Multi-modal water-saving sprinkler system based on internet of things

Abstract

The application belongs to the technical field of crop irrigation control, and specifically discloses a multi-modal water-saving sprinkling irrigation system based on the Internet of Things, which comprises a data acquisition module, a stress analysis module connected with the data acquisition module, a sprinkling irrigation control module connected with the stress analysis module, and a fault processing module connected with the sprinkling irrigation control module. The physiological dynamics of crops are monitored in a multi-modal manner through the cooperation of an embedded stem micro-variation sensor and an infrared thermal imager, so that the occurrence of water stress can be accurately identified at an early stage. When water stress is identified, a capillary pulse sprinkling irrigation mode is automatically started, so that water supply is realized in a demand-oriented, accurate, small-flow and high-frequency manner. Not only is the timeliness and physiological adaptability of the irrigation response significantly improved, but also the root zone suitable water potential can be effectively maintained, water efficient absorption and utilization are promoted, water resource waste is reduced, and normal physiological metabolism of crops is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of crop irrigation control, and specifically discloses a multi-modal water-saving sprinkling irrigation system based on the Internet of Things. BACKGROUND

[0002] In the process of crop growth and development, water, as a solvent for cell metabolic reactions, a carrier for nutrient transport, and a key medium for photosynthesis and energy conversion, deeply participates in physiological processes such as protoplasm flow, enzymatic reactions, stomatal regulation, and assimilate distribution, and has a decisive influence on the growth rate, organ differentiation, biomass accumulation, yield formation, and quality formation of crops.

[0003] However, plants cannot synthesize water themselves, and the water in their bodies is mainly absorbed from the soil environment through the root system and dynamically transported in the soil-plant-atmosphere continuum driven by transpiration. Due to the continuous consumption of water caused by transpiration, which is much greater than the metabolic generation amount, crops must rely on external water sources to supplement to maintain water balance and normal physiological functions. Therefore, in agricultural production, irrigation is a core means of artificially regulating crop water supply.

[0004] In the prior art, crop irrigation decision-making mainly relies on monitoring of soil moisture content, i.e., soil water content, to indirectly infer the water requirement state of crops. This method has a significant lag effect because the change in soil water content often lags behind the actual physiological water stress condition of crops. This lag may lead to misjudgment of drought, i.e., when the soil water content is monitored to be insufficient, the crop has actually experienced a long period of water stress, thereby affecting its growth and development and final yield.

[0005] Most current irrigation systems mainly supply water to the root system of crops, however, this irrigation method is difficult to achieve precise pulse irrigation, i.e., dynamically adjusting the irrigation frequency and water amount according to the instantaneous water requirement of crops. Due to the lack of precise response mechanism to the real-time water requirement of crops, on the one hand, it is easy to cause excessive irrigation, which not only wastes valuable water resources, but also may cause problems such as secondary soil salinization, nutrient loss, and groundwater pollution, on the other hand, it causes limited growth of crops, and uneven or untimely irrigation will cause intermittent water deficit or excessive wetting of crops, which will interfere with normal physiological processes such as stomatal regulation, photosynthesis, and nutrient absorption, thereby inhibiting the growth of crops and reducing yield and quality. SUMMARY

[0006] In view of this, the present application aims to propose a multi-modal water-saving sprinkling irrigation system based on the Internet of Things, which can realize real-time sensing of the water state of crops and dynamic adjustment of the irrigation scheme according to the water state of crops, so as to ensure that every drop of water can be effectively utilized by crops, thereby achieving water-saving effect.

[0007] The object of the present application can be achieved by the following technical solution: a multi-modal water-saving sprinkling irrigation system based on the Internet of Things, comprising the following modules: a data acquisition module: real-time capture of crop stem diameter change data through an embedded stem micro-change sensor, while carrying an infrared thermal imager to scan and generate a canopy temperature distribution thermal map.

[0008] A stress analysis module: extract the shrinkage and expansion amplitude characteristics from the stem diameter change data, and divide the canopy thermal map into grids to calculate the high-temperature grid proportion, when the stem shrinkage and expansion amplitude exceeds the crop species reference value and the canopy high-temperature grid proportion exceeds the set proportion, output a water stress signal.

[0009] A sprinkling irrigation control module: continuously monitor the light intensity at the top of the canopy, and lock the sprinkling irrigation system when the intensity is lower than the photosynthetic compensation point, trigger the high-speed electromagnetic valve to control the opening and closing of the capillary network based on the water stress signal, and dynamically adjust the pulse sprinkling irrigation frequency according to the crop species drought resistance characteristic library.

[0010] A fault handling module: compare the adjacent capillary branch flowmeter data, and perform hierarchical unblocking operation when the flow difference exceeds the tolerance range.

[0011] Compared with the prior art, the present application has the following beneficial effects: 1. The present application cooperates with the multi-modal monitoring of crop physiological dynamics through the embedded stem micro-change sensor and infrared thermal imager, which can early and accurately identify the occurrence of water stress, and automatically start the capillary pulse sprinkling irrigation mode when water stress is identified, realizing on-demand, accurate, small-flow and high-frequency water supply, which not only significantly improves the timeliness and physiological adaptability of irrigation response, but also effectively maintains the appropriate water potential in the root zone, promotes efficient water absorption and utilization, reduces water resource waste, and at the same time guarantees normal physiological metabolism of crops.

[0012] 2. The present application introduces a real-time comparison mechanism of adjacent branch flowmeter data during capillary sprinkling irrigation of crops, which realizes early identification of local blockage failure by monitoring the flow consistency between branches, and starts a hierarchical unblocking strategy when the flow deviation exceeds the preset tolerance threshold, significantly improving the reliability and self-healing ability of the irrigation system, effectively preventing uneven irrigation, local drought and other problems caused by clogging of drips or pipes, ensuring the continuity and accuracy of the irrigation process, and reducing the cost of manual inspection and maintenance. BRIEF DESCRIPTION OF DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0014] Figure 1A schematic diagram of the system composition in the present application.

[0015] Figure 2 An implementation flowchart for dividing the crown thermal map into a grid to calculate the high-temperature grid proportion in the present application.

[0016] Figure 3 An operation schematic diagram for outputting a water stress signal when the stem contraction and expansion amplitude exceeds the crop type reference value and the crown high-temperature grid proportion exceeds the set proportion in the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] Referring to Figure 1 The present application proposes a multi-modal water-saving sprinkler system based on the Internet of Things, which comprises a data acquisition module, a stress analysis module connected with the data acquisition module, a sprinkler control module connected with the stress analysis module, and a fault processing module connected with the sprinkler control module.

[0019] The data acquisition module is used to capture crop stem diameter change data in real time through an embedded stem micro-change sensor, and simultaneously scan a crown temperature distribution thermal map by using an infrared thermal imager.

[0020] In the optional implementation of the above module, the real-time capture of crop stem diameter change data by the embedded stem micro-change sensor comprises the following contents: an annular support structure is fixed outside the crop stem, and a plurality of piezoresistive strain sensors are uniformly distributed in the circumferential direction of the annular support structure to collect the interface stress changes caused by the slight deformation of the stem due to physiological changes in real time.

[0021] The physical relationship between stress and strain is used to inverse the ellipticity of the stem cross section at different times as the time series characteristics of the dynamic changes of the stem diameter.

[0022] It should be understood that the piezoresistive strain sensor is adhered or crimped to the inner wall of the annular support structure and is in contact with the stem surface. When the stem slightly expands or contracts due to water absorption or transpiration, the diameter change of the stem causes the annular support structure to produce slight deformation, which in turn causes the stress sheet to be strained. According to Hooke's law, the stress and strain satisfy a linear relationship: , wherein Elastic modulus of the stress sheet material, the local stress distribution on the ring support structure can be deduced by measuring the strain values in each direction.

[0023] A plurality of piezoresistive strain sensors are arranged circumferentially on the ring support structure to record the strain signals at different azimuth angles, which reflect the stem deformation trend in that direction. The stem cross-section is approximated to an ellipse that changes over time, and its geometric shape is characterized by the major axis length , the minor axis length and the major axis direction . The inversion model can be established by multi-point strain data as follows:

[0024] The stem displacement at each sensor position is obtained by strain integration, assuming that the cross-section deformation is elastic small deformation, and the least squares method is used to fit all data points to solve the optimal ellipse parameters, so that , where is the theoretical stem displacement of the ellipse model at angle .

[0025] Once the major axis length and the minor axis length are fitted, the ellipticity parameter can be defined as:

[0026] .

[0027] The ellipticity defined above is , and when the value is 0, it represents a perfect circle, and the closer to 1, the flatter, the greater the ellipticity.

[0028] It should be pointed out that the above physical relationship between stress and strain inverts the ellipticity of the stem cross-section at different times, which belongs to the prior art.

[0029] In another optional embodiment of the above module, the infrared thermal imager scanning generates a canopy temperature distribution thermogram in the following manner: the infrared thermal imager moves along the crop row to scan, captures the infrared energy radiated by the crop canopy, and generates a thermogram representing the canopy surface temperature distribution according to the relationship between temperature and infrared radiation intensity. The thermogram can intuitively reflect the local temperature difference in the crop population, and provides an important visual basis for evaluating the water status and environmental adaptability of crops.

[0030] The crop stem diameter and the canopy temperature are used as the physiological water stress sensing indicators of the crop because the change in the stem diameter directly reflects the dynamic balance of water in the plant body. When the plant experiences water deficit, the decrease in the cell turgor pressure leads to the contraction of the stem; conversely, under the condition of sufficient water, the stem expands. Therefore, the change in the stem diameter can timely and accurately reflect the water condition inside the crop.

[0031] The canopy temperature is closely related to the transpiration of the plant. Under the condition of sufficient water, the transpiration can effectively reduce the leaf temperature; while under the condition of water deficit, the stomata are closed, the transpiration is reduced, and the canopy temperature rises. Therefore, the thermal map of the canopy temperature distribution provides intuitive phenotypic information of the water condition of the crop.

[0032] The measurement of the soil water content can provide the information of the soil water content, but the change thereof is often lagged behind the actual water demand of the crop. Due to the time difference between the water transmission between the soil and the root system, the change in the soil humidity cannot timely reflect whether the crop is in the water stress state. In addition, the differences in the soil type, texture, structure and the like also affect the water transmission efficiency, further increasing the prediction error. Compared with the soil water content, the change in the stem diameter and the distribution of the canopy temperature can directly and rapidly respond to the change in the water condition of the crop, and there is almost no time delay. This makes the irrigation decision more timely and accurate, and avoids the misjudgment or delay caused by the lag of the soil water.

[0033] The stress analysis module is used to extract the contraction and expansion amplitude feature from the stem diameter change data, divide the canopy thermal map into grids, calculate the high-temperature grid proportion, and output the water stress signal when the stem contraction and expansion amplitude exceeds the crop species reference value and the canopy high-temperature grid proportion exceeds the set proportion.

[0034] Optionally, the specific process of extracting the contraction and expansion amplitude feature from the stem diameter change data in the above scheme is as follows: the original time series data of the stem diameter change is divided into a plurality of time unit sub-sequences according to a preset time window.

[0035] In the example of the above implementation, the preset time window can be daily, because the stem diameter of the crop is not constant, but presents a significant circadian rhythm change, which is mainly driven by the following processes:

[0036] From morning to noon: the root system is active in water absorption, the cell turgor pressure is restored, and the stem expands;

[0037] From afternoon to evening: the transpiration is enhanced, the water loss is greater than the absorption, and the stem contracts;

[0038] At night: the transpiration is weakened, the water is re-accumulated, and the stem slowly recovers.

[0039] It can be seen that the change of the stem diameter of the crop in a 24-hour cycle shows a regular fluctuation pattern.

[0040] The maximum value and the minimum value of the stem diameter sub-sequence in each time unit are extracted, and the difference between the two is calculated as the stem diameter shrinkage-expansion peak-valley difference in the time unit.

[0041] The stem diameter time sequence in the same time unit is analyzed by point-by-point first-order difference, the change rate sequence between adjacent time points is obtained, and whether the stem is in the expansion stage or the shrinkage stage is identified according to the positive and negative signs of the difference result.

[0042] In the above scheme, specifically, when the difference sign is positive, it is the expansion stage, and when the difference sign is negative, it is the shrinkage stage.

[0043] The arithmetic mean of the absolute values of all positive difference items of the stem diameter time sequence in the expansion stage is taken to obtain the stem expansion rate.

[0044] The arithmetic mean of the absolute values of all negative difference items of the stem diameter time sequence in the shrinkage stage is taken to obtain the stem shrinkage rate.

[0045] The stem diameter shrinkage-expansion peak-valley difference, the expansion rate and the shrinkage rate characteristics in each time unit are jointly used as the feature vector of the crop stem shrinkage-expansion amplitude in the period.

[0046] It should be understood that the stem diameter shrinkage-expansion peak-valley difference in the feature vector of the stem shrinkage-expansion amplitude extracted in each preset time window is used to quantify the maximum deformation amplitude of the stem in the cycle, reflecting the activity degree of its physiological activity; the stem expansion rate represents the root water absorption and cell turgor pressure recovery process, reflecting the recovery ability of the plant in the water sufficient period; the stem shrinkage rate represents the water loss speed driven by transpiration, reflecting the transpiration water loss and tissue dehydration degree. When the crop encounters water stress, the expansion amplitude decreases, the shrinkage degree intensifies or advances, and the overall peak-valley difference decreases significantly, the expansion rate decreases, and the shrinkage rate accelerates. Therefore, the peak-valley difference and the expansion and shrinkage rate parameters can be used as early sensitive indicators of water stress.

[0047] In the above scheme, further optionally, referring to Figure 2 As shown in the figure, the crown layer thermogram is divided into a grid to calculate the high-temperature grid proportion as follows: the obtained crop crown layer thermogram is uniformly divided into several regular grid units according to a preset spatial resolution.

[0048] As can be understood, the above discrete of the crop crown layer thermogram into regular grids facilitates subsequent quantitative statistics and parallel computing. The grid size can be set according to the sensor resolution to realize spatial scale matching. Although uniform division does not consider the temperature boundary, it provides a unified analysis unit for subsequent clustering and statistics.

[0049] The standard deviation of all pixel temperature values inside each grid is calculated and compared with a pre-set temperature uniformity threshold.

[0050] The temperature standard deviation reflects the dispersion degree of pixel values within the grid. The smaller the standard deviation, the more uniform the temperature distribution. The larger the standard deviation, the greater the temperature difference, which may include dry hot patches or shadow interference. By setting the temperature uniformity threshold as a decision switch, an adaptive representative temperature selection strategy is achieved.

[0051] The temperature uniformity threshold can be obtained by collecting multiple canopy thermal samples under sufficient water supply and non-stress conditions, and statistically analyzing the temperature standard deviation of typical grids to obtain the distribution range under normal conditions, such as mean ± 2 times standard deviation. The upper limit of this range is used as the temperature uniformity threshold.

[0052] If the temperature standard deviation of a grid is less than the threshold, it indicates that the temperature distribution inside the grid is relatively uniform. Then, clustering analysis is further performed on the pixel temperature within the grid to identify different temperature regions, and then the area of each temperature region is calculated based on the clustering results. The largest temperature cluster is selected, and the average temperature of all pixel points in the dominant temperature cluster is taken as the representative temperature of the grid.

[0053] It should be noted that in a grid with relatively uniform temperature distribution, the clustering algorithm can automatically identify several temperature clusters. Selecting the largest temperature cluster and taking its average temperature as the representative value is equivalent to using the mode temperature zone mean method, which effectively suppresses the influence of edge noise or small-range abnormal points.

[0054] If the temperature standard deviation of a grid is greater than or equal to the threshold, it indicates that there is significant temperature heterogeneity inside the grid. In this case, the highest temperature of all pixels within the grid is used as the representative temperature. This is because in the crop canopy, local high-temperature regions are usually closely related to water stress. When crops encounter water stress, stomata close to reduce water loss, resulting in reduced transpiration and an increase in leaf temperature. Therefore, local high-temperature regions are often the most severely water-stressed parts. Using the highest temperature as the representative temperature ensures that even if there are a few high-temperature regions within the grid, they can be identified in a timely manner, avoiding the masking of these critical information due to averaging. Compared with using average temperature or other statistical quantities such as median and mode, the highest temperature can detect potential water stress signals earlier. This is because local high-temperature often occurs in the early stages of water stress, while other regions may not have been affected.

[0055] The representative temperature of each grid is compared with the high-temperature threshold, which can be set based on crop species, growth stage, and environmental background. If the average temperature within a grid exceeds the high-temperature threshold, the grid is marked as a high-temperature grid.

[0056] The proportion of all grids marked as high temperature to the total number of grids is calculated to obtain the proportion of high temperature grids.

[0057] It is important to understand that plants regulate their body temperature through transpiration. When there is sufficient water, the cooling effect of transpiration is significant and the canopy temperature is low. When water stress occurs, the stomata close, transpiration weakens, and the canopy temperature rises. Therefore, high-temperature areas often correspond to areas of plants or groups with limited transpiration and water deficit.

[0058] Alternatively, see Figure 3 As shown, when the stem shrinkage and expansion amplitude exceeds the benchmark value of the crop species and the proportion of high-temperature grids in the canopy exceeds the set ratio, the water stress signal is output, which includes the following process: based on the current monitored crop species, the benchmark stem shrinkage and expansion amplitude feature vector of the species under sufficient water supply and healthy growth state is retrieved from the pre-built crop physiological characteristics knowledge base.

[0059] It should be pointed out that the above-mentioned benchmark stem shrinkage and expansion amplitude characteristic vector can be dynamically adjusted with the crop growth period to ensure matching of the physiological characteristics of different growth stages.

[0060] The measured shrinkage and expansion characteristic vectors of crop stems in each time unit are compared and analyzed dimension by dimension with the benchmark characteristic vectors of the corresponding species. If two or more characteristic dimensions exceed their benchmark values ​​and the proportion of high-temperature grids in the canopy exceeds the set ratio, a water stress signal is output.

[0061] If only a single feature dimension exceeds the baseline value, it is judged as a potential anomaly or local disturbance. At this time, the stress signal is not output immediately, but the shrinkage and expansion feature vector of the next time unit is monitored. If the abnormal feature vector evolves into a multi-dimensional feature vector that exceeds the baseline value and the proportion of high-temperature grids in the canopy exceeds the set ratio, a water stress signal is output. If the abnormal feature vector maintains or returns to normal, no water stress signal is output.

[0062] It should be made clear that the physiological response of crops when subjected to water stress is not limited to isolated changes in a single indicator, but rather manifests as coordinated changes in multiple physiological parameters, such as a decrease in the peak-to-valley difference in stem diameter, a decrease in expansion rate, an abnormal contraction rate, and an increase in canopy temperature. This phenomenon of multi-dimensional parameters synchronously deviating from the normal range is a typical physiological characteristic of true water stress. In contrast, fluctuations in a single indicator may be caused by non-stress interference factors, such as instantaneous environmental changes, sensor noise, or partial shading. Relying solely on a single parameter threshold to trigger irrigation can easily lead to misjudgments and ineffective operations.

[0063] By constructing a multi-parameter joint criterion and introducing a dynamic tracking mechanism, the true stress signal can be effectively distinguished from temporary disturbances, significantly improving the specificity and robustness of the diagnosis, thereby reducing the risk of false irrigation caused by environmental fluctuations or measurement noise, and achieving more accurate and reliable intelligent water management.

[0064] In a further innovative implementation of the above solution, the stress analysis module further includes environmental interference exclusion, and the specific operation is as follows: when it is detected that the stem contraction and expansion feature vector presents a decrease in contraction and expansion rate while the crown layer high-temperature grid proportion decreases, the dew point sensor is activated to obtain the thickness data of the condensation water layer on the leaf surface in real time.

[0065] The leaf surface dew thickness is compared with the false alarm protection threshold value. If the leaf surface dew thickness exceeds the protection threshold value, the current water stress signal is shielded.

[0066] It should be noted that in the real-time monitoring process, the physiological response characteristics of the stem and the trend of the crown layer thermal environment are analyzed simultaneously. When it is detected that the stem contraction and expansion feature vector presents a decrease in contraction amplitude, a decrease in expansion rate, and other stress characteristics, and at the same time the crown layer high-temperature grid proportion not only does not increase but significantly decreases, the system determines that the abnormal pattern may be caused by non-water deficiency factors. Typical scenarios include nighttime or early morning high-humidity conditions, where plant transpiration is inhibited and stem flow rate is passively decreased. By detecting the leaf surface dew thickness, when the leaf surface dew thickness exceeds the protection threshold value, it indicates that the abnormal change of the current stem physiological signal is most likely caused by transpiration inhibition due to the free water film on the leaf surface, rather than root zone water deficit, which can effectively avoid false irrigation decisions caused by false physiological stress caused by high humidity environment.

[0067] The false alarm protection threshold value mentioned above is used to determine whether the leaf surface dew thickness is sufficient to cause significant inhibition of transpiration, thereby avoiding the misidentification of false water stress caused by high humidity environment as true root zone water deficit. Specifically, under normal growth conditions, i.e., without water stress and suitable environment, multiple sets of crown layer thermal maps and corresponding leaf surface dew thickness data are collected, and these data are statistically analyzed to determine the maximum dew thickness in the 95% confidence interval as the preliminary threshold value.

[0068] The sprinkler control module is used to continuously monitor the light intensity at the top of the crown layer. When the intensity is lower than the photosynthetic compensation point, the sprinkler system is locked. Based on the water stress signal, a high-speed electromagnetic valve is triggered to control the pulse opening and closing of the capillary network, and the pulse sprinkling frequency is dynamically adjusted according to the drought resistance characteristics library of the crop species.

[0069] In an implementable manner of the above module, the light intensity at the top of the canopy is continuously monitored, and when the intensity is lower than the photosynthetic compensation point, the sprinkler system is locked as follows: the light intensity is detected in real time by the quantum sensor deployed at the top of the crop canopy, and compared with the photosynthetic compensation point of the species. When the instantaneous light intensity is detected to be lower than the threshold value of the photosynthetic compensation point of the species, the low light duration tracking is started, and the insufficient light time is accumulated.

[0070] The accumulated insufficient light time is compared with a preset sprinkler locking trigger threshold duration. If the accumulated insufficient light time reaches the threshold duration, it is determined that the current environment is not suitable for water absorption and photosynthetic metabolism. In order to avoid the risks of root hypoxia, water retention and nutrient leaching, an automatic control instruction is output, the sprinkler system is locked, that is, the pulse sprinkling on-off operation is suspended, and the low light dormant irrigation mode is entered. When it is detected that the light intensity has recovered to above the compensation point, it is determined that the photosynthetic activity recovery condition is met, the sprinkler system is unlocked, and the normal stress response driven irrigation control logic is restored.

[0071] It should be noted that the photosynthetic compensation point refers to the light intensity at which the photosynthetic rate of a plant is equal to the respiration rate. At this time, the net photosynthesis is zero, and the specific value is set according to the crop species. When the light intensity is lower than this point, the plant cannot effectively assimilate carbon, but consumes the organic matter stored in the body, and the whole body is in an energy negative balance state. At this time, photosynthesis is inhibited, stomatal conductance decreases, and transpiration is significantly weakened, and the root water absorption power decreases. Plant root water absorption mainly depends on the driving force of transpiration. When the light is insufficient to cause the stomata to close and the transpiration to weaken, the xylem negative pressure decreases, and the water upward transport capacity decreases. If irrigation is carried out at this time, it is easy to cause water retention in the root zone, increase the risk of hypoxia, root rot and nutrient loss. However, when the insufficient light does not last for a period of time, it may be a transient shadow such as cloud shadow or mechanical movement. At this time, irrigation locking should not be triggered. Only when the insufficient light lasts for a certain duration, it indicates that the environment has entered a truly low light state.

[0072] The sprinkler locking trigger threshold duration in the above embodiment reflects the maximum time limit within which the crop can maintain normal physiological functions under low light conditions without additional irrigation. The setting of this threshold directly affects the accuracy and timeliness of the system's judgment of water demand under low light conditions, ensuring irrigation when the plant really needs water, while avoiding unnecessary waste of water resources and potential root health problems. Specifically, the typical shade tolerance time of different crops can be determined as a reference basis for the sprinkler locking trigger threshold, according to the specific crop species and its growth stage, and referring to existing research data or agricultural guidelines.

[0073] In another implementable manner of the above module, the pulse sprinkling frequency is dynamically adjusted according to the drought resistance characteristics library of the crop species as follows: for shallow-rooted crops, a high-frequency short-pulse mode is set, which increases the number of pulses per unit time but shortens the duration of each pulse.

[0074] Need to explain, in the output of water stress signal for sprinkling irrigation, its root zone distribution is shallow, weak water holding capacity, transpiration response sensitive, by enabling high frequency short pulse mode, improve the frequency of sprinkling irrigation on-off in unit time. Shorten the duration of single irrigation. Realize the intermittent shallow replenishment of water, avoid deep seepage, maintain the appropriate humidity of surface root zone, improve water use efficiency.

[0075] For deep-rooted crops, set low-frequency long-pulse mode, reduce the number of pulses in unit time, but extend the duration of single irrigation.

[0076] Need to explain, for deep-rooted crops, its root system is developed, distributed widely, strong water buffer capacity, enable low-frequency long-time pulse mode, reduce pulse frequency, prolong single irrigation duration, promote water infiltration to deep soil, guide root growth downward, use soil reservoir effect, reduce irrigation frequency, enhance drought resistance.

[0077] The above automatically matches the root physiological and ecological characteristics of the current planted crops according to the type information of the crops, implements differentiated pulse sprinkling irrigation control strategy, avoids excessive water supply or local drought caused by one-size-fits-all irrigation, realizes on-demand, precise and dynamic control, and significantly reduces the risk of water and fertilizer loss.

[0078] The fault handling module is used to compare the adjacent capillary branch flow meter data, and perform hierarchical unblocking operation when the flow difference exceeds the tolerance range.

[0079] The specific implementation of the above module is as follows: real-time acquisition and synchronous comparison of the monitoring data of the flow meters at the outlets of adjacent capillary branch pipes, calculation of the flow difference value, and comparison of the difference value with the preset flow difference tolerance threshold.

[0080] The above-mentioned tolerance threshold is set according to the pipe network design parameters such as pipe diameter, pressure range, flow rate consistency requirement and crop partition uniformity requirement, and is usually ±10% to 15% of the rated flow.

[0081] When the flow difference between a branch and its adjacent branch continuously exceeds the tolerance threshold and reaches the determination duration, the branch is marked as an abnormal branch.

[0082] The above-mentioned determination duration reflects the confirmation delay threshold of the system to the flow abnormal state, and its core function is to distinguish between transient disturbance and persistent blockage fault, and to avoid misjudgment caused by short-time flow fluctuation. The determination duration can be reasonably set based on the flow monitoring frequency of the capillary pipe network, and can be defined as an integer multiple of the flow monitoring period.

[0083] Inject a set duration of compressed air pulse into the abnormal branch, if the flow is not restored after the compressed air pulse treatment, close the abnormal branch and activate the standby capillary pipe network.

[0084] The above-mentioned hierarchical response mechanism is started when an abnormal branch is identified, and a first-stage unclogging operation is first performed: a low-pressure compressed air pulse with a preset duration of 5-15 seconds is injected into the abnormal branch, and the gas-liquid / gas-solid two-phase flow disturbance effect is used to impact the pipe wall, thereby removing the biofilm, micro-particles or deposits attached to the dripper or pipe inner wall, and achieving non-disassembly online dredging.

[0085] If the flow of the branch returns to within the tolerance range after the unclogging operation is completed, it is determined that the unclogging is successful, normal operation is restored, and an event log is recorded.

[0086] If the flow is not restored, the system determines that it is a serious blockage or a structural failure, and then automatically performs a second-stage isolation measure: the electric valve or electromagnetic valve of the fault branch is closed to block the water flow and prevent local pressure imbalance from affecting the overall pipe network operation.

[0087] At the same time, a preset backup capillary network channel is activated to switch the water supply path and ensure uninterrupted water supply to the corresponding irrigation area.

[0088] It needs to be explained that the monitoring of the flow difference between adjacent capillary tubes in the crop capillary irrigation system is to ensure the uniformity and reliability of the entire irrigation system, because each capillary tube is responsible for the water supply of crops in a specific area. If there is a significant difference in water supply between different branches, it will cause local over-wetting or drought, affecting the consistency and yield of crop growth. On the other hand, it can also timely discover potential faults and ensure the efficient and stable operation of the system. Specifically, small particles suspended in water may accumulate at the inlet of the dripper and gradually form a blockage, causing the flow of the branch to decrease. Pipe rupture or joint loosening caused by mechanical work or other external factors will cause part of the water flow to flow from the damaged point, rather than all of it reaching the end dripper. These problems will cause uneven irrigation, and by monitoring the flow difference in real time, the problem can be discovered at an early stage, avoiding more extensive system failure or crop damage due to long-term neglect.

[0089] The above-mentioned embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above-mentioned embodiments can be realized in the form of a computer program product, wholly or partially.

[0090] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0091] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0092] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any modification or replacement within the technical range disclosed by the present application can be easily thought by any person skilled in the art, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0093] Finally, the above is merely preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. The multi-modal water-saving sprinkler irrigation system based on the Internet of Things is characterized by: Includes the following modules: Data acquisition module: The embedded stem micro-variation sensor captures crop stem diameter change data in real time, while an infrared thermal imager is used to scan and generate a thermographic map of canopy temperature distribution. Stress analysis module: Extracts shrinkage and expansion amplitude characteristics from stem diameter change data, divides the canopy thermal map into grids, and calculates the proportion of high-temperature grids. When the stem shrinkage and expansion amplitude exceeds the benchmark value for the crop type and the proportion of high-temperature grids in the canopy exceeds the set ratio, a water stress signal is output; Sprinkler control module: Continuously monitors the light intensity at the top of the canopy, locks the sprinkler system when the intensity falls below the photosynthetic compensation point, triggers high-speed solenoid valves to control the pulse opening and closing of the capillary network based on water stress signals, and dynamically adjusts the pulse sprinkler frequency based on the crop species' drought resistance characteristics database; Fault handling module: compares the data of adjacent capillary branch flowmeters and performs graded clearing operations when the flow difference exceeds the tolerance range; The specific process of extracting the shrinkage and expansion amplitude characteristics from the stem diameter change data is as follows: The original time series data of stem diameter change is divided into subsequences within a number of time units according to a preset time window; Extract the maximum and minimum values ​​of the stem diameter subsequence within each time unit, and calculate the difference between the two as the peak-to-valley difference of the stem diameter contraction and expansion within the time unit; The first-order difference analysis of the stem diameter time series within the same time unit was performed point by point, and the expansion or contraction stage of the stem was identified based on the positive or negative signs of the difference results. The stem expansion rate is obtained by taking the arithmetic mean of the absolute values ​​of all positive difference terms in the time series of stem diameter in the expansion stage; The stem shrinkage rate is obtained by taking the arithmetic average of the absolute values ​​of all negative difference terms in the time series of stem diameter in the shrinkage stage; The peak-to-valley difference of stem diameter contraction and expansion, expansion rate and contraction rate characteristics under each time unit are taken as the characteristic vector of crop stem contraction and expansion amplitude in this period. The canopy thermal map is divided into grids to calculate the high temperature grid ratio as follows: The obtained crop canopy thermal map is evenly divided into several regular grid cells according to the preset spatial resolution; For each grid, the standard deviation of the temperature values ​​of all pixels within it is calculated and compared with the pre-set temperature uniformity threshold; If the temperature standard deviation of a grid is less than the threshold, the pixel temperatures in the grid are further clustered to identify different temperature regions. The pixel areas occupied by each temperature region are then counted based on the clustering results. The temperature cluster with the largest area is selected, and the average temperature of all pixels in the temperature cluster with the largest area is used as the representative temperature of the grid. If the standard deviation of the temperature of a grid is greater than or equal to the threshold, the highest temperature of all pixels in the grid is used as the representative temperature; Compare the representative temperature of each grid with the high temperature threshold. If the representative temperature in a grid exceeds the high temperature threshold, mark the grid as a high temperature grid. Count the proportion of all grids marked as high temperature to the total number of grids, that is, the proportion of high temperature grids; When the stem shrinkage and expansion amplitude exceeds the reference value of the crop type and the proportion of high-temperature grids in the canopy exceeds the set ratio, outputting the water stress signal includes the following process: Based on the currently monitored crop species, the benchmark stem shrinkage and expansion amplitude feature vector of the species under sufficient water supply and healthy growth state is retrieved from the pre-built crop physiological characteristics knowledge base; The measured shrinkage and expansion feature vectors of crop stems in each time unit are compared dimension by dimension with the benchmark feature vectors of the corresponding species. If two or more feature dimensions exceed their benchmark values ​​and the proportion of high-temperature grids in the canopy exceeds the set ratio, a water stress signal is output. If only a single characteristic dimension exceeds the baseline value, the shrinkage and expansion characteristic vector of the next time unit will continue to be monitored. If the abnormal characteristic vector evolves into a multidimensional characteristic vector that exceeds the baseline value and the proportion of high-temperature grids in the canopy exceeds the set ratio, a water stress signal will be output. If the abnormal characteristic vector maintains or returns to normal, no water stress signal will be output.

2. The multimodal water-saving sprinkler irrigation system based on the Internet of Things according to claim 1, characterized in that: The real-time capture of crop stem diameter change data by the embedded stem micro-change sensor includes the following: A ring-shaped support structure is fixed to the outside of the crop stem, and multiple piezoresistive strain sensors are evenly distributed around it to collect real-time interfacial stress changes caused by tiny deformations of the stem due to physiological changes. The physical relationship between stress and strain is used to invert the ellipticity of the stem cross section at different times as the time series characteristic of the dynamic change of the stem diameter.

3. The multimodal water-saving sprinkler irrigation system based on the Internet of Things according to claim 1, characterized in that: The method for generating the canopy temperature distribution thermodynamic map by scanning with an infrared thermal imager is as follows: An infrared thermal imager is used to scan crops along crop rows. By capturing the infrared energy radiated by the crop canopy, a thermodynamic map representing the temperature distribution on the canopy surface is generated based on the relationship between temperature and infrared radiation intensity.

4. The multimodal water-saving sprinkler irrigation system based on the Internet of Things according to claim 1, characterized in that: The stress analysis module also includes environmental interference elimination, and the specific operations are as follows: When it is detected that the stem shrinkage and expansion characteristic vector shows a decrease in the shrinkage and expansion rate and the proportion of high-temperature grids in the canopy decreases, the dew point sensor is activated to obtain the thickness data of the condensation water layer on the leaf surface in real time; The condensation thickness on the leaf surface is compared with the protection threshold for preventing misjudgment. If the condensation thickness on the leaf surface exceeds the protection threshold, the current water stress signal is shielded.

5. The multimodal water-saving sprinkler irrigation system based on the Internet of Things according to claim 1, characterized in that: The method of continuously monitoring the light intensity at the top of the canopy and locking the sprinkler system when the intensity falls below the photosynthetic compensation point is as follows: Quantum sensors deployed at the top of the crop canopy detect light intensity in real time and compare it with the photosynthetic compensation point of the crop species. When the instantaneous light intensity is detected to be lower than the photosynthetic compensation point threshold of the species, low-light duration tracking is initiated, and the insufficient light time is accumulated. The accumulated insufficient light time is compared with the preset sprinkler lock trigger threshold time. If the accumulated insufficient light time reaches the threshold time, the output system locks the sprinkler system. When the light intensity is detected to rise above the compensation point, the sprinkler system lock is released.

6. The multimodal water-saving sprinkler irrigation system based on the Internet of Things according to claim 1, characterized in that: The dynamic adjustment of the pulse sprinkler irrigation frequency according to the crop drought resistance characteristic library is performed as follows: A high-frequency short-pulse mode is set for shallow-rooted crops, which increases the number of pulses per unit time but shortens the duration of a single pulse. A low-frequency long-pulse mode is set for deep-rooted crops to reduce the number of pulses per unit time but extend the duration of a single pulse.

7. The multi-modal water-saving sprinkler irrigation system based on the Internet of Things according to claim 1, characterized in that: The fault handling module includes the following contents: Collect and compare the monitoring data of the flow meters at the outlets of adjacent capillary branches in real time, calculate the flow difference, and compare the difference with the preset flow difference tolerance limit; When the flow difference between a branch and its adjacent branches exceeds the tolerance limit continuously and reaches the judgment time, the branch is marked as an abnormal branch; A compressed air pulse of a set duration is injected into the abnormal branch. If the flow is not restored after the compressed air pulse treatment, the abnormal branch is closed and the backup capillary network is activated.

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