Garden Environment Monitoring and Management System Based on Internet of Things Technology

Abstract

The present invention belongs to the technical field of intelligent irrigation. The present invention discloses a garden environment monitoring and management system based on Internet of Things technology, including a soil moisture monitoring module, which obtains the moisture content of the root layer of garden vegetation through an implanted TDR soil moisture sensor array; a carbon flux sensing module, which monitors carbon dioxide flux and vegetation photosynthetic efficiency respectively through an eddy covariance tower and a chlorophyll fluorometer to obtain carbon flux data and photosynthetic efficiency parameters; based on the carbon flux data and photosynthetic efficiency parameters, obtains the net carbon exchange amount; a data coupling module, which performs coupling analysis on the moisture content of the root layer and the net carbon exchange amount, establishes a water-carbon fixation correlation model, calculates the carbon sink efficiency index, and determines whether to generate an initial irrigation demand signal; while improving the water resource utilization rate, it also takes into account the ecological benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent irrigation, and more specifically, to a garden environment monitoring and management system based on Internet of Things technology. Background Art

[0002] Existing garden environment monitoring and management systems based on Internet of Things technology mainly have the following problems:

[0003] In traditional garden environment monitoring and management, it mainly relies on fixed irrigation time and quantitative water supply, without considering the current soil moisture status or the actual water demand of vegetation, which is likely to cause over - supply or insufficient water supply, resulting in water resource waste or vegetation water shortage; the traditional water volume calculation method relies on fixed parameters and look - up tables, and cannot compensate for the phenomenon of reduced photosynthetic capacity due to water shortage, resulting in a large deviation between the theoretical water demand and the actual demand, leading to a lack of accuracy in irrigation decision - making;

[0004] Traditional methods lack real - time monitoring and closed - loop feedback mechanisms, and mostly rely on preset timed irrigation plans rather than adjusting irrigation plans based on dynamic changes; after water supply, the water content in the root layer and the vegetation carbon flux may have changed, but the system fails to respond and adjust quickly, resulting in subsequent irrigation plans still not being well - matched with the actual demand, and the ecological benefits are difficult to reach the optimal state; traditional irrigation only focuses on water supply, ignores the carbon sequestration function and ecological benefits of vegetation, and cannot balance water resource conservation and the improvement of ecosystem functions. A single irrigation strategy may lead to the carbon absorption capacity of the garden ecosystem not meeting the standards.

[0005] In view of this, the present invention proposes a garden environment monitoring and management system based on Internet of Things technology to solve the above problems. Summary of the Invention

[0006] In order to overcome the above - mentioned defects of the prior art and to achieve the above - mentioned purpose, the present invention provides the following technical solutions: A garden environment monitoring and management system based on Internet of Things technology, including:

[0007] A soil moisture monitoring module, which obtains the water content in the root layer of garden vegetation through an implanted TDR soil moisture sensor array;

[0008] A carbon flux sensing module, which monitors carbon dioxide flux and vegetation photosynthetic efficiency through an eddy covariance tower and a chlorophyll fluorometer respectively, obtains carbon flux data and photosynthetic efficiency parameters; based on the carbon flux data and photosynthetic efficiency parameters, obtains the net carbon exchange amount;

[0009] A data coupling module, which couples and analyzes the water content in the root layer and the net carbon exchange amount, establishes a water - carbon fixation correlation model, calculates the carbon sequestration efficiency index, and determines whether to generate an initial irrigation demand signal;

[0010] Dynamic irrigation decision-making module. If an initial irrigation demand signal is generated, the transpiration-osmosis dynamic balance algorithm is adopted. Based on the reference evapotranspiration, combined with the crop coefficient and the carbon efficiency correction factor, the actual water requirement of the vegetation is obtained to correct the initial irrigation demand signal and obtain an accurate irrigation signal.

[0011] Intelligent drip irrigation module. Based on the accurate irrigation signal, the water supply instruction is executed through the intelligent drip irrigation control terminal, and after executing the accurate irrigation signal, the updated moisture content of the root layer and the net carbon exchange amount are periodically collected to form irrigation result data.

[0012] Feedback optimization module. The retrieved leaf area index is obtained, and combined with the preset extinction coefficient, a carbon storage estimation model is established. The irrigation result data is input into the carbon storage estimation model to calculate the deviation between the carbon storage estimated value and the expected value, and the extinction coefficient is dynamically corrected. The moisture-carbon fixation correlation model is updated using the corrected extinction coefficient to re-obtain the carbon sink efficiency index.

[0013] Preferably, the method for obtaining the moisture content of the root layer of the garden vegetation includes:

[0014] According to the distribution of the garden vegetation, n TDR probes are buried in the soil area where the roots of the vegetation are distributed to form a three-dimensional monitoring network. The TDR device is used to emit electromagnetic pulse signals to each TDR probe, and the time delay for each TDR probe to receive the electromagnetic pulse signal and send back the reflected signal to the TDR device is measured. According to the time delay and the length of the TDR probe, the actual propagation speed of the electromagnetic pulse signal in the soil area is calculated, and combined with the speed of the electromagnetic wave in a vacuum, the dielectric constant of different soil areas is calculated. The dielectric constants of different soil areas are substituted into the Topp equation to calculate the volumetric water content of different soil areas. The volumetric water contents of the soil areas at the same depth are weighted and averaged to obtain the moisture content of the root layer of the garden vegetation.

[0015] Preferably, the method for obtaining the net carbon exchange amount includes:

[0016] According to the height of the garden vegetation, an eddy covariance tower is built, and an anemometer and a gas analyzer are installed on the eddy covariance tower synchronously. The instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration are obtained through measurement. The average value of the product of the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration is calculated and spectrum correction is performed to obtain the carbon flux data. According to the distribution of the garden vegetation, a fixed chlorophyll fluorometer is installed, and a timed sampling program is preset to perform periodic monitoring on the surrounding vegetation to obtain the photosynthetic efficiency parameters.

[0017] A nocturnal respiration model is constructed. A temperature response function is constructed through the continuous nocturnal carbon flux data and the nocturnal temperature, and the total respiration amount of the garden vegetation is calculated and obtained. The photosynthetic efficiency parameters are subtracted from the total respiration amount of the garden vegetation to obtain the net carbon exchange amount.

[0018] Preferably, the method for establishing the water-carbon fixation correlation model includes:

[0019] Denote the water content rate of the root layer of garden vegetation as , and denote the net carbon exchange amount as . Perform time synchronization processing, eliminate outliers and use a sliding window to construct continuous time series sample pairs ; According to the physiological mechanism that plant photosynthesis is limited by water supply, select a water-carbon fixation correlation model to fit the functional relationship between the water content rate and the carbon fixation ability. The water-carbon fixation correlation model is ; where represents the water-carbon exchange response curve; represents the disturbance term; represents the index of the time point; By solving the first-order derivative of the water-carbon fixation correlation model, extract the carbon sink efficiency index.

[0020] Preferably, the method for determining whether to generate an initial irrigation demand signal includes:

[0021] Preset a carbon sink efficiency index threshold, and compare the carbon sink efficiency index with the preset carbon sink efficiency index threshold; if the carbon sink efficiency index is less than the preset carbon sink efficiency index threshold, it is determined that water replenishment irrigation is required, and an initial irrigation demand signal is generated; if the carbon sink efficiency index is greater than or equal to the preset carbon sink efficiency index threshold, it is determined that water replenishment irrigation is not required, and no initial irrigation demand signal is generated; The initial irrigation demand signal is the target irrigation area number that requires water replenishment irrigation.

[0022] Preferably, the method for obtaining the actual water demand of the vegetation includes:

[0023] Collect meteorological data and calculate the reference evapotranspiration through the transpiration-osmosis dynamic balance algorithm; The meteorological data includes net radiation, air temperature, wind speed and relative humidity; Analyze the types and growth periods of garden vegetation, and use the look-up table method to determine the crop coefficient according to the different water use characteristics of different garden vegetation during their growth cycles; By calculating the product of the reference evapotranspiration and the crop coefficient, further obtain the potential water demand of the vegetation ;

[0024] Based on the photosynthetic efficiency parameter and the net carbon exchange amount, evaluate the impact of water stress on the photosynthesis of the vegetation, and further obtain the carbon efficiency correction factor ; where is the ideal value of the net carbon exchange amount under the optimal water supply; is the actually measured net carbon exchange amount; is an adjustment coefficient; multiplying the potential water requirement of the vegetation by the carbon efficiency correction factor to obtain the actual water requirement of the vegetation.

[0025] Preferably, the method for obtaining the precise irrigation signal includes:

[0026] Based on the actual water requirement of the vegetation, correct the initial irrigation demand signal, set the actual water requirement of the vegetation as the minimum irrigation water volume, and calculate the irrigation duration required based on the minimum irrigation water volume according to the preset flow value of the drip irrigation equipment; integrate the initial irrigation demand signal, the minimum irrigation water volume and the irrigation duration to obtain the precise irrigation signal.

[0027] Preferably, the method for the intelligent drip irrigation control terminal to execute the water supply instruction includes:

[0028] The precise irrigation signal is transmitted to the intelligent drip irrigation control terminal through the Internet of Things protocol, and the intelligent drip irrigation control terminal analyzes the precise irrigation signal; the precise irrigation signal includes the target irrigation area number, the minimum irrigation water volume and the irrigation duration; the intelligent drip irrigation control terminal retrieves whether the corresponding drip irrigation execution solenoid valve is in the standby state according to the received target irrigation area number and synchronizes the state; the intelligent drip irrigation control terminal sends an irrigation start instruction to the drip irrigation execution solenoid valve to open the drip irrigation execution solenoid valve to supply water for irrigation to the garden vegetation in the target irrigation area.

[0029] Preferably, the method for dynamically correcting the extinction coefficient includes:

[0030] Use ground instruments to directly observe the light transmittance of the vegetation canopy and inversely deduce the retrieved leaf area index LAL. The preset extinction coefficient is , and construct a carbon storage estimation model based on the retrieved leaf area index and the extinction coefficient;

[0031] Preset the target carbon storage, and use the preset target carbon storage as the carbon storage expectation value , and subtract the carbon storage estimated value from the carbon storage expectation value to obtain the deviation of the carbon storage estimated value ; dynamically correct the extinction coefficient through the extinction coefficient correction formula. The extinction coefficient correction formula is: ; where, represents the corrected extinction coefficient; represents the adjustment factor for controlling the correction intensity;

[0032] If , it is judged that the carbon storage is insufficient and the extinction coefficient is increased; if , it is judged that the carbon storage is redundant and the extinction coefficient is decreased.

[0033] Preferably, the method for re-obtaining the carbon sink efficiency index includes: substituting the corrected extinction coefficient for the preset extinction coefficient, updating the water-carbon fixation correlation model, re-solving the first derivative of the water-carbon fixation correlation model, and extracting the carbon sink efficiency index.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] Through the transpiration-osmosis dynamic balance algorithm, combined with the reference evapotranspiration, crop coefficient and carbon efficiency correction factor, the present invention fully considers the influence of meteorological conditions, vegetation types and growth periods, and water stress on vegetation photosynthesis, accurately calculates the actual water demand of vegetation, and avoids the blindness of "irrigation determined by water" in traditional irrigation; the introduction of the carbon efficiency correction factor upgrades the traditional irrigation mode to "water-carbon collaborative intelligent irrigation", which not only considers the water demand of vegetation, but also takes into account ecological benefits such as carbon fixation, achieving a double breakthrough in water resource utilization efficiency and ecological benefits;

[0036] The initial irrigation signal is triggered based on the carbon sink efficiency threshold, and the actual water demand is obtained through the transpiration-osmosis balance algorithm and the carbon efficiency correction factor, forming a closed loop of "demand trigger → water volume calibration → precise execution" to ensure that the maximum ecological benefit is generated by every drop of water; the updated root layer moisture content and net carbon exchange amount after irrigation are input into the carbon storage estimation model, combined with the dynamic correction of the retrieved leaf area index (LAI) and extinction coefficient, to achieve a positive feedback loop of "irrigation effect evaluation → model parameter optimization → carbon sink target approximation". The extinction coefficient is adjusted by deviation drive, so that the carbon storage estimation value gradually converges to the preset target, ensuring long-term ecological benefits;

[0037] Compared with traditional timed / quantitative irrigation, the system reduces ineffective irrigation through water-carbon collaborative decision-making, achieving a double improvement in water conservation and carbon sink; through Internet of Things perception, water-carbon coupling model and closed-loop control technology, the traditional irrigation is upgraded to a smart management mode of "determining demand by carbon and supplying water by water", achieving the triple goals of water resource conservation, carbon sink capacity improvement and maximization of garden ecological value, providing a feasible technical solution for smart cities. Brief Description of the Drawings

[0038] Figure 1 It is a schematic structural diagram of a garden environment monitoring and management system based on Internet of Things technology of the present invention;

[0039] Figure 2 It is a schematic flowchart of a garden environment monitoring and management method based on Internet of Things technology of the present invention;

[0040] Figure 3 It is a technical roadmap of a garden environment monitoring and management based on Internet of Things technology of the present invention. Detailed Embodiments

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Embodiment 1

[0043] Please refer to Figure 1 and Figure 3 As shown, this Embodiment 1 further illustrates the garden environment monitoring and management system based on the Internet of Things technology proposed by the present invention, including:

[0044] In garden maintenance, the rational utilization of water resources has always been a difficult problem. For a long time, the traditional irrigation mode has seriously relied on manual experience, which makes it difficult to achieve precise irrigation when facing the complex and variable water demand characteristics of garden vegetation. In different seasons and weather conditions, the water demand of garden vegetation varies greatly. For example, in summer when the temperature is high, the water evaporation is fast and the water demand of plants increases sharply; while in spring and autumn, the relative demand decreases. Moreover, for different vegetation types, such as trees, shrubs, and herbaceous plants, the root depths and water absorption capacities are different, and the water demands are also different. Blind irrigation is likely to cause water resource waste or affect the growth of vegetation due to insufficient water supply. Therefore, the research and development of an advanced technology that can monitor the soil moisture condition in real time and accurately and implement precise irrigation regulation accordingly has become an urgent need in the garden maintenance industry.

[0045] And this need coincides with the concept of modern garden management that pursues refinement. Modern garden management pursues refinement and expects to monitor and manage the garden environment in an all-round and real-time manner. Under the traditional garden management mode, the frequency of obtaining environmental data is low and the scope is limited, making it difficult to detect the subtle changes in the garden environment. If these potential problems cannot be discovered and addressed in a timely manner, it is very likely to cause large-scale damage to the vegetation. With the continuous expansion of the scale of urban gardens and the increasing diversification of functions, the traditional management method has become increasingly inadequate and unable to meet the actual needs. Against this background, the rise of the Internet of Things technology has brought new hope for garden environment management.

[0046] In recent years, the Internet of Things technology has made rapid progress, providing strong technical support for solving many problems in garden management. Among them, the remarkable progress of sensor technology is particularly crucial. Now, various environmental parameter sensors have the characteristics of smaller size, higher precision, and lower power consumption, making it possible to deploy sensors on a large scale and in all directions in the garden environment. At the same time, the innovation of communication technology also ensures that data can be transmitted at high speed and stably, so as to timely and accurately collect the data collected by sensors distributed in every corner of the garden to the management platform. In addition, the substantial improvement of data processing and analysis technology makes it possible to deeply mine and scientifically model a large amount of environmental data, providing a scientific and reliable basis for garden management decisions.

[0047] In traditional garden environment monitoring and management, it mainly relies on fixed irrigation time and quantitative water supply. Regardless of the current soil moisture status or the actual water demand of the vegetation, irrigation is carried out according to the preset plan, lacking dynamic evaluation based on real-time meteorological conditions, vegetation types, crop growth periods, and the impact of water stress, unable to accurately calculate the actual water demand of the vegetation, easily causing overwatering or insufficient water supply, resulting in water resource waste or vegetation water shortage.

[0048] The traditional water volume calculation method relies on fixed parameters and look-up tables, without introducing a carbon efficiency correction factor, unable to compensate for the phenomenon of reduced photosynthetic capacity due to water shortage, resulting in a large deviation between the theoretical water demand and the actual demand, and thus the irrigation decision lacks accuracy; with the changes in environmental conditions, canopy structure, and vegetation types, fixed parameters are likely to lead to deviations in carbon storage estimation, and the lack of feedback correction means in traditional methods makes it difficult to achieve long-term ecological goals, and it is difficult to maximize the ecological value of the garden.

[0049] Traditional methods lack real-time monitoring and closed-loop feedback mechanisms, mostly relying on preset timed irrigation plans rather than adjusting irrigation plans based on dynamic changes; after water supply, the moisture content in the root layer and the vegetation carbon flux may have changed, but the system fails to respond and adjust quickly, resulting in the subsequent irrigation plan still not being well matched with the actual demand, and it is difficult to achieve the optimal ecological benefit state; traditional irrigation only focuses on water supply, does not incorporate carbon fixation, vegetation growth, and ecological benefits into the irrigation decision-making process, lacks water-carbon collaborative decision-making, ignores the carbon sink function and ecological benefits of vegetation, cannot balance water resource conservation and ecosystem function improvement, and a single irrigation strategy may lead to the carbon absorption capacity of the garden ecosystem not meeting the standards.

[0050] In this context, the present invention proposes a garden environment monitoring and management system based on the Internet of Things technology, including:

[0051] A soil moisture monitoring module, which obtains the moisture content in the root layer of garden vegetation through an implanted TDR soil moisture sensor array.

[0052] A carbon flux sensing module monitors carbon dioxide flux and vegetation photosynthetic efficiency through an eddy covariance tower and a chlorophyll fluorometer respectively, to obtain carbon flux data and photosynthetic efficiency parameters; based on the carbon flux data and photosynthetic efficiency parameters, it obtains the net carbon exchange amount;

[0053] A data coupling module performs a coupling analysis on the root zone water content and the net carbon exchange amount, establishes a water-carbon fixation correlation model, calculates the carbon sink efficiency index, and determines whether to generate an initial irrigation demand signal;

[0054] A dynamic irrigation decision-making module, if an initial irrigation demand signal is generated, then uses a transpiration-osmosis dynamic balance algorithm, through the reference evapotranspiration, combined with the crop coefficient and the carbon efficiency correction factor, to obtain the actual water requirement of the vegetation, and corrects the initial irrigation demand signal to obtain an accurate irrigation signal;

[0055] An intelligent drip irrigation module, based on the accurate irrigation signal, executes a water supply instruction through an intelligent drip irrigation control terminal, and regularly collects the updated root zone water content and net carbon exchange amount after executing the accurate irrigation signal to form irrigation result data;

[0056] A feedback optimization module obtains the retrieved leaf area index, and combined with a preset extinction coefficient, establishes a carbon storage estimation model; inputs the irrigation result data into the carbon storage estimation model, calculates the deviation between the carbon storage estimated value and the expected value, and dynamically corrects the extinction coefficient; uses the corrected extinction coefficient to update the water-carbon fixation correlation model and re-obtains the carbon sink efficiency index.

[0057] The method for obtaining the root zone water content of landscape vegetation includes:

[0058] According to the distribution of landscape vegetation, n TDR probes are buried in the soil area where the vegetation roots are distributed to form a three-dimensional monitoring network; the TDR probes are three-dimensionally distributed in space, and the TDR probes are evenly arranged in the horizontal direction within the same depth layer, so that the monitoring point intervals can cover the vegetation distribution range, and the TDR probes are respectively buried in the vertical direction within different depth layers to realize the monitoring of the water content of the surface, middle and deep roots of the vegetation; the TDR method is a non-destructive in-situ monitoring means, which can obtain real data without excavating or disturbing the soil. By arranging TDR probes in layers (vertical direction) and evenly (horizontal direction) in the vegetation root distribution area, a three-dimensional monitoring network with multi-point layout, stratified sampling and continuous response is constructed, effectively covering all spatial areas of the vegetation root activities, obtaining the soil water content from the surface layer to the deep layer, and realizing the visualization of the water content at different positions and different depths in the landscape soil area;

[0059] Use a TDR device to send an electromagnetic pulse signal to each TDR probe, and measure the time delay for each TDR probe to receive the electromagnetic pulse signal and send a reflected signal back to the TDR device ;Depending on the time delay and TDR probe length , calculate the actual propagation speed of the electromagnetic pulse signal in the soil area , and combined with the speed of electromagnetic waves in a vacuum, the dielectric constant of different soil areas is calculated ; Substitute the dielectric constants of different soil areas into the Topp equation to calculate the volumetric moisture content of different soil areas; take the weighted average of the volumetric moisture content of soil areas at the same depth to obtain the moisture content of the root layer of garden vegetation.

[0060] Methods for obtaining net carbon exchange include:

[0061] An eddy covariance tower is constructed according to the height of the garden vegetation, and an anemometer and gas analyzer are installed on the eddy covariance tower simultaneously. The instantaneous deviation of vertical wind speed and carbon dioxide concentration are measured. The average value of the product of the instantaneous deviation of vertical wind speed and carbon dioxide concentration is calculated, and the spectrum is corrected to obtain carbon flux data. According to the distribution of garden vegetation, a fixed chlorophyll fluorescence meter is installed, and a timed sampling program is preset to periodically monitor the surrounding vegetation to obtain photosynthetic efficiency parameters.

[0062] The eddy covariance tower can monitor changes in carbon dioxide concentration in situ without disturbance and has micro-meteorological response capabilities. The carbon flux data calculated by combining wind speed and instantaneous deviation of carbon dioxide concentration are highly temporally and spatially representative and flux conservation. The chlorophyll fluorescence meter provides photosynthetic efficiency indicators, reflecting the response of vegetation to environmental stresses (drought, temperature, etc.). After integration with carbon flux data, it can dynamically monitor changes in the carbon sequestration capacity of vegetation at different times (such as morning and afternoon) or weather conditions (cloudy and sunny). Combining micro-meteorological monitoring technology with plant physiological response methods forms a complete process from CO2 flux monitoring to photosynthetic efficiency assessment to respiratory modeling to net exchange capacity calculation, realizing refined assessment of the carbon efficiency of garden vegetation and optimized management of carbon sequestration function.

[0063] A nighttime respiration model was constructed, and a temperature response function was constructed using continuous nighttime carbon flux data and nighttime temperature to calculate and obtain the total respiration of garden vegetation. The net carbon exchange was obtained by subtracting the total respiration of garden vegetation from the photosynthetic efficiency parameter.

[0064] Methods for modeling the water-carbon sequestration linkage include:

[0065] The root layer moisture content of garden vegetation is recorded as , and the net carbon exchange amount is recorded as , perform time synchronization processing, remove outliers and use sliding windows to construct continuous time series sample pairs ; Based on the physiological mechanism that plant photosynthesis is limited by water supply, a water-carbon fixation correlation model is selected to fit the functional relationship between water content and carbon fixation ability. The water-carbon fixation correlation model is ; where represents the water-carbon exchange response curve; represents the disturbance term; represents the index of the time point; By taking the first derivative of the water-carbon fixation correlation model, the carbon sink efficiency index is extracted.

[0066] Based on the Gaussian response model, a water-carbon exchange response curve is constructed, and the optimal water content range is accurately fitted. ; where represents the maximum net carbon exchange; represents the response curve curvature control coefficient; is the central value of the optimal soil water content range; By using the least squares method to optimize the fitted Gaussian curve, the central value of the optimal soil water content range is obtained.

[0067] The construction of the water-carbon fixation correlation model is based on the physiological mechanism that plant photosynthesis depends on water supply, fitting the influence response relationship of root water content on net carbon exchange, transforming the plant's response mechanism to water into a mathematical function, realizing the explicit modeling of the carbon sink ability with respect to water supply, thereby judging the marginal effect of the current water level on the carbon sink, finding the optimal irrigation range, avoiding "inefficient irrigation" or "over-irrigation", providing a quantitative basis and control means for achieving precise irrigation and intelligent ecological management, and being a key bridge module in the "Internet of Things + ecological feedback" system.

[0068] The method for judging whether to generate an initial irrigation demand signal includes:

[0069] Presetting a carbon sink efficiency index threshold, and comparing the carbon sink efficiency index with the preset carbon sink efficiency index threshold; If the carbon sink efficiency index is less than the preset carbon sink efficiency index threshold, it is judged that water replenishment irrigation is required, and an initial irrigation demand signal is generated; If the carbon sink efficiency index is greater than or equal to the preset carbon sink efficiency index threshold, it is judged that water replenishment irrigation is not required, and no initial irrigation demand signal is generated; The initial irrigation demand signal is the target irrigation area number that requires water replenishment irrigation.

[0070] The method for obtaining the actual water demand of vegetation includes:

[0071] Collect meteorological data and calculate the reference evapotranspiration through the transpiration-osmosis dynamic balance algorithm. , which is used to describe the theoretical evapotranspiration capacity of vegetation without water stress under standard conditions; the meteorological data includes net radiation, air temperature, wind speed and relative humidity; analyze the types and growth periods of garden vegetation, and determine the crop coefficient by using the look-up table method according to the different water use characteristics of different garden vegetation in their growth cycles ; by calculating the product of the reference evapotranspiration and the crop coefficient, the potential water demand of the vegetation can be obtained , which is used to describe the theoretical water demand under no water stress;

[0072] "Transpiration-osmosis dynamic balance algorithm" is a precise irrigation algorithm constructed on the basis of plant water physiology, which is used to dynamically regulate the balance relationship between the water demand of plants and the irrigation water supplement. Plants consume water through stomatal transpiration, and its rate is affected by multiple environmental factors, such as net radiation, air temperature, wind speed and relative humidity. By combining meteorological factors through the FAO Penman-Monteith equation, the reference evapotranspiration can be calculated; this algorithm comprehensively considers the actual water demand of plant transpiration and the limitation of water supply, and by coupling "water demand" and "water supply", the amount of irrigation water is more in line with the actual demand, avoiding "over-irrigation" or "under-irrigation", and realizing intelligent correction and optimal control of irrigation behavior under dynamically changing environmental conditions;

[0073] Based on the photosynthetic efficiency parameters and net carbon exchange, evaluate the impact of water stress on the photosynthesis of vegetation, and then obtain the carbon efficiency correction factor , which reflects the adjustment of the water demand of vegetation under the actual water stress state. The initially calculated water demand can be dynamically corrected through this factor; among them, is the ideal value of the net carbon exchange under the best water supply; is the actually measured net carbon exchange; is the adjustment coefficient; multiply the potential water demand of the vegetation by the carbon efficiency correction factor to obtain the actual water demand of the vegetation.

[0074] Principle: Since the soil water condition will affect the photosynthesis efficiency and water use effect of vegetation, a carbon efficiency correction factor is introduced to quantitatively evaluate the impact of water stress on the photosynthesis of vegetation; the carbon efficiency correction factor is used to measure the change in the photosynthesis efficiency of plants under actual water conditions. Based on the net carbon exchange and photosynthetic efficiency parameters collected by the carbon flux sensing module, the carbon efficiency correction factor is compared with the "carbon exchange capacity under the ideal photosynthetic state" to reflect the impact of the current water stress on the physiological process of vegetation; when the soil water is insufficient, the actually measured net carbon exchange will be lower than the ideal value of the net carbon exchange under the best water supply, resulting in the carbon efficiency correction factor being less than 1 (reflecting a decrease in water demand). On the contrary, when the soil water is sufficient, the carbon efficiency correction factor will be greater than or equal to 1;

[0075] The introduction of the carbon efficiency correction factor not only takes into account the environmental evapotranspiration conditions but also incorporates the feedback of the plant's own physiological state, upgrading the traditional "irrigation determined by water" to "water-carbon collaborative intelligent irrigation", achieving a double breakthrough in water resource utilization efficiency and ecological benefits, providing new ideas for intelligent garden management; achieving water conservation and meeting the needs of vegetation through adaptive regulation, optimizing irrigation decision-making and water management; enabling the originally obtained theoretical water demand to be corrected to a water demand closer to the actual state, avoiding the phenomena of "over-irrigation" or "under-irrigation", supplying water as needed, and significantly improving irrigation accuracy and water conservation effects; regulating irrigation through carbon efficiency feedback to effectively maintain the optimal photosynthetic level and carbon sequestration capacity of plants, contributing to maximizing the ecological functions of garden green spaces.

[0076] The methods for obtaining accurate irrigation signals include:

[0077] Correct the initial irrigation demand signal based on the actual water demand of the vegetation, set the actual water demand of the vegetation as the minimum irrigation water volume, and calculate the irrigation duration required based on the minimum irrigation water volume according to the preset flow value of the drip irrigation equipment; integrate the initial irrigation demand signal, the minimum irrigation water volume, and the irrigation duration to obtain an accurate irrigation signal.

[0078] For example: The tree irrigation area A-12 (with an area of 50 m²) in an urban ecological park triggers an irrigation demand: The accurate irrigation signal is: the target irrigation area number A-12, the minimum irrigation water volume of 330 L (6.6 mm per minute), and the irrigation duration of 110 min.

[0079] The methods for the intelligent drip irrigation control terminal to execute the water supply instruction include:

[0080] The accurate irrigation signal is transmitted to the intelligent drip irrigation control terminal through the Internet of Things protocol, and the intelligent drip irrigation control terminal analyzes the accurate irrigation signal; the accurate irrigation signal includes the target irrigation area number, the minimum irrigation water volume, and the irrigation duration; the intelligent drip irrigation control terminal retrieves whether the corresponding drip irrigation execution solenoid valve is in a standby state according to the received target irrigation area number and synchronizes the state; the intelligent drip irrigation control terminal sends an irrigation start instruction to the drip irrigation execution solenoid valve to open the drip irrigation execution solenoid valve to supply water for irrigation to the garden vegetation in the target irrigation area.

[0081] The methods for dynamically correcting the extinction coefficient include:

[0082] Use ground instruments to directly observe the light transmittance of the vegetation canopy and inversely deduce the retrieved leaf area index LAL, and preset the extinction coefficient as ; construct a carbon storage estimation model based on the retrieved leaf area index and the extinction coefficient: ; where represents the estimated value of carbon storage; Indicates the carbon fixation capacity per unit leaf area;

[0083] The preset target carbon storage is used as the expected value of carbon storage. , and the expected value of carbon storage is subtracted from the estimated value of carbon storage to obtain the deviation of the estimated value of carbon storage. ; The extinction coefficient is dynamically corrected through the extinction coefficient correction formula, and the extinction coefficient correction formula is: ; where represents the corrected extinction coefficient; represents the adjustment factor for controlling the correction intensity;

[0084] If , it is determined that the carbon storage is insufficient, and the extinction coefficient is increased; if , it is determined that the carbon storage is redundant, and the extinction coefficient is decreased.

[0085] Accuracy improvement: By correcting the extinction coefficient in real time, the model can dynamically correct the estimation error caused by canopy structure, species differences, or environmental changes, so that the carbon storage prediction result is closer to the preset target carbon storage;

[0086] Adaptive control: Through the mechanism of "physical model of light attenuation + target-driven feedback", dynamic self-calibration of carbon storage estimation is achieved, with strong adaptability and robustness;

[0087] Guiding management decisions: Accurate carbon storage estimation not only provides a basis for ecological compensation and green finance, but also can guide the optimal configuration and management measures of garden vegetation, thus achieving a double improvement in ecological and economic benefits.

[0088] The method for re-obtaining the carbon sink efficiency index includes: replacing the preset extinction coefficient with the corrected extinction coefficient, updating the water-carbon fixation correlation model, re-solving the first derivative of the water-carbon fixation correlation model, and extracting the carbon sink efficiency index.

[0089] The preset extinction coefficient is set by the staff based on the historical data analysis results. This historical analysis process includes the system collecting the extinction coefficients of multiple data points and calculating their average value as a reference; similarly, the preset timing sampling program, the preset carbon sink efficiency index threshold, the preset drip irrigation equipment flow value, and the preset target carbon storage are also set by the staff according to the system historical operation data and the specific application scenario requirements. These preset thresholds can be adjusted by the staff according to the actual situation during the system operation.

[0090] In this embodiment, through the transpiration-osmosis dynamic balance algorithm, combined with the reference evapotranspiration, crop coefficient and carbon efficiency correction factor, the impacts of meteorological conditions, vegetation types, growth periods and water stress on vegetation photosynthesis are fully considered, and the actual water requirement of vegetation is accurately calculated, avoiding the blindness of "irrigation determined by water volume" in traditional irrigation; the introduction of the carbon efficiency correction factor upgrades the traditional irrigation mode to "water-carbon collaborative intelligent irrigation", which not only considers the water demand of vegetation, but also takes into account ecological benefits such as carbon fixation, achieving a double breakthrough in water resource utilization efficiency and ecological benefits;

[0091] The initial irrigation signal is triggered based on the carbon sink efficiency threshold, and the actual water requirement is obtained through the transpiration-osmosis balance algorithm and the carbon efficiency correction factor, forming a closed loop of "demand trigger → water volume calibration → precise execution" to ensure that the maximum ecological benefit is generated from each drop of water; the updated root layer moisture content and net carbon exchange amount after irrigation are input into the carbon storage estimation model, and combined with the inversion of leaf area index (LAI) and extinction coefficient for dynamic correction, realizing a positive feedback loop of "irrigation effect evaluation → model parameter optimization → carbon sink target approximation". The extinction coefficient is adjusted by deviation drive, making the carbon storage estimation value gradually converge to the preset target carbon storage to ensure long-term ecological benefits;

[0092] Compared with traditional fixed-time / fixed-volume irrigation, the system reduces ineffective irrigation through water-carbon collaborative decision-making, achieving a double improvement in water conservation and carbon sink; through Internet of Things perception, water-carbon coupling model and closed-loop control technology, the traditional irrigation is upgraded to an intelligent management mode of "determining water supply based on carbon demand", achieving the triple goals of water resource conservation, carbon sink capacity improvement and maximization of garden ecological value, providing a practical technical solution for smart cities.

[0093] Embodiment 2

[0094] Please refer to Figure 2 As shown, for the parts not described in detail in this embodiment, refer to the description content of Embodiment 1. A garden environment monitoring and management method based on Internet of Things technology is provided, including:

[0095] S1. Obtain the root layer moisture content of garden vegetation through an implanted TDR soil moisture sensor array;

[0096] S2. Monitor the carbon dioxide flux and vegetation photosynthetic efficiency through an eddy covariance tower and a chlorophyll fluorometer respectively to obtain carbon flux data and photosynthetic efficiency parameters; based on the carbon flux data and photosynthetic efficiency parameters, obtain the net carbon exchange amount;

[0097] S3. Conduct a coupling analysis on the root layer moisture content and the net carbon exchange amount, establish a water-carbon fixation correlation model, calculate the carbon sink efficiency index, and determine whether to generate an initial irrigation demand signal;

[0098] S4. If an initial irrigation demand signal is generated, the transpiration-osmosis dynamic balance algorithm is adopted. By combining the reference evapotranspiration, crop coefficient, and carbon efficiency correction factor, the actual water requirement of the vegetation is obtained to correct the initial irrigation demand signal and obtain an accurate irrigation signal.

[0099] S5. Based on the accurate irrigation signal, the intelligent drip irrigation control terminal executes the water supply instruction, and after executing the accurate irrigation signal, the updated water content of the root layer and net carbon exchange amount are periodically collected to form irrigation result data.

[0100] S6. The inversion leaf area index is obtained, and combined with the preset extinction coefficient, a carbon storage estimation model is established. The irrigation result data is input into the carbon storage estimation model to calculate the deviation between the carbon storage estimation value and the expected value, and the extinction coefficient is dynamically corrected. The water-carbon fixation correlation model is updated using the corrected extinction coefficient to re-obtain the carbon sink efficiency index.

[0101] Since the electronic device introduced in this embodiment is the electronic device adopted by the garden environment monitoring and management system based on the Internet of Things technology in the embodiments of the present application, based on the garden environment monitoring and management system based on the Internet of Things technology introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device realizes the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device adopted by the garden environment monitoring and management system based on the Internet of Things technology in the embodiments of the present application, it falls within the protection scope of the present application.

[0102] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0103] The above is only the preferred implementation manner of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those ordinary technical operators in the technical field, several improvements and retouches made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A garden environment monitoring and management system based on Internet of Things technology, characterized in that Including: A soil moisture monitoring module, which obtains the water content rate of the root layer of garden vegetation through an implanted TDR soil moisture sensor array; A carbon flux sensing module, which respectively monitors the carbon dioxide flux and the photosynthetic efficiency of vegetation through an eddy covariance tower and a chlorophyll fluorometer, obtains carbon flux data and photosynthetic efficiency parameters; and based on the carbon flux data and the photosynthetic efficiency parameters, obtains the net carbon exchange amount; A data coupling module, which performs coupling analysis on the water content rate of the root layer and the net carbon exchange amount, establishes a water-carbon fixation association model, calculates a carbon sink efficiency index, and determines whether to generate an initial irrigation demand signal; The method for establishing the water-carbon fixation association model includes: Denote the water content of the root layer of garden vegetation as , and denote the net carbon exchange as . Perform time synchronization processing, remove outliers and use a sliding window to construct continuous time series sample pairs ; Based on the physiological mechanism that plant photosynthesis is limited by water supply, select the water-carbon fixation correlation model to fit the functional relationship between water content and carbon fixation ability. The water-carbon fixation correlation model is ; where represents the water-carbon exchange response curve; represents the disturbance term; represents the index of the time point; By solving the first-order derivative of the water-carbon fixation correlation model, extract the carbon sink efficiency index; Construct a moisture-carbon exchange response curve based on a Gaussian response model and accurately fit the optimal moisture content interval. ; Among them, represents the maximum net carbon exchange amount; represents the response curve curvature control coefficient; is the central value of the optimal soil moisture content interval; The central value of the optimal soil moisture content interval is obtained by optimizing the fitted Gaussian curve through the least squares method. A dynamic irrigation decision-making module, if an initial irrigation demand signal is generated, then adopts a transpiration-osmosis dynamic balance algorithm, and through the reference evapotranspiration, combines the crop coefficient and the carbon efficiency correction factor to obtain the actual water requirement of the vegetation, and corrects the initial irrigation demand signal to obtain an accurate irrigation signal; An intelligent drip irrigation module, based on the accurate irrigation signal, executes a water supply instruction through an intelligent drip irrigation control terminal, and regularly collects the updated water content rate of the root layer and the net carbon exchange amount after executing the accurate irrigation signal to form irrigation result data; A feedback optimization module, which obtains the retrieved leaf area index, and combines the preset extinction coefficient to establish a carbon storage estimation model; inputs the irrigation result data into the carbon storage estimation model, calculates the deviation between the carbon storage estimation value and the expected value, and dynamically corrects the extinction coefficient; updates the water-carbon fixation association model with the corrected extinction coefficient, and re-obtains the carbon sink efficiency index.

2. The garden environment monitoring and management system based on Internet of Things technology according to claim 1, wherein The method for obtaining the water content rate of the root layer of the garden vegetation includes: According to the distribution of the garden vegetation, n TDR probes are buried in the soil area where the vegetation roots are distributed to form a three-dimensional monitoring network; the TDR device is used to emit electromagnetic pulse signals to each TDR probe, and the time delay for each TDR probe to receive the electromagnetic pulse signal and send back the reflected signal to the TDR device is measured; according to the time delay and the length of the TDR probe, the actual propagation speed of the electromagnetic pulse signal in the soil area is calculated, and combined with the speed of the electromagnetic wave in a vacuum, the dielectric constant of different soil areas is calculated; the dielectric constants of different soil areas are substituted into the Topp equation to calculate the volumetric water content rate of different soil areas; the volumetric water content rates of the soil areas at the same depth are weighted and averaged to further obtain the water content rate of the root layer of the garden vegetation.

3. The garden environment monitoring and management system based on the Internet of Things technology according to claim 2, wherein, The method for obtaining the net carbon exchange amount includes: An eddy covariance tower frame is built according to the height of the garden vegetation, and an anemometer and a gas analyzer are synchronously installed on the eddy covariance tower frame, and the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration are obtained through measurement; the average value of the product of the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration is calculated and spectrum correction is performed to further obtain the carbon flux data; according to the distribution of the garden vegetation, a fixed chlorophyll fluorometer is installed, and a timing sampling program is preset to perform periodic monitoring on the surrounding vegetation to further obtain the photosynthetic efficiency parameters; Construct a nighttime respiration model, construct a temperature response function through continuous nighttime carbon flux data and nighttime temperature, calculate and obtain the total respiration of garden vegetation; subtract the total respiration of garden vegetation from the photosynthetic efficiency parameter to obtain the net carbon exchange amount.

4. The garden environment monitoring and management system based on the Internet of Things technology according to claim 3, wherein The method for determining whether to generate an initial irrigation demand signal includes: Preset a carbon sink efficiency index threshold, and compare the carbon sink efficiency index with the preset carbon sink efficiency index threshold; if the carbon sink efficiency index is less than the preset carbon sink efficiency index threshold, it is determined that supplementary irrigation is required and an initial irrigation demand signal is generated; if the carbon sink efficiency index is greater than or equal to the preset carbon sink efficiency index threshold, it is determined that supplementary irrigation is not required and no initial irrigation demand signal is generated; the initial irrigation demand signal is the target irrigation area number that requires supplementary irrigation.

5. The garden environment monitoring and management system based on the Internet of Things technology according to claim 4, characterized in that, The method for obtaining the actual water demand of the vegetation includes: Collect meteorological data and calculate the reference evapotranspiration through the transpiration-osmosis dynamic equilibrium algorithm ; The meteorological data includes net radiation, air temperature, wind speed and relative humidity; Analyze the types and growth periods of garden vegetation, and determine the crop coefficient by using the table lookup method according to the different water use characteristics of different garden vegetation in their growth cycles ; Obtain the potential water requirement of vegetation by calculating the product of the reference evapotranspiration and the crop coefficient ; Based on photosynthetic efficiency parameters and net carbon exchange, evaluate the impact of water stress on vegetation photosynthesis, and then obtain the carbon efficiency correction factor ; among them, is the ideal value of the net carbon exchange under optimal water supply; is the net carbon exchange obtained through actual measurement; is the adjustment coefficient; multiply the potential water demand of the vegetation by the carbon efficiency correction factor to obtain the actual water demand of the vegetation.

6. The garden environment monitoring and management system based on the Internet of Things technology according to claim 5, characterized in that, The method for obtaining the precise irrigation signal includes: Based on the actual water demand of the vegetation, correct the initial irrigation demand signal, set the actual water demand of the vegetation as the minimum irrigation water volume, and calculate the irrigation duration required based on the minimum irrigation water volume according to the preset drip irrigation equipment flow value; integrate the initial irrigation demand signal, the minimum irrigation water volume, and the irrigation duration to obtain the precise irrigation signal.

7. The garden environment monitoring and management system based on the Internet of Things technology according to claim 6, characterized in that, The method for executing the water supply instruction through the intelligent drip irrigation control terminal includes: The precise irrigation signal is transmitted to the intelligent drip irrigation control terminal through the Internet of Things protocol, and the intelligent drip irrigation control terminal analyzes the precise irrigation signal; the precise irrigation signal includes the target irrigation area number, the minimum irrigation water volume, and the irrigation duration; the intelligent drip irrigation control terminal retrieves whether the corresponding drip irrigation execution solenoid valve is in the standby state according to the received target irrigation area number and synchronizes the state; the intelligent drip irrigation control terminal sends an irrigation start instruction to the drip irrigation execution solenoid valve to open the drip irrigation execution solenoid valve to supply water to the garden vegetation in the target irrigation area.

8. The garden environment monitoring and management system based on the Internet of Things technology according to claim 7, characterized in that, The method for dynamically correcting the extinction coefficient includes: Using ground-based instruments to directly observe the light transmittance of the vegetation canopy and inversely derive the leaf area index LAI, with a preset extinction coefficient of , constructing a carbon storage estimation model based on the inverted leaf area index and extinction coefficient; Set a preset target carbon storage, and use the preset target carbon storage as the expected value of carbon storage , subtract the estimated value of carbon storage from the expected value of carbon storage to obtain the deviation of the estimated value of carbon storage ; dynamically correct the extinction coefficient through the extinction coefficient correction formula, and the extinction coefficient correction formula is: ; where represents the corrected extinction coefficient; represents the adjustment factor for controlling the correction intensity; If , it is determined that the carbon storage is insufficient and the extinction coefficient is increased; if , it is determined that the carbon storage is redundant and the extinction coefficient is decreased.

9. The garden environment monitoring and management system based on the Internet of Things technology according to claim 8, characterized in that, The method for re-obtaining the carbon sink efficiency index includes: replacing the preset extinction coefficient with the corrected extinction coefficient, updating the water-carbon fixation correlation model, re-solving the first derivative of the water-carbon fixation correlation model, and extracting the carbon sink efficiency index.

Images