Intelligent garden sprinkler device and system based on remote control

Abstract

The present application relates to the field of intelligent sprinkler technology, and specifically to an intelligent garden sprinkler device and system based on remote control. The device comprises: an environmental information acquisition module for acquiring real-time garden temperature data, wind speed data, and soil moisture data for each area within the garden; an environmental information analysis module for acquiring the sprinkler demand coefficient for each area for each sprinkler irrigation. The specific process is as follows: acquiring each humidity rise interval and humidity change degree for each area; acquiring each temperature change interval and the amount of change in soil moisture data therein for each area, and combining the time difference between the sudden change in soil moisture data and temperature data for each area to acquire the humidity change sensitivity for each area; acquiring the sprinkler demand coefficient for each area; and a sprinkler control module for controlling the sprinkler duration for each area. The present application aims to achieve refined control of garden sprinkler irrigation by controlling the sprinkler duration for each area within the garden.

Description

Technical Field

[0001] The present application relates to the field of intelligent sprinkler irrigation technology, and in particular to an intelligent garden sprinkler irrigation device and system based on remote control. Background Art

[0002] With the advancement of agricultural technology, precision agriculture has become a key development direction in modern agriculture. Agricultural sprinkler irrigation uses sprinkler nozzles to spray water into the air, forming fine droplets that are then evenly distributed across farmland. In precision agriculture, automated sprinkler control is a key tool for achieving high efficiency, water conservation, and increased production. Currently, automated sprinkler irrigation is not only used in large-scale agricultural plantings but is also gradually being adopted in garden plantings.

[0003] Existing technologies use wireless terminals and sensor networks to collect garden environmental data, such as temperature, wind speed, and soil moisture, to achieve automated sprinkler irrigation control. However, existing technologies often use threshold control, such as turning on sprinkler irrigation only when soil moisture is below a certain threshold. However, compared to the agricultural sector, the plant density in a garden is relatively low, and flowers may not be planted in every part of the garden. Therefore, the water demand of different areas of the garden varies. Controlling sprinkler irrigation solely based on thresholds may result in insufficient irrigation in areas with high water demand, affecting plant growth, or excessive irrigation in areas with low water demand, thereby wasting water resources. Therefore, it is necessary to determine the actual soil water demand in the garden and then implement more refined sprinkler irrigation control. Summary of the Invention

[0004] In view of the above, it is necessary to provide an intelligent garden sprinkler device and system based on remote control. Compared with the traditional intelligent garden sprinkler device and system based on remote control, it can achieve refined control of garden sprinkler irrigation by controlling the sprinkler irrigation time of each area in the garden.

[0005] In a first aspect, an embodiment of the present application provides a remote-controlled intelligent garden sprinkler device, wherein the device comprises:

[0006] Environmental information collection module, used to obtain real-time temperature data, wind speed data, and soil moisture data of each preset area in the garden;

[0007] The environmental information analysis module is used to obtain the irrigation demand coefficient of each area for each irrigation by using the temperature data, wind speed data, and soil moisture data within a preset time period before the irrigation. The specific process is as follows:

[0008] The humidity increase intervals of each region are obtained by analyzing the growth of soil moisture data in each region. The humidity change degree of each region is obtained by analyzing the moment when the soil moisture data of each region undergoes a sudden change, the lowest value of the soil moisture data, and the speed of change of the soil moisture data within each humidity increase interval, and combining the correlation between the degree of change of the soil moisture data in each region and the degree of change of the wind speed data.

[0009] The temperature change intervals of each region are obtained by the changes in the temperature data of each region; the humidity change sensitivity of each region is obtained by the changes in the soil moisture data within each temperature change interval of each region and the time difference between the mutations of the soil moisture data and the temperature data of each region.

[0010] Obtaining the sprinkler irrigation demand coefficient of each area through the humidity change degree and the humidity change sensitivity;

[0011] The sprinkler control module is used to control the sprinkler irrigation duration of each area through the sprinkler irrigation demand coefficient.

[0012] In one embodiment, obtaining the humidity rising interval includes:

[0013] The soil moisture data of each area within the preset time period are arranged in time series to form each soil moisture data sequence; the time interval between any minimum point and the moment of the next maximum point in each soil moisture sequence is taken as a humidity rising interval.

[0014] In one embodiment, obtaining the humidity change degree includes:

[0015] The collection moments within the preset time period are numbered in time sequence to obtain the number of the collection moment at which the first mutation point in the soil moisture data sequence of each region is located;

[0016] Obtaining the absolute value of the slope of the fitted straight line of the soil moisture data in each humidity rising interval; calculating the average value of the absolute value in all humidity rising intervals in each region;

[0017] Counting the minimum value of all soil moisture data in each area within the preset time period;

[0018] Calculating the absolute values ​​of the elements in the first-order difference sequence of the soil moisture data sequence of each region, and calculating the correlation coefficient between the absolute values ​​of the elements and the wind speed data in each region within the preset time period in time;

[0019] The humidity change degree is the reciprocal of the sum of the normalized value of the number, the normalized value of the average value, the normalized value of the minimum value, and the correlation coefficient.

[0020] In one embodiment, obtaining the temperature variation range includes:

[0021] When the time series variation between any two temperature data is greater than or equal to a preset value, the time interval between the moments of the any two temperature data is taken as a temperature variation interval.

[0022] In one embodiment, obtaining the humidity change sensitivity includes:

[0023] Calculate the difference between the last soil moisture data and the first soil moisture data in each humidity change interval; record the average of the differences in all temperature change intervals of each region as a first average;

[0024] Arrange the temperature data within the time period in time sequence to form a temperature data sequence; obtain mutation points in the soil moisture data sequence of each region and number them respectively in time sequence; obtain mutation points in the temperature data sequence and number them respectively in time sequence; calculate the time difference between the soil moisture data sequence and the temperature data sequence of each region at the time of each mutation point with the same number, and record the average of all the time differences in each region as the second average;

[0025] The humidity change sensitivity is directly proportional to the first average value and inversely proportional to the second average value.

[0026] In one embodiment, the humidity change sensitivity calculation process is: calculating the sum of the second mean value and a preset positive number; and the humidity change sensitivity is the ratio of the first mean value to the sum.

[0027] In one embodiment, the sprinkler irrigation demand coefficient is the product of the normalized value of the humidity change sensitivity and the humidity change degree.

[0028] In one embodiment, controlling the sprinkling duration of each area includes: increasing the sprinkling duration for areas where the sprinkling demand coefficient is greater than or equal to a preset humidity threshold; and reducing the sprinkling duration for areas where the sprinkling demand coefficient is less than the preset humidity threshold.

[0029] In one embodiment, the expression for the irrigation duration of each area is:

[0030] Where, T i represents the duration of irrigation in the i-th area; K i represents the humidity change sensitivity of the i-th region; H represents the preset irrigation duration; γ represents the preset humidity threshold.

[0031] In a second aspect, an embodiment of the present application also provides an intelligent garden sprinkler system based on remote control, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned intelligent garden sprinkler devices based on remote control.

[0032] This application has at least the following beneficial effects:

[0033] By dividing the garden, we can obtain soil moisture data for each area, which will facilitate targeted irrigation according to the soil moisture conditions of each area. Taking into account the different water requirements of different areas, we can obtain the temperature change degree by combining the minimum soil moisture value, the rate of humidity increase, and the correlation between soil moisture changes and wind speed changes. This can more accurately reflect the actual changes in soil moisture in each area, assess the water demand of each area, and avoid the one-sidedness caused by single-factor evaluation.

[0034] Furthermore, taking into account the mutual influence between adjacent areas, evaluating the sensitivity of soil moisture to temperature changes and adjusting the water demand of each area can more comprehensively and accurately evaluate the actual water demand of each area in the garden, provide targeted sprinkler irrigation basis for each area, and enable sprinkler irrigation control to be differentiated and adjusted according to the actual water demand of different areas; and then control the sprinkler irrigation time according to the water demand of each area, avoiding the waste of water resources or poor plant growth caused by the use of a uniform sprinkler irrigation volume for all areas in the traditional sprinkler irrigation method, and realizing refined control of garden sprinkler irrigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description 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.

[0036] Figure 1 A block diagram of the remote-controlled smart garden sprinkler system provided for this application;

[0037] Figure 2 This is a flow chart for obtaining the sprinkler irrigation demand coefficient;

[0038] Figure 3 This is a flow chart of an intelligent garden sprinkler system based on remote control;

[0039] Figure 4 This is a flowchart for obtaining the irrigation duration. DETAILED DESCRIPTION

[0040] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application relates. The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. It should be understood that, unless otherwise indicated, " / " represents or.

[0042] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0043] The specific scheme of the remote control-based intelligent garden sprinkler device and system provided by this application is described in detail below with reference to the accompanying drawings.

[0044] See also Figure 1 , which shows a block diagram of an intelligent garden sprinkler device based on remote control provided by an embodiment of the present invention. The device includes: an environmental information acquisition module 101, a mobile terminal 102, an environmental information analysis module 103, a sprinkler control module 104, a sprinkler controller 105, a booster water pump 106 and a solenoid valve 107.

[0045] The environmental information collection module 101 is used to obtain the temperature data, wind speed data of the garden, and soil moisture data of each preset area in the garden in real time.

[0046] The temperature sensor is used to collect the temperature data of the garden in real time, the wind speed sensor is used to collect the wind speed data of the garden in real time, and the soil moisture sensor is used to collect the soil moisture data of each preset area in the garden in real time.

[0047] In this embodiment, the time interval for the temperature sensor to collect temperature data, the wind speed sensor to collect wind speed data, and the soil moisture sensor to collect soil moisture data are all 2 minutes. The value of the collection time interval is preset manually and can be set by the implementer. This application does not impose any special restrictions.

[0048] In this embodiment, the area of ​​the garden is 20 square meters, and the number of soil moisture sensors is 5. The implementer can set the number of soil moisture sensors according to the area of ​​the garden. The garden is evenly divided into zones, and the number of zones is the same as the number of soil moisture sensors.

[0049] The mobile terminal 102 is used to install a garden sprinkler mobile application program and transmit the control instructions of each sprinkler irrigation to the environmental information analysis module.

[0050] The user selects the automatic control mode in the garden sprinkler mobile app and sets the values ​​for each parameter, such as the garden sprinkler cycle, to control the sprinkler on and off. The garden sprinkler mobile app transmits the user-set control commands to the environmental information analysis module via the wireless communication network.

[0051] In this embodiment, the mobile terminal is a mobile phone.

[0052] The environmental information analysis module 103 is used to obtain the irrigation demand coefficient of each area by analyzing the soil moisture data of each area within a preset time period before the irrigation, as well as the temperature data and wind speed data within the preset time period.

[0053] After receiving the control instruction, the environmental information analysis module analyzes the soil moisture data of each area in the preset time period before the irrigation, as well as the temperature data and wind speed data in the preset time period, to obtain the irrigation demand coefficient of each area. The specific analysis process is as follows:

[0054] For the soil in the garden, the part where flowers are planted is relatively loose, with high air circulation and faster water evaporation. Since the roots of flowers absorb water, the moisture content of the soil where flowers are planted will decrease rapidly with the increase in temperature. The greater the density of flowers, the faster the rate of decrease in soil moisture. At the same time, due to the obstruction of flowers, the rate of increase of soil moisture is disturbed to a certain extent during the sprinkler irrigation process, and the rate of increase is slowed down. In contrast, for the soil where flowers are not planted, the soil is relatively compact, there are no plants to absorb water, and the evaporation rate of water in the soil is slow. Usually, the change in soil moisture is relatively gentle. Although the moisture content in the soil will also decrease due to the increase in temperature, since only water evaporates and the soil is compact, the change rate of soil moisture is slow, and the overall water content is higher than that of the soil where flowers are planted.

[0055] Different regions plant different flower varieties and their growth stages. Therefore, different regions actually have different water requirements within the same timeframe. For example, varieties like gerbera and petunia require higher soil moisture, while varieties like geranium require less. Furthermore, the seedling growth stage requires less water than the flowering stage. As the temperature rises, different varieties and flowers at different growth stages absorb different amounts of soil moisture. Specifically, as the temperature rises, the soil moisture in regions with higher water requirements decreases earlier. When the temperature reaches the highest point of the day, the minimum soil moisture in each region varies due to the varying degrees of soil moisture absorption. The smaller the minimum soil moisture, the more water the region requires.

[0056] In addition, when the wind speed on the soil surface is higher, the water evaporation efficiency is faster. When the flower density in the area is higher and the volume of the flowers is larger, the wind speed on the soil surface in the area will be lower than that of the soil surface where flowers are not planted or in the germination and seedling growth period due to the obstruction of the flowers themselves, and water evaporation is relatively slow. Therefore, the stronger the correlation between the degree of change in the soil moisture data in the area and the degree of change in the garden wind speed data, the more likely it is that there are no flowers planted in the area or the currently planted flowers are in a stage with low water demand, and the soil in the area has less demand for water.

[0057] For each sprinkler irrigation, the soil moisture data of each area within the preset time period are arranged in time sequence to form a soil moisture data sequence for each area; the temperature data within the time period are arranged in time sequence to form a temperature data sequence; and the wind speed data within the time period are arranged in time sequence to form a wind speed data sequence.

[0058] In this embodiment, the preset time period is the 24 hours before the start time of each sprinkler irrigation. The implementer can set the length of the preset time period according to actual conditions, and this application does not impose any special restrictions.

[0059] To characterize the rate of increase and decrease in soil moisture data, we obtained the extreme points in each soil moisture data series. The time interval between the instant of a minimum point and the instant of the next maximum point in each soil moisture series was defined as a moisture increase interval. A mutation point detection algorithm was used to obtain mutation points in each soil moisture data series and in each temperature data series.

[0060] In this embodiment, an extreme point detection algorithm is used to obtain the extreme points in each soil moisture data sequence. The extreme point detection algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to obtain the extreme points in each soil moisture data sequence, the implementer may adopt other existing technologies, such as peak and trough detection algorithms, and this application does not impose any special restrictions.

[0061] In this embodiment, the mutation point detection algorithm is the Pettitt mutation point detection algorithm. The Pettitt mutation point detection algorithm is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to obtain each mutation point in each soil moisture data sequence and each mutation point in the temperature data sequence, the implementer may adopt other existing technologies, such as the Bayesian mutation point detection algorithm, the Mann-Kendall mutation point detection algorithm, etc., and this application does not impose any special restrictions.

[0062] Based on the above analysis, the humidity change degree of each region is obtained by the time when the soil moisture data of each region undergoes a sudden change, the lowest value of the soil moisture data, and the change speed of the soil moisture data in each humidity rising interval, and the correlation between the change degree of the soil moisture data in each region and the change degree of the wind speed data. The expression is:

[0063] Where A i represents the humidity change degree of the i-th region; norm() represents the arctangent normalization function; the collection moments within the preset time period are numbered in time sequence, b i Indicates the number of the first mutation point in the soil moisture data sequence of the i-th region at the time of collection; obtains the absolute value of the slope of the fitting line of the soil moisture data in each humidity rising interval; c i represents the average value of the absolute values ​​in all humidity rising intervals of the i-th region; calculate the absolute values ​​of the elements in the first-order difference sequence of the soil moisture data sequence of each region, and arrange them in time sequence to form the soil moisture speed sequence of each region; Z i represents the correlation coefficient between the soil moisture speed sequence and the wind speed data sequence of the ith region. The calculation of the slope is a well-known technique and will not be described in detail in this application.

[0064] In this embodiment, the least squares method is used to obtain a fitting straight line for the soil moisture data in each humidity rising interval. The least squares method is a well-known technology and will not be described in detail in this application. As other implementation methods, on the basis of being able to obtain a fitting straight line for the soil moisture data in each humidity rising interval, the implementer may adopt other existing technologies, such as the weighted least squares method, etc., and this application does not impose any special restrictions.

[0065] In this embodiment, the correlation coefficient between the soil moisture speed change sequence and the wind speed data sequence is the Pearson correlation coefficient. In the process of calculating the Pearson correlation coefficient, if the lengths of the soil moisture speed change sequence and the wind speed data sequence are not equal, the longest sequence is truncated to make the lengths of the longest sequence equal to the shortest sequence; as other implementation methods, on the basis of being able to measure the correlation between the soil moisture speed change sequence and the wind speed data sequence, the implementer may adopt other existing technologies, such as the Spearman correlation coefficient, the Kendall rank correlation coefficient, etc., and this application does not impose any special restrictions.

[0066] It should be noted that: when the humidity in the i-th region decreases faster and increases slower, and the minimum soil moisture is smaller, the correlation between soil moisture change and wind speed change is stronger, it means that the flowers planted in the i-th region have a greater demand for water, stronger absorption energy for soil moisture, and a higher degree of soil shading during sprinkler irrigation. The overall humidity variability in the i-th region is higher, the higher the water demand of the i-th region, and the more water resources are needed for sprinkler irrigation in the i-th region.

[0067] Furthermore, the temperature change intervals of each region are obtained through the changes in the temperature data of each region; the humidity change sensitivity of each region is obtained through the changes in the soil moisture data within each temperature change interval of each region, combined with the time difference of the mutation between the soil moisture data and the temperature data of each region.

[0068] Due to the different growth conditions of plants, when the flower density in a certain area is high or the planted flower varieties themselves are large, their branches and leaves will cover the adjacent areas. Moreover, since the plant root system is relatively developed, it may also extend to the adjacent areas to absorb water, thereby causing the soil moisture in some areas where no flowers are planted to change faster. Calculation only through the above method may still misjudge the areas where no flowers are planted as areas with high water demand, and then spray more water resources, ultimately resulting in a waste of water resources. Therefore, further judgment is needed.

[0069] For planted flowers, as the sun rises, the illumination increases, the temperature gradually rises, and the photosynthesis and transpiration of flowers gradually increase. Therefore, flowers will need more water to support life activities. The more water this area needs, the greater the impact of the ambient temperature. Therefore, the soil moisture changes in areas with higher water requirements will be more sensitive to changes in ambient temperature. This is mainly manifested in that when the temperature data changes, the soil moisture changes in areas with a high density of flower planting are closer in time and the change amount is larger than in areas without flower planting.

[0070] In order to characterize the sensitivity of soil moisture changes to temperature, for the temperature data sequence, starting from the first temperature data, traversing the temperature data backward, every time the temperature changes by n°C, that is, when the change in the time series between any two temperature data is greater than or equal to n°C, the time interval between the moments of the arbitrary two temperature data is taken as a temperature change interval.

[0071] In this embodiment, the value of n is 5. The value of n is preset and can be set by the implementer. This application does not impose any special restrictions. For example, if the temperature data collected from the first collection moment to the fifth collection moment is 15.1°C, 17.2°C, 18.0°C, 18.5°C, and 20.2°C, the time interval between 15.1°C and 20.2°C is considered as a temperature change interval, and the temperature change interval includes the collection moment of 15.1°C and the collection moment of 20.2°C.

[0072] Based on the above analysis, the humidity change sensitivity of each region is obtained by combining the change amount of soil moisture data in each temperature change interval of each region with the time difference between the mutation of soil moisture data and temperature data of each region. The expression is:

[0073] Where, E i The sensitivity of humidity change in the i-th region; calculate the difference between the last soil moisture data and the first soil moisture data in each humidity change interval, F i represents the mean of the difference values ​​in all temperature change intervals of the i-th region; α represents a preset positive number, which is used to avoid the denominator being 0. The value of α is preset manually. In order to avoid affecting the calculation result of the humidity change sensitivity, in this embodiment, α is a very small positive number of 0.01; the mutation points in each soil moisture data sequence are numbered in time sequence, and the mutation points in the temperature data sequence are numbered in time sequence, and the time difference between the mutation points with the same number between the soil moisture data sequence and the temperature data sequence of each region is calculated, H i represents the mean of all the time differences in the ith region. i Recorded as the first mean, H i Recorded as the second mean.

[0074] In this embodiment, the difference between the last soil moisture data and the first soil moisture data is the absolute value of the difference.

[0075] It should be noted that: when the change in soil moisture in the i-th region is greater within the temperature change range, and the change time of the soil moisture data is closer to the temperature change time, it means that the soil moisture in the i-th region is more sensitive to temperature changes, and it is more likely to be a soil area with a large water demand, and more water resources are needed for sprinkler irrigation in the i-th region.

[0076] Furthermore, the irrigation demand coefficient of each region is obtained by the humidity change and humidity change sensitivity of each region. The expression is:

[0077] K i =A i +norm(E i );where K i is the sprinkler irrigation demand coefficient of the i-th area; A i represents the humidity change of the i-th region; norm() represents the arc tangent normalization function; E i The sensitivity of humidity change in the i-th region.

[0078] It should be noted that: when the density of flower planting in the i-th region is higher, the soil area has a greater demand for water, and the humidity change is more sensitive to temperature, it may mean that the higher the density of flower planting in the i-th region, the greater the demand for water, and the more water should be sprinkled in the i-th region. The flowchart for obtaining the sprinkler demand coefficient is as follows: Figure 2 shown.

[0079] The sprinkling control module 104 is used to control the sprinkling duration of each area according to the sprinkling demand coefficient.

[0080] For areas where the irrigation demand coefficient is greater than or equal to the preset humidity threshold, the irrigation time is increased; for areas where the irrigation demand coefficient is less than the preset humidity threshold, the irrigation time is reduced. The expression for the irrigation time of each area is:

[0081] Where, T i represents the duration of irrigation in the i-th area; K i represents the humidity change sensitivity of the i-th area; H represents the preset irrigation time; γ represents the preset humidity threshold. The flow chart of the intelligent garden sprinkler system based on remote control is as follows: Figure 3 The flow chart of obtaining the irrigation duration is shown in Figure 4 shown.

[0082] In this embodiment, the preset sprinkler irrigation time is 5 minutes. The preset sprinkler irrigation time is manually preset and can be set by the implementer according to actual conditions. This application does not impose any special restrictions.

[0083] In this embodiment, the value of the preset humidity threshold is 0.6. The value of the preset humidity threshold is preset manually and the implementer can set it according to actual conditions. This application does not impose any special restrictions.

[0084] For each sprinkler irrigation, the sprinkler control module issues a control instruction, which is transmitted to the sprinkler controller through the wireless communication network. The sprinkler controller controls the booster pump and solenoid valve to irrigate each area. The duration of sprinkler irrigation in each area is the calculated sprinkler duration.

[0085] Based on the same inventive concept as the above method, an embodiment of the present application also provides an intelligent garden sprinkler system based on remote control, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the above-mentioned intelligent garden sprinkler device based on remote control is implemented.

[0086] In summary, by dividing the garden and obtaining soil moisture data for each area, it is convenient to carry out targeted sprinkler irrigation according to the soil moisture conditions of each area. Taking into account the different water requirements of different areas, the minimum value of soil moisture, the rate of humidity increase, and the correlation between soil moisture changes and wind speed changes are comprehensively considered to obtain the temperature change degree. This can more accurately reflect the actual changes in soil moisture in each area, evaluate the water demand of each area, and avoid the one-sidedness caused by single-factor evaluation.

[0087] Furthermore, taking into account the mutual influence between adjacent areas, evaluating the sensitivity of soil moisture to temperature changes and adjusting the water demand of each area can more comprehensively and accurately evaluate the actual water demand of each area in the garden, provide targeted sprinkler irrigation basis for each area, and enable sprinkler irrigation control to be differentiated and adjusted according to the actual water demand of different areas; and then control the sprinkler irrigation time according to the water demand of each area, avoiding the waste of water resources or poor plant growth caused by the use of a uniform sprinkler irrigation volume for all areas in the traditional sprinkler irrigation method, and realizing refined control of garden sprinkler irrigation.

[0088] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architectures, functions and operations of the systems, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0089] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from all perspectives, the above embodiments of the present application should be regarded as exemplary and non-restrictive.

Claims

1. An intelligent garden sprinkler system based on remote control, characterized in that: The device contains: Environmental information collection module, used to obtain real-time temperature data, wind speed data, and soil moisture data of each preset area in the garden; The environmental information analysis module is used to obtain the irrigation demand coefficient of each area for each irrigation by using the temperature data, wind speed data, and soil moisture data within a preset time period before the irrigation. The specific process is as follows: The humidity increase intervals of each region are obtained by analyzing the growth of soil moisture data in each region. The humidity change degree of each region is obtained by analyzing the moment when the soil moisture data of each region undergoes a sudden change, the minimum value of the soil moisture data, and the change speed of the soil moisture data within each humidity increase interval, and combining the correlation between the change degree of soil moisture data in each region and the change degree of wind speed data. The temperature change intervals of each region are obtained by the changes in the temperature data of each region; the humidity change sensitivity of each region is obtained by the changes in the soil moisture data within each temperature change interval of each region and the time difference between the mutations of the soil moisture data and the temperature data of each region. Obtaining the sprinkler irrigation demand coefficient of each area through the humidity change degree and the humidity change sensitivity; A sprinkler irrigation control module, configured to control the duration of sprinkler irrigation in each area according to the sprinkler irrigation demand coefficient; The acquisition of the humidity change degree includes: The collection moments within the preset time period are numbered in time sequence to obtain the number of the collection moment at which the first mutation point in the soil moisture data sequence of each region is located; Obtaining the absolute value of the slope of the fitted straight line of the soil moisture data in each humidity rising interval; calculating the average value of the absolute value in all humidity rising intervals in each region; Counting the minimum value of all soil moisture data in each area within the preset time period; Calculating the absolute values ​​of the elements in the first-order difference sequence of the soil moisture data sequence of each region, and calculating the correlation coefficient between the absolute values ​​of the elements and the wind speed data in each region within the preset time period in time; The humidity change degree is the reciprocal of the sum of the normalized value of the number, the normalized value of the average value, the normalized value of the minimum value, and the correlation coefficient; The acquisition of the humidity change sensitivity includes: Calculate the difference between the last soil moisture data and the first soil moisture data in each humidity change interval; record the average of the differences in all temperature change intervals of each region as a first average; Arrange the temperature data within the time period in time sequence to form a temperature data sequence; obtain mutation points in the soil moisture data sequence of each region and number them respectively in time sequence; obtain mutation points in the temperature data sequence and number them respectively in time sequence; calculate the time difference between the soil moisture data sequence and the temperature data sequence of each region at the time of each mutation point with the same number, and record the average of all the time differences in each region as the second average; The humidity change sensitivity is directly proportional to the first average value and inversely proportional to the second average value.

2. The remote control-based intelligent garden sprinkler device according to claim 1, characterized in that: The acquisition of the humidity rising interval includes: The soil moisture data of each area within the preset time period are arranged in time series to form each soil moisture data sequence; the time interval between any minimum point and the moment of the next maximum point in each soil moisture sequence is taken as a humidity rising interval.

3. The remote control-based intelligent garden sprinkler device according to claim 1, characterized in that: The acquisition of the temperature variation range includes: When the time series variation between any two temperature data is greater than or equal to a preset value, the time interval between the moments of the any two temperature data is taken as a temperature variation interval.

4. The remote control-based intelligent garden sprinkler device according to claim 1, characterized in that: The humidity change sensitivity calculation process is: calculating the sum of the second mean value and a preset positive number; the humidity change sensitivity is the ratio of the first mean value to the sum value.

5. The remote control-based intelligent garden sprinkler device according to claim 1, characterized in that: The sprinkler irrigation demand coefficient is the product of the normalized value of the humidity change sensitivity and the humidity change degree.

6. The remote control-based intelligent garden sprinkler device according to claim 1, characterized in that: The controlling of the sprinkling duration of each area includes: increasing the sprinkling duration for an area where the sprinkling demand coefficient is greater than or equal to a preset humidity threshold; and reducing the sprinkling duration for an area where the sprinkling demand coefficient is less than the preset humidity threshold.

7. The remote control-based intelligent garden sprinkler device according to claim 6, characterized in that: The expression of the irrigation duration of each area is: Where, represents the duration of sprinkler irrigation in the i-th area; represents the sprinkler irrigation demand coefficient of the i-th area; H represents the preset sprinkler irrigation duration; γ represents the preset humidity threshold.

8. An intelligent garden sprinkler system based on remote control, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the intelligent garden sprinkler device based on remote control as described in any one of claims 1 to 7 is implemented.

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