A dry land spring maize configuration method and system based on efficient rainwater utilization

Abstract

The application discloses a dry land spring maize configuration method and system based on efficient rainwater utilization, relates to the rainwater utilization configuration technical field, and is used for solving the problems of uneven rainwater resource distribution and low utilization efficiency. The plant height uniformity rate and yield reduction of dry land spring maize on the harvest day are acquired through a historical database, scheduling characteristics are calculated, it is judged whether component distance antagonistic relations exist in the rainwater resources of multiple land blocks, if the component distance antagonistic relations exist, candidate scheduling land blocks are screened, water quality data are collected, near-source water transfer matching values are calculated to determine the scheduling land blocks, component distance antagonistic relation data are acquired, rainwater content weight and path time consumption weight are generated, rainwater resource configuration rules are determined by monitoring rainwater quantity and irrigation pipeline transmission time consumption, combining the rainwater content weight and the path time consumption weight, and accurate rainwater matching and efficient utilization are realized.

Description

Technical Field

[0001] This invention relates to the field of rainwater utilization configuration technology, and more specifically, to a method and system for configuring spring maize in dryland areas based on efficient rainwater utilization. Background Technology

[0002] As an important grain crop in my country, the yield and quality of spring maize in dryland areas are greatly affected by rainfall conditions. Especially in multi-plot planting environments, the effective scheduling and utilization of rainwater resources is the key to ensuring stable growth and high yield of maize. In existing technologies, rainwater resource scheduling mostly relies on a single indicator for allocation, mainly focusing on the allocation of nearby water sources.

[0003] The existing technology has the following shortcomings:

[0004] Currently, existing methods do not adequately consider water quality data and irrigation path time, lack comprehensive regulation and analysis of rainwater resources among multiple plots, and are difficult to accurately identify the compositional distance antagonistic relationship between rainwater resources, resulting in uneven distribution of rainwater resources and low utilization efficiency. Therefore, a method and system for configuring spring maize in dryland based on efficient rainwater utilization is proposed.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for configuring spring maize in dryland areas based on efficient rainwater utilization. This method utilizes historical database analysis, sensor data acquisition, and multi-dimensional weighted model fusion techniques to address the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for configuring spring maize in dryland areas based on efficient rainwater utilization, comprising the following steps:

[0008] Step S1: Call the historical database to obtain the uniformity of plant height on the harvest day of spring maize in dryland and the reduction in spring maize yield, and calculate the scheduling characteristics of rainwater resources in multiple plots at present.

[0009] Step S2: Determine whether there is a component distance antagonism relationship between the rainwater resources of multiple plots through scheduling characteristics. If there is a component distance antagonism relationship between the rainwater resources of multiple plots, select candidate scheduling plots based on the uniformity of plant height on the harvest day of spring corn in dryland.

[0010] Step S3: Collect water quality data of candidate scheduling sites through water quality acquisition sensors, calculate the near-source water diversion matching value based on the water quality data and determine the scheduling sites, and use the underground soil moisture sensors laid on the scheduling sites to obtain the component distance antagonistic relationship data of the scheduling sites.

[0011] Step S4: Generate rainwater content weight and path time weight based on the component distance adversarial relationship data. Obtain the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline based on the bidirectional monitoring of rainwater path. Combine the corresponding rainwater content weight and path time weight to determine the rainwater resource allocation rules for the scheduling plot.

[0012] In a preferred embodiment, in step S1, the dryland spring maize harvest day is the harvest time point of the dryland spring maize collected in the past. A past period is selected as the historical time period. The historical database is accessed to retrieve the plant height of multiple dryland spring maize harvest days in the historical time period. The standard deviation of the plant height for each dryland spring maize harvest day is calculated and its reciprocal is taken as the average plant height value of the corresponding dryland spring maize harvest day.

[0013] In a preferred embodiment, in step S1, the average value of the uniform plant height on the harvest day of dryland spring maize is selected as the marked uniform height value, the sum of the marked uniform height value and 1 is used as the calibration benchmark, and the ratio of the marked uniform height value to the calibration benchmark is used as the uniformity rate of plant height on the harvest day of dryland spring maize.

[0014] The dryland spring maize harvest date where the uniform height of the marker is located is taken as the marker harvest date. The spring maize yield of the marker harvest date is obtained by subtracting the spring maize yield of the previous harvest date from the spring maize yield of the marker harvest date collected from the historical database.

[0015] In a preferred embodiment, in step S1, the logarithm of the spring maize yield reduction on the marked harvest date is taken as the spring maize yield reduction rate on the marked harvest date.

[0016] The difference between the yield reduction rate of spring maize on the marked harvest date and the uniformity of plant height of spring maize on the harvest date in dryland areas is used as the current scheduling characteristics of rainwater resources in multiple plots.

[0017] In a preferred embodiment, in step S2, when the scheduling characteristics exceed a preset scheduling threshold, it is determined that there is a component distance antagonistic relationship between the rainwater resources of the current multiple plots; otherwise, it is determined that there is no component distance antagonistic relationship between the rainwater resources of the current multiple plots.

[0018] When there is a component distance antagonism in the rainwater resources of multiple plots, the average plant height of each plot to be tested in the multiple plot areas during the marked harvest day is calculated, and plots with plant height lower than the average plant height are selected as candidate plots for scheduling.

[0019] In a preferred embodiment, in step S3, based on the nutrient concentration of near-source rainwater collected by the water quality acquisition sensor and compared with the preset minimum effective nutrient threshold, the nutrient supply item set of the current rainwater sample is divided. Then, the set of dryland spring maize nutrient requirements corresponding to the nutrient supply item set of the current rainwater sample is called from the crop database to construct a matching supply and demand pair. The supply and demand deviation normalization formula of rainwater nutrient components is substituted into the formula to calculate the near-source water diversion matching value.

[0020] The matching value of near-source water diversion is compared with the preset matching threshold. If the matching value of near-source water diversion is greater than or equal to the preset matching threshold, it is not marked as a dispatched plot.

[0021] If the matching value of the near-source water diversion is less than the preset matching threshold, it is marked as a dispatched plot.

[0022] In a preferred embodiment, in step S3, the component distance adversarial relationship data of the dispatched plot includes the matching value of rainwater components in each rainwater harvesting device with the required components of the dispatched plot, as well as the distance between each rainwater harvesting device and the dispatched plot.

[0023] The system collects the required nutrient components of the scheduled plots using underground soil moisture sensors. It also extracts the concentration values ​​of various nutrients detected in the current rainwater of each rainwater collection device using water quality sensors, constructs a set of available nutrients, extracts the intersection of the required items of the scheduled plots and the available items in the rainwater, and calculates the supply-demand difference value for each nutrient in the intersection. Finally, it uses a normalized weighted formula to calculate the matching value between the rainwater components in each rainwater collection device and the required components of the scheduled plots.

[0024] The geographical location information of all rainwater harvesting devices and each dispatched plot is obtained by underground soil moisture sensors and represented by two-dimensional plane coordinates. Based on the two-dimensional plane coordinates, the shortest water resource transmission path between the rainwater harvesting devices and each dispatched plot is constructed, and the distance between each rainwater harvesting device and the dispatched plot is obtained.

[0025] In a preferred embodiment, in step S4, the rainwater components in each rainwater collection device, the demand components of the dispatched plot, and the distance between each rainwater collection device and the dispatched plot are standardized to generate rainwater content weight and path time weight.

[0026] Based on bidirectional monitoring of rainwater path, the rainwater volume of each rainwater collection device and the transmission time of the corresponding irrigation pipeline are obtained;

[0027] By deploying water level sensors inside each rainwater harvesting device, the current liquid level height in the rainwater harvesting device is detected in real time. Combined with the cross-sectional area data of the device, the amount of rainwater collected by each rainwater harvesting device is obtained through the volume calculation formula.

[0028] Based on the actual irrigation path between the rainwater harvesting device and the plot of land to be used, the difference between the irrigation start time recorded by the timer and the feedback time of the water volume sensor at the end of the irrigation is calculated to obtain the transmission time of the irrigation pipeline.

[0029] In a preferred embodiment, in step S4, after standardizing the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline, the matching score between the scheduling plot and each rainwater collection device is obtained by combining the corresponding rainwater content weight and the path time weight.

[0030] Based on the compatibility score between the dispatched plots and each rainwater collection device and the near-source water diversion matching value of the dispatched plots, the dispatched plots are sorted in ascending order according to the near-source water diversion matching value. The plot with the highest compatibility score between the dispatched plot and each rainwater collection device is selected as the rainwater collection device for the rainwater resource allocation of the dispatched plot.

[0031] If the rainwater collection device corresponding to the largest dispatched plot and the matching score of each rainwater collection device is a configured rainwater collection device, then the process will continue to the next rainwater collection device until all rainwater resources of the dispatched plot are configured.

[0032] If all rainwater harvesting devices corresponding to the largest dispatched plot and the matching scores of each rainwater harvesting device have been configured, then the current dispatched plot is set to be idle, and rainwater resource allocation rules are not applied to the dispatched plot.

[0033] A dryland spring maize configuration system based on efficient rainwater utilization includes a historical recall module, a plot screening module, a weight generation module, and a rule determination module, with each module connected by electrical signals.

[0034] The history recall module is used to collect the uniformity of plant height on the harvest day of spring maize in dryland and the reduction in spring maize yield, and then transmit it to the plot screening module.

[0035] The plot screening module receives the uniformity of plant height on the harvest day of spring maize in dryland and the spring maize yield reduction calculation scheduling characteristics, and determines whether there is a component distance adversarial relationship of rainwater resources in multiple plots and screens candidate scheduling plots, and sends the candidate scheduling plots to the weight generation module.

[0036] The weight generation module collects water quality data of candidate scheduling sites and calculates the near-source water transfer matching value. After determining the scheduling sites based on the near-source water transfer matching value, it obtains component distance adversarial relationship data. It then uses the component distance adversarial relationship data to calculate and generate rainwater content weight and path time weight, which are then uploaded to the rule determination module.

[0037] The rule determination module monitors and obtains the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline, and determines the rainwater resource allocation rules for the scheduling plots by combining the corresponding rainwater content weight and path time weight.

[0038] The technical effects and advantages of this invention are as follows:

[0039] This invention obtains the uniformity of plant height and yield reduction of spring maize on the harvest day in dryland from a historical database and calculates scheduling characteristics. Based on these characteristics, it determines whether there is a component distance antagonism relationship among rainwater resources in multiple plots. When such a relationship exists, candidate plots for scheduling are selected. Water quality data for these candidate plots is collected, and a near-source water diversion matching value is calculated based on the water quality data to determine the scheduling plots. The component distance antagonism data for each scheduling plot are used to generate rainwater content weights and path time weights. By monitoring the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline, and combining the corresponding rainwater content weights and path time weights, rainwater resource allocation rules for the scheduling plots are determined. This achieves the beneficial effects of matching rainwater to plots and efficient rainwater utilization, improving the accuracy of rainwater resource scheduling. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating the implementation of a method for configuring spring maize in dryland areas based on efficient rainwater utilization, as described in this invention.

[0041] Figure 2 This is a modular framework diagram of a dryland spring maize configuration system based on efficient rainwater utilization according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0043] This invention obtains the uniformity of plant height and yield reduction of spring maize on the harvest day in dryland from a historical database and calculates scheduling characteristics. Based on these characteristics, it determines whether there is a component distance antagonism relationship among rainwater resources in multiple plots. When such a relationship exists, candidate plots for scheduling are selected. Water quality data for these candidate plots is collected, and a near-source water diversion matching value is calculated based on the water quality data to determine the scheduling plots. The component distance antagonism data for each scheduling plot is used to generate rainwater content weights and path time weights. By monitoring the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline, and combining the corresponding rainwater content weights and path time weights, rainwater resource allocation rules for the scheduling plots are determined, thereby achieving the beneficial effects of matching rainwater to plots and efficient rainwater utilization.

[0044] Example 1: A method for configuring spring maize in dryland areas based on efficient rainwater utilization, such as... Figure 1 As shown, it includes the following steps:

[0045] Step S1: Call the historical database to obtain the uniformity of plant height on the harvest day of spring maize in dryland and the reduction in spring maize yield, and calculate the scheduling characteristics of rainwater resources in multiple plots at present.

[0046] Step S2: Determine whether there is a component distance antagonism relationship between the rainwater resources of multiple plots through scheduling characteristics. If there is a component distance antagonism relationship between the rainwater resources of multiple plots, select candidate scheduling plots based on the uniformity of plant height on the harvest day of spring corn in dryland.

[0047] Step S3: Collect water quality data of candidate scheduling sites through water quality acquisition sensors, calculate the near-source water diversion matching value based on the water quality data and determine the scheduling sites, and use the underground soil moisture sensors laid on the scheduling sites to obtain the component distance antagonistic relationship data of the scheduling sites.

[0048] Step S4: Generate rainwater content weight and path time weight based on the component distance adversarial relationship data. Obtain the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline based on the bidirectional monitoring of rainwater path. Combine the corresponding rainwater content weight and path time weight to determine the rainwater resource allocation rules for the scheduling plot.

[0049] The specific implementation is as follows:

[0050] In step S1, the harvest day of dryland spring maize is the harvest time point of dryland spring maize collected in the historical database in the past. A period of time in the past is selected as the historical time period. The historical database is accessed to retrieve the plant height of multiple dryland spring maize harvest days in the historical time period. The standard deviation of the plant height of each dryland spring maize harvest day is calculated and its reciprocal is taken as the average plant height of the corresponding dryland spring maize harvest day.

[0051] The higher the standard deviation of plant height on the harvest day of dryland spring maize, the lower the uniformity of plant height on the corresponding harvest day, and the more uneven the plant height on the corresponding harvest day.

[0052] The average value of plant height on the harvest day of dryland spring maize was selected as the marked height uniformity value. The sum of the marked height uniformity value and 1 was used as the calibration benchmark. The ratio of the marked height uniformity value to the calibration benchmark was used as the plant height uniformity rate on the harvest day of dryland spring maize.

[0053] The lower the uniformity of plant height on the harvest day of spring maize in dryland areas, and the more uneven the trend of plant height on the harvest day of spring maize in dryland areas over a historical period, the more necessary it is to implement rainwater resource allocation measures.

[0054] The dryland spring maize harvest date where the uniform height of the marker is located is taken as the marker harvest date. The spring maize yield of the marker harvest date is collected from the historical database and the spring maize yield of the previous harvest date is subtracted to obtain the spring maize yield reduction of the marker harvest date.

[0055] The greater the reduction in spring corn yield, the lower the yield of spring corn at the marked harvest date, and the greater the need for rainwater resource allocation.

[0056] It should be noted that the yield reduction of spring maize on the marked harvest date may be negative, indicating that the yield of spring maize on the marked harvest date has increased, and no rainwater resource allocation treatment is required.

[0057] The logarithm of the yield reduction of spring maize on the marked harvest date is taken as the yield reduction rate of spring maize on the marked harvest date;

[0058] The difference between the yield reduction rate of spring maize on the marked harvest date and the uniformity of plant height of spring maize on the harvest date in dryland is used as the current scheduling characteristics of rainwater resources in multiple plots.

[0059] The higher the yield reduction rate of spring maize on the marked harvest date in a multi-plot area during the historical period, or the lower the uniformity of plant height of spring maize in dryland areas on the harvest date, the lower the rainwater utilization efficiency in the multi-plot area during the historical period, the greater the current rainwater resource scheduling characteristics in the multi-plot area, and the more necessary the current rainwater resource scheduling in the multi-plot area.

[0060] It should be noted that the historical database is a comprehensive data system used to collect and manage rainwater resource information for different plots of land. This includes accessing historical data on rainwater resources from multiple plots of land, such as the uniformity of plant height on the day of spring corn harvest in dryland areas and changes in spring corn yield.

[0061] In step S2, when determining whether there is a component distance antagonistic relationship between the rainwater resources of multiple plots through scheduling features, the scheduling features are compared with a preset scheduling threshold. If the scheduling features exceed the scheduling threshold, it is determined that there is a component distance antagonistic relationship between the rainwater resources of multiple plots; otherwise, it is determined that there is no component distance antagonistic relationship between the rainwater resources of multiple plots.

[0062] When there is a component distance antagonism in the rainwater resources of multiple plots, the average plant height of each plot to be tested in the multiple plot areas during the marked harvest day is calculated, and plots with plant height lower than the average plant height are selected as candidate plots for scheduling.

[0063] It should be noted that when the scheduling characteristic is larger, the spatial heterogeneity of rainwater resources across multiple plots is more significant, and the current scheduling rules are more likely to have a component distance antagonistic relationship. Conversely, when the scheduling characteristic is smaller, the distribution of rainwater resources among multiple plots tends to be more consistent, and the current scheduling rules are less likely to have a component distance antagonistic relationship.

[0064] In step S3, water quality data of the candidate scheduling plots are collected using water quality acquisition sensors;

[0065] Calculate the matching value for near-source water transfer based on water quality data;

[0066] The calculation process of the near-source water transfer matching value is as follows: based on the nutrient concentration of the near-source rainwater collected by the water quality acquisition sensor, the nutrient supply item set of the current rainwater sample is divided according to the comparison with the preset minimum effective nutrient threshold. Then, the set of dryland spring maize demand nutrients corresponding to the nutrient supply item set of the current rainwater sample is called in the crop database to construct a matching supply and demand pair. The supply and demand deviation normalization formula of rainwater nutrient components is substituted into the formula to calculate the near-source water transfer matching value.

[0067] It should be noted that near-source rainwater refers to rainwater resources provided by rainwater collection devices with physical water conveyance capabilities within the preset water diversion radius of the dispatched plot. The classification rules and minimum effective nutrient thresholds were set by the researchers of this experiment based on the physiological nutrient curves of spring maize at different growth stages and the average nutrient stability model of rainwater sources, which will not be elaborated here.

[0068] The set of nutrient contributions for the current rainwater sample is expressed as follows:

[0069] ;

[0070] In the formula, It is a collection of nutrients that can be provided by nearby rainwater. The concentration of the kth nutrient in near-source rainwater is shown. This represents the minimum effective nutrient threshold for component k.

[0071] Where k = 1, 2, ..., K, and K is the number of available nutrient types detected in the current rainwater sample;

[0072] Specifically, the normalized formula for the supply and demand deviation of rainwater nutrients is expressed as follows:

[0073] ;

[0074] In the formula, This is the near-source water diversion matching value between plot i and the near-source rainwater, i.e., the near-source water diversion matching value (ranging from 0 to 1, with the closer to 1 indicating a better match). Let k be the required concentration of the k-th nutrient for spring maize in dryland plot i. The required weights for nutrient type k are set based on the physiological model of spring maize in dryland areas. The intersection of the set of nutrient supplies and the set of land demand.

[0075] Where i = 1, 2, ..., I, and I is the number of candidate plots for scheduling that participate in the current round of scheduling determination;

[0076] It should be noted that the numerator represents the weighted absolute deviation of the difference between nutrient supply and demand, and the denominator is the weighted total of the corresponding demand, which serves as a normalization factor and will not be elaborated here.

[0077] Among them, the larger the near-source water diversion matching value, the higher the supply and demand matching degree between the current plot and its corresponding near-source rainwater sample in terms of nutrient concentration and composition structure, and the stronger the ability of rainwater resources to meet the needs of crop growth.

[0078] The matching value of near-source water diversion is compared with the preset matching threshold. If the matching value of near-source water diversion is greater than or equal to the preset matching threshold, it indicates that the plot and its corresponding near-source rainwater are highly matched in terms of nutrient supply and demand, and therefore the plot is not marked as a diversion plot.

[0079] If the matching value of the near-source water diversion is less than the preset matching threshold, it indicates that there is a large nutrient supply and demand deviation between the plot and the corresponding near-source rainwater, and the current water supply cannot meet the needs of crops, so it is marked as a plot to be diverted.

[0080] It should be noted that the matching threshold was set by the researchers based on the nutritional requirements model of spring maize in dryland at different growth stages and the fluctuation characteristics of nutrients in near-source rainwater, which will not be elaborated here.

[0081] Furthermore, after determining the scheduling plots, the component distance antagonistic relationship data of the scheduling plots are obtained by using underground soil moisture sensors laid on the scheduling plots.

[0082] Among them, the component distance adversarial relationship data of the dispatched plots includes the matching value of rainwater components in each rainwater harvesting device with the required components of the dispatched plots, as well as the distance between each rainwater harvesting device and the dispatched plots;

[0083] The matching value between the rainwater composition of each rainwater harvesting device and the required composition of the dispatched plot refers to the comprehensive degree of adaptation between the concentration of various nutrients required by the current dispatched plot and the concentration of the corresponding components actually contained in the rainwater of each rainwater harvesting device. It indicates that the rainwater in the rainwater harvesting device has a higher degree of supply and demand adaptation for the current dispatched plot. The acquisition logic is to collect the set of required nutrients of the dispatched plot through the underground moisture sensor, extract the concentration values ​​of various nutrients detected in the current rainwater of each rainwater harvesting device using the water quality acquisition sensor, construct a set of nutrients that can be provided, extract the intersection of the required items of the dispatched plot and the provided items in the rainwater, and calculate the supply and demand difference value of each nutrient in the intersection. Then, the normalized weighted formula is used to calculate the matching value between the rainwater composition of each rainwater harvesting device and the required composition of the dispatched plot.

[0084] It should be noted that the normalized weighted formula is consistent with the above-mentioned normalized formula for the supply and demand deviation of rainwater nutrients, which has been described above and will not be repeated here.

[0085] Furthermore, the order of constructing the set of available nutrients and the set of nutrients required by the scheduled plots is to first collect the set of nutrients required by the scheduled plots, so as to determine the rainwater harvesting device that is most suitable for the current scheduled plot.

[0086] The distance between each rainwater harvesting device and the dispatched plot refers to the actual transmission path distance between the dispatched plot and each candidate rainwater harvesting device. It is used to measure the time delay of the water transfer path. The acquisition logic is to obtain the geographical location information of all rainwater harvesting devices and each dispatched plot through the underground soil moisture sensor and represent it in two-dimensional plane coordinates. Based on the two-dimensional plane coordinates, the shortest water resource transmission path between the rainwater harvesting device and each dispatched plot is constructed to obtain the distance between each rainwater harvesting device and the dispatched plot.

[0087] Among them, the underground soil moisture sensor obtains the latitude and longitude data of its location in real time through the embedded GNSS positioning module, and performs data projection transformation on the latitude and longitude according to the coordinate analysis algorithm to form a unified two-dimensional plane coordinate representation.

[0088] The method for constructing the shortest water resource transmission path is based on a preset topology map of the water conservancy pipeline network between fields. A set of connected paths from rainwater harvesting devices to each scheduling plot is constructed in two-dimensional space. The shortest path algorithm is used to start from the rainwater harvesting device node and traverse to the target scheduling plot node under the conditions of satisfying the path connectivity and traversability constraints.

[0089] The total distance of the shortest transmission path is obtained by summing the distances between consecutive coordinate point segments in the path. If the path contains n path segments, the specific calculation formula for the water diversion distance between the j-th rainwater harvesting device and the i-th dispatching plot is as follows:

[0090] ;

[0091] In the formula, The distance between the j-th rainwater harvesting device and the ith dispatch plot is the distance of the n-th road segment. Let m be the two-dimensional planar coordinates of the m-th consecutive coordinate point on the path segment;

[0092] It should be noted that the shortest path algorithm can be either Dijkstra's shortest path algorithm or A* heuristic path search algorithm; the specific algorithm chosen will not be elaborated here.

[0093] Among them, the geographic coordinates uniformly adopt the WGS84 or CGCS2000 geographic reference system to ensure the consistency of multi-source coordinate data. Furthermore, the path construction process considers the existing irrigation infrastructure, crop interval barriers and water flow capacity constraints. Optionally, when there are water flow capacity constraints, edge weight factors can be introduced for path weighted optimization. Here, the method for realizing the distance between each rainwater collection device and the dispatch plot is not limited.

[0094] It's important to note that in real-world agricultural scenarios, a typical contradiction exists: the most suitable rainwater often comes from more distant collection points, while the water quality composition of the nearest rainwater source may be mismatched. This is determined by the characteristics of the planting location. When there are natural differences between the soil environment, habitat conditions, and irrigation needs of the planting location, crops exhibit significant differences in their adaptability to water quality components (such as pH value, mineral concentration, and organic matter content) across different plots. Furthermore, the layout of rainwater harvesting devices is often optimized based on topography and terrain, and the rainwater composition in the collection area is also affected by the concentration of surrounding air pollutants, surface runoff paths, and equipment materials. Therefore, a closer collection device does not necessarily provide the optimal water source, and the composition of some distant water sources may be closer to the growth needs of specific crops. In this context, the objectives of water diversion path optimization (shortest time) and water quality suitability (most matching components) present a typical conflicting relationship. If water diversion is based solely on distance or cost, it can easily lead to decreased crop yields or unbalanced plant growth. Therefore, it is imperative to introduce a component-path fusion perception and multi-dimensional weight allocation mechanism to coordinate and resolve this issue.

[0095] In step S4, based on the component distance adversarial relationship data, namely the rainwater components in each rainwater harvesting device and the demand components of the dispatched plot, as well as the distance between each rainwater harvesting device and the dispatched plot;

[0096] The rainwater composition in each rainwater harvesting device is standardized in relation to the demand components of the dispatched plots, as well as the distance between each rainwater harvesting device and the dispatched plots, to generate rainwater content weights and path time weights.

[0097] The purpose of standardization is to ensure that the rainwater composition in each rainwater harvesting device is in the same dimension as the required composition of the dispatched plot and the distance between each rainwater harvesting device and the dispatched plot.

[0098] It should be noted that the standardization methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization method, or normalization method based on nonlinear mapping function. The application methods of standardization will not be elaborated here.

[0099] Based on bidirectional monitoring of rainwater path, the rainwater volume of each rainwater collection device and the transmission time of the corresponding irrigation pipeline are obtained;

[0100] The logic for obtaining the amount of rainwater in each rainwater collection device is to use water level sensors deployed inside each rainwater collection device to detect the current liquid level height in the rainwater collection device in real time, and combine the cross-sectional area data of the device to obtain the amount of rainwater in each rainwater collection device through the volume calculation formula.

[0101] The logic for obtaining the transmission time of the irrigation pipeline is based on the actual irrigation path between the rainwater harvesting device and the plot of land. The difference between the irrigation start time recorded by the timer and the feedback time of the water volume sensor at the end of the irrigation is calculated to obtain the transmission time of the irrigation pipeline.

[0102] After standardizing the rainwater volume of each rainwater harvesting device and the transmission time of its corresponding irrigation pipeline, the matching score between the scheduled plot and each rainwater harvesting device is obtained by combining the corresponding rainwater content weight and the path time weight.

[0103] The specific calculation formula is as follows:

[0104] ;

[0105] In the formula, To assess the compatibility between the land parcels and each rainwater harvesting device, Weighted by rainfall content, As the weight of path time, The amount of rainwater collected by each rainwater harvesting device after standardization treatment. Transmission time for standardized irrigation pipelines;

[0106] Based on the compatibility score between the dispatched plots and each rainwater collection device and the near-source water diversion matching value of the dispatched plots, the dispatched plots are sorted in ascending order according to the near-source water diversion matching value. The plot with the highest compatibility score between the dispatched plot and each rainwater collection device is selected as the rainwater collection device for the rainwater resource allocation of the dispatched plot.

[0107] Furthermore, if the rainwater collection device corresponding to the largest dispatched plot and the matching score of each rainwater collection device is a configured rainwater collection device, then the process will continue to the next rainwater collection device until all rainwater resources of the dispatched plot are configured.

[0108] It should be noted that if all rainwater collection devices corresponding to the maximum scheduling plot and the matching scores of each rainwater collection device have been configured, the current scheduling plot will be left unused, and rainwater resource allocation rules will not be applied to that scheduling plot.

[0109] For example, if there are five plots of land to be allocated, corresponding to A, B, C, D, and F respectively, and four rainwater harvesting devices, corresponding to A, B, C, and D respectively, then the rainwater resource allocation rules for the allocated plots are as follows:

[0110] If the matching scores of the maximum scheduling plots A and B with each rainwater harvesting device correspond to the rainwater harvesting device 'a', then the near-source water diversion matching value of scheduling plots A and B is called. It is found that the near-source water diversion matching value of scheduling plot A is less than that of scheduling plot B. Therefore, the rainwater harvesting device corresponding to scheduling plot A is 'a'. Then, the matching score of the second-ranked maximum scheduling plot of scheduling plot B with each rainwater harvesting device is called. The corresponding rainwater harvesting device is 'c'. Then, the rainwater harvesting device corresponding to scheduling plot B is extended to 'c', and so on.

[0111] Once the four scheduling plots A, B, C, and D are configured, when configuring the F scheduling plot, it is checked that all four rainwater harvesting devices have been configured. If so, the F scheduling plot is left unused, and no rainwater resource configuration rules are applied to the F scheduling plot.

[0112] It should be noted that in the above example, the order of the near-source water diversion matching values ​​corresponding to the five dispatched plots is: A < B < C < D < F, which will not be elaborated here.

[0113] Example 2: A dryland spring maize configuration system based on efficient rainwater utilization, such as... Figure 2 As shown, a method for configuring spring maize in dryland areas based on efficient rainwater utilization is used, which includes a historical data recall module, a plot selection module, a weight generation module, and a rule determination module. The modules are connected by electrical signals.

[0114] The functions of each module are as follows:

[0115] The history recall module is used to collect the uniformity of plant height on the harvest day of spring maize in dryland and the reduction in spring maize yield, and then transmit it to the plot screening module.

[0116] The plot screening module receives the uniformity of plant height on the harvest day of spring maize in dryland and the spring maize yield reduction calculation scheduling characteristics, and determines whether there is a component distance adversarial relationship of rainwater resources in multiple plots and screens candidate scheduling plots, and sends the candidate scheduling plots to the weight generation module.

[0117] The weight generation module collects water quality data of candidate scheduling sites and calculates the near-source water transfer matching value. After determining the scheduling sites based on the near-source water transfer matching value, it obtains component distance adversarial relationship data. It then uses the component distance adversarial relationship data to calculate and generate rainwater content weight and path time weight, which are then uploaded to the rule determination module.

[0118] The rule determination module monitors and obtains the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline, and determines the rainwater resource allocation rules for the scheduling plots by combining the corresponding rainwater content weight and path time weight.

[0119] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0120] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0121] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0122] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0123] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dry land spring maize configuration method based on efficient utilization of rainwater, characterized by: Includes the following steps: Step S1: Call the historical database to obtain the uniformity of plant height on the harvest day of spring maize in dryland and the reduction in spring maize yield, and calculate the scheduling characteristics of rainwater resources in multiple plots at present. Step S2: Determine whether there is a component distance antagonism relationship between the rainwater resources of multiple plots through scheduling characteristics. If there is a component distance antagonism relationship between the rainwater resources of multiple plots, select candidate scheduling plots based on the uniformity of plant height on the harvest day of spring corn in dryland. Step S3: Collect water quality data of candidate scheduling sites through water quality acquisition sensors, calculate the near-source water diversion matching value based on the water quality data and determine the scheduling sites, and use the underground soil moisture sensors laid on the scheduling sites to obtain the component distance antagonistic relationship data of the scheduling sites. In step S3, based on the nutrient concentration of near-source rainwater collected by the water quality acquisition sensor and compared with the preset minimum effective nutrient threshold, the nutrient supply item set of the current rainwater sample is divided. Then, the set of dryland spring maize nutrient requirements corresponding to the nutrient supply item set of the current rainwater sample is called from the crop database to construct a matching supply and demand pair. The supply and demand deviation normalization formula of rainwater nutrient components is substituted into the formula to calculate the near-source water diversion matching value. The set of nutrient contributions for the current rainwater sample is expressed as follows: ； In the formula, a set of nutrient components available to near-source rainwater, a detected concentration of the kth nutrient component in near-source rainwater, a minimum nutrient effective threshold value for component k; Where k = 1, 2, ..., K, and K is the number of available nutrient types detected in the current rainwater sample; Specifically, the normalized formula for the supply and demand deviation of rainwater nutrients is expressed as follows: ； In the formula, is a near-source water matching value of the i-th land block to near-source rainwater, that is, the near-source water matching value ranges from 0 to 1, and the closer to 1 indicates the better matching, is a demand concentration of the i-th land block to the k-th type of nutrient at present, is a demand weight of the k-th type of nutrient, which is set according to a dryland spring maize physiological model, is an intersection of the nutrient giving item set and the land block demand set; Where i = 1, 2, ..., I, and I is the number of candidate plots for scheduling that participate in the current round of scheduling determination; The matching value of near-source water diversion is compared with the preset matching threshold. If the matching value of near-source water diversion is greater than or equal to the preset matching threshold, it is not marked as a dispatched plot. If the matching value of the near-source water diversion is less than the preset matching threshold, it is marked as a dispatched plot; Step S4: Generate rainwater content weight and path time weight based on the component distance adversarial relationship data. Obtain the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline based on the bidirectional monitoring of rainwater path. Combine the corresponding rainwater content weight and path time weight to determine the rainwater resource allocation rules for the scheduling plot.

2. The method for configuring spring maize in dryland areas based on efficient rainwater utilization according to claim 1, characterized in that: In step S1, the dryland spring maize harvest day is the harvest time point of dryland spring maize collected in the past. A past period is selected as the historical time period. The historical database is accessed to retrieve the plant height of multiple dryland spring maize harvest days in the historical time period. The standard deviation of the plant height for each dryland spring maize harvest day is calculated and its reciprocal is taken as the average plant height value of the corresponding dryland spring maize harvest day.

3. A method for configuring spring maize in dryland areas based on efficient rainwater utilization according to claim 2, characterized in that: In step S1, the average value of the plant height uniformity on the harvest day of dryland spring maize is selected as the marked height uniformity value. The sum of the marked height uniformity value and 1 is used as the calibration benchmark. The ratio of the marked height uniformity value to the calibration benchmark is used as the plant height uniformity rate on the harvest day of dryland spring maize. The dryland spring maize harvest date where the uniform height of the marker is located is taken as the marker harvest date. The spring maize yield of the marker harvest date is obtained by subtracting the spring maize yield of the previous harvest date from the spring maize yield of the marker harvest date collected from the historical database.

4. A method for configuring spring maize in dryland areas based on efficient rainwater utilization, as described in claim 3, is characterized in that: In step S1, the logarithm of the yield reduction of spring maize on the marked harvest date is taken as the yield reduction rate of spring maize on the marked harvest date; The difference between the yield reduction rate of spring maize on the marked harvest date and the uniformity of plant height of spring maize on the harvest date in dryland areas is used as the current scheduling characteristics of rainwater resources in multiple plots.

5. A method for configuring spring maize in dryland areas based on efficient rainwater utilization according to claim 4, characterized in that: In step S2, if the scheduling characteristics exceed the preset scheduling threshold, it is determined that there is a component distance antagonistic relationship between the rainwater resources of the current multiple plots; otherwise, it is determined that there is no component distance antagonistic relationship between the rainwater resources of the current multiple plots. When there is a component distance antagonism in the rainwater resources of multiple plots, the average plant height of each plot to be tested in the multiple plot areas during the marked harvest day is calculated, and plots with plant height lower than the average plant height are selected as candidate plots for scheduling.

6. A method for configuring spring maize in dryland areas based on efficient rainwater utilization according to claim 1, characterized in that: In step S3, the component distance adversarial relationship data of the dispatched plots includes the matching value of rainwater components in each rainwater harvesting device with the required components of the dispatched plots, as well as the distance between each rainwater harvesting device and the dispatched plots; The system collects the required nutrient components of the scheduled plots using underground soil moisture sensors. It also extracts the concentration values ​​of various nutrients detected in the current rainwater of each rainwater collection device using water quality sensors, constructs a set of available nutrients, extracts the intersection of the required items of the scheduled plots and the available items in the rainwater, and calculates the supply-demand difference value for each nutrient in the intersection. Finally, it uses a normalized weighted formula to calculate the matching value between the rainwater components in each rainwater collection device and the required components of the scheduled plots. The geographical location information of all rainwater harvesting devices and each dispatched plot is obtained by underground soil moisture sensors and represented by two-dimensional plane coordinates. Based on the two-dimensional plane coordinates, the shortest water resource transmission path between the rainwater harvesting devices and each dispatched plot is constructed, and the distance between each rainwater harvesting device and the dispatched plot is obtained.

7. A method for configuring spring maize in dryland areas based on efficient rainwater utilization according to claim 6, characterized in that: In step S4, the rainwater composition in each rainwater collection device is standardized with the demand components of the dispatched plot and the distance between each rainwater collection device and the dispatched plot to generate rainwater content weight and path time weight. Based on bidirectional monitoring of rainwater path, the rainwater volume of each rainwater collection device and the transmission time of the corresponding irrigation pipeline are obtained; By deploying water level sensors inside each rainwater harvesting device, the current liquid level height in the rainwater harvesting device is detected in real time. Combined with the cross-sectional area data of the device, the amount of rainwater collected by each rainwater harvesting device is obtained through the volume calculation formula. Based on the actual irrigation path between the rainwater harvesting device and the plot of land to be used, the difference between the irrigation start time recorded by the timer and the feedback time of the water volume sensor at the end of the irrigation is calculated to obtain the transmission time of the irrigation pipeline.

8. A method for configuring spring maize in dryland areas based on efficient rainwater utilization according to claim 7, characterized in that: In step S4, after standardizing the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline, the matching score between the scheduling plot and each rainwater collection device is obtained by combining the corresponding rainwater content weight and the path time weight. Based on the compatibility score between the dispatched plots and each rainwater collection device and the near-source water diversion matching value of the dispatched plots, the dispatched plots are sorted in ascending order according to the near-source water diversion matching value. The plot with the highest compatibility score between the dispatched plot and each rainwater collection device is selected as the rainwater collection device for the rainwater resource allocation of the dispatched plot. If the rainwater collection device corresponding to the largest dispatched plot and the matching score of each rainwater collection device is a configured rainwater collection device, then the process will continue to the next rainwater collection device until all rainwater resources of the dispatched plot are configured. If all rainwater harvesting devices corresponding to the largest dispatched plot and the matching scores of each rainwater harvesting device have been configured, then the current dispatched plot is set to be idle, and rainwater resource allocation rules are not applied to the dispatched plot.

9. A rainwater high-efficiency utilization based spring maize configuration system for realizing the rainwater high-efficiency utilization based spring maize configuration method in any one of claims 1-8. It includes a historical data retrieval module, a land parcel screening module, a weight generation module, and a rule determination module, with each module connected by electrical signals; The history recall module is used to collect the uniformity of plant height on the harvest day of spring maize in dryland and the reduction in spring maize yield, and then transmit it to the plot screening module. The plot screening module receives the uniformity of plant height on the harvest day of spring maize in dryland and the spring maize yield reduction calculation scheduling characteristics, and determines whether there is a component distance adversarial relationship of rainwater resources in multiple plots and screens candidate scheduling plots, and sends the candidate scheduling plots to the weight generation module. The weight generation module collects water quality data of candidate scheduling sites and calculates the near-source water transfer matching value. After determining the scheduling sites based on the near-source water transfer matching value, it obtains component distance adversarial relationship data. It then uses the component distance adversarial relationship data to calculate and generate rainwater content weight and path time weight, which are then uploaded to the rule determination module. The rule determination module monitors and obtains the rainwater volume of each rainwater collection device and the transmission time of its corresponding irrigation pipeline, and determines the rainwater resource allocation rules for the scheduling plots by combining the corresponding rainwater content weight and path time weight.

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