Graded evaluation water and fertilizer management method for yield and quality of greenhouse tomatoes

Abstract

The invention belongs to the technical field of greenhouse crop water and fertilizer management and green efficient sustainable production, and particularly relates to a greenhouse tomato yield and quality grading evaluation water and fertilizer management method, which is characterized by sequentially comprising the steps of data collection, weight determination, sorting, grading and optimal mode determination, weight determination adopts an integrated entropy weight method, sorting adopts a TOPSIS model, and the optimal mode determination adopts a TOPSIS model. And an AISM model is adopted in a grading manner, and finally an EWM-TOPSIS-AISM comprehensive evaluation model is constructed. The method has the advantages that an EWM-TOPSIS-AISM comprehensive evaluation model is constructed on the basis of field experiments, water and nitrogen management of greenhouse drip irrigation tomatoes in the northeast region is optimized on the basis of multiple targets, an optimal water and nitrogen supply mode is established, and a theoretical basis and technical support are provided for formulating a precise drip irrigation and fertilization strategy for greenhouse tomatoes in the northeast region; (2) the EWM-TOPSIS-AISM comprehensive evaluation model has the advantage that the problems of subjectivity and inconsistent results of a single model are effectively solved through a combined evaluation method, and (3) the evaluation result is stable and reliable through bidirectional verification of UP type and DOWN type topological hierarchy directed graphs.

Description

Technical Field

[0001] This invention belongs to the field of greenhouse crop water and fertilizer management and green, efficient and sustainable production technology, and in particular relates to a water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes. Background Technology

[0002] Tomatoes are one of the most widely cultivated vegetables in the world. Currently, the global annual tomato production is approximately 18 billion tons. China ranks first in the world in both tomato planting area and output. The harsh climate of Northeast my country is unfavorable for tomato growth. However, farmers cultivate tomatoes in greenhouses, utilizing solar greenhouses to regulate temperature, humidity, and light, providing a favorable growing environment. In recent years, due to rapid population growth and global warming, many regions of the world are facing water shortages. Previous studies have shown that both over- and under-irrigation are detrimental to tomato growth. Over-irrigation reduces the soluble sugar content of tomatoes and increases water vapor content in greenhouses, leading to increased pests and diseases. Under-irrigation, on the other hand, affects the physiological processes of tomatoes, causing premature aging of plants and increased susceptibility to various diseases, ultimately limiting crop growth and reducing yield. Therefore, precise control of irrigation is crucial for the growth of greenhouse tomatoes.

[0003] Current research on water and fertilizer management optimization models has shifted from single-method evaluation to multi-attribute comprehensive evaluation systems. These systems integrate statistical methods such as principal component analysis, TOPSIS, grey relational analysis, and combined weighting methods to systematically assess the impact of water and nitrogen regulation on crop yield, quality, and resource utilization efficiency. However, due to differences in calculation methods or human interference, the evaluation results often contain significant errors, which are detrimental to practical production. Therefore, to address the inconsistency in the calculation results of single evaluation models and obtain highly objective and accurate evaluation results, it is essential to adopt combined evaluation models. This overcomes the subjectivity and variability of single-model results, improves the objectivity and accuracy of decision-making, and provides scientific support for efficient and environmentally friendly agricultural production. Summary of the Invention

[0004] The purpose of this invention is to provide a graded evaluation method for water and fertilizer management of greenhouse tomatoes to assess yield and quality, overcome the shortcomings of existing technologies, and construct an EWM-TOPSIS-AISM comprehensive evaluation model based on field experiments. This model optimizes water and nitrogen management for greenhouse drip-irrigated tomatoes in Northeast China based on multiple objectives, establishes the optimal water and nitrogen supply mode, and provides theoretical basis and technical support for the formulation of precision drip irrigation fertilization strategies for greenhouse tomatoes in Northeast China.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes includes data collection, weight determination, ranking, grading, and determination of the optimal model. Weight determination uses the integrated entropy weight method, ranking uses the TOPSIS model, grading uses the AISM model, and finally, an EWM-TOPSIS-AISM integrated evaluation model is constructed. The specific processing steps are as follows: 1) Data collection: Under the same greenhouse conditions, several experimental plots were determined according to different water and nitrogen conditions; specific indicators were measured and recorded during the key growth and harvest periods of tomatoes for subsequent comprehensive evaluation. 2) Weight determination: The weights of the specific indicators measured in step 1) in the comprehensive evaluation system are objectively calculated using the entropy weight method (EWM). 3) Sorting: Input the standardized data of each specific indicator weight processing into the TOPSIS model, calculate the closeness of each processing to the ideal solution, and perform preliminary sorting; 4) Hierarchical structure: Using the AISM model, a relationship matrix and reachability matrix are generated based on the standardized data of each specific indicator. UP-type and DOWN-type topological hierarchical directed graphs are drawn to intuitively display the hierarchical relationship of superiority and inferiority among each processing. 5) Determine the optimal mode: combine the ranking results of the TOPSIS model with the hierarchical structure of the AISM model to jointly determine the water and nitrogen treatment combination with the best overall performance.

[0006] The number of test plots shall not be less than 27.

[0007] The specific indicators include, but are not limited to: plant height, stem diameter, leaf area index (L), dry matter of each organ, number of fruits per plant, weight of a single fruit, total yield (Y), vitamin C content (VC), soluble solids content (TSS), soluble sugar content (SS), organic acid content (OA), lycopene content (L), nitrogen use efficiency (NUE), and water productivity (WP).

[0008] The organs referred to are any one of the following: fruit FDM, stem SDM, root RDM, and leaf LDM.

[0009] The steps for calculating the index weights using the Entropy Weight Method (EWM) are as follows: i) Construct the original matrix. Assuming n evaluation indicators are selected to evaluate m samples, construct the original matrix: In the formula: rij is the data value corresponding to the j-th sample under the i-th indicator; ii) Indicator standardization: Since different indicators have different units and trends, the extreme value method is used to standardize the indicators. The calculation formula for positive indicators is as follows: The formula for calculating negative indicators is as follows: In the formula: and They represent the maximum and minimum values ​​under the same indicator; xij is the value corresponding to the j-th sample under the i-th indicator after unification; iii) Calculate the entropy of the index, first according to the formula Calculate the proportion Pij of the j-th sample under the i-th indicator; according to the formula... Calculate the coefficient K; finally, according to the formula... Calculate the entropy under the i-th index, when hour, ; (iii) The weights of the indicators are based on the formula Calculate the weight Wi of the i-th indicator, where .

[0010] The positive indicators refer to those that are as high as possible, including but not limited to yield or vitamin C content.

[0011] The negative indicator is one where the smaller the value, the better; this includes, but is not limited to, organic acid content.

[0012] The calculation steps of the TOPSIS model are as follows: a) Index unification: The same extreme value method as the entropy weight method is used to unify the indexes, resulting in a unified matrix; b) Calculate the weighted normalization matrix: according to the formula Calculate the weighted normalization matrix to determine the positive and negative ideal solutions: Positive ideal solution Negative ideal solution In the formula: and This represents the maximum and minimum values ​​under the same indicator; c) Calculate the distance from the sample to the positive and negative ideal solutions: according to the formula Calculate the distance from the j-th sample to the positive ideal solution; according to the formula... Calculate the distance from the j-th sample to the negative ideal solution; d) Calculate the proximity coefficient and sort: according to the formula Calculate the proximity coefficient of the j-th sample. The larger the proximity coefficient, the closer the sample is to the ideal point and the better the treatment effect. Sort the different water and nitrogen treatment combinations according to the size of the proximity coefficient.

[0013] The steps for analyzing the hierarchical relationship of samples using the AISM model are as follows: I) For a decision matrix (D) with m columns, there are m different index dimensions; positive indices are labeled p1, p2...pm, and negative indices are labeled q1, q2...qm. For any two rows x and y in the decision matrix (D): Negative indicators include: , 、·····、 , Positive indicators include: , 、·····、 ; The partial order relation between elements x and y is denoted as x < y, indicating that element y is superior to element x; that is, given a partially ordered set (D, <), we have , If dj < di, denote aij = 1; if dj > di, denote aij = 0; the decision matrix (D) can be used to obtain the relation matrix through the partial order rule. , representing the results of comparisons between samples under different indicator dimensions, where: ; Ⅱ) Calculation of the reachability matrix (R) of the relation matrix (A) In the formula: B is the multiplication matrix; I is an m-order Boolean matrix with 1s on the diagonal; R is the reachability matrix; The skeleton matrix (S) is obtained by performing Boolean operations on the reachability matrix (R). The calculation process is as follows: ; Ⅲ) UP-type hierarchical graph: the hierarchy is divided according to the result priority, and the rule is R(ei) = T(ei). The extracted samples are placed in a top-down order. The division of the hierarchical graph is determined by the preceding set (Q), the common set (T), and the reachable set (R). Taking the relation matrix (A) as an example, its elements satisfy: the preceding set of ei is Q(ei), which is all elements with a corresponding column of 1; the reachable set of ei is R(ei), which is all elements with a corresponding row of 1; the common set of ei is T(ei), which is the intersection of Q(ei) and R(ei). IV) DOWN type hierarchical diagram, which divides the hierarchy according to cause priority, with the rule Q(ei) = T(ei), and the extracted samples are placed in order from bottom to top; UP type and DOWN type are a set of opposing extraction results, with the Pareto optimal sample at the top level and the worst sample at the bottom level, thus obtaining the evaluation results of the samples and determining the order of the samples.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1) Based on field experiments, an EWM-TOPSIS-AISM integrated evaluation model was constructed to optimize water and nitrogen management of greenhouse drip irrigation tomatoes in Northeast China based on multi-objective optimization, establish the optimal water and nitrogen supply mode, and provide theoretical basis and technical support for the formulation of precision drip irrigation fertilization strategies for greenhouse tomatoes in Northeast China.

[0015] 2) The core advantage of the EWM-TOPSIS-AISM integrated evaluation model lies in its ability to effectively overcome the subjectivity and inconsistency of results of a single model through combined evaluation methods. The Entropy Weight Method (EWM) can objectively determine the weight of each evaluation index and avoid human interference. The TOPSIS model makes full use of the original data information and ranks the samples by calculating the closeness of the samples to the ideal solution, which intuitively reflects the merits of the solutions. The introduction of the Adversarial Explanatory Structure Model (AISM) further enhances the analytical ability of the model. By constructing partial order relations and hierarchical directed graphs, it intuitively shows the adversarial relationship between the merits of the samples. It can not only identify Pareto optimal solutions, but also clearly present the hierarchical structure of different processing solutions, which enhances the interpretability of the results and the decision support strength. 3) The model of this invention is verified by bidirectional directed graphs of UP and DOWN topological hierarchy, and the evaluation results are stable and reliable. Attached Figure Description

[0016] Figure 1 This is a flowchart of the model calculation steps in an embodiment of the present invention; Figure 2 This is a topologically directed graph based on upward and downward types of data for a certain year, as described in an embodiment of the present invention. Figure 3 The data for the following year in this embodiment of the invention is based on a topologically directed graph of upward and downward types. Detailed Implementation

[0017] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The components of the embodiments of the present invention described and shown in the accompanying drawings can typically be arranged and designed in many different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention.

[0019] Experimental conditions and material preparation for this invention embodiment: The "Pink Crown No. 1" tomato variety was selected and planted in a standard greenhouse using a double-row, ridge-based, drip-irrigation system under plastic film. Israeli pressure-compensating drip irrigation tapes were used, with a flow rate of 1.6 L·h per tape. -1 The working pressure is 0.1-0.3 MPa. Two drip irrigation tapes are arranged in parallel in each test plot, with a spacing of 0.5 meters.

[0020] See Figure 1 This is a flowchart of the model calculation steps in an embodiment of the present invention, which includes data collection, weight determination, sorting, grading, and determination of the optimal mode. The weight determination adopts the integrated entropy weight method, the sorting adopts the TOPSIS model, the grading adopts the AISM model, and finally the EWM-TOPSIS-AISM comprehensive evaluation model is constructed. The specific processing steps are as follows: 1) Data collection: Under the same greenhouse conditions, several experimental plots were determined according to different water and nitrogen conditions; specific indicators were measured and recorded during the key growth and harvest periods of tomatoes for subsequent comprehensive evaluation. This invention employs a two-factor completely randomized block design. The experimental factors and levels are as follows: Soil moisture content (upper and lower limits of irrigation) and nitrogen application rate. Soil moisture content (W): Three levels were set: W1: 65%-75% field capacity (θFC), W2: 75%-85% θFC, W3: 85%-95% θFC; Nitrogen application rate (N): Three levels were set: N1: 120 kg·hm² -2 N2: 180 kg·hm -2 N3: 240 kg·hm -2 The above irrigation and nitrogen application levels were fully combined to obtain a total of 9 treatments (N1W1, N1W2, N1W3, N2W1, N2W2, N2W3, N3W1, N3W2, and N3W3). Each treatment was replicated three times, for a total of 27 experimental plots.

[0021] Specific indicator data collection and measurement: During the key growth and harvest periods of tomatoes, the following indicators were measured and recorded for subsequent comprehensive evaluation: Growth indicators: plant height, stem diameter, leaf area index (L), and dry matter mass of each organ (fruit FDM, stem SDM, root RDM, leaf LDM). Yield and quality indicators: number of fruits per plant, single fruit weight, total yield (Y), vitamin C content (VC), soluble solids content (TSS), soluble sugar content (SS), organic acid content (OA), and lycopene content (L). Resource utilization efficiency indicators: nitrogen use efficiency (NUE) and water productivity (WP).

[0022] 2) Weight determination: The weights of each specific indicator measured in step 1) in the comprehensive evaluation system are objectively calculated using the Entropy Weight Method (EWM). The steps for calculating the indicator weights using the Entropy Weight Method (EWM) are as follows: i) Construct the original matrix. Assuming n evaluation indicators are selected to evaluate m samples, the original matrix is ​​constructed using formula (1): Formula (1) In the formula: rij is the data value corresponding to the j-th sample under the i-th indicator; ii) Standardize indicators. Since different indicators have different units and trends, the extreme value method is used to standardize the indicators. The calculation formula (2) for positive indicators (positive indicators refer to those that are as large as possible, including but not limited to output or VC content) is as follows: Formula (2) The calculation formula (3) for negative indicators (negative indicators are those with smaller values, including but not limited to organic acid content) is as follows: Formula (3) In the formula: and They represent the maximum and minimum values ​​under the same indicator; xij is the value corresponding to the j-th sample under the i-th indicator after unification; iii) Calculate the entropy of the index, first according to formula (4) Calculate the proportion Pij of the j-th sample under the i-th indicator; according to formula (5) Calculate the coefficient K; finally, according to formula (6) Calculate the entropy under the i-th index, when hour, ; (ii) The weights of the indicators are determined according to formula (7). Calculate the weight Wi of the i-th indicator, where .

[0023] 3) Sorting: Input the standardized data of each specific indicator weight processing into the TOPSIS model, calculate the closeness of each processing to the ideal solution, and perform preliminary sorting; The calculation steps for the TOPSIS model are as follows: a) Index unification: The same extreme value method as the entropy weight method is used to unify the indexes, resulting in a unified matrix; b) Calculate the weighted standardized matrix: according to formula (8) Calculate the weighted normalization matrix to determine the positive and negative ideal solutions: Positive ideal solution Negative ideal solution Formula (9) Formula (10) In the formula: and This represents the maximum and minimum values ​​under the same indicator; c) Calculate the distance from the sample to the positive and negative ideal solutions: according to formula (11) Calculate the distance from the j-th sample to the positive ideal solution; according to formula (12) Calculate the distance from the j-th sample to the negative ideal solution; d) Calculate the proximity coefficient and sort: according to formula (13) Calculate the proximity coefficient of the j-th sample. The larger the proximity coefficient, the closer the sample is to the ideal point and the better the treatment effect. Sort the different water and nitrogen treatment combinations according to the size of the proximity coefficient.

[0024] 4) Hierarchical structure: Using the AISM model, a relationship matrix and reachability matrix are generated based on the standardized data of each specific indicator. UP-type and DOWN-type topological hierarchical directed graphs are drawn to intuitively display the hierarchical relationship of superiority and inferiority among each processing. The steps for analyzing the hierarchical relationship of samples using the AISM model are as follows: I) For a decision matrix (D) with m columns, there are m different index dimensions; positive indices are labeled p1, p2...pm, and negative indices are labeled q1, q2...qm. For any two rows x and y in the decision matrix (D): Negative indicators include: , 、·····、 , formula (14) Positive indicators include: , 、·····、 ; Formula (15) The partial order relation between elements x and y is denoted as x < y, indicating that element y is superior to element x; that is, given a partially ordered set (D, <), we have , If dj < di, denote aij = 1; if dj > di, denote aij = 0; the decision matrix (D) can be used to obtain the relation matrix through the partial order rule. , representing the results of comparisons between samples under different indicator dimensions, where: ; Formula (16) Ⅱ) Calculation of the reachability matrix (R) of the relation matrix (A) Formula (17) In the formula: B is the multiplication matrix; I is an m-order Boolean matrix with 1s on the diagonal; R is the reachability matrix; The skeleton matrix (S) is obtained by performing Boolean operations on the reachability matrix (R), and the calculation process is as shown in formula (18): ; Formula (18) Ⅲ) UP-type hierarchical graph: the hierarchy is divided according to the result priority, and the rule is R(ei) = T(ei). The extracted samples are placed in a top-down order. The division of the hierarchical graph is determined by the preceding set (Q), the common set (T), and the reachable set (R). Taking the relation matrix (A) as an example, its elements satisfy: the preceding set of ei is Q(ei), which is all elements with a corresponding column of 1; the reachable set of ei is R(ei), which is all elements with a corresponding row of 1; the common set of ei is T(ei), which is the intersection of Q(ei) and R(ei). IV) DOWN type hierarchical diagram, which divides the hierarchy according to cause priority, with the rule Q(ei) = T(ei), and the extracted samples are placed in order from bottom to top; UP type and DOWN type are a set of opposing extraction results, with the Pareto optimal sample at the top level and the worst sample at the bottom level, thus obtaining the evaluation results of the samples and determining the order of the samples.

[0025] 5) Determine the optimal model, and finally, combine the ranking results of the TOPSIS model with the hierarchical structure of the AISM model, compare them with the test target, and jointly verify and determine N2W2 (soil moisture content 75-85% θ). FC Nitrogen application rate: 180 kg·hm -2 () represents the optimal combination of water and nitrogen management models.

[0026] Through the above complete implementation scheme, this invention has been verified through two years of repeated experiments and comprehensively evaluated using the EWM-TOPSIS-AISM model. The optimal water and nitrogen management mode of this invention has been determined to be: maintaining soil moisture content at field capacity (FMC). The nitrogen application rate should be controlled within the range of 75%-85% (W2) and the nitrogen application rate should be controlled at 180 kg·hm. -2 (N2), or N2W2 treatment. This mode can significantly improve fruit quality and water and nitrogen resource utilization efficiency while ensuring high and stable tomato yields, thus achieving cost reduction, efficiency improvement, and sustainable development in greenhouse tomato production.

[0027] Figure 2 and Figure 3 The figures represent the topologically ordered graphs obtained from the two years of experiments. As can be seen from the figures, the N2W2 treatment was optimal in both years, while the N1 series was the least effective. The different hierarchical divisions in the two years may be due to differences in the experimental data leading to different weightings of the indicators, thus affecting the AISM analysis. The figures also show that the better treatments were mostly those with nitrogen in the water, while the worse treatments were all those with low nitrogen. This is consistent with our experience and the conclusions drawn from entropy-weighted TOPSIS, indicating that AISM can effectively evaluate different treatment combinations in agricultural production.

[0028] Table 1 shows the weighting coefficients of each indicator calculated based on EWM in 2020 and 2021.

[0029] The rankings of each treatment calculated based on the EWM-TOPSIS model in 2020 and 2021 are shown in Table 2.

[0030] Table 1 Note: In the table, the indicators VC, TSS, SS, OA, L, Y, NUE, WP, FDM, SDM, RDM, and LDM represent the content of vitamin C, soluble solids, soluble sugar, organic acids, and lycopene in tomato fruit; yield, nitrogen use efficiency, and water productivity; and the dry matter content of tomato fruit, stem, root, and leaves, respectively. The corresponding data on the right represent the weights of these indicators calculated using the entropy weight method (EWM).

[0031] The table shows that during the two-year trial, the highest weighting of an indicator was 12.08%, and the lowest was 6.20%, with most others falling between 8% and 9%, indicating little difference in weight among the indicators. This reflects the good objectivity of the entropy weighting method in assigning weights, preventing any single indicator from having an excessively large weight. Furthermore, this method considers the correlation between indicators, thereby improving the reliability of the evaluation results.

[0032] Table 2 Note: d + d represents the distance between each sample and the positive ideal solution. - T represents the distance between each sample and the negative ideal solution. i This is the proximity coefficient.

[0033] Table 2 shows the ranking results of each treatment calculated using TOPSIS based on the objective weights of each indicator. The TOPSIS calculation simultaneously considers the distance between the chosen solution and the optimal / worst solution, making the results more scientific and reasonable, thus further improving the reliability of the evaluation results. Combining these two methods, compared to the TOPSIS method alone, represents a significant improvement in both data processing and result acquisition, exhibiting more advanced objectivity and accuracy.

[0034] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for grading and evaluating the yield and quality of greenhouse tomatoes and for water and fertilizer management, characterized in that, The process includes data collection, weight determination, sorting, grading, and determining the optimal model. Weight determination uses the integrated entropy weighting method, sorting uses the TOPSIS model, grading uses the AISM model, and finally, an EWM-TOPSIS-AISM integrated evaluation model is constructed. The specific processing steps are as follows: 1) Data collection: Under the same greenhouse conditions, several experimental plots were determined according to different water and nitrogen conditions; specific indicators were measured and recorded during the key growth and harvest periods of tomatoes for subsequent comprehensive evaluation. 2) Weight determination: The weights of the specific indicators measured in step 1) in the comprehensive evaluation system are objectively calculated using the entropy weight method (EWM). 3) Sorting: Input the standardized data of each specific indicator weight processing into the TOPSIS model, calculate the closeness of each processing to the ideal solution, and perform preliminary sorting; 4) Hierarchical structure: Using the AISM model, a relationship matrix and reachability matrix are generated based on the standardized data of each specific indicator. UP-type and DOWN-type topological hierarchical directed graphs are drawn to intuitively display the hierarchical relationship of superiority and inferiority among each processing. 5) Determine the optimal mode: combine the ranking results of the TOPSIS model with the hierarchical structure of the AISM model to jointly determine the water and nitrogen treatment combination with the best overall performance.

2. The water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 1, characterized in that, The number of test plots shall not be less than 27.

3. The water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 1, characterized in that, The specific indicators include, but are not limited to: plant height, stem diameter, leaf area index (L), dry matter of each organ, number of fruits per plant, weight of a single fruit, total yield (Y), vitamin C content (VC), soluble solids content (TSS), soluble sugar content (SS), organic acid content (OA), lycopene content (L), nitrogen use efficiency (NUE), and water productivity (WP).

4. The water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 1, characterized in that, The organs referred to are any one of the following: fruit FDM, stem SDM, root RDM, and leaf LDM.

5. The water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 1, characterized in that, The steps for calculating the index weights using the Entropy Weight Method (EWM) are as follows: i) Construct the original matrix. Assuming n evaluation indicators are selected to evaluate m samples, construct the original matrix: In the formula: rij is the data value corresponding to the j-th sample under the i-th indicator; ii) Indicator standardization: Since different indicators have different units and trends, the extreme value method is used to standardize the indicators. The calculation formula for positive indicators is as follows: The formula for calculating negative indicators is as follows: In the formula: and This represents the maximum and minimum values ​​under the same indicator; xij is the value corresponding to the j-th sample under the i-th indicator after unification; iii) Calculate the entropy of the index, first according to the formula Calculate the weight Pij of the j-th sample under the i-th indicator; according to the formula Calculate the coefficient K; finally, according to the formula... Calculate the entropy under the i-th index, when hour, ; (iii) The weights of the indicators are based on the formula Calculate the weight Wi of the i-th indicator, where .

6. A water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 5, characterized in that, The positive indicators refer to those that are as high as possible, including but not limited to yield or vitamin C content.

7. The water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 5, characterized in that, The negative indicator is one where the smaller the value, the better; this includes, but is not limited to, organic acid content.

8. The water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 1, characterized in that, The calculation steps of the TOPSIS model are as follows: a) Index unification: The same extreme value method as the entropy weight method is used to unify the indexes, resulting in a unified matrix; b) Calculate the weighted normalization matrix: according to the formula Calculate the weighted normalization matrix to determine the positive and negative ideal solutions: Positive ideal solution Negative ideal solution In the formula: and This represents the maximum and minimum values ​​under the same indicator; c) Calculate the distance from the sample to the positive and negative ideal solutions: according to the formula Calculate the distance from the j-th sample to the positive ideal solution; according to the formula... Calculate the distance from the j-th sample to the negative ideal solution; d) Calculate the proximity coefficient and sort: according to the formula Calculate the proximity coefficient of the j-th sample. The larger the proximity coefficient, the closer the sample is to the ideal point and the better the treatment effect. Sort the different water and nitrogen treatment combinations according to the size of the proximity coefficient.

9. A water and fertilizer management method for grading and evaluating the yield and quality of greenhouse tomatoes according to claim 1, characterized in that, The steps for analyzing the hierarchical relationship of samples using the AISM model are as follows: I) For a decision matrix (D) with m columns, there are m different index dimensions; positive indices are labeled p1, p2...pm, and negative indices are labeled q1, q2...qm. For any two rows x and y in the decision matrix (D): Negative indicators include: 、 、······、 ， Positive indicators include: 、 、······、 ； The partial order relation between elements x and y is denoted as x < y, indicating that element y is superior to element x; that is, given a partially ordered set (D, <), we have , If dj < di, denote aij = 1; if dj > di, denote aij = 0; the decision matrix (D) can be used to obtain the relation matrix through the partial order rule. , representing the results of comparisons between samples under different indicator dimensions, where: ； Ⅱ) Calculation of the reachability matrix (R) of the relation matrix (A) In the formula: B is the multiplication matrix; I is an m-order Boolean matrix with 1s on the diagonal; R is the reachability matrix; The skeleton matrix (S) is obtained by performing Boolean operations on the reachability matrix (R). The calculation process is as follows: ； Ⅲ) UP-type hierarchical graph: the hierarchy is divided according to the result priority, and the rule is R(ei) = T(ei). The extracted samples are placed in a top-down order. The division of the hierarchical graph is determined by the preceding set (Q), the common set (T), and the reachable set (R). Taking the relation matrix (A) as an example, its elements satisfy: the preceding set of ei is Q(ei), which is all elements with a corresponding column of 1; the reachable set of ei is R(ei), which is all elements with a corresponding row of 1; the common set of ei is T(ei), which is the intersection of Q(ei) and R(ei). IV) DOWN type hierarchical diagram, which divides the hierarchy according to cause priority, with the rule Q(ei) = T(ei), and the extracted samples are placed in order from bottom to top; UP type and DOWN type are a set of opposing extraction results, with the Pareto optimal sample at the top level and the worst sample at the bottom level, thus obtaining the evaluation results of the samples and determining the order of the samples.

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