Intelligent garden irrigation control method and device

Abstract

The invention provides an intelligent garden irrigation control method and device, and belongs to the technical field of irrigation management, and the method comprises the steps: for each crop plot in a garden, obtaining plants with a good growth state in the crop plot, obtaining the irrigation water quantity per unit area of the crop plot in the combined state of the parameter values of the environmental conditions of each type; obtaining a parameter value of each type of environmental condition of each crop plot on the current day; comparing to obtain a plurality of good environment condition parameter value combinations suitable for the day; and obtaining the required irrigation water quantity per unit area of each crop plot on the current day according to the combination of the suitable good environmental condition parameter values of each crop plot on the current day. According to the intelligent garden irrigation control method and device provided by the invention, accurate self-adaptive irrigation of the garden is realized.

Description

Technical Field

[0001] The present application belongs to the field of irrigation management technology, and more specifically, relates to a smart garden irrigation control method and device. Background Art

[0002] Garden irrigation is a core component of garden maintenance, and its water resource utilization efficiency and management directly impact the sustainable development and ecological benefits of gardens. However, traditional garden irrigation methods rely primarily on manual experience or fixed-time program control. These fixed irrigation durations fail to account for factors such as soil moisture and weather fluctuations, easily leading to over- or under-irrigation. For example, sprinkler systems are still activated according to preset programs on rainy days, resulting in water waste. Existing automated systems often rely on a single soil moisture sensor and fail to integrate environmental parameters such as light intensity, temperature, and wind speed. This makes it impossible to dynamically adjust irrigation strategies, resulting in a mismatch between plant water demand and water supply. Summary of the Invention

[0003] The purpose of this application is to provide a smart garden irrigation control method and device to achieve accurate and adaptive garden irrigation.

[0004] In a first aspect of an embodiment of the present application, a smart garden irrigation control method is provided, comprising: For each crop plot in the garden, obtain the plants in good growth state in the crop plot, and obtain the irrigation water volume per unit area of the crop plot under the combination of parameter values of each type of environmental condition, and summarize the irrigation water volume required per unit area for each crop plot corresponding to different combinations of good environmental condition parameter values; Obtain parameter values of each type of environmental conditions for each crop plot on that day; For each crop plot, several combinations of good environmental condition parameter values applicable to the day are obtained by comparison; The irrigation water demand per unit area of each crop plot on that day is obtained based on the irrigation water demand per unit area corresponding to different good environmental condition parameter value combinations of each crop plot and the good environmental condition parameter value combination applicable to each crop plot on that day.

[0005] A second aspect of the embodiments of the present application provides a smart garden irrigation control method, comprising: Irrigation modules are set up separately in each crop plot of the garden; Receive the irrigation water demand per unit area of each crop plot on that day; The irrigation module in each crop plot is controlled to irrigate according to the irrigation water demand per unit area of each crop plot on that day.

[0006] A third aspect of the embodiments of the present application provides a smart garden irrigation control device, comprising: Sensors for detecting soil moisture content; Weather information interface, used to obtain average temperature, sunshine duration, average humidity and average wind speed; The irrigation prediction module is used to obtain, for each crop plot in the garden, the number of plants in good growth condition in the crop plot, and the irrigation water volume per unit area of the crop plot under the combination of parameter values of each type of environmental condition, and summarize the irrigation water volume required per unit area for each crop plot corresponding to different combinations of good environmental condition parameter values; Obtain parameter values of each type of environmental conditions for each crop plot on that day; For each crop plot, several combinations of good environmental condition parameter values applicable to the day are obtained by comparison; The irrigation water demand per unit area of each crop plot on the day is obtained according to the irrigation water demand per unit area of each crop plot corresponding to different good environmental condition parameter value combinations and the good environmental condition parameter value combination applicable to each crop plot on the day; An irrigation execution module is used to separately set up irrigation modules in each crop plot of the garden; Receive the irrigation water demand per unit area of each crop plot on that day; The irrigation module in each crop plot is controlled to irrigate according to the irrigation water demand per unit area of each crop plot on that day.

[0007] The beneficial effects of the smart garden irrigation control method and device provided by the present application are as follows: an irrigation prediction module analyzes the parameter values of each type of environmental condition collected by sensors and a weather information interface, compares and analyzes the environmental parameters of plants in good growth within a plot, and determines the daily irrigation water requirement per unit area for each crop plot. Finally, an irrigation execution module implements differentiated and precise irrigation based on the actual needs of each crop plot. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0009] Figure 1 A schematic diagram of the functional modules and information flow of a smart garden irrigation control device provided in one embodiment of the present application; Figure 2 A schematic diagram of the steps of the irrigation prediction module provided in one embodiment of the present application; Figure 3 A schematic diagram of the steps of the irrigation execution module provided in one embodiment of the present application; Figure 4 A schematic diagram of the process flow of step S3 provided in one embodiment of the present application; Figure 5 A schematic diagram of the process flow of step S32 provided in one embodiment of the present application; Figure 6 A schematic diagram of the process flow of step S4 provided in one embodiment of the present application; Figure 7 A schematic block diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0010] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0011] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0012] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the functional modules and information flow of a smart garden irrigation control device provided in one embodiment of the present application. The device functionally comprises a sensor 1, a weather information interface 2, an irrigation prediction module 3, and an irrigation execution module 4. Sensor 1 detects soil moisture, while weather information interface 2 obtains average temperature, sunshine duration, average humidity, and average wind speed. Irrigation prediction module 3 estimates irrigation water usage for each plot, while irrigation execution module 4 executes irrigation.

[0013] Please refer to Figures 1 to 3 In this solution, irrigation prediction module 3 estimates the irrigation needs of each crop plot by first executing step S1. For each crop plot within the garden, the module obtains the number of plants in good growth within the plot and the irrigation water volume per unit area for each combination of environmental condition parameters. The module then summarizes the irrigation water volume per unit area for each crop plot under different combinations of good environmental condition parameters. The environmental condition types include soil moisture content, average temperature, sunshine duration, average humidity, and average wind speed. Next, the module executes step S2 to obtain the parameter values for each environmental condition for each crop plot on that day.

[0014] See also Figure 3 and 4 As shown, after obtaining the environmental conditions of the day, step S3 can be performed next to each crop plot to compare and obtain several good environmental condition parameter value combinations applicable to the day. Specifically for each crop plot, step S31 can be performed first to use the parameter values of each type of environmental conditions of the crop plot in each crop plot on the day as the environmental condition parameter value combination of the day. Next, step S32 can be performed to select several good environmental condition parameter value combinations that have commonality with the environmental conditions of the day based on the difference between the environmental condition parameter value combination of the day and the parameter values of each type of environmental conditions of each good environmental condition parameter value combination as several good environmental condition parameter value combinations applicable to the crop plot on the day.

[0015] See also Figure 5 As shown, in the process of selecting several good environmental condition parameter value combinations applicable for the day, step S321 can be first performed. For each environmental condition parameter value combination, including the environmental condition parameter value combination for the day and each good environmental condition parameter value combination, the cumulative value of the difference between the parameter values of each type of environmental condition between the environmental condition parameter value combinations is used as the environmental divergence between the environmental condition parameter value combinations, and the good environmental condition parameter value combinations with the largest and smallest environmental divergence from the environmental condition parameter value combination for the day are respectively used as different good environmental condition parameter value combinations and the same good environmental condition parameter value combination. Next, step S322 can be performed to calculate and obtain the environmental divergence between the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations and each other environmental condition parameter value combination. Next, step S323 can be performed to classify each environmental condition parameter value combination other than the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations, together with the different good environmental condition parameter value combinations or the same good environmental condition parameter value combinations with the smallest environmental divergence, into the same environmental condition parameter value combination library. At this time, the good environmental condition parameter value combination contained in the environmental condition parameter value combination library where the environmental condition parameter value combination of that day is located is the good environmental condition parameter value combination applicable to that day.

[0016] However, the good environmental condition parameter value combinations obtained above may not all be applicable on the day, so it is also necessary to verify whether the environmental conditions have commonality. First, step S324 can be executed to calculate and obtain the mean value of the parameter values of each type of environmental conditions of all environmental condition parameter value combinations contained in each environmental condition parameter value combination library as the simulated environmental condition parameter value combination of the environmental condition parameter value combination library. Next, step S425 can be executed to use the environmental condition parameter value combination with the smallest environmental divergence between each environmental condition parameter value combination library and the corresponding simulated environmental condition parameter value combination as the different good environmental condition parameter value combination and the same good environmental condition parameter value combination after switching, wherein the one with the larger environmental divergence between the environmental condition parameter value combination and the environmental condition parameter value combination of the day is used as the different good environmental condition parameter value combination after switching, and the one with the smaller environmental divergence between the environmental condition parameter value combination and the environmental condition parameter value combination of the day is used as the same good environmental condition parameter value combination after switching.

[0017] If the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations before and after the switch are unchanged, it means that the good environmental condition parameter value combinations contained in the environmental condition parameter value combination library where the environmental condition parameter value combination of the current day is located all have common environmental conditions. Therefore, step S326 can be executed to determine all the good environmental condition parameter value combinations contained in the environmental condition parameter value combination library where the environmental condition parameter value combination of the current day is located as the good environmental condition parameter value combinations that have commonality with the environmental conditions of the current day.

[0018] If the different good environmental condition parameter value combinations or the same good environmental condition parameter value combinations before and after the switch change, it means that the good environmental condition parameter value combinations contained in the environmental condition parameter value combination library where the environmental condition parameter value combination of the day is located do not completely have the commonality of the environmental conditions. Therefore, it is necessary to iterate again. Therefore, step S327 can be executed next to divide the environmental condition parameter value combination library after the switch according to the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations after the switch. Next, step S328 can be executed to continue calculating and generating different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations after the switch in the environmental condition parameter value combination library after the switch. Steps S326 to S328 are continuously iterated until the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations after the switch do not change. At this time, all the good environmental condition parameter value combinations contained in the environmental condition parameter value combination library where the environmental condition parameter value combination of the day is located are regarded as the good environmental condition parameter value combinations that have commonality with the environmental conditions of the day.

[0019] In order to avoid output response delays caused by too many iterations, if the number of times the environmental condition parameter value combination library is divided and switched exceeds the set number, the good environmental condition parameter value combination with the smallest environmental divergence from the environmental condition parameter value combination of the day among all good environmental condition parameter value combinations will be used as the only good environmental condition parameter value combination that has commonality with the environmental conditions of the day.

[0020] To supplement the implementation of steps S321 to S328, we provide the source code for some functional modules, with cross-references and explanations provided in the comments. To prevent the leakage of data involving commercial secrets, data that does not affect the implementation of the solution is desensitized. The same applies below.

[0021] #include <iostream> #include <vector> #include <map> #include <cmath> #include <algorithm> #include <limits> / / Environment parameter structure struct EnvironmentParams { double soil_moisture; / / soil moisture content (%) double temperature; / / average temperature (℃) double sunshine; / / duration of sunshine (hours) double humidity; / / average humidity (%) double wind_speed; / / Average wind speed (m / s) / / Print parameters (for debugging) void print() const { std::cout<<"["< <soil_moisture<<"%, "<<temperature<<"℃, " < <sunshine<<"h, "<<humidity<<"%, "<<wind_speed<<"m / s]"; } }; / / Calculate the divergence (Manhattan distance) between two environment combinations double calculateDivergence(const EnvironmentParams&a, constEnvironmentParams&b) { return fabs(a.soil_moisture - b.soil_moisture) + fabs(a.temperature - b.temperature) + fabs(a.sunshine - b.sunshine) + fabs(a.humidity - b.humidity) + fabs(a.wind_speed - b.wind_speed); } / / Environment combination classifier class EnvironmentClassifier { private: std::vector <environmentparams>all_conditions; / / All good environment combinations EnvironmentParams today_condition; / / Today's environment combination int max_iterations; / / Maximum number of iterations / / Find the combination with the largest / smallest divergence void findExtremeConditions( const std::vector <environmentparams>&conditions, EnvironmentParams&min_div, EnvironmentParams&max_div) { double min_val = std::numeric_limits <double>::max(); double max_val = std::numeric_limits <double>::min(); for (const auto&cond : conditions) { double div = calculateDivergence(today_condition, cond); if (div <min_val) { min_val = div; min_div = cond; } if (div>max_val) { max_val = div; max_div = cond; } } } / / Classify to the nearest center (maximum / minimum divergence combination) std::map <int, std::vector <environmentparams>>classifyConditions( const EnvironmentParams&min_cond, const EnvironmentParams&max_cond) { std::map<int, std::vector <environmentparams>>clusters; clusters[0].push_back(min_cond); / / minimum divergence library clusters[1].push_back(max_cond); / / Maximum divergence library for (const auto&cond : all_conditions) { if (cond == min_cond || cond == max_cond) continue; double div_to_min = calculateDivergence(cond, min_cond); double div_to_max = calculateDivergence(cond, max_cond); if (div_to_min<= div_to_max) { clusters[0].push_back(cond); } else { clusters[1].push_back(cond); } } return clusters; } / / Calculate the simulated environment combination (mean) EnvironmentParams calculateMeanCondition( const std::vector <environmentparams>&conditions) { EnvironmentParams mean{0,0,0,0,0}; for (const auto&cond : conditions) { mean.soil_moisture += cond.soil_moisture; mean.temperature += cond.temperature; mean.sunshine += cond.sunshine; mean.humidity += cond.humidity; mean.wind_speed += cond.wind_speed; } double size = conditions.size(); mean.soil_moisture / = size; mean.temperature / = size; mean.sunshine / = size; mean.humidity / = size; mean.wind_speed / = size; return mean; } public: EnvironmentClassifier(const std::vector <environmentparams>&conditions, const EnvironmentParams&today, int max_iter = 10) : all_conditions(conditions), today_condition(today), max_iterations(max_iter) {} / / Main algorithm: Get a common environment combination std::vector <environmentparams>getCommonConditions() { if (all_conditions.empty()) return {}; EnvironmentParams current_min, current_max; findExtremeConditions(all_conditions, current_min, current_max); int iter = 0; while (iter++ <max_iterations) { / / Classify into two libraries auto clusters = classifyConditions(current_min, current_max); / / Computational Simulation Center EnvironmentParams new_min = calculateMeanCondition(clusters[0]); EnvironmentParams new_max = calculateMeanCondition(clusters[1]); / / Find the actual sample closest to the simulated center in the library EnvironmentParams actual_min, actual_max; findExtremeConditions(clusters[0], actual_min, actual_max); findExtremeConditions(clusters[1], actual_max, actual_min); / / Note the order of parameters / / Check for convergence if (actual_min == current_min&&actual_max == current_max) { / / Return all combinations of the library on that day double div_to_min = calculateDivergence(today_condition, actual_min); double div_to_max = calculateDivergence(today_condition, actual_max); return (div_to_min<= div_to_max) ? clusters[0] : clusters[1]; } / / Update the current center current_min = actual_min; current_max = actual_max; } / / If the maximum number of iterations is exceeded, return the single sample with the smallest divergence EnvironmentParams best_match; double min_div = std::numeric_limits <double>::max(); for (const auto& cond : all_conditions) { double div = calculateDivergence(today_condition, cond); if (div < min_div) { min_div = div; best_match = cond; } } return {best_match}; } }; int main() { / / Example data std::vector <environmentparams>good_conditions = { {25.0, 28.5, 6.2, 65.0, 2.1}, {23.5, 30.2, 5.8, 70.0, 1.5}, {27.0, 25.8, 7.0, 60.0, 2.5}, {26.2, 27.3, 6.5, 62.0, 2.0}, {24.8, 29.0, 6.1, 67.0, 1.9} }; EnvironmentParams today = {24.8, 29.1, 6.0, 68.0, 1.8}; / / Create a classifier and execute the algorithm EnvironmentClassifier classifier(good_conditions, today); auto common_conditions = classifier.getCommonConditions(); / / Output the result std::cout<<"A good environment combination that is common to the current environment:"< <std::endl; for (const auto&cond : common_conditions) { cond.print(); std::cout<<" (divergence:"< <calculateDivergence(today, cond)<<")\n"; } return 0; } This code implements an environmental parameter combination screening algorithm based on dynamic classification. During execution, it first calculates divergence, using the Manhattan distance to quantify the differences between environmental combinations, comprehensively considering five parameters, such as soil moisture and temperature. An iterative classification mechanism then identifies the two extreme combinations with the greatest and least divergence from the daily environment as initial classification centers. Over multiple rounds (up to 10), the classification is dynamically adjusted to gradually optimize the partitioning of the combination library. Each round, the proposed center (parameter mean) of each library is recalculated and the closest actual sample is found. Finally, an intelligent termination condition is implemented: when the classification center no longer changes, all combinations in the library containing the daily environment are returned as the final result. To avoid excessive iteration wait times, when the maximum number of iterations is exceeded, the algorithm automatically degenerates to returning a single optimal combination with the smallest divergence.

[0022] This algorithm can effectively identify historically favorable combinations with similar environmental characteristics to those of the current environment, providing a basis for accurate irrigation decision-making in smart garden systems. Compared to simple nearest neighbor methods, its classification process better reflects the overall distribution characteristics of environmental parameters.

[0023] See also Figure 3 and 4 As shown, after obtaining several good environmental condition parameter value combinations applicable to a certain crop plot on the day, step S33 can be finally executed to summarize and obtain the good environmental condition parameter value combinations applicable to each crop plot on the day.

[0024] See also Figure 2 and 6 As shown, after finding the good environmental condition parameter value combination applicable to the day, step S4 can be executed next to obtain the irrigation water demand per unit area of each crop plot on the day according to the irrigation water demand per unit area corresponding to the different good environmental condition parameter value combinations of each crop plot and the good environmental condition parameter value combination applicable to each crop plot on the day. Specifically for each crop plot, step S41 can first be executed to calculate the environmental divergence between the environmental condition parameter value combination of the crop plot on the day and each good environmental condition parameter value combination applicable on the day. Next, step S42 can be executed to use the environmental divergence of each good environmental condition parameter value combination applicable on the day and the environmental condition parameter value combination applicable on the day as a weighted coefficient, and calculate the weighted average of the irrigation water demand per unit area corresponding to the good environmental condition parameter value combination applicable on the day as the irrigation water demand per unit area of the crop plot on the day. Finally, step S43 can be executed to summarize and obtain the irrigation water demand per unit area of each crop plot on the day.

[0025] Please continue reading Figures 1 to 3 As shown, during operation, the irrigation execution module 4 in this solution can first execute step S041 to separately set up an irrigation module within each crop plot in the garden. After the irrigation prediction module 3 determines the irrigation demand of each crop plot, step S042 can be executed to receive the irrigation water demand per unit area of each crop plot on that day. Finally, step S043 can be executed to control the irrigation module within each crop plot to irrigate according to the irrigation water demand per unit area of each crop plot on that day.

[0026] See also Figure 7 , Figure 7 This is a schematic block diagram of an electronic device provided in one embodiment of the present application. Figure 7 The electronic device 500 in the embodiment shown may include: one or more processors 501, one or more input devices 502, one or more output devices 503, and one or more memories 504. The processors 501, input devices 502, output devices 503, and memories 504 communicate with each other via a communication bus 505. The memory 504 is used to store computer programs, which include program instructions. The processor 501 is used to execute the program instructions stored in the memory 504. The processor 501 is configured to call the program instructions to execute the functions of each module / unit in the embodiment of the smart garden irrigation control device, such as Figure 1 The functions of the irrigation prediction module 3 and the irrigation execution module 4 are shown.

[0027] It should be understood that in the embodiments of the present application, the processor 501 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0028] The input device 502 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 503 may include a display (LCD, etc.), a speaker, etc.

[0029] The memory 504 may include a read-only memory and a random access memory, and provides instructions and data to the processor 501. A portion of the memory 504 may also include a non-volatile random access memory. For example, the memory 504 may also store information about the device type.

[0030] In a specific implementation, the processor 501, input device 502, and output device 503 described in the embodiment of the present application can execute the implementation method described in an embodiment of a smart garden irrigation control method provided in the embodiment of the present application, and can also execute the implementation method of the electronic device described in the embodiment of the present application, which will not be repeated here.

[0031] In another embodiment of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0032] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0033] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0034] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0035] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0036] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0037] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0038] The above are only specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.< / environmentparams> < / double> < / environmentparams> < / environmentparams> < / environmentparams> < / environmentparams> < / environmentparams> < / double> < / double> < / environmentparams> < / environmentparams> < / limits> < / algorithm> < / cmath> < / map> < / vector> < / iostream>

Claims

1. A smart garden irrigation control method, characterized in that: include: For each crop plot in the garden, obtain the plants in good growth state in the crop plot, and obtain the irrigation water volume per unit area of the crop plot under the combination of parameter values of each type of environmental condition, and summarize the irrigation water volume required per unit area for each crop plot corresponding to different combinations of good environmental condition parameter values; Obtain parameter values of each type of environmental conditions for each crop plot on that day; For each crop plot, several combinations of good environmental condition parameter values applicable to the day are obtained by comparison; The irrigation water demand per unit area of each crop plot on that day is obtained based on the irrigation water demand per unit area corresponding to different good environmental condition parameter value combinations of each crop plot and the good environmental condition parameter value combination applicable to each crop plot on that day.

2. The smart garden irrigation control method according to claim 1, characterized in that: Types of environmental conditions include soil moisture content, average temperature, sunshine duration, average humidity, and average wind speed.

3. The smart garden irrigation control method according to claim 1, characterized in that: The step of comparing and obtaining a plurality of good environmental condition parameter value combinations applicable to the day for each crop plot includes: For each crop plot, perform the following steps separately, The parameter values of each type of environmental conditions of each crop plot on that day are used as the environmental condition parameter value combination of that day. According to the difference between the parameter value of each type of environmental condition of the environmental condition parameter value combination on the day and each good environmental condition parameter value combination, a plurality of good environmental condition parameter value combinations having commonality with the environmental conditions on the day are selected as the plurality of good environmental condition parameter value combinations applicable to the crop plot on the day; The combination of good environmental condition parameter values applicable to each crop plot on that day is obtained through summary.

4. The smart garden irrigation control method according to claim 3, characterized in that: The step of selecting a plurality of good environmental condition parameter value combinations having commonality with the environmental conditions of the day based on the difference between the parameter value combination of the environmental conditions of the day and the parameter value of each type of environmental conditions of each good environmental condition parameter value combination, include, For each environmental condition parameter value combination including the environmental condition parameter value combination of the day and each good environmental condition parameter value combination, the accumulated value of the difference between the parameter values of each type of environmental condition between the environmental condition parameter value combinations is used as the environmental divergence between the environmental condition parameter value combinations, and the good environmental condition parameter value combinations with the largest and smallest environmental divergence from the environmental condition parameter value combination of the day are respectively used as different good environmental condition parameter value combinations and the same good environmental condition parameter value combination; Calculate the environmental divergence between different good environmental condition parameter value combinations and the same good environmental condition parameter value combination and each other environmental condition parameter value combination; Each environmental condition parameter value combination other than the different good environmental condition parameter value combination and the same good environmental condition parameter value combination is classified into the same environmental condition parameter value combination library together with the different good environmental condition parameter value combination or the same good environmental condition parameter value combination with the smallest environmental divergence.

5. The smart garden irrigation control method according to claim 4, characterized in that: The step of selecting a plurality of good environmental condition parameter value combinations having commonality with the environmental conditions of the day based on the difference between the parameter value combination of the environmental conditions of the day and the parameter value of each type of environmental conditions of each good environmental condition parameter value combination also includes: Calculating and obtaining the mean value of the parameter value of each type of environmental condition of all environmental condition parameter value combinations contained in each environmental condition parameter value combination library as the simulated environmental condition parameter value combination of the environmental condition parameter value combination library; The environmental condition parameter value combination with the smallest degree of environmental divergence between each environmental condition parameter value combination library and the corresponding simulated environmental condition parameter value combination is used as the different good environmental condition parameter value combination and the same good environmental condition parameter value combination after switching, wherein the environmental divergence between the environmental condition parameter value combination with the larger degree of environmental divergence and the environmental condition parameter value combination of the day is used as the different good environmental condition parameter value combination after switching, and the environmental divergence between the environmental condition parameter value combination with the smaller degree of environmental divergence and the environmental condition parameter value combination of the day is used as the same good environmental condition parameter value combination after switching; If the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations before and after the switch have not changed, all the good environmental condition parameter value combinations contained in the environmental condition parameter value combination library where the environmental condition parameter value combination of the day is located will be regarded as the good environmental condition parameter value combinations that are common with the environmental conditions of the day.

6. The smart garden irrigation control method according to claim 5, characterized in that: The step of selecting a plurality of good environmental condition parameter value combinations having commonality with the environmental conditions of the day based on the difference between the parameter value combination of the environmental conditions of the day and the parameter value of each type of environmental conditions of each good environmental condition parameter value combination also includes: If the different good environmental condition parameter value combinations or the same good environmental condition parameter value combinations before and after the switching are changed, the environmental condition parameter value combination library after the switching is obtained according to the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations after the switching; Continue to calculate and generate different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations after switching in the environmental condition parameter value combination library after switching, until the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations after switching have not changed, and take all the good environmental condition parameter value combinations contained in the environmental condition parameter value combination library where the environmental condition parameter value combination of the day is located at this time as the good environmental condition parameter value combinations that have commonality with the environmental conditions of the day.

7. The method according to claim 6, characterized in that If the number of times the environmental condition parameter value combination library is divided and switched exceeds the set number, the good environmental condition parameter value combination with the smallest environmental divergence from the environmental condition parameter value combination of the day among all good environmental condition parameter value combinations will be used as the only good environmental condition parameter value combination that has commonality with the environmental conditions of the day.

8. The method according to any one of claims 4 to 7, characterized in that The step of obtaining the irrigation water demand per unit area of each crop plot on the day according to the irrigation water demand per unit area corresponding to different good environmental condition parameter value combinations of each crop plot and the good environmental condition parameter value combination applicable to each crop plot on the day, include, For each crop plot, perform the following steps separately, Calculate the environmental divergence between the combination of environmental condition parameter values of the crop plot on that day and the combination of good environmental condition parameter values applicable on each day; The environmental divergence between each combination of good environmental condition parameter values applicable on the day and the combination of environmental condition parameter values on the day is used as a weighting coefficient, and the weighted average of the irrigation water requirements per unit area corresponding to the good environmental condition parameter value combinations applicable on the day is calculated as the irrigation water requirements per unit area of the crop plot on the day; The irrigation water requirement per unit area of each crop plot on that day is obtained by summarizing.

9. A smart garden irrigation control method, characterized in that: include, Irrigation modules are set up separately in each crop plot of the garden; Receiving the irrigation water demand per unit area of each crop plot on the day in the smart garden irrigation control method according to any one of claims 1 to 8; The irrigation module in each crop plot is controlled to irrigate according to the irrigation water demand per unit area of each crop plot on that day.

10. A smart garden irrigation control device, characterized in that: include, Sensors for detecting soil moisture content; Weather information interface, used to obtain average temperature, sunshine duration, average humidity and average wind speed; The irrigation prediction module is used to obtain, for each crop plot in the garden, the number of plants in good growth condition in the crop plot, and the irrigation water volume per unit area of the crop plot under the combination of parameter values of each type of environmental condition, and summarize the irrigation water volume required per unit area for each crop plot corresponding to different combinations of good environmental condition parameter values; Obtain parameter values of each type of environmental conditions for each crop plot on that day; For each crop plot, several combinations of good environmental condition parameter values applicable to the day are obtained by comparison; The irrigation water demand per unit area of each crop plot on the day is obtained according to the irrigation water demand per unit area of each crop plot corresponding to different good environmental condition parameter value combinations and the good environmental condition parameter value combination applicable to each crop plot on the day; An irrigation execution module is used to separately set up irrigation modules in each crop plot of the garden; Receive the irrigation water demand per unit area of each crop plot on that day; The irrigation module in each crop plot is controlled to irrigate according to the irrigation water demand per unit area of each crop plot on that day.

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