A smart garden irrigation control method and device

Abstract

This application provides a smart garden irrigation control method and device, belonging to the field of irrigation management technology. The method includes: for each crop plot within the garden, acquiring the number of plants in good growth condition within that plot, and acquiring the irrigation water volume per unit area of ​​that plot under various combinations of parameter values ​​for different environmental conditions; acquiring the parameter values ​​for each type of environmental condition for each crop plot on a given day; comparing these values ​​to obtain several combinations of favorable environmental condition parameter values ​​applicable to that day; and obtaining the required irrigation water volume per unit area for each crop plot on that day based on the applicable combinations of favorable environmental condition parameter values. The smart garden irrigation control method and device provided in this application achieve accurate adaptive irrigation for the garden.

Description

Technical Field

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

[0002] As a core aspect of garden maintenance, irrigation directly impacts the sustainable development and ecological benefits of gardens through its water resource utilization efficiency and management level. However, traditional garden irrigation methods rely primarily on manual experience or fixed-time program control. Fixed-duration irrigation fails to consider factors such as soil moisture and weather changes, easily leading to over-irrigation or under-irrigation. For example, activating sprinkler systems according to preset programs on rainy days wastes water resources. Existing automated systems often rely on single soil moisture sensors, failing to integrate environmental parameters such as light intensity, temperature, and wind speed, thus unable to dynamically adjust irrigation strategies, resulting in a mismatch between plant water needs and 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 adaptive irrigation in gardens.

[0004] A first aspect of this application provides a smart garden irrigation control method, including:

[0005] For each crop plot in the garden, obtain the plants with good growth status in the crop plot, and obtain the irrigation water volume per unit area of ​​the crop plot under the parameter value combination of each type of environmental condition. Summarize to obtain the irrigation water demand per unit area of ​​each crop plot under different combinations of parameter values ​​of good environmental conditions.

[0006] Obtain the parameter values ​​of each type of environmental condition for each crop plot on that day;

[0007] For each crop plot, several combinations of favorable environmental condition parameter values ​​applicable to the day are obtained through comparison;

[0008] The irrigation water requirement per unit area for each crop plot on a given day is obtained by considering the combination of favorable environmental conditions parameter values ​​for different crop plots and the combination of favorable environmental conditions parameter values ​​applicable to each crop plot on that day.

[0009] A second aspect of this application provides a smart garden irrigation control method, including:

[0010] Irrigation modules are set up separately in each crop plot in the garden;

[0011] Receive the irrigation water demand per unit area for each crop plot on that day;

[0012] The irrigation modules within each crop plot are controlled to irrigate according to the irrigation water demand per unit area of ​​each crop plot on that day.

[0013] A third aspect of this application provides a smart garden irrigation control device, comprising:

[0014] Sensors are used to detect soil moisture content;

[0015] Weather information interface, used to obtain average temperature, sunshine duration, average humidity and average wind speed;

[0016] The irrigation prediction module is used to obtain the number of plants with good growth status in each crop plot in the garden, and to obtain the irrigation water volume per unit area of ​​the crop plot under the combination of parameter values ​​of each type of environmental conditions. The module is then used to summarize the required irrigation water volume per unit area of ​​each crop plot under different combinations of parameter values ​​of good environmental conditions.

[0017] Obtain the parameter values ​​of each type of environmental condition for each crop plot on that day;

[0018] For each crop plot, several combinations of favorable environmental condition parameter values ​​applicable to the day are obtained through comparison;

[0019] The irrigation water requirement per unit area for each crop plot on a given day is obtained by considering the combination of favorable environmental conditions parameter values ​​for different crop plots and the combination of favorable environmental conditions parameter values ​​applicable to each crop plot on that day.

[0020] An irrigation execution module is used to set up irrigation modules separately in each crop plot of the garden;

[0021] Receive the irrigation water demand per unit area for each crop plot on that day;

[0022] The irrigation modules within each crop plot are controlled to irrigate according to the irrigation water demand per unit area of ​​each crop plot on that day.

[0023] The beneficial effects of the smart garden irrigation control method and device provided in this application are as follows: The irrigation prediction module analyzes the parameter values ​​of each type of environmental condition collected by sensors and weather information interfaces, compares and analyzes the environmental parameters of plants with good growth status within the crop plot, and determines the daily irrigation water requirement per unit area for each crop plot. Finally, the irrigation execution module performs differentiated and precise irrigation according to the actual needs of each crop plot. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A schematic diagram of the functional modules and information flow of a smart garden irrigation control device provided in an embodiment of this application;

[0026] Figure 2 This is a flowchart illustrating the steps of an irrigation prediction module provided in an embodiment of this application.

[0027] Figure 3 This is a flowchart illustrating the steps of an irrigation execution module provided in an embodiment of this application;

[0028] Figure 4 This is a flowchart illustrating step S3 provided in an embodiment of this application;

[0029] Figure 5 This is a flowchart illustrating step S32 provided in an embodiment of this application;

[0030] Figure 6 This is a flowchart illustrating step S4 provided in an embodiment of this application;

[0031] Figure 7 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0032] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0033] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0034] Please refer to Figure 1 , Figure 1This application provides a schematic diagram of the functional modules and information flow of a smart garden irrigation control device according to an embodiment. The device, functionally divided, includes a sensor 1, a weather information interface 2, an irrigation prediction module 3, and an irrigation execution module 4. The sensor 1 is used to detect soil moisture content, and the weather information interface 2 is used to obtain average temperature, sunshine duration, average humidity, and average wind speed. The irrigation prediction module 3 is used to estimate the irrigation water consumption for each plot, and the irrigation execution module 4 is used to execute irrigation.

[0035] Please refer to Figures 1 to 3 In this scheme, the irrigation prediction module 3, in estimating the irrigation needs of each crop plot, first executes step S1: for each crop plot within the garden, it obtains the number of plants in good growth condition within that plot and the irrigation water volume per unit area under each combination of environmental conditions. This is then summarized to obtain the required irrigation water volume per unit area for each crop plot under different combinations of favorable environmental conditions. The environmental conditions include soil moisture content, average temperature, sunshine duration, average humidity, and average wind speed. Next, step S2 is executed to obtain the parameter values ​​for each type of environmental condition for each crop plot on that day.

[0036] Please see Figure 3 and 4 As shown, after obtaining the environmental conditions for the day, step S3 can be executed to compare and obtain several combinations of favorable environmental condition parameter values ​​applicable to the day for each crop plot. Specifically, for each crop plot, step S31 can first be executed to obtain the parameter values ​​of each type of environmental condition for each crop plot on the day as the combination of environmental condition parameter values ​​for the day. Next, step S32 can be executed to select several combinations of favorable environmental condition parameter values ​​that have commonalities with the environmental conditions of the day based on the difference between the combination of environmental condition parameter values ​​for the day and the parameter values ​​of each type of environmental condition in each combination of favorable environmental condition parameter values, as several combinations of favorable environmental condition parameter values ​​applicable to the crop plot on the day.

[0037] Please see Figure 5As shown, in the process of selecting several suitable combinations of environmental condition parameter values ​​for the day, step S321 can be executed first. For each combination of environmental condition parameter values, including the combination of environmental condition parameter values ​​for the day and each combination of good environmental condition parameter values, the cumulative value of the difference in parameter values ​​for each type of environmental condition between the combinations of environmental condition parameter values ​​is used as the environmental divergence degree between the combinations of environmental condition parameter values. The good environmental condition parameter value combinations with the largest and smallest environmental divergence degrees with the combination of environmental condition parameter values ​​for the day are respectively designated as dissimilar good environmental condition parameter value combinations and identical good environmental condition parameter value combinations. Next, step S322 can be executed to calculate and obtain the environmental divergence degree between the dissimilar good environmental condition parameter value combinations and identical good environmental condition parameter value combinations and all other combinations of environmental condition parameter values. Next, step S323 can be executed to classify each combination of environmental condition parameter values ​​other than the dissimilar good environmental condition parameter value combinations and identical good environmental condition parameter value combinations, along with the dissimilar good environmental condition parameter value combination or identical good environmental condition parameter value combination with the smallest environmental divergence degree, into the same environmental condition parameter value combination library. At this time, the combination of favorable environmental conditions parameter values ​​contained in the environmental condition parameter value combination library where the combination of environmental condition parameter values ​​for that day is located is the combination of favorable environmental condition parameter values ​​applicable for that day.

[0038] However, the aforementioned combinations of favorable environmental condition parameter values ​​may not all be applicable to the current day. Therefore, it is necessary to verify whether the environmental conditions have commonalities. First, step S324 can be executed to calculate the average value of the parameter values ​​for each type of environmental condition in all environmental condition parameter value combinations contained in each environmental condition parameter value combination library, which is used as the proposed environmental condition parameter value combination for that environmental condition parameter value combination library. Next, step S425 can be executed to select the environmental condition parameter value combination with the smallest environmental discrepancy with the corresponding proposed environmental condition parameter value combination in each environmental condition parameter value combination library as the switched dissimilar favorable environmental condition parameter value combination and the same favorable environmental condition parameter value combination. Among them, the combination with a larger environmental discrepancy with the environmental condition parameter value combination of the current day is selected as the switched dissimilar favorable environmental condition parameter value combination, and the combination with a smaller environmental discrepancy with the environmental condition parameter value combination of the current day is selected as the switched same favorable environmental condition parameter value combination.

[0039] If neither the different nor the same combinations of favorable environmental condition parameter values ​​change before and after the switch, it indicates that the favorable environmental condition parameter value combinations contained in the environmental condition parameter value combination library for that day all share common environmental conditions. Therefore, step S326 can be executed next to select all favorable environmental condition parameter value combinations contained in the environmental condition parameter value combination library for that day as favorable environmental condition parameter value combinations that share common environmental conditions with that day.

[0040] If the combinations of dissimilar or identical favorable environmental condition parameter values ​​change before and after the switch, it indicates that the favorable environmental condition parameter value combinations contained in the environmental condition parameter value combination library for that day do not completely share environmental conditions. Therefore, iterative processing is required again. Step S327 is then executed to divide the environmental condition parameter value combination library after the switch based on the dissimilar and identical favorable environmental condition parameter value combinations. Step S328 continues to calculate and generate dissimilar and identical favorable environmental condition parameter value combinations within the library. Steps S326 to S328 are iterated until both dissimilar and identical favorable environmental condition parameter value combinations remain unchanged. At this point, all favorable environmental condition parameter value combinations contained in the environmental condition parameter value combination library for that day are considered as favorable environmental condition parameter value combinations that share environmental conditions with that day.

[0041] To avoid output response delays caused by excessive iterations, if the number of times the environmental condition parameter value combination library is divided after switching exceeds the set number, the environmental condition parameter value combination with the smallest environmental discrepancy among all good environmental condition parameter value combinations will be taken as the only good environmental condition parameter value combination that has commonality with the environmental conditions of the day.

[0042] To supplement the explanation of the implementation process of steps S321 to S328 above, source code for some functional modules is provided, with comparative explanations in the comments. To avoid data leakage involving trade secrets, data that does not affect the implementation of the solution has been anonymized; the same applies below.

[0043] #include <iostream>

[0044] #include <vector>

[0045] #include <map>

[0046] #include <cmath>

[0047] #include <algorithm>

[0048] #include <limits>

[0049] / / Environment parameter structure

[0050] struct EnvironmentParams {

[0051] double soil_moisture; / / Soil moisture content (%)

[0052] double temperature; / / Average temperature (°C)

[0053] double sunshine; / / Sunshine duration (hours)

[0054] double humidity; / / Average humidity (%)

[0055] double wind_speed; / / Average wind speed (m / s)

[0056] / / Print parameters (for debugging)

[0057] void print() const {

[0058] std::cout<<"["< <soil_moisture<<"%, "<<temperature<<"℃, "

[0059] < <sunshine<<"h, "<<humidity<<"%, "<<wind_speed<<"m / s]";

[0060] }

[0061] };

[0062] / / Calculate the divergence (Manhattan distance) between two environment combinations.

[0063] double calculateDivergence(const EnvironmentParams&a, constEnvironmentParams&b) {

[0064] return fabs(a.soil_moisture - b.soil_moisture) +

[0065] fabs(a.temperature - b.temperature) +

[0066] fabs(a.sunshine - b.sunshine) +

[0067] fabs(a.humidity - b.humidity) +

[0068] fabs(a.wind_speed - b.wind_speed);

[0069] }

[0070] / / Environment Combination Classifier

[0071] class EnvironmentClassifier {

[0072] private:

[0073] std::vector <environmentparams>all_conditions; / / All good condition combinations

[0074] EnvironmentParams today_condition; / / Today's environment condition

[0075] int max_iterations; / / Maximum number of iterations

[0076] / / Find the combination with the highest / lowest divergence

[0077] void findExtremeConditions(

[0078] const std::vector <environmentparams>&conditions,

[0079] EnvironmentParams&min_div,

[0080] EnvironmentParams&max_div) {

[0081] double min_val = std::numeric_limits <double>::max();

[0082] double max_val = std::numeric_limits <double>::min();

[0083] for (const auto&cond : conditions) {

[0084] double div = calculateDivergence(today_condition, cond);

[0085] if (div <min_val) {

[0086] min_val = div;

[0087] min_div = cond;

[0088] }

[0089] if (div>max_val) {

[0090] max_val = div;

[0091] max_div = cond;

[0092] }

[0093] }

[0094] }

[0095] / / Classify to the nearest center (maximum / minimum divergence combination)

[0096] std::map <int, std::vector <environmentparams>>classifyConditions(

[0097] const EnvironmentParams&min_cond,

[0098] const EnvironmentParams&max_cond) {

[0099] std::map<int, std::vector <environmentparams>>clusters;

[0100] clusters[0].push_back(min_cond); / / Minimum branching database

[0101] clusters[1].push_back(max_cond); / / Maximum branching cluster

[0102] for (const auto&cond : all_conditions) {

[0103] if (cond == min_cond || cond == max_cond) continue;

[0104] double div_to_min = calculateDivergence(cond, min_cond);

[0105] double div_to_max = calculateDivergence(cond, max_cond);

[0106] if (div_to_min<= div_to_max) {

[0107] clusters[0].push_back(cond);

[0108] } else {

[0109] clusters[1].push_back(cond);

[0110] }

[0111] }

[0112] return clusters;

[0113] }

[0114] / / Calculate the mean of the simulated environment combination

[0115] EnvironmentParams calculateMeanCondition(

[0116] const std::vector <environmentparams>&conditions) {

[0117] EnvironmentParams mean{0,0,0,0,0};

[0118] for (const auto&cond : conditions) {

[0119] mean.soil_moisture += cond.soil_moisture;

[0120] mean.temperature += cond.temperature;

[0121] mean.sunshine += cond.sunshine;

[0122] mean.humidity += cond.humidity;

[0123] mean.wind_speed += cond.wind_speed;

[0124] }

[0125] double size = conditions.size();

[0126] mean.soil_moisture / = size;

[0127] mean.temperature / = size;

[0128] mean.sunshine / = size;

[0129] mean.humidity / = size;

[0130] mean.wind_speed / = size;

[0131] return mean;

[0132] }

[0133] public:

[0134] EnvironmentClassifier(const std::vector <environmentparams>&conditions,

[0135] const EnvironmentParams&today,

[0136] int max_iter = 10)

[0137] : all_conditions(conditions), today_condition(today), max_iterations(max_iter) {}

[0138] / / Main algorithm: Obtaining common environment combinations

[0139] std::vector <environmentparams>getCommonConditions() {

[0140] if (all_conditions.empty()) return {};

[0141] EnvironmentParams current_min, current_max;

[0142] findExtremeConditions(all_conditions, current_min, current_max);

[0143] int iter = 0;

[0144] while (iter++ <max_iterations) {

[0145] / / Categorized into two libraries

[0146] auto clusters = classifyConditions(current_min, current_max);

[0147] / / Computational Simulation Center

[0148] EnvironmentParams new_min = calculateMeanCondition(clusters[0]);

[0149] EnvironmentParams new_max = calculateMeanCondition(clusters[1]);

[0150] / / Find the actual sample in the database that is closest to the simulated center.

[0151] EnvironmentParams actual_min, actual_max;

[0152] findExtremeConditions(clusters[0], actual_min, actual_max);

[0153] findExtremeConditions(clusters[1], actual_max, actual_min); / / Note the parameter order

[0154] / / Check if convergence

[0155] if (actual_min == current_min&&actual_max == current_max) {

[0156] / / Returns all combinations in the database for the current day

[0157] double div_to_min = calculateDivergence(today_condition, actual_min);

[0158] double div_to_max = calculateDivergence(today_condition, actual_max);

[0159] return (div_to_min<= div_to_max) ? clusters[0] : clusters[1];

[0160] }

[0161] / / Update the current center

[0162] current_min = actual_min;

[0163] current_max = actual_max;

[0164] }

[0165] / / If the maximum number of iterations has been exceeded, return the single sample with the smallest divergence.

[0166] EnvironmentParams best_match;

[0167] double min_div = std::numeric_limits <double>::max();

[0168] for (const auto&cond : all_conditions) {

[0169] double div = calculateDivergence(today_condition, cond);

[0170] if (div<min_div) {

[0171] min_div = div;

[0172] best_match = cond;

[0173] }

[0174] }

[0175] return {best_match};

[0176] }

[0177] };

[0178] int main() {

[0179] / / Example data

[0180] std::vector <environmentparams>good_conditions = {

[0181] {25.0, 28.5, 6.2, 65.0, 2.1},

[0182] {23.5, 30.2, 5.8, 70.0, 1.5},

[0183] {27.0, 25.8, 7.0, 60.0, 2.5},

[0184] {26.2, 27.3, 6.5, 62.0, 2.0},

[0185] {24.8, 29.0, 6.1, 67.0, 1.9}

[0186] };

[0187] EnvironmentParams today = {24.8, 29.1, 6.0, 68.0, 1.8};

[0188] / / Create a classifier and execute the algorithm

[0189] EnvironmentClassifier classifier(good_conditions, today);

[0190] auto common_conditions = classifier.getCommonConditions();

[0191] / / Output results

[0192] std::cout<<"A good environmental combination that shares similarities with the current day's environment:"< <std::endl;

[0193] for (const auto&cond : common_conditions) {

[0194] cond.print();

[0195] std::cout<<" (Divergence:"< <calculateDivergence(today, cond)<<")\n";

[0196] }

[0197] return 0;

[0198] }

[0199] This code implements a dynamic classification-based environmental parameter combination selection algorithm. During execution, it first calculates the divergence degree, using Manhattan distance to quantify the differences between environmental combinations, comprehensively considering five parameters including soil moisture content and air temperature. Then, an iterative classification mechanism is employed, initially identifying the two extreme combinations with the highest and lowest divergence from the current day's environment as initial classification centers. Multiple iterations (up to 10) are performed to dynamically adjust the classification, progressively optimizing the combination library. In each round, the proposed centers (parameter mean) for each library are recalculated, and the closest actual sample is found. Finally, an intelligent termination condition is established: when the classification centers no longer change, all combinations in the current day's environment library are returned as the final result. To avoid excessively long iteration wait times, when the maximum number of iterations is exceeded, it automatically degenerates to returning the single optimal combination with the lowest divergence degree.

[0200] This algorithm can effectively identify historically favorable combinations that resemble current environmental characteristics, providing precise irrigation decision-making support for smart garden systems. Compared to simple nearest neighbor methods, its classification process better reflects the overall distribution characteristics of environmental parameters.

[0201] Please see Figure 3 and 4 As shown, after obtaining the combination of several favorable environmental condition parameter values ​​applicable to a certain crop plot on that day, step S33 can be executed to summarize the combination of favorable environmental condition parameter values ​​applicable to each crop plot on that day.

[0202] Please see Figure 2 and 6 As shown, after finding the optimal combination of environmental conditions for the day, step S4 can be executed to obtain the required irrigation water per unit area for each crop plot based on the required irrigation water per unit area for each crop plot under different optimal environmental condition parameter combinations and the optimal environmental condition parameter combinations applicable to each crop plot on that day. Specifically, for each crop plot, step S41 can be executed first to calculate the environmental divergence between the optimal combination of environmental conditions for the day and each optimal combination of environmental conditions applicable to that day. Next, step S42 can be executed to use the environmental divergence between each optimal combination of environmental conditions applicable to that day and the optimal combination of environmental conditions for the day as a weighting coefficient to calculate the weighted average of the required irrigation water per unit area corresponding to the optimal combination of environmental conditions applicable to that day as the required irrigation water per unit area for that crop plot on that day. Finally, step S43 can be executed to summarize the required irrigation water per unit area for each crop plot on that day.

[0203] Please continue reading. Figures 1 to 3 As shown, in this scheme, the irrigation execution module 4 can first execute step S041 to set up irrigation modules separately in each crop plot of the garden. After the irrigation prediction module 3 obtains the irrigation demand of each crop plot, it can execute step S042 to receive the irrigation water demand per unit area of ​​each crop plot on that day. Finally, it can execute step S043 to control the irrigation modules in each crop plot to irrigate according to the irrigation water demand per unit area of ​​each crop plot on that day.

[0204] See Figure 7 , Figure 7 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 7 The electronic device 500 in this embodiment 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 memories 504 store computer programs, including program instructions. The processors 501 execute the program instructions stored in the memories 504. Specifically, the processors 501 are configured to invoke the program instructions to execute the functions of each module / unit in the above-described intelligent garden irrigation control device embodiment, for example... Figure 1 The functions of irrigation prediction module 3 and irrigation execution module 4 are shown.

[0205] It should be understood that, in the embodiments of this application, the processor 501 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), 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.

[0206] Input device 502 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 503 may include a display (LCD, etc.), a speaker, etc.

[0207] The memory 504 may include read-only memory and random access memory, and provides instructions and data to the processor 501. A portion of the memory 504 may also include non-volatile random access memory. For example, the memory 504 may also store device type information.

[0208] In specific implementations, the processor 501, input device 502, and output device 503 described in the embodiments of this application can execute the implementation methods described in the embodiments of the smart garden irrigation control method provided in this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.

[0209] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0210] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing 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, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic 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 will be output.

[0211] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0212] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0213] 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 illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.

[0214] The units described as separate components may or may not be physically separate. 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 can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0215] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0216] The above are merely specific embodiments of this application, but the scope of protection of this 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 these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope 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 with good growth status in the crop plot, and obtain the irrigation water volume per unit area of ​​the crop plot under the parameter value combination of each type of environmental condition. Summarize to obtain the irrigation water demand per unit area of ​​each crop plot under different combinations of parameter values ​​of good environmental conditions. Obtain the parameter values ​​of each type of environmental condition for each crop plot on that day; For each crop plot, several combinations of favorable environmental condition parameter values ​​applicable to the day are obtained through comparison. This includes performing the following steps for each crop plot: The parameter values ​​of each type of environmental condition for each crop plot on that day are used as the combination of environmental condition parameter values ​​for that day. Based on the difference between the combination of environmental condition parameter values ​​for the day and the parameter values ​​of each type of environmental condition in each combination of good environmental condition parameter values, several combinations of good environmental condition parameter values ​​that have commonalities with the environmental conditions for the day are selected as several combinations of good environmental condition parameter values ​​applicable to the crop plot on that day. This includes, for each combination of environmental condition parameter values ​​that includes the combination of environmental condition parameter values ​​for the day and each combination of good environmental condition parameter values, the cumulative value of the difference between the parameter values ​​of each type of environmental condition in the combination of environmental condition parameter values ​​is used as the degree of environmental divergence between the combination of environmental condition parameter values, and the good environmental condition parameter value combinations with the largest and smallest degree of environmental divergence with the combination of environmental condition parameter values ​​for the day are respectively used as different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations. Calculate the degree of environmental divergence between different combinations of favorable environmental condition parameter values ​​and combinations of the same favorable environmental condition parameter values ​​and each other combination of environmental condition parameter values. Each combination of environmental condition parameter values ​​other than the combinations of dissimilar good environmental condition parameter values ​​and the combinations of identical good environmental condition parameter values ​​is classified into the same environmental condition parameter value combination library along with the combination of dissimilar good environmental condition parameter values ​​or the combination of identical good environmental condition parameter values ​​with the lowest degree of environmental discrepancy. The combination of favorable environmental condition parameter values ​​applicable to each crop plot on that day is obtained by summarizing them. The irrigation water requirement per unit area for each crop plot on a given day is determined based on the combination of favorable environmental condition parameters for different crop plots and the applicable combination of favorable environmental condition parameters for each crop plot on that day. This includes performing the following steps for each crop plot: Calculate the environmental divergence between the combination of environmental condition parameter values ​​for the crop plot on that day and the combination of favorable environmental condition parameter values ​​applicable on that day; Using the environmental divergence between each combination of favorable environmental conditions parameters applicable on a given day and the combination of environmental conditions parameters applicable on a given day as a weighting coefficient, the weighted average value of the required irrigation water per unit area corresponding to the combination of favorable environmental conditions parameters applicable on a given day is calculated as the required irrigation water per unit area of ​​the crop plot on that day. The total irrigation water requirement per unit area for each crop plot on that day is calculated.

2. The intelligent garden irrigation control method as described in claim 1, characterized in that, Environmental conditions include soil moisture content, average temperature, sunshine duration, average humidity, and average wind speed.

3. The intelligent garden irrigation control method as described in claim 1, characterized in that, The step of selecting several combinations of favorable environmental condition parameter values ​​that share commonalities with the environmental conditions of the day based on the difference between the combination of environmental condition parameter values ​​for the day and the parameter values ​​for each type of environmental condition in each combination of favorable environmental condition parameter values ​​also includes, The mean value of the parameter values ​​for each type of environmental condition in all environmental condition parameter value combinations contained in each environmental condition parameter value combination library is calculated and used as the simulated environmental condition parameter value combination for that environmental condition parameter value combination library. The environmental condition parameter value combination with the smallest environmental discrepancy among the corresponding proposed environmental condition parameter value combination in each environmental condition parameter value combination library is used as the switched different good environmental condition parameter value combination and the same good environmental condition parameter value combination. Among them, the combination with a larger environmental discrepancy among the environmental condition parameter value combination of the day is used as the switched different good environmental condition parameter value combination, and the combination with a smaller environmental discrepancy among the environmental condition parameter value combination of the day is used as the switched same good environmental condition parameter value combination. If the different combinations of favorable environmental condition parameter values ​​and the same combinations of favorable environmental condition parameter values ​​remain unchanged before and after the switch, then all favorable 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 will be regarded as favorable environmental condition parameter value combinations that have commonalities with the environmental conditions of the current day.

4. The intelligent garden irrigation control method as described in claim 3, characterized in that, The step of selecting several combinations of favorable environmental condition parameter values ​​that share commonalities with the environmental conditions of the day based on the difference between the combination of environmental condition parameter values ​​for the day and the parameter values ​​for each type of environmental condition in each combination of favorable environmental condition parameter values ​​also includes, If the different or identical combinations of favorable environmental condition parameter values ​​change before and after the switch, the environmental condition parameter value combination library after the switch is obtained based on the different and identical combinations of favorable environmental condition parameter values ​​after the switch. The system continues to calculate and generate different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations in the environmental condition parameter value combination library after the switch, until the different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations after the switch remain unchanged. At this time, all 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 are taken as good environmental condition parameter value combinations that have commonity with the environmental conditions of the current day.

5. The method according to claim 4, characterized in that, If the number of times the environmental condition parameter value combination library is divided and switched exceeds the set number, then the environmental condition parameter value combination with the smallest environmental difference degree among all the good environmental condition parameter value combinations and the environmental condition parameter value combination of the day will be regarded as the only good environmental condition parameter value combination that has commonality with the environmental conditions of the day.

6. A smart garden irrigation control method, characterized in that, include, Irrigation modules are set up separately in each crop plot in the garden; The method for controlling irrigation in a smart garden according to any one of claims 1 to 5 receives the daily irrigation water demand per unit area for each crop plot. The irrigation modules within each crop plot are controlled to irrigate according to the irrigation water demand per unit area of ​​each crop plot on that day.

7. A smart garden irrigation control device, characterized in that, include, Sensors are used to detect 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 the number of plants with good growth status in each crop plot in the garden, and to obtain the irrigation water volume per unit area of ​​the crop plot under the combination of parameter values ​​of each type of environmental conditions. The module is then used to summarize the required irrigation water volume per unit area of ​​each crop plot under different combinations of parameter values ​​of good environmental conditions. Obtain the parameter values ​​of each type of environmental condition for each crop plot on that day; For each crop plot, several combinations of favorable environmental condition parameter values ​​applicable to the day are obtained through comparison. This includes performing the following steps for each crop plot: The parameter values ​​of each type of environmental condition for each crop plot on that day are used as the combination of environmental condition parameter values ​​for that day. Based on the difference between the combination of environmental condition parameter values ​​for the day and the parameter values ​​of each type of environmental condition in each combination of good environmental condition parameter values, several combinations of good environmental condition parameter values ​​that have commonalities with the environmental conditions for the day are selected as several combinations of good environmental condition parameter values ​​applicable to the crop plot on that day. This includes, for each combination of environmental condition parameter values ​​that includes the combination of environmental condition parameter values ​​for the day and each combination of good environmental condition parameter values, the cumulative value of the difference between the parameter values ​​of each type of environmental condition in the combination of environmental condition parameter values ​​is used as the degree of environmental divergence between the combination of environmental condition parameter values, and the good environmental condition parameter value combinations with the largest and smallest degree of environmental divergence with the combination of environmental condition parameter values ​​for the day are respectively used as different good environmental condition parameter value combinations and the same good environmental condition parameter value combinations. Calculate the degree of environmental divergence between different combinations of favorable environmental condition parameter values ​​and combinations of the same favorable environmental condition parameter values ​​and each other combination of environmental condition parameter values. Each combination of environmental condition parameter values ​​other than the combinations of dissimilar good environmental condition parameter values ​​and the combinations of identical good environmental condition parameter values ​​is classified into the same environmental condition parameter value combination library along with the combination of dissimilar good environmental condition parameter values ​​or the combination of identical good environmental condition parameter values ​​with the lowest degree of environmental discrepancy. The combination of favorable environmental condition parameter values ​​applicable to each crop plot on that day is obtained by summarizing them. The irrigation water requirement per unit area for each crop plot on a given day is determined based on the combination of favorable environmental condition parameters for different crop plots and the applicable combination of favorable environmental condition parameters for each crop plot on that day. This includes performing the following steps for each crop plot: Calculate the environmental divergence between the combination of environmental condition parameter values ​​for the crop plot on that day and the combination of favorable environmental condition parameter values ​​applicable on that day; Using the environmental divergence between each combination of favorable environmental conditions parameters applicable on a given day and the combination of environmental conditions parameters applicable on a given day as a weighting coefficient, the weighted average value of the required irrigation water per unit area corresponding to the combination of favorable environmental conditions parameters applicable on a given day is calculated as the required irrigation water per unit area of ​​the crop plot on that day. The total irrigation water requirement per unit area for each crop plot on that day was calculated. An irrigation execution module is used to set up irrigation modules separately in each crop plot of the garden; Receive the irrigation water demand per unit area for each crop plot on that day; The irrigation modules within each crop plot are controlled to irrigate according to the irrigation water demand per unit area of ​​each crop plot on that day.

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