Intelligent garden sprinkling irrigation device and system based on remote control

Abstract

The invention relates to the technical field of intelligent sprinkling irrigation, in particular to an intelligent garden sprinkling irrigation device and system based on remote control, and the device comprises an environment information collection module which is used for obtaining temperature data and wind speed data of a garden and soil humidity data of each area in the garden in real time; the environment information analysis module is used for acquiring the sprinkling irrigation demand coefficient of each area for each sprinkling irrigation, and the specific process is as follows: acquiring each humidity rising interval and humidity change degree of each area; obtaining each temperature change interval of each area and the change quantity of the soil humidity data in the temperature change interval, and obtaining the humidity change sensitivity of each area in combination with the time difference of sudden change between the soil humidity data and the temperature data of each area; obtaining a spray irrigation demand coefficient of each region; and the spray irrigation control module controls the spray irrigation duration of each area. According to the invention, precise control of garden sprinkling irrigation is realized by controlling the sprinkling irrigation duration of each area in the garden.

Description

Technical Field

[0001] This application relates to the technical field of intelligent sprinkler irrigation, and specifically relates to an intelligent garden sprinkler irrigation device and system based on remote control. Background Art

[0002] With the development of agricultural technology, precision agriculture has become an important development direction of modern agriculture. Agricultural sprinkler irrigation is an irrigation method that uses sprinkler heads to spray water into the air, forming fine water droplets, and then evenly sprinkling them onto farmland. In precision agriculture, automatic irrigation control of farmland is one of the important means to achieve high efficiency, water conservation, and increased production. Currently, automatic sprinkler irrigation is not only applied to large-scale agricultural planting but also gradually extended to the field of garden planting.

[0003] The prior art collects garden environmental data such as temperature, wind speed, and soil humidity through wireless terminals and sensor networks to achieve automatic sprinkler irrigation control. However, the prior art mostly adopts a threshold control method. For example, the sprinkler irrigation is only turned on when the soil humidity is less than a certain threshold. However, compared with the agricultural field, the plant density in the garden is relatively low, and it is possible that not every area in the garden is planted with flowers. Therefore, there are certain differences in the water demand levels of different areas in the garden. Only controlling the sprinkler irrigation through thresholds may result in insufficient sprinkler irrigation in areas with high water demand, affecting plant growth, or over-irrigation in areas with low water demand, thereby wasting water resources. Therefore, it is necessary to judge the actual soil water demand in the garden and then perform more refined control over the sprinkler irrigation. Summary of the Invention

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

[0005] In a first aspect, an embodiment of the present application provides an intelligent garden sprinkler irrigation device based on remote control. In the device, there are:

[0006] An environmental information acquisition module for real-time acquisition of the temperature data, wind speed data of the garden, and the soil humidity data of each preset area in the garden;

[0007] An environmental information analysis module for obtaining the sprinkler irrigation demand coefficient of each area for each sprinkler irrigation through the temperature data, wind speed data, and soil humidity data within a preset time period before sprinkler irrigation. The specific process is as follows:

[0008] Based on the growth of soil moisture data in each area, obtain the humidity increase intervals for each area; based on the moments when the soil moisture data in each area undergoes mutations, the minimum values of the soil moisture data, and the change rates of the soil moisture data within each humidity increase interval, and combine the correlation between the change degree of the soil moisture data and the change degree of the wind speed data within each area to obtain the humidity change degree for each area;

[0009] Based on the changes in temperature data in each area, obtain the temperature change intervals for each area; based on the change amounts of the soil moisture data within each temperature change interval in each area, and combine the time difference of mutations between the soil moisture data and the temperature data in each area to obtain the humidity change sensitivity for each area;

[0010] Based on the humidity change degree and the humidity change sensitivity, obtain the sprinkler irrigation demand coefficient for each area;

[0011] The sprinkler irrigation control module is used to control the sprinkler irrigation duration for each area based on the sprinkler irrigation demand coefficient.

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

[0013] Arrange the soil moisture data in each area within the preset time period in chronological order to form each soil moisture data sequence; take the time interval between any minimum value point and the subsequent maximum value point adjacent to it in each soil moisture sequence as a humidity increase interval.

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

[0015] Number the acquisition moments within the preset time period in chronological order, and obtain the number of the acquisition moment where the first mutation point is located in the soil moisture data sequence of each area;

[0016] Obtain the absolute value of the slope of the fitting line of the soil moisture data within each humidity increase interval; calculate the average value of the absolute values within all humidity increase intervals in each area;

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

[0018] Calculate the absolute values of the elements in the first-order difference sequence of the soil moisture data sequence in each area, and calculate the correlation coefficient between the absolute values and the wind speed data in chronological order within each area within the preset time period;

[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, the acquisition of the temperature change range includes:

[0021] When the change amount of any two temperature data in time series is greater than or equal to a preset value, the time interval between the times corresponding to the any two temperature data is used as a temperature change range.

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

[0023] Calculate the difference between the last soil humidity data and the first soil humidity data within each humidity change range; Denote the average value of the differences within all temperature change ranges of each region as the first average value;

[0024] Arrange the temperature data within the time period in time series to form a temperature data sequence; Obtain the mutation points in the soil humidity data sequence of each region and number them respectively in time series; Obtain the mutation points in the temperature data sequence and number them in time series; Calculate the time difference between the times corresponding to the mutation points with the same number in the soil humidity data sequence and the temperature data sequence of each region, and denote the average value of all the time differences of each region as the second average value;

[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 process of calculating the humidity change sensitivity is: Calculate the sum of the second average value and a preset positive number; The humidity change sensitivity is the ratio of the first average value to the sum value.

[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 sprinkler irrigation duration of each region includes: For the regions where the sprinkler irrigation demand coefficient is greater than or equal to a preset humidity threshold, increase the sprinkler irrigation duration; For the regions where the sprinkler irrigation demand coefficient is less than the preset humidity threshold, reduce the sprinkler irrigation duration.

[0029] In one embodiment, the expression of the sprinkler irrigation duration of each region is:

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

[0031] In a second aspect, an embodiment of the present application further provides an intelligent garden irrigation 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 intelligent garden irrigation device based on remote control described in any one of the above is implemented.

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

[0033] By dividing the garden and obtaining the soil humidity data of each area, it is convenient to perform targeted irrigation according to the soil humidity conditions of each area in the follow-up; considering that the water requirements of different areas are different, by comprehensively considering the lowest value of soil humidity, the humidity rising speed, and the correlation between soil humidity change and wind speed change, the temperature change degree is obtained, which can more accurately reflect the actual change of soil humidity in each area, evaluate the water requirement degree of each area, and avoid the one-sidedness brought by single-factor evaluation;

[0034] Furthermore, considering the mutual influence between adjacent areas, by evaluating the sensitivity of soil humidity to temperature change and adjusting the water requirement degree of each area, the actual water requirement situation of each area in the garden can be evaluated more comprehensively and accurately, providing a targeted irrigation basis for each area, so that the irrigation control can be differentially adjusted according to the actual water requirements of different areas; and then, according to the water requirements of each area, the irrigation duration is controlled, avoiding the problems of water resource waste or poor plant growth caused by using the same irrigation amount for all areas in the traditional irrigation method, and realizing the refined control of garden irrigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0036] Figure 1 It is a block diagram of the intelligent garden irrigation device based on remote control provided by the present application;

[0037] Figure 2 It is a schematic flowchart for obtaining the irrigation demand coefficient;

[0038] Figure 3 It is a flowchart of the intelligent garden irrigation device based on remote control;

[0039] Figure 4 It is a schematic flowchart for obtaining the irrigation duration. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example", etc. is intended to present relevant concepts in a specific manner.

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

[0042] In addition, it should be noted that the terms "first" and "second" in this application are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0043] The following specifically describes the specific solutions of the intelligent garden sprinkler irrigation device and system based on remote control provided by this application in conjunction with the accompanying drawings.

[0044] Please refer to Figure 1 , which shows a block diagram of an intelligent garden sprinkler irrigation 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 irrigation control module 104, a sprinkler irrigation controller 105, a booster pump 106, and a solenoid valve 107.

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

[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 humidity sensor is used to collect the soil humidity data of each preset area in the garden in real time.

[0047] In this embodiment, the time intervals for the temperature sensor to collect temperature data, the wind speed sensor to collect wind speed data, and the soil humidity sensor to collect soil humidity data are all 2 minutes. The value of the collection time interval is preset by humans, and the implementer can set it by himself. This application does not make special restrictions.

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

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

[0050] The user selects the automatic control mode in the mode configuration of the garden irrigation mobile application and sets the values of each parameter, such as the irrigation cycle of the garden, to control the on / off situation of irrigation. The garden irrigation mobile application transmits the control instructions set by the user to the environmental information analysis module through 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 analyze the soil humidity data of each area within a preset time period before irrigation, as well as the temperature data and wind speed data within the preset time period for each irrigation, so as to obtain the irrigation demand coefficient of each area.

[0053] After receiving the control instructions, the environmental information analysis module analyzes the soil humidity data of each area within a preset time period before irrigation, as well as the temperature data and wind speed data within the preset time period for each irrigation, so as 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 of the soil where flowers are planted is relatively loose, with high air permeability, fast water evaporation, and because the roots of the flowers will absorb water, the soil moisture of the flower-planting soil will rapidly decrease as the temperature rises, and the greater the flower density, the faster the soil moisture drops. At the same time, due to the obstruction of the flowers, the rising speed of the soil humidity is interfered to a certain extent during irrigation, and thus the rising speed slows down. In contrast, for the soil where no flowers are planted, the soil is relatively compact, there is no plant to absorb water, and the evaporation speed of water in the soil is slow. Usually, the humidity change of the soil is relatively gentle. Although the water in the soil will also decrease due to the rise in temperature, due to only water evaporation and the compact soil, the change speed of the soil humidity is slow, and the overall water content is higher than that of the flower-planting soil.

[0055] The flower varieties planted in different regions are different, and the growth stages of the flowers vary. Therefore, in fact, there are certain differences in the water demand of different regions within the same time range. For example, varieties such as Gerbera jamesonii and Petunia hybrida have higher requirements for soil moisture, while varieties such as Pelargonium hortorum have lower requirements for soil moisture. Also, for example, the water required during the seedling growth stage is less than that during the flowering stage. As the temperature rises, there are certain differences in the amount of water absorbed by flowers of different varieties and at different growth stages from the soil. Specifically, as the temperature rises, the time point when the soil humidity drops in the area with a higher water demand appears earlier. When reaching the highest temperature of the day, due to the different degrees of water absorption in the soil, there are also differences in the minimum soil humidity in each area. The smaller the minimum soil humidity, the more water is required in that area.

[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, due to the obstruction of the flowers themselves, the wind speed on the soil surface in the area is lower than that of the soil surface without planted flowers or in the germination stage and seedling growth stage, and the water evaporation is relatively slow. Therefore, the stronger the correlation between the change degree of the soil humidity data and the change degree of the garden wind speed data in the area, the more likely it is that there are no planted flowers in that area or the currently planted flowers are in the stage with low water demand, and the less water is required for the soil in that area.

[0057] For each irrigation, arrange the soil humidity data of each area within the preset time period in sequence to form the soil humidity data sequence of each area; arrange the temperature data within the time period in sequence to form the temperature data sequence; arrange the wind speed data within the time period in sequence to form the wind speed data sequence.

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

[0059] In order to characterize the change speed of the rise and fall of the soil humidity data, obtain each extreme point in each soil humidity data sequence, and use the time interval between the moment of any minimum value point and the moment of the adjacent subsequent maximum value point in each soil humidity sequence as a humidity rise interval. Use the mutation point detection algorithm to obtain each mutation point in each soil humidity data sequence and each mutation point in the temperature data sequence respectively.

[0060] In this embodiment, an extreme point detection algorithm is used to obtain each extreme point in each soil humidity data sequence. The extreme point detection algorithm is a well-known technology and will not be elaborated in this application. As other implementation manners, on the basis of being able to obtain each extreme point in each soil humidity data sequence, the implementer can adopt other existing technologies, such as a peak-valley detection algorithm. This application does not make 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 elaborated in this application. As other implementation manners, on the basis of being able to obtain each mutation point in each soil humidity data sequence and each mutation point in the temperature data sequence, the implementer can adopt other existing technologies, such as the Bayesian mutation point detection algorithm, the Mann-Kendall mutation point detection algorithm, etc. This application does not make special restrictions.

[0062] Based on the above analysis, by the moment when the soil humidity data in each region mutates, the lowest value of the soil humidity data, and the change rate of the soil humidity data within each humidity rising interval, and combining the correlation between the change degree of the soil humidity data and the change degree of the wind speed data within each region, the humidity change degree of each region is obtained. The expression is:

[0063] In the formula, A i represents the humidity change degree of the i-th region; norm() represents the arctangent normalization function; the acquisition times within the preset time period are numbered in chronological order, and b i represents the number of the acquisition time where the first mutation point is located in the soil humidity data sequence of the i-th region; the absolute value of the slope of the fitting straight line of the soil humidity data within each humidity rising interval is obtained; c i represents the average value of the absolute values within all humidity rising intervals of the i-th region; the absolute values of the elements in the first-order difference sequence of the soil humidity data sequence of each region are calculated and arranged in chronological order to form the soil humidity variable speed sequence of each region; Z i represents the correlation coefficient between the soil humidity variable speed sequence and the wind speed data sequence of the i-th region. Among them, the calculation of the slope is a well-known technology and will not be elaborated in this application.

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

[0065] In this embodiment, the correlation coefficient between the soil humidity variable-speed sequence and the wind speed data sequence is the Pearson correlation coefficient. During the calculation of the Pearson correlation coefficient, if the lengths of the soil humidity variable-speed sequence and the wind speed data sequence are not equal, the longest sequence is truncated to make the lengths of the longest sequence and the shortest sequence equal. As other implementation manners, on the basis of being able to measure the correlation between the soil humidity variable-speed sequence and the wind speed data sequence, implementers can adopt other existing technologies, such as the Spearman correlation coefficient, the Kendall rank correlation coefficient, etc., and this application does not make special restrictions.

[0066] It should be noted that: when the humidity decrease speed in the i-th area is faster, the increase speed is slower, and the minimum value of the soil humidity is smaller, the stronger the correlation between the soil humidity change and the wind speed change, it indicates that the flowers planted in the i-th area have a greater demand for water, a stronger ability to absorb soil moisture, and a higher degree of soil shielding during sprinkler irrigation. The overall humidity change degree in the i-th area is higher, and the water demand degree in the i-th area is higher, and more water resources need to be sprinkled in the i-th area.

[0067] Furthermore, through the change situation of the temperature data in each area, each temperature change interval in each area is obtained; through the change amount of the soil humidity data within each temperature change interval in each area, combined with the time difference of mutation between the soil humidity data and the temperature data in each area, the humidity change sensitivity of each area is obtained.

[0068] Since the growth conditions of plants are different, when the flower density in a certain area is relatively high or the flower varieties planted are relatively large, their branches and leaves will cover adjacent areas, and due to the relatively developed plant roots, they may also extend to adjacent areas to absorb water, resulting in a relatively large change speed of the soil humidity in some areas where no flowers are planted. Calculating only through the above method may still misjudge the areas where no flowers are planted as areas with high water demand, and then sprinkle more water resources, ultimately causing waste of water resources. Therefore, further judgment is still needed.

[0069] For the planted flowers, as the sun rises and the light intensity increases, the air temperature gradually rises, and the photosynthesis and transpiration of the flowers gradually increase. Therefore, the flowers will need more water to support their life activities. This area needs more water and is more affected by the environmental temperature. Therefore, the soil humidity change in the area with a large water demand is more sensitive to the change in the environmental temperature, mainly manifested as when the temperature data changes, the time when the soil humidity in the area with a large flower planting density changes is closer to that in the area where no flowers are planted, and the change amount is larger.

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

[0071] In this embodiment, the value of n is 5, and the value of n is preset manually. The implementer can set it by himself, and this application does not make special restrictions. For example, the temperature data collected from the first collection time to the fifth collection time are 15.1°C, 17.2°C, 18.0°C, 18.5°C, and 20.2°C. Then the time interval between 15.1°C and 20.2°C is used as a temperature change interval, and the temperature change interval includes the collection time where 15.1°C is located and the collection time where 20.2°C is located.

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

[0073] In the formula, E i The humidity change sensitivity of the i-th region; calculate the difference between the last soil moisture data and the first soil moisture data within each humidity change interval, F i represents the mean value of the difference within all temperature change intervals in the i-th region; α represents a preset positive number 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 0.01; number the mutation points in each soil moisture data sequence according to the time series, number the mutation points in the temperature data sequence according to the time series, and calculate the time difference between the times of the mutation points with the same number in the soil moisture data sequence and temperature data sequence in each region, H i represents the mean value of all the time differences in the i-th region. Denote F i as the first mean value, and denote H i as the second mean value.

[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: in the i-th region, the greater the change amount of soil moisture within the temperature change interval, and the closer the change time of the soil moisture data is to the change time of the temperature, it indicates that the soil moisture in the i-th region is more sensitive to temperature changes, and it is more likely to be a soil region with a large water demand, and more water resources need to be sprayed in the i-th region.

[0076] Further, the sprinkler irrigation demand coefficient of each area is obtained through the humidity change degree and humidity change sensitivity of each area, and the expression is:

[0077] K i = A i + norm(E i ); In the formula, K i is the sprinkler irrigation demand coefficient of the i-th area; A i represents the humidity change degree of the i-th area; norm() represents the arctangent normalization function; E i is the humidity change sensitivity of the i-th area.

[0078] It should be noted that: in the i-th area, the higher the flower planting density, the greater the water demand of the soil area, and the greater the sensitivity of the humidity change to temperature, it may indicate that the higher the flower planting density in the i-th area, the greater the water demand, and the more water should be sprinkler-irrigated in the i-th area. The schematic diagram of the acquisition process of the sprinkler irrigation demand coefficient is as Figure 2 shown.

[0079] The sprinkler irrigation control module 104 is used to control the sprinkler irrigation duration of each area through the sprinkler irrigation demand coefficient.

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

[0081] In the formula, T i represents the sprinkler irrigation duration of the i-th area; K i represents the humidity change sensitivity of the i-th area; H represents the preset sprinkler irrigation duration; γ represents the preset humidity threshold. The flow chart of the intelligent garden sprinkler irrigation device based on remote control is as Figure 3 shown. The schematic diagram of the acquisition process of the sprinkler irrigation duration is as Figure 4 shown.

[0082] In this embodiment, the value of the preset sprinkler irrigation duration is 5 min, and the value of the preset sprinkler irrigation duration is preset manually. The implementer can set it according to the actual situation, and this application does not make special restrictions.

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

[0084] For each irrigation, the irrigation control module issues a control instruction, which is transmitted to the irrigation controller through the wireless communication network. The irrigation controller controls the booster pump and the solenoid valve, so as to perform irrigation on each area, and the irrigation duration for each area is the calculated irrigation duration.

[0085] Based on the same inventive concept as the above method, an embodiment of the present application further provides an intelligent garden irrigation 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 irrigation device based on remote control is implemented.

[0086] In summary, by dividing the garden and obtaining the soil humidity data of each area, it is convenient to perform targeted irrigation according to the soil humidity conditions of each area in the follow-up; considering that the water requirements of different areas are different, by comprehensively considering the lowest value of soil humidity, the humidity rising speed, and the correlation between soil humidity change and wind speed change, the temperature change degree is obtained, which can more accurately reflect the actual change of soil humidity in each area, evaluate the water requirement degree of each area, and avoid the one-sidedness brought by single-factor evaluation;

[0087] Furthermore, considering the mutual influence between adjacent areas, by evaluating the sensitivity of soil humidity to temperature change and adjusting the water requirement degree of each area, the actual water requirement situation of each area in the garden can be evaluated more comprehensively and accurately, providing a targeted irrigation basis for each area, so that the irrigation control can be differentially adjusted according to the actual water requirements of different areas; and then controlling the irrigation duration according to the water requirements of each area, avoiding the problems of water resource waste or poor plant growth caused by the same irrigation amount for all areas in the traditional irrigation method, and realizing the refined control of garden irrigation.

[0088] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of code, or a portion thereof that contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps may in fact be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

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

Claims

1. An intelligent garden sprinkler device based on remote control, characterized in that, In the described device, there are: An environmental information collection module, which is used to obtain the temperature data, wind speed data of the garden in real time, and the soil humidity data of each preset area in the garden; An environmental information analysis module, which is used to obtain the irrigation demand coefficient of each area for each irrigation by using the temperature data, wind speed data and soil humidity data within a preset time period before irrigation. The specific process is as follows: Obtain each humidity rising interval of each area through the growth situation of the soil humidity data of each area; obtain the humidity change degree of each area through the moment when the soil humidity data of each area mutates, the minimum value of the soil humidity data, and the change speed of the soil humidity data within each humidity rising interval, and combine the correlation between the change degree of the soil humidity data and the change degree of the wind speed data within each area; Obtain each temperature change interval of each area through the change situation of the temperature data of each area; obtain the humidity change sensitivity of each area through the change amount of the soil humidity data within each temperature change interval of each area and combine the time difference of mutation between the soil humidity data and the temperature data of each area; Obtain the irrigation demand coefficient of each area through the humidity change degree and the humidity change sensitivity; An irrigation control module, which is used to control the irrigation duration of each area through the irrigation demand coefficient.

2. The intelligent garden sprinkler device based on remote control according to claim 1, characterized in that The acquisition of the humidity rising interval includes: Arrange the soil humidity data of each area within the preset time period in sequence respectively to form each soil humidity data sequence; take the time interval between any minimum value point and the moment of the adjacent subsequent maximum value point in each soil humidity sequence as a humidity rising interval.

3. The intelligent garden sprinkler device based on remote control according to claim 2, characterized in that, The acquisition of the humidity change degree includes: Number the acquisition moments within the preset time period in sequence, and obtain the number of the acquisition moment where the first mutation point in the soil humidity data sequence of each area is located; Obtain the absolute value of the slope of the fitting straight line of the soil humidity data within each humidity rising interval; calculate the average value of the absolute values within all humidity rising intervals of each area; Statistical minimum value of all soil humidity data of each area within the preset time period; Calculate the absolute value of the elements in the first-order difference sequence of the soil humidity data sequence of each area, and calculate the correlation coefficient between the absolute values of the elements of each area and the wind speed data in time sequence within the preset time period; 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.

4. The intelligent garden sprinkler device based on remote control according to claim 1, wherein, The acquisition of the temperature change interval includes: When the change amount of any two temperature data in time sequence is greater than or equal to a preset value, take the time interval between the moments where the any two temperature data are located as a temperature change interval.

5. The intelligent garden sprinkler device based on remote control according to claim 2, characterized in that, The acquisition of the humidity change sensitivity includes: Calculate the difference amount between the last soil humidity data and the first soil humidity data within each humidity change interval; record the average value of the difference amounts within all temperature change intervals of each area as the first average value; Arrange the temperature data within the time period in chronological order to form a temperature data sequence; obtain the mutation points in the soil humidity data sequences of each region and number them respectively in chronological order; obtain the mutation points in the temperature data sequence and number them in chronological order; calculate the time differences at the moments of the mutation points with the same number in the soil humidity data sequences and the temperature data sequence of each region, and denote the mean value of all the time differences of each region as the second mean value; The humidity change sensitivity is directly proportional to the first mean value and inversely proportional to the second mean value.

6. The intelligent garden sprinkler device based on remote control according to claim 5, characterized in that The calculation process of the humidity change sensitivity is as follows: calculate the sum value 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.

7. The intelligent garden sprinkler device based on remote control 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.

8. The intelligent garden sprinkler device based on remote control according to claim 1, characterized in that Controlling the sprinkler irrigation duration of each region includes: for the regions where the sprinkler irrigation demand coefficient is greater than or equal to the preset humidity threshold, increasing the sprinkler irrigation duration; for the regions where the sprinkler irrigation demand coefficient is less than the preset humidity threshold, reducing the sprinkler irrigation duration.

9. The intelligent garden sprinkler device based on remote control according to claim 8, characterized in that The expression for the sprinkler irrigation duration of each region is: In the formula, T i represents the sprinkler irrigation duration of the i-th area; K i represents the humidity change sensitivity of the i-th area; H represents the preset sprinkler irrigation duration; γ represents the preset humidity threshold.

10. An intelligent garden irrigation 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, it implements the intelligent garden sprinkler irrigation device based on remote control as described in any one of claims 1-9.

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