A regional agricultural high water efficiency development evaluation method and system

Abstract

The present application belongs to the field of agricultural water-saving and yield-increasing technology of agricultural resource and environment monitoring, and particularly relates to a regional agricultural high water efficiency development evaluation method and system. The method first acquires time series data of crop planting area, yield per unit and water use efficiency of each evaluation unit in the target region; processes the time series data to obtain characteristic parameters representing the change state of each index; analyzes the coordination relationship between crop production indexes and resource utilization indexes based on the characteristic parameters; and determines the agricultural high water efficiency development type of each evaluation unit according to the coordination relationship. The present application provides a specific evaluation system of regional distributed high water efficiency agriculture, effectively solves the problem that the existing evaluation method is difficult to reveal the synergistic trade-off relationship among area, yield and water efficiency, realizes the spatial explicit diagnosis and quantitative evaluation of regional distributed agricultural high water efficiency development, and provides decision support for agricultural regional development and management.

Description

Technical Field

[0001] This invention belongs to the field of agricultural water-saving and yield-increasing technology in agricultural resource and environmental monitoring, specifically involving a method and system for evaluating the high water efficiency development of regional agriculture. Background Technology

[0002] Current evaluations of efficient agricultural water resource utilization and sustainable development in regions still heavily rely on traditional statistical yearbook data and single-point ground monitoring data. Evaluation dimensions are mostly concentrated on single or discrete indicators such as crop yield, farmland water consumption, and irrigation efficiency, making it difficult to form a systematic assessment framework. Existing research either focuses on the spatial expansion analysis of agricultural production scale or independently conducts static calculations of production efficiency and water use efficiency, failing to effectively link the dynamic relationship between cultivated land expansion, improved agricultural production efficiency, and optimized water use efficiency. This makes it impossible to accurately identify the synergistic effects or trade-offs between scale expansion and efficient resource utilization in agricultural development. Furthermore, limited by data sources and evaluation methods, most studies can only achieve coarse-grained evaluations at the administrative unit and annual scales, lacking high spatiotemporal resolution dynamic analysis capabilities based on a combination of remote sensing inversion, mechanistic models, and empirical analysis. This makes it difficult to characterize the spatial heterogeneity and temporal continuity of water efficiency evolution in agricultural systems, resulting in insufficient understanding of the intrinsic driving mechanisms and bottlenecks in the development of high water efficiency in regional agriculture.

[0003] Existing evaluation systems have significant limitations in terms of indicator coupling, dynamic diagnosis, and practical guidance. They cannot achieve precise multi-dimensional coupling of agricultural production scale, production efficiency, and water resource utilization efficiency, nor can they construct dynamic evaluation models with clear mechanisms and reliable empirical evidence. Therefore, they fail to meet the demands of refined water resource management for high-quality agricultural development in the new era. Against the backdrop of large-scale agricultural expansion and increasingly tight water resource constraints, traditional evaluation methods struggle to accurately diagnose the spatiotemporal matching relationship among "area-yield-water efficiency," easily leading to deviations in agricultural development planning and water resource allocation decisions. Therefore, there is an urgent need to construct a regional agricultural high-water-efficiency development evaluation system that integrates multi-source data, multi-dimensional indicators, and multi-scale analysis to achieve organic coupling of area expansion, production efficiency, and water use efficiency, providing precise spatiotemporal diagnosis and scientific decision-making support for the coordinated improvement of "area-yield-water efficiency" in the agricultural system. Summary of the Invention

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

[0005] Therefore, the first objective of this invention is to propose a method for evaluating the development of high water efficiency in regional agriculture, comprising the following steps:

[0006] S1, Obtain time-series data on crop planting area, yield per unit area, and water use efficiency for each evaluation unit within the target area; S2, process the time series data to obtain characteristic parameters that characterize the change state of each indicator; S3, Analyze the coordination relationship between crop production indicators and resource utilization indicators based on the aforementioned characteristic parameters; S4. Determine the agricultural high-water-efficiency development type of each evaluation unit based on the coordination relationship.

[0007] In one embodiment of the present invention, S2 includes: S21. For each evaluation unit, calculate the changing trends of its crop planting area, yield per unit area and water use efficiency within the time series data to obtain the judgment result that each indicator shows an upward or downward trend. S22, combine the trend judgment results of crop planting area, yield per unit area and water use efficiency in each evaluation unit, and classify them into predefined changes; S23, determine the situation where the three trends are in the same direction as synchronous change paths, and determine the situation where the three trends are in opposite directions as asynchronous change paths.

[0008] In one embodiment of the present invention, the variations include: Scenario 1: Increased planting area, increased yield per unit area, and increased water use efficiency; Scenario 2: Decrease in planting area, decrease in yield per unit area, and decrease in water use efficiency; Scenario 3: Increased planting area, increased yield per unit area, and decreased water use efficiency; Scenario 4: Increased planting area, decreased yield per unit area, and increased water use efficiency; Scenario 5: Increased planting area, decreased yield per unit area, and decreased water use efficiency; Situation 6: Decrease in planting area, decrease in yield per unit area, and increase in water use efficiency; Situation 7: Decreased planting area, increased yield per unit area, and decreased water use efficiency; Situation 8: Planting area decreases, yield per unit area increases, and water use efficiency increases. Case 1 and Case 2 are classified as synchronous change paths, while Case 3 to Case 8 are classified as asynchronous change paths.

[0009] In one embodiment of the present invention, S3 includes: S31. Construct an evaluation index system that includes an agricultural production subsystem and a resource and environment subsystem. The agricultural production subsystem shall include at least an index reflecting changes in crop planting area and an index reflecting changes in grain yield per unit area. S32, standardize the data of each indicator and use the coefficient of variation method to determine the weight of each indicator, so as to obtain the standardized data and indicator weights. S33. Based on the standardized data and indicator weights, calculate the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem respectively. Calculate the coupling degree and comprehensive evaluation index based on the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem, and then obtain the coupling coordination degree.

[0010] In one embodiment of the present invention, the formula for calculating the evaluation value of the agricultural production subsystem is as follows: ; in, and For weight values, Let be the standardized value of the first indicator in region i. This is the standardized value of the second indicator in region i; The formula for calculating the evaluation value of the resource and environment subsystem is as follows: ; in, For the region i The standardized value of the third indicator.

[0011] In one embodiment of the present invention, the formula for calculating the coupling coordination degree is: ; in, C For coupling degree, T The formulas for calculating the coupling degree and the comprehensive evaluation index are as follows:

[0012] ; in, E 1 represents the evaluation value of the agricultural production subsystem. E 2 represents the evaluation value of the resource and environment subsystem. α Contribution to the evaluation value of the agricultural production subsystem β Contribution to the evaluation value of the resource and environment subsystem. k This is for adjusting the coefficient.

[0013] In one embodiment of the present invention, the formula for calculating the standardized value is: ; in, X ij Let max{ be the actual value of the j-th indicator in analysis region i; X j} represents the maximum value; min{ X j} is the minimum value. Y ijStandardized values; in, ; ; ; , ; In the formula w j As weight.

[0014] In one embodiment of the present invention, S4 includes: Based on the evaluation values ​​of the agricultural production subsystem, the resource and environment subsystem, and the magnitude and relationship of coupling coordination, the evaluation units are divided into different types of high-water-efficiency agricultural development; these different types of high-water-efficiency agricultural development include production lag type, resource plundering type, low-level equilibrium type, and high-level synergy type. The situation where the evaluation value of the agricultural production subsystem is less than the evaluation value of the resource and environment subsystem is classified as a production lag type. The situation where the evaluation value of the agricultural production subsystem is greater than the evaluation value of the resource and environment subsystem is classified as resource plundering. The case where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are low, and the CCD is also low, is classified as a low-level equilibrium type. A high level of synergy is defined as a situation where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are high, and the CCD is also high.

[0015] To achieve the above objectives, a second aspect of the present invention provides a regional agricultural high-water-efficiency development evaluation system, comprising: The data acquisition module is used to acquire time-series data on crop planting area, yield per unit area, and water use efficiency of each evaluation unit within the target area. The feature parameter generation module is used to process the time series data to obtain feature parameters that characterize the change state of each indicator; The coordination relationship judgment module is used to analyze the coordination relationship between crop production indicators and resource utilization indicators based on the feature parameters. The type determination module is used to determine the agricultural high-water-efficiency development type of each evaluation unit based on the coordination relationship.

[0016] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0017] The methods, systems, and storage media of this invention effectively solve the problem that existing evaluation methods are unable to reveal the synergistic trade-off between crop planting area, yield per unit area, and water use efficiency, and realize spatial explicit diagnosis and quantitative evaluation of high water efficiency development in regional agriculture.

[0018] 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

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a regional agricultural high-water-efficiency development evaluation method according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the steps of the regional agricultural high-water-efficiency development evaluation method according to an embodiment of the present invention; Figure 3 This is a classification diagram of agricultural changes according to an embodiment of the present invention; Figure 4 This is a classification diagram of high-water-efficiency agricultural development types according to an embodiment of the present invention; Figure 5 This is a structural diagram of a regional agricultural high water efficiency development evaluation system according to an embodiment of the present invention. Detailed Implementation

[0020] 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.

[0021] 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.

[0022] The following describes, with reference to the accompanying drawings, a regional agricultural high-water-efficiency development evaluation method and system proposed according to an embodiment of the present invention.

[0023] Example 1 Figure 1 This is a flowchart of a regional agricultural high-water-efficiency development evaluation method according to an embodiment of the present invention.

[0024] like Figure 1As shown, the evaluation method for high water efficiency development in regional agriculture includes the following steps: S1: Acquire multi-source observation and simulation data of the target area, and analyze the time series data of crop planting area, yield per unit area and water use efficiency of each evaluation unit.

[0025] This step aims to construct a data foundation for regional agricultural water efficiency evaluation. By integrating multi-source observation and simulation data, time-series data on crop planting area, yield per unit area, and water use efficiency for each evaluation unit within the target area are obtained. The multi-source observation and simulation data encompasses satellite remote sensing imagery and crop model simulation results, ensuring the spatiotemporal coverage and mechanistic accuracy of the data. During data processing, to address the inconsistency in spatial resolution between data from different sources, spatial matching techniques are employed to unify the data to a benchmark evaluation unit. For example, using a specific grid coordinate system as a benchmark, neighborhood operations or resampling methods are used to upscale or downscale other data, thereby forming a spatially explicit time-series dataset. As one implementation method, the planting area distribution can be extracted from satellite remote sensing imagery, combined with APSIM model simulation to obtain yield and water use efficiency data, and then the coordinates and resolution can be unified using geographic information system software.

[0026] This step, by integrating observational and simulation data, overcomes the limitations of single data sources in terms of spatiotemporal resolution or mechanism representation, providing accurate and consistent multidimensional data support for subsequent synchronization analysis and coupling coordination degree calculation, thus ensuring the reliability and scientific nature of the evaluation of high water efficiency development in regional agriculture.

[0027] S2, based on the time series data, calculate the changing trend characteristics or standardization development level characteristics of crop planting area, yield per unit area and water use efficiency of each evaluation unit.

[0028] Specifically, for trend characteristics, statistical methods can be used to determine the increase or decrease of indicators over time, yielding a qualitative conclusion of rise or fall. For standardization development level characteristics, methods to eliminate the influence of dimensions are used to map the original data to a unified interval for multi-dimensional comparison.

[0029] Specifically, step S2 includes: S21. For each evaluation unit, calculate the changing trends of its crop planting area, yield per unit area and water use efficiency within the time series data to obtain the result of whether each indicator shows an upward or downward trend.

[0030] S22, combine the trend judgment results of crop planting area, yield per unit area and water use efficiency in each evaluation unit, and classify them into predefined changes.

[0031] As one implementation method, variations include: Scenario 1: Increased planting area, increased yield per unit area, and increased water use efficiency; Scenario 2: Decrease in planting area, decrease in yield per unit area, and decrease in water use efficiency; Scenario 3: Increased planting area, increased yield per unit area, and decreased water use efficiency; Scenario 4: Increased planting area, decreased yield per unit area, and increased water use efficiency; Scenario 5: Increased planting area, decreased yield per unit area, and decreased water use efficiency; Situation 6: Decrease in planting area, decrease in yield per unit area, and increase in water use efficiency; Situation 7: Decreased planting area, increased yield per unit area, and decreased water use efficiency; Situation 8: Planting area decreases, yield per unit area increases, and water use efficiency increases. Case 1 and Case 2 are classified as synchronous change paths, while Case 3 to Case 8 are classified as asynchronous change paths.

[0032] S23, determine the situation where the three trends are in the same direction as synchronous change paths, and determine the situation where the three trends are in opposite directions as asynchronous change paths.

[0033] This step eliminates differences in dimensions and orders of magnitude among different indicators by performing unified feature processing on multi-source heterogeneous data, ensuring the compatibility and accuracy of the evaluation system. Simultaneously, the dual feature extraction mechanism of trend and standardization levels can flexibly adapt to the needs of different evaluation scenarios, comprehensively reflecting the spatiotemporal dynamic characteristics of high water efficiency development in agriculture.

[0034] S3. Based on the combined characteristics or coordinated characteristics of the development level of crop planting area, yield per unit area and water use efficiency, determine the high water efficiency agricultural development type of each evaluation unit.

[0035] The core of this step lies in establishing a classification mapping mechanism between multi-source indicator characteristics and agricultural development status. This process aims to reveal the inherent synergistic or trade-off relationships between area expansion, production efficiency, and water use efficiency, where the synergistic characteristics reflect the degree of matching between production development and resource utilization. As one implementation method, multiple change scenarios can be predefined. Changes where the three trends are in the same direction are identified as synchronous change paths, representing ideal high-water-efficiency, sustainable, and intensive development paths. Changes with opposite trends are identified as asynchronous change paths, representing areas for improvement.

[0036] Specifically, step S3 includes the following steps: S31. Construct an evaluation index system that includes an agricultural production subsystem and a resource and environment subsystem. The agricultural production subsystem shall include at least an index reflecting changes in crop planting area and an index reflecting changes in grain yield per unit area.

[0037] The formula for calculating the evaluation value of the agricultural production subsystem is as follows: ; in, and For weight values, For the region i The standardized value of the first indicator. For the region i The standardized value of the second indicator; The formula for calculating the evaluation value of the resource and environment subsystem is: ; in, For the region i The standardized value of the third indicator.

[0038] S32 standardizes the data of each indicator and uses the coefficient of variation method to determine the weight of each indicator, thus obtaining the standardized data and indicator weights.

[0039] As one implementation method, the extreme value method can be used to standardize the index data. The specific formula is as follows:

[0040] In the formula, X ij For the analysis area i The Middle j The actual value of each indicator; max{ X j} represents the maximum value; min{ X j} is the minimum value. Y ij These are standardized values.

[0041] in, ; ; ; , ; In the formula w j As weight.

[0042] Through the above processing, the original observation data is transformed into measurable feature parameters, providing a data foundation for subsequent type determination.

[0043] S33. Based on the standardized data and indicator weights, calculate the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem respectively. Calculate the coupling degree and comprehensive evaluation index based on the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem, and then obtain the coupling coordination degree.

[0044] The formula for calculating the coupling coordination degree is as follows: ; in, C For coupling degree, T The formulas for calculating the coupling degree and the comprehensive evaluation index are as follows:

[0045] ; in, E 1 represents the evaluation value of the agricultural production subsystem. E 2 represents the evaluation value of the resource and environment subsystem. α Contribution to the evaluation value of the agricultural production subsystem β Contribution to the evaluation value of the resource and environment subsystem. k This is for adjusting the coefficient.

[0046] This step overcomes the limitations of single-indicator evaluation through the coupled analysis of multi-dimensional features, enabling precise spatiotemporal diagnosis and quantitative evaluation of high water efficiency development in regional agriculture, and providing decision optimization support for the development and governance of agricultural regions.

[0047] S4 generates spatial distribution results containing the high water efficiency agricultural development type of each evaluation unit, which is used for diagnosis and decision-making regarding high water efficiency development in regional agriculture.

[0048] This process aims to transform abstract evaluation and classification results into geographically referenced spatial data, thereby visually demonstrating the spatial clustering characteristics and heterogeneous distribution of different development types within the target area.

[0049] Specifically, based on the evaluation values ​​of the agricultural production subsystem, the resource and environment subsystem, and the magnitude and relationship of coupling coordination, the evaluation units are divided into different types of high-water-efficiency agricultural development; these different types of high-water-efficiency agricultural development include production-lagging type, resource-exploiting type, low-level equilibrium type, and high-level collaborative type. The situation where the evaluation value of the agricultural production subsystem is less than the evaluation value of the resource and environment subsystem is classified as a production lag type. The situation where the evaluation value of the agricultural production subsystem is greater than the evaluation value of the resource and environment subsystem is classified as resource plundering. The case where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are low, and the CCD is also low, is classified as a low-level equilibrium type. A high level of synergy is defined as a situation where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are high, and the CCD is also high.

[0050] By generating spatial distribution results, it is possible to achieve a spatial explicit diagnosis of the high water efficiency development status of regional agriculture, reveal the development differences and synergistic relationships between different regions, provide an intuitive spatial decision-making basis for formulating differentiated agricultural water-saving and yield-increasing management strategies, and enhance the practical application value and guiding significance of the evaluation results.

[0051] As one embodiment, the method proposed in this invention can perform only steps S1 and S2 to evaluate high water efficiency agriculture in each region based on synchronicity and asynchronicity.

[0052] In one implementation, the method proposed in this invention can perform only steps S1, S3, and S4 to evaluate high-water-efficiency agriculture in each region based on the evaluation value and coupling coordination degree.

[0053] Example 2 like Figure 2 As shown below, the method proposed in this invention will be described using Xinjiang and the Hexi Corridor region of China as examples.

[0054] Data on maize planting area, yield, and water use efficiency at various grid scales in the region from 2000 to 2019 were obtained through APSIM model simulation and satellite observation analysis. The planting area distribution data had a resolution of 1 km and a coordinate system of Asia North Albers Equal Area Conic; the yield and water use efficiency data had a resolution of 5′ and a coordinate system of WGS1984. Using Geographic Information System (GIS) software, based on the 5′ grid and WGS1984 coordinate system, the planting area distribution data was upscaled to a 5′ grid cell and WGS1984 coordinate system. The temporal trends of each indicator were calculated to obtain their upward or downward trends, and they were classified according to eight types of synchronicity or asynchronicity. Figure 3 Where "A" represents "planting area", "Y" represents "yield per unit area", "W" represents "water use efficiency", "I" represents "increase", "D" represents "decrease", and "&" represents "and". This diagram does not show a case where "AD&Y-D&W-D" (i.e., all three decrease) occurs; this corresponds to case two in Example 1.

[0055] It is worth noting that if the spatial resolutions of the multi-source data are inconsistent, one resolution should be used as a benchmark, and the other data should be upscaled or downscaled. Upscaling uses the neighborhood operation method in the geographic information system software, while downscaling uses the nearest neighbor method in the resampling function of the geographic information system software.

[0056] It can be seen that most areas exhibit synchronous growth, indicating that the development of high-efficiency agriculture in these areas is progressing well. The remaining asynchronous situations mainly consist of a decrease in planting area but an increase in yield per unit area and water use efficiency, reflecting a situation where there is room for improvement in agricultural water efficiency development.

[0057] The area, yield, and water use efficiency data of each 5′ grid were exported using Geographic Information System (GIS) software, and then calculated grid-by-grid according to Method 2. E 1. E 2. And CCD, according to the evaluation principles, the coupling coordination degree is classified. Specifically, as follows... Figure 4 As shown, in this example, the four evaluation results account for almost the same proportion.

[0058] Example 3 like Figure 5 As shown, a regional agricultural high-water-efficiency development evaluation system 10 includes: The data acquisition module 100 is used to acquire time-series data on crop planting area, yield per unit area, and water use efficiency of each evaluation unit within the target area.

[0059] The data acquisition module 100 integrates multi-source observation and simulation data to obtain time-series data on crop planting area, yield per unit area and water use efficiency of each evaluation unit in the target area. This multi-source observation and simulation data covers satellite remote sensing images and crop model simulation results, ensuring the spatiotemporal coverage and mechanism accuracy of the data.

[0060] The feature parameter generation module 200 is used to process the time series data to obtain feature parameters that characterize the change state of each indicator.

[0061] The feature parameter generation module 200 first calculates the changing trends of crop planting area, yield per unit area and water use efficiency in the time series data for each evaluation unit, and obtains the results of determining whether each indicator shows an upward or downward trend. Then, it combines the trend determination results of crop planting area, yield per unit area and water use efficiency in each evaluation unit and classifies them into predefined changes.

[0062] Specifically, the changes include: Scenario 1: Increased planting area, increased yield per unit area, and increased water use efficiency; Scenario 2: Decrease in planting area, decrease in yield per unit area, and decrease in water use efficiency; Scenario 3: Increased planting area, increased yield per unit area, and decreased water use efficiency; Scenario 4: Increased planting area, decreased yield per unit area, and increased water use efficiency; Scenario 5: Increased planting area, decreased yield per unit area, and decreased water use efficiency; Situation 6: Decrease in planting area, decrease in yield per unit area, and increase in water use efficiency; Situation 7: Decreased planting area, increased yield per unit area, and decreased water use efficiency; Situation 8: Planting area decreases, yield per unit area increases, and water use efficiency increases. Case 1 and Case 2 are classified as synchronous change paths, while Case 3 to Case 8 are classified as asynchronous change paths.

[0063] When the trends of the three are in the same direction, they are identified as synchronous change paths; when the trends of the three are in opposite directions, they are identified as asynchronous change paths.

[0064] The coordination relationship judgment module 300 is used to analyze the coordination relationship between crop production indicators and resource utilization indicators based on the feature parameters.

[0065] The formula for calculating the evaluation value of the agricultural production subsystem is as follows: ; in, and For weight values, For the region i The standardized value of the first indicator. For the region i The standardized value of the second indicator; The formula for calculating the evaluation value of the resource and environment subsystem is: ; in, For the region i The standardized value of the third indicator.

[0066] The type determination module 400 is used to determine the agricultural high water efficiency development type of each evaluation unit based on the coordination relationship.

[0067] Specifically, the type determination module 400 divides the evaluation unit into different types of high-water-efficiency agricultural development based on the evaluation values ​​of the agricultural production subsystem, the resource and environment subsystem, and the magnitude and relationship of the coupling coordination degree; the different types of high-water-efficiency agricultural development include production lag type, resource plundering type, low-level equilibrium type, and high-level synergy type. The situation where the evaluation value of the agricultural production subsystem is less than the evaluation value of the resource and environment subsystem is classified as a production lag type. The situation where the evaluation value of the agricultural production subsystem is greater than the evaluation value of the resource and environment subsystem is classified as resource plundering. The case where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are low, and the CCD is also low, is classified as a low-level equilibrium type. A high level of synergy is defined as a situation where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are high, and the CCD is also high.

[0068] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables an evaluation method for achieving high water efficiency in agriculture in the aforementioned region.

[0069] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are 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.

[0070] 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 evaluating the development of high water efficiency in regional agriculture, characterized in that, Includes the following steps: S1, Obtain time-series data on crop planting area, yield per unit area, and water use efficiency for each evaluation unit within the target area; S2, process the time series data to obtain characteristic parameters that characterize the change state of each indicator; S3, Analyze the coordination relationship between crop production indicators and resource utilization indicators based on the aforementioned characteristic parameters; S4. Determine the agricultural high-water-efficiency development type of each evaluation unit based on the coordination relationship.

2. The method according to claim 1, characterized in that, S2 includes: S21. For each evaluation unit, calculate the changing trends of its crop planting area, yield per unit area and water use efficiency within the time series data to obtain the judgment result that each indicator shows an upward or downward trend. S22, combine the trend judgment results of crop planting area, yield per unit area and water use efficiency in each evaluation unit, and classify them into predefined changes; S23, determine the situation where the three trends are in the same direction as synchronous change paths, and determine the situation where the three trends are in opposite directions as asynchronous change paths.

3. The method according to claim 2, characterized in that, The changes include: Scenario 1: Increased planting area, increased yield per unit area, and increased water use efficiency; Scenario 2: Decrease in planting area, decrease in yield per unit area, and decrease in water use efficiency; Scenario 3: Increased planting area, increased yield per unit area, and decreased water use efficiency; Scenario 4: Increased planting area, decreased yield per unit area, and increased water use efficiency; Scenario 5: Increased planting area, decreased yield per unit area, and decreased water use efficiency; Situation 6: Decrease in planting area, decrease in yield per unit area, and increase in water use efficiency; Situation 7: Decreased planting area, increased yield per unit area, and decreased water use efficiency; Situation 8: Planting area decreases, yield per unit area increases, and water use efficiency increases. Case 1 and Case 2 are classified as synchronous change paths, while Case 3 to Case 8 are classified as asynchronous change paths.

4. The method according to claim 1, characterized in that, S3 includes: S31. Construct an evaluation index system that includes an agricultural production subsystem and a resource and environment subsystem. The agricultural production subsystem shall include at least an index reflecting changes in crop planting area and an index reflecting changes in grain yield per unit area. S32, standardize the data of each indicator and use the coefficient of variation method to determine the weight of each indicator, so as to obtain the standardized data and indicator weights. S33. Based on the standardized data and indicator weights, calculate the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem respectively. Calculate the coupling degree and comprehensive evaluation index based on the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem, and then obtain the coupling coordination degree.

5. The method according to claim 4, characterized in that, The formula for calculating the evaluation value of the agricultural production subsystem is as follows: ； in, and For weight values, Let be the standardized value of the first indicator in region i. This is the standardized value of the second indicator in region i; The formula for calculating the evaluation value of the resource and environment subsystem is as follows: ； in, This is the standardized value of the third indicator in region i.

6. The method according to claim 5, characterized in that, The formula for calculating the coupling coordination degree is: ； in, C For coupling degree, T The formulas for calculating the coupling degree and the comprehensive evaluation index are as follows: ； in, E 1 represents the evaluation value of the agricultural production subsystem. E 2 represents the evaluation value of the resource and environment subsystem. α Contribution to the evaluation value of the agricultural production subsystem β Contribution to the evaluation value of the resource and environment subsystem. k This is for adjusting the coefficient.

7. The method according to claim 5, characterized in that, The formula for calculating the standardized value is: ； in, X ij Let max{ be the actual value of the j-th indicator in analysis region i; X j } represents the maximum value; min{ X j } is the minimum value. Y ij Standardized values; in, ； ； ； , ； In the formula w j For weights.

8. The method according to claim 1, characterized in that, S4 includes: Based on the evaluation values ​​of the agricultural production subsystem, the resource and environment subsystem, and the magnitude and relationship of coupling coordination, the evaluation units are divided into different types of high-water-efficiency agricultural development; these different types of high-water-efficiency agricultural development include production lag type, resource plundering type, low-level equilibrium type, and high-level synergy type. The situation where the evaluation value of the agricultural production subsystem is less than the evaluation value of the resource and environment subsystem is classified as a production lag type. The situation where the evaluation value of the agricultural production subsystem is greater than the evaluation value of the resource and environment subsystem is classified as resource plundering. The case where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are low, and the CCD is also low, is classified as a low-level equilibrium type. A high level of synergy is defined as a situation where both the evaluation values ​​of the agricultural production subsystem and the resource and environment subsystem are high, and the CCD is also high.

9. A regional agricultural high-water-efficiency development evaluation system, characterized in that, include: The data acquisition module is used to acquire time-series data on crop planting area, yield per unit area, and water use efficiency of each evaluation unit within the target area. The feature parameter generation module is used to process the time series data to obtain feature parameters that characterize the change state of each indicator; The coordination relationship judgment module is used to analyze the coordination relationship between crop production indicators and resource utilization indicators based on the feature parameters. The type determination module is used to determine the agricultural high-water-efficiency development type of each evaluation unit based on the coordination relationship.

10. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as claimed in any one of claims 1-8.

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