efficiency model as the target layer, the various factors that affect the research results of different planting scenarios in the study area as the criterion layer, and the various optimized index parameters obtained in step S3 as the indicator layer;

[0010] S5. Calculate and analyze the subjective weight and objective weight of the indicator layer and the criterion layer. Use the analytic hierarchy process to calculate the subjective weight, and use the CRITIC weight method to calculate the objective weight. Perform a weighted calculation again on the subjective weight obtained by the analytic hierarchy process and the objective weight obtained by the CRITIC weight method to finally obtain the comprehensive weight of the criterion layer and the indicator layer.

[0011] S6. Combine steps S4 and S5 to obtain the evaluation results of the crop efficient water use model.

[0012] According to the embodiment of the present invention, a crop model and a random forest algorithm are used to simulate and optimize various indicators and factors that affect the efficient water use of crops, and a crop efficient water use model is established in combination with the hierarchical analysis method. The process of obtaining the crop efficient water use model by the hierarchical analysis method is further optimized by coupling subjective and objective weight distribution, thereby obtaining a more scientific crop efficient water use evaluation model, and then promoting the implementation of water-saving and water conservation measures according to the efficient water use model to improve the utilization rate of water resources.

[0013] Optionally, the crop model adopts a DSSAT model.

[0014] Furthermore, the multiple index parameters obtained in step S2 include water utilization rate, crop water productivity, and nitrogen fertilizer utilization efficiency.

[0015] Furthermore, the various factors affecting the research results of different planting scenarios in the research area described in step S4 include resource input and utilization efficiency, yield and quality, and environmental benefit factors.

[0016] Furthermore, in step S3, the regression decision tree of the random forest algorithm is expressed as: Where h(x) is the final prediction result of the random forest algorithm, h(x,θ i ) is the prediction result of a regression decision tree in the random forest algorithm, x is the set of corresponding feature variables used by the decision tree, θ i is an independent and identically distributed random vector, K is the number of decision trees, and N is the number of training sample subsets.

[0017] Furthermore, the corresponding characteristic variables used by the decision tree include water use efficiency (WUE), crop water productivity (WP), and nitrogen use efficiency (NUE), where:

[0018] WUE=Y / W

[0019] Where WUE is water use efficiency; Y is grain yield per unit area; W is water resource input;

[0020] WP=Y / ET

[0021] Where WP is crop water productivity; Y is grain yield per unit area; ET is field evapotranspiration;

[0022] NUE=Y / N

[0023] Where NUE is nitrogen fertilizer utilization efficiency; Y is grain yield per unit area; N is nitrogen application amount.

[0024] Furthermore, the optimization of the crop model using the random forest algorithm includes the following steps:

[0025] S31. Crop yield, soil water content, water use efficiency (WUE), crop water productivity (WP), and nitrogen utilization efficiency (NUE) were used as characteristic variables of the random forest algorithm;

[0026] S32, adjusting and optimizing the crop parameters, the soil parameters and the irrigation and fertilization parameters according to the random forest algorithm, and obtaining an optimized crop model accordingly;

[0027] S33, inputting different combinations of meteorological data, soil data, crop yield and irrigation and fertilization system data into the adjusted and optimized crop model for simulation, and outputting optimized simulation values ​​for various index parameters obtained in step S2;

[0028] S34. The output optimized simulation values ​​are used as the indicator layer of the hierarchical analysis method.

[0029] Furthermore, in said S34, the output simulation values ​​include crop yield, simulated crop irrigation water utilization efficiency, simulated crop fertilizer utilization efficiency, and simulated crop soil moisture content.

[0030] Furthermore, in step S5, calculating the subjective weight using the hierarchical analysis method includes the following steps:

[0031] Construct an expert judgment matrix and perform pairwise comparison for scaling; calculate the judgment matrix weights using the arithmetic mean method; perform consistency checks; and calculate subjective weights.

[0032] Further, in step S5, calculating the objective weight using the CRITIC weight method includes the following steps:

[0033] Construct the original data matrix; standardize the positive and negative indicators; calculate the information carrying capacity; calculate the objective weight.

[0034] Furthermore, in step S5, when calculating the comprehensive level of the crop water efficiency model, the comprehensive weighted formula is as follows:

[0035] Q i =a×v i +(1-a)w i ;

[0036] Among them, Q i The total weight of the i-th factor, v i is the objective weight of the ith factor, w i is the subjective weight of the ith factor, and a is the coefficient of the combined weight.

[0037] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0039] Figure 1 A schematic diagram of a method flow of a crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods according to the present invention;

[0040] Figure 2 It is a schematic diagram of the steps of optimizing the crop model by using the S2 step random forest algorithm of a crop efficient water use model coupled with multiple evaluation indicators and multiple weighting methods according to the present invention;

[0041] Figure 3 The present invention is a schematic diagram of the weighted results of various indicators after objective weighting is calculated by the CRITIC weighting method according to an embodiment of a crop efficient water use model coupled with multiple evaluation indicators and multiple weighting methods. DETAILED DESCRIPTION

[0042] Embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but should not be understood as limiting the present invention.

[0043] The present invention provides a crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods. Figures 1 to 3 A detailed description is given, including the following steps:

[0044] S1. Obtain relevant data in the study area, including meteorological data, soil data, water resources data, and irrigation and fertilization system in the study area;

[0045] S2. According to the planting scenarios of different crops and the relevant data obtained in the corresponding scenarios, they are input into the crop model for simulation to obtain various indicator parameters that affect the research results of different planting scenarios in the study area;

[0046] S3, using the various parameter indicators obtained in S2 as predictive variables, constructing sample data, and inputting them into the random forest algorithm to compensate for the deviation caused by unclear information or insufficient knowledge reserve in the crop model, optimize the crop model, and output the optimized various parameter indicators;

[0047] S4. Use the analytic hierarchy process to establish a crop water efficiency evaluation model, taking the crop water efficiency model as the target layer, the various factors that affect the research results of different planting scenarios in the study area as the criterion layer, and the various optimized index parameters obtained in step S3 as the indicator layer;

[0048] S5. Calculate and analyze the subjective weight and objective weight of the indicator layer and the criterion layer. Use the analytic hierarchy process to calculate the subjective weight, and use the CRITIC weight method to calculate the objective weight. Perform a weighted calculation again on the subjective weight obtained by the analytic hierarchy process and the objective weight obtained by the CRITIC weight method to finally obtain the comprehensive weight of the criterion layer and the indicator layer.

[0049] S6. Combine steps S4 and S5 to obtain the evaluation results of the crop efficient water use model.

[0050] The present invention utilizes crop models and random forest algorithms to simulate and optimize various indicators and factors that affect efficient water use of crops, and combines the analytic hierarchy process to establish a crop efficient water use model. The process of obtaining the crop efficient water use model by the analytic hierarchy process is further optimized by coupling subjective and objective weight distribution, thereby obtaining a more scientific crop efficient water use evaluation model, and then promoting the implementation of water-saving and water conservation measures according to the efficient water use model to improve the utilization rate of water resources.

[0051] In some embodiments, the relevant data in the study area in S1 includes data at multiple time and space scales, that is, meteorological data, soil data, water resource data, irrigation and fertilization system, crop quality data and crop yield data in the study area are obtained at one or more time points.

[0052] Among them, meteorological data include daily precipitation, minimum temperature, minimum temperature, sunshine duration, winter wheat sowing date, development period data, and water resource data, which can all be obtained through official data. The total shortwave radiation on the ground can be calculated through the obtained sunshine-related data. In some embodiments, the total shortwave radiation on the ground can be included in the relevant data in the study area. The calculation method is calculated by referring to the FAO-Penman Monteith method, and the formula is as follows:

[0053]

[0054]

[0055] ω s =cos -1 [-tan(ψ)tan(δ)];

[0056]

[0057] Where R s is shortwave radiation; n is the actual sunshine hours; N is the maximum possible sunshine hours; is relative sunshine; G SC Solar constant, 0.082; R a is the extraterrestrial radiation; a s , b s is the regression constant; d r is the relative distance between the sun and the earth; δ is the solar inclination; ω is the sunset angle; and J is the day sequence.

[0058] Since irrigation is done manually, meteorological data should also include data such as irrigation methods, irrigation quotas, irrigation quotas and irrigation time.

[0059] Soil data include the depth, texture, total nitrogen content, soil bulk density, pH value and organic carbon content of each soil layer. Soil data can be obtained through official data and existing field experiment data.

[0060] Bring the various acquired data into the crop model and proceed to step S2.

[0061] In some embodiments, when performing step S2, the crop model selected is the DSSAT model. The DSSAT model is a decision support system for agricultural technology transfer (Decision Support System for Agrotechnology Transfer), which is one of the most widely used crop model systems at present. It is a comprehensive computer model jointly developed and jointly developed by multiple scientific research units under the sponsorship and support of the International Benchmark Network for Agricultural Technology Transfer (IBSNAT). The DSSAT model has a standardized input and output format, powerful functions, simple operation, and can be applied to a variety of widely planted crops. It can provide decisions and countermeasures for rationally and effectively utilizing water, nitrogen, fertilizer and other various natural resources to improve agricultural production efficiency.

[0062] After completing the simulation of the DSSAT model, the first simulation values ​​of various indicator parameters that affect the research results of different planting scenarios in the study area will be obtained. The various indicator parameters obtained in the S2 step include but are not limited to water utilization rate, crop water productivity, and nitrogen fertilizer utilization efficiency.

[0063] Since the DSSAT model may have some deviations due to unclear information or insufficient knowledge reserves during use, the random forest algorithm in step S3 is introduced to optimize the first simulation value obtained by the DSSAT model.

[0064] In some embodiments, in step S3, the regression decision tree of the random forest algorithm is represented as: Where h(x) is the final prediction result of the random forest algorithm, h(x,θ i ) is the prediction result of a regression decision tree in the random forest algorithm, x is the set of corresponding feature variables used by the decision tree, θ i is an independent and identically distributed random vector, K is the number of decision trees, and N is the number of training sample subsets.

[0065] The corresponding characteristic variables used by the decision tree of the random forest algorithm include but are not limited to water use efficiency (WUE), crop water productivity (WP), and nitrogen utilization efficiency (NUE), where:

[0066] WUE=Y / W

[0067] Where WUE is water use efficiency; Y is grain yield per unit area; W is water resource input;

[0068] WP=Y / ET

[0069] Where WP is crop water productivity; Y is grain yield per unit area; ET is field evapotranspiration;

[0070] NUE=Y / N

[0071] Where NUE is nitrogen fertilizer utilization efficiency; Y is grain yield per unit area; N is nitrogen application amount.

[0072] In some embodiments, in step S3, optimizing the crop model using the random forest algorithm includes the following steps:

[0073] S31. Crop yield, soil water content, water use efficiency (WUE), crop water productivity (WP), and nitrogen utilization efficiency (NUE) were used as characteristic variables of the random forest algorithm;

[0074] S32, adjusting and optimizing the crop parameters, the soil parameters and the irrigation and fertilization parameters according to the random forest algorithm, and obtaining an optimized crop model accordingly;

[0075] S33, inputting different combinations of meteorological data, soil data, crop yield and irrigation and fertilization system data into the adjusted and optimized crop model for simulation, and outputting optimized simulation values ​​for various index parameters obtained in step S2;

[0076] S34. The output optimized simulation values ​​are used as the indicator layer of the hierarchical analysis method.

[0077] The simulation values ​​optimized by the random forest algorithm include water use efficiency, nitrogen fertilizer use efficiency, water and fertilizer usage; crop yield, crop quality, sustainability, planting area; water leakage, water runoff, and nitrogen leaching / surplus data.

[0078] The above-mentioned simulation values ​​will correspond to the various factors affecting the research results of different planting scenarios in the study area described in step S4, and respectively correspond to the indicator layer and the criterion layer of step S4.

[0079] Among them, in said S34, the output simulation values ​​include crop yield, simulated crop irrigation water utilization efficiency, simulated crop fertilizer utilization efficiency, and simulated crop soil moisture content.

[0080] In some embodiments, when performing step S4, the multiple factors affecting the research results of different planting scenarios in the study area include resource input and utilization efficiency, yield and quality, and environmental benefit factors. These three factors constitute the criterion layer of the hierarchical analysis method.

[0081] In step S5, the calculation of subjective weights using the analytic hierarchy process includes the following steps:

[0082] Construct an expert judgment matrix and perform pairwise comparison for scaling; calculate the judgment matrix weights using the arithmetic mean method; perform consistency checks; and calculate subjective weights.

[0083] Taking the criterion layer as an example, the expert judgment matrix is ​​as follows:

[0084]

[0085] Among them, a1 represents the scale corresponding to the resource input and utilization efficiency criteria, a2 represents the scale corresponding to the crop yield and quality criteria, and a3 represents the scale corresponding to the environmental benefit criteria.

[0086] At the same time, step S5 is performed to calculate the judgment matrix weight using the arithmetic mean method, that is, to calculate the subjective weight of the hierarchical analysis method. The calculation process is as follows:

[0087]

[0088] After completion, a consistency check is performed and the one-time index CI is calculated.

[0089]

[0090] Among them, i is the matrix order, λ max is the largest characteristic root of A;

[0091] CI = 0, with complete consistency;

[0092] The CI is close to 0, which means there is satisfactory consistency;

[0093] The larger the CI, the more serious the inconsistency.

[0094] Due to λ max Continuous dependence on a ij , then λ max The larger the value is than i (matrix order), the more serious the inconsistency of A is. The larger the judgment error caused is. Therefore, we can use λ max The size of the -i value is used to measure the inconsistency of A.

[0095] Define the consistency ratio:

[0096] Where: RI is the random consistency index, which is generally obtained by looking up the table, as shown in the following table:

[0097] n 1 2 3 4 5 6 7 8 9 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0098] Where n is the matrix order.

[0099] From the table, we can get RI = 0.52.

[0100] When CR is less than 0.1, it is considered that the inconsistency of A is within the allowable range and passes the one-time test; otherwise, the judgment matrix A is reconstructed.

[0101] In the above method, in one embodiment, the indicator layer includes A1, water use efficiency; A2, nitrogen fertilizer use efficiency; A3, irrigation water usage; A4, nitrogen fertilizer usage; B1, yield; B2, quality; B3, planting area; B4, production sustainability; B5, labor productivity; C1, water leakage; C2, water runoff;

[0102] C3, nitrogen leaching; C4, nitrogen surplus. The criteria layer includes: resource input and utilization efficiency criteria A, yield and quality criteria B and environmental benefits C.

[0103] Taking the criterion layer as an example, according to the calculation principle of the hierarchical analysis method and the expert scoring results, the criterion layer indicator judgment matrix (E) is obtained:

[0104]

[0105] The maximum characteristic root λ of the quasi-layer judgment matrix (E) can be calculated max =3.064, CI = 0.032, where The consistency check is passed.

[0106] Calculate the subjective weight, and the matrix E is normalized to

[0107] The subjective weight values ​​are:

[0108]

[0109] In some embodiments, in step S5, calculating the objective weight using the CRITIC weight method includes the following steps:

[0110] Construct the original data matrix; standardize the positive and negative indicators; calculate the information carrying capacity; calculate the objective weight. Use the CRITIC weight method to perform objective weight analysis and calculation. The CRITIC weight method is an objective weighting method based on data volatility. The idea is to use two indicators, namely volatility (contrast intensity) and conflict (correlation) indicators. The contrast intensity is represented by the standard deviation. If the data standard deviation is larger, the volatility is larger, and the weight will be higher; the conflict is represented by the correlation coefficient. If the correlation coefficient value between the indicators is larger, the conflict is smaller, and the weight is lower. When calculating the weight, the contrast intensity is multiplied by the conflict indicator, and normalized, and the final weight is obtained.

[0111] The steps of the CRITIC weighting method are as follows:

[0112] 1) Each indicator is normalized according to the number of each option,

[0113] For positive indicators:

[0114] For negative indicators:

[0115] 2) Calculate the standard deviation to show the indicator variability: Where S j represents the standard deviation of the jth indicator;

[0116] 3) Calculate the correlation coefficient to show the conflict of indicators: Where R j Represents the correlation coefficient between evaluation indicators i and j;

[0117] 4) Calculate the information content of the indicator:

[0118] 5) Calculate objective weight:

[0119] According to the above method, in one embodiment, each indicator of the indicator layer is constructed into a set, and actual data is used as an example to construct the following set:

[0120] A1, water use efficiency; A2, nitrogen fertilizer use efficiency; A3, irrigation water usage; A4, nitrogen fertilizer usage; B1, yield; B2, quality; B3, planting area; B4, production sustainability; B5, labor productivity; C1, water leakage; C2, water runoff; C3, nitrogen leaching; C4, nitrogen surplus.

[0121] After normalizing the data of each indicator, the following table is obtained:

[0122] A1 A2 A3 A4 B1 B2 B3 B4 B5 C1 C2 C3 C4 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.696 0.814 0.757 0.779 0.731 0.636 0.618 0.809 0.801 0.528 0.625 0.803 0.764 0.403 0.483 0.465 0.443 0.395 0.421 0.366 0.567 0.565 0.434 0.375 0.417 0.457 0.260 0.244 0.264 0.214 0.134 0.221 0.229 0.298 0.317 0.066 0.208 0.205 0.307 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

[0123] The standard deviation S of the normalized data j The calculation results are as follows:

[0124] A1 A2 A3 A4 B1 B2 B3 B4 B5 C1 C2 C3 C4 0.388 0.407 0.395 0.407 0.414 0.385 0.384 0.398 0.394 0.403 0.387 0.414 0.390

[0125] Calculate the correlation coefficient between each indicator:

[0126]

[0127] Calculate the amount of information C j :

[0128] A1 A2 A3 A4 B1 B2 B3 B4 B5 C1 C2 C3 C4 0.007 0.005 0.003 0.004 0.028 0.020 0.030 0.029 0.029 0.048 0.019 0.029 0.027

[0129] Calculate the weight W of each indicator j :

[0130] A1 A2 A3 A4 B1 B2 B3 B4 B5 C1 C2 C3 C4 0.386 0.272 0.149 0.192 0.209 0.150 0.218 0.212 0.211 0.385 0.156 0.237 0.222

[0131] The final weighted results of each indicator layer of the comprehensive evaluation system for crop efficient water use are as follows: Figure 3 shown.

[0132] After obtaining the objective weights and subjective weights, in step S5, when calculating the comprehensive level of the crop efficient water use model, the comprehensive weighted formula is as follows:

[0133] Q i =a×v i +(1-a)w i ;

[0134] Among them, Q i The total weight of the i-th factor, v i is the objective weight of the ith factor, w i is the subjective weight of the ith factor, and a is the coefficient of the combined weight.

[0135] Taking the subjective weight of the AHP and the objective weight of the CRITIC weight method as an example, the comprehensive weighting formula Q i =a×v i +(1-a)w i The comprehensive weights are calculated to complete the evaluation of the multi-objective function. The evaluation results of the efficient water use model are obtained. According to the evaluation results, they can be fed back to the crop irrigation in the actual study area, so as to achieve water conservation and improve the utilization rate of water resources.

[0136] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0137] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0138] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0139] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0140] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0141] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods, characterized in that: The following steps are involved: S1. Obtain relevant data in the study area, including meteorological data, soil data, water resources data, and irrigation and fertilization system in the study area; S2. According to the planting scenarios of different crops and the relevant data obtained in the corresponding scenarios, they are input into the crop model for simulation to obtain various indicator parameters that affect the research results of different planting scenarios in the study area; S3, using the various parameter indicators obtained in S2 as predictive variables, constructing sample data, and inputting them into the random forest algorithm to compensate for the deviation caused by unclear information or insufficient knowledge reserve in the crop model, optimize the crop model, and output the optimized various parameter indicators; S4. Use the analytic hierarchy process to establish a crop water efficiency evaluation model, taking the crop water efficiency model as the target layer, the various factors that affect the research results of different planting scenarios in the study area as the criterion layer, and the various optimized index parameters obtained in step S3 as the indicator layer; S5. Calculate and analyze the subjective weight and objective weight of the indicator layer and the criterion layer. Use the analytic hierarchy process to calculate the subjective weight, and use the CRITIC weight method to calculate the objective weight. Perform a weighted calculation again on the subjective weight obtained by the analytic hierarchy process and the objective weight obtained by the CRITIC weight method to finally obtain the comprehensive weight of the criterion layer and the indicator layer. S6. Combine steps S4 and S5 to obtain the evaluation results of the crop efficient water use model.

2. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 1, characterized in that: The crop model adopts the DSSAT model.

3. A crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 2, characterized in that: The various index parameters obtained in step S2 include water utilization rate, crop water productivity, and nitrogen fertilizer utilization efficiency.

4. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 1, characterized in that: The various factors affecting the research results of different planting scenarios in the study area described in step S4 include resource input and utilization efficiency, yield and quality, and environmental benefit factors.

5. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 1, characterized in that: In step S3, the regression decision tree of the random forest algorithm is expressed as: Where h(x) is the final prediction result of the random forest algorithm, h(x,θ i ) is the prediction result of a regression decision tree in the random forest algorithm, x is the set of corresponding feature variables used by the decision tree, θ i is an independent and identically distributed random vector, K is the number of decision trees, and N is the number of training sample subsets.

6. A crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 5, characterized in that: The corresponding feature variables used in the decision tree include water use efficiency (WUE), crop water productivity (WP), and nitrogen use efficiency (NUE), where: WUE=Y / W Where WUE is water use efficiency; Y is grain yield per unit area; W is water resource input; WP=Y / ET Where WP is crop water productivity; Y is grain yield per unit area; ET is field evapotranspiration; NUE=Y / N Where NUE is nitrogen fertilizer utilization efficiency; Y is grain yield per unit area; N is nitrogen application amount.

7. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 6, characterized in that: The optimization of crop models using the random forest algorithm includes the following steps: S31. Crop yield, soil water content, water use efficiency (WUE), crop water productivity (WP), and nitrogen utilization efficiency (NUE) were used as characteristic variables of the random forest algorithm; S32, adjusting and optimizing the crop parameters, the soil parameters and the irrigation and fertilization parameters according to the random forest algorithm, and obtaining an optimized crop model accordingly; S33, inputting different combinations of meteorological data, soil data, crop yield and irrigation and fertilization system data into the adjusted and optimized crop model for simulation, and outputting optimized simulation values ​​for various index parameters obtained in step S2; S34. The output optimized simulation values ​​are used as the indicator layer of the hierarchical analysis method.

8. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 7, characterized in that: In the above S34, the output simulation values ​​include crop yield, simulated crop irrigation water utilization efficiency, simulated crop fertilizer utilization efficiency, and simulated crop soil moisture content.

9. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 1, characterized in that: In step S5, the calculation of subjective weights using the analytic hierarchy process includes the following steps: Construct an expert judgment matrix and perform pairwise comparison for scaling; calculate the judgment matrix weights using the arithmetic mean method; perform consistency checks; and calculate subjective weights.

10. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 1, characterized in that: In step S5, the calculation of objective weights using the CRITIC weight method includes the following steps: Construct the original data matrix; standardize the positive and negative indicators; calculate the information carrying capacity; calculate the objective weight.

11. The crop water efficiency model coupled with multiple evaluation indicators and multiple weighting methods as claimed in claim 1, characterized in that: In step S5, when calculating the comprehensive level of the crop water efficiency model, the comprehensive weighted formula is as follows: Q i =a×v i +(1-a)w i ; Among them, Q i The total weight of the i-th factor, v i is the objective weight of the ith factor, w i is the subjective weight of the ith factor, and a is the coefficient of the combined weight.

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