Garden environment monitoring management system based on Internet of Things technology

Abstract

The invention belongs to the technical field of intelligent irrigation, and discloses a garden environment monitoring and management system based on the Internet of Things technology, and the system comprises a soil moisture monitoring module which obtains the moisture content of a root layer of garden vegetation through an implantable TDR soil moisture sensor array; the carbon flux sensing module is used for respectively monitoring carbon dioxide flux and vegetation photosynthetic efficiency through a vortex covariance tower and a chlorophyll luminoscope and acquiring carbon flux data and photosynthetic efficiency parameters; obtaining net carbon exchange capacity based on the carbon flux data and the photosynthetic efficiency parameters; the data coupling module is used for performing coupling analysis on the moisture content of the root system layer and the net carbon exchange capacity, establishing a moisture-carbon fixed correlation model, calculating a carbon sink efficiency index and judging whether an initial irrigation demand signal is generated or not; the utilization rate of water resources is increased; and meanwhile, ecological benefits are considered.

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] The existing garden environment monitoring and management systems based on Internet of Things technology mainly have the following problems: In traditional garden environment monitoring and management, it mainly relies on fixed irrigation time and quantitative water replenishment, without considering the current soil moisture status or the actual water demand of vegetation, which is likely to cause excessive 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; 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 replenishment, the moisture content of 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 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, cannot balance water resource conservation and the improvement of ecosystem functions, and a single irrigation strategy may lead to the carbon absorption capacity of the garden ecosystem not meeting the standards.

[0003] 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

[0004] In order to overcome the above defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A garden environment monitoring and management system based on Internet of Things technology, comprising: 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 respectively monitors the carbon dioxide flux and the photosynthetic efficiency of vegetation 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 the photosynthetic efficiency parameters, the net carbon exchange amount is obtained; A data coupling module, which couples and analyzes the moisture content of 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; The dynamic irrigation decision-making module, if an initial irrigation demand signal is generated, adopts the transpiration-percolation dynamic balance algorithm. By using the reference evapotranspiration, combined with the crop coefficient and the carbon efficiency correction factor, it obtains the actual water requirement of the vegetation, corrects the initial irrigation demand signal, and obtains an accurate irrigation signal; The intelligent drip irrigation module, based on the accurate irrigation signal, executes the water supply instruction through the intelligent drip irrigation control terminal, and regularly collects the updated water content of the root layer and the net carbon exchange amount after executing the accurate irrigation signal to form irrigation result data; The feedback optimization module obtains the retrieved leaf area index, and combines it with 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 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.

[0005] Preferably, the method for obtaining the water content 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 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 measure the time delay for each TDR probe to receive the electromagnetic pulse signal and send back the reflected signal to the TDR device; according to the time delay and the length of the TDR probe, calculate the actual propagation speed of the electromagnetic pulse signal in the soil area, and combine it with the speed of the electromagnetic wave in a vacuum to calculate the dielectric constant of different soil areas; substitute the dielectric constants of different soil areas into the Topp equation to calculate the volumetric water content of different soil areas; perform weighted averaging on the volumetric water content of the soil areas at the same depth to obtain the water content of the root layer of the garden vegetation.

[0006] Preferably, the method for obtaining the net carbon exchange amount includes: Build a eddy covariance tower according to the height of the garden vegetation, and synchronously install an anemometer and a gas analyzer on the eddy covariance tower. Obtain the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration through measurement; calculate the average value of the product of the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration, and perform spectral correction to obtain the carbon flux data; according to the distribution of the garden vegetation, install a fixed chlorophyll fluorometer, and preset a timing sampling program to perform periodic monitoring on the surrounding vegetation to obtain the photosynthetic efficiency parameters; Construct a night respiration model, build a temperature response function through continuous night carbon flux data and night temperature, calculate and obtain the total respiration amount of the garden vegetation; subtract the total respiration amount of the garden vegetation from the photosynthetic efficiency parameters to obtain the net carbon exchange amount.

[0007] Preferably, the method for establishing the water-carbon fixation correlation model includes: Record the water content of the root layer of garden vegetation as ; record the net carbon exchange 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 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.

[0008] Preferably, 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 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.

[0009] Preferably, 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 balance algorithm ; the meteorological data includes net radiation, air temperature, wind speed and relative humidity; analyze the type and growth period of garden vegetation, and determine the crop coefficient using the look-up table method 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 ; Based on the photosynthetic efficiency parameter and the net carbon exchange, 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 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 further obtain the actual water demand of the vegetation.

[0010] Preferably, the method for obtaining the precise irrigation signal includes: Based on the actual water requirement of the vegetation, the initial irrigation demand signal is corrected. The actual water requirement of the vegetation is set as the minimum irrigation water volume, and according to the preset flow value of the drip irrigation equipment, the irrigation duration required based on the minimum irrigation water volume is calculated. The initial irrigation demand signal, the minimum irrigation water volume, and the irrigation duration are integrated to obtain an accurate irrigation signal.

[0011] Preferably, the method for the intelligent drip irrigation control terminal to execute the water supply instruction includes: 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 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.

[0012] Preferably, the method for dynamically correcting the extinction coefficient includes: Using ground instruments to directly observe the light transmittance of the vegetation canopy and inversely derive the retrieved leaf area index LAL. The preset extinction coefficient is Based on the retrieved leaf area index and the extinction coefficient, a carbon storage estimation model is constructed; A preset target carbon storage is set, and the preset target carbon storage is used as the carbon storage expectation value The carbon storage expectation value Subtracting the carbon storage estimated value To obtain the deviation of the carbon storage estimated value The extinction coefficient is dynamically corrected through the extinction coefficient correction formula. The extinction coefficient correction formula is: Among them, 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.

[0013] Preferably, 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, and re-solving the first derivative of the water-carbon fixation correlation model to extract the carbon sink efficiency index.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention, through the transpiration-osmosis dynamic balance algorithm, combines the reference evapotranspiration, crop coefficient, and carbon efficiency correction factor, fully considering the meteorological conditions, vegetation type, growth period, and the impact of water stress on vegetation photosynthesis, accurately calculates the actual water requirement of vegetation, and avoids 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; The initial irrigation signal is triggered based on the carbon sink efficiency threshold, while 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 each drop of water generates the maximum ecological benefit; the updated root layer water content and net carbon exchange amount after irrigation are input into the carbon storage estimation model, and combined with the inversion of the leaf area index (LAI) and dynamic correction of the extinction coefficient, a positive feedback loop of "irrigation effect evaluation → model parameter optimization → carbon sink target approximation" is realized. The extinction coefficient is adjusted by deviation drive, making the carbon storage estimation value gradually converge to the preset target to ensure long-term ecological benefits; 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 an intelligent management mode of "determining demand based on carbon and supplying water based on demand", realizing the triple goals of water resource conservation, carbon sink capacity improvement, and maximization of the ecological value of gardens, providing a feasible technical solution for smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic structural diagram of the garden environment monitoring and management system based on Internet of Things technology of the present invention; Figure 2 It is a schematic flow diagram of the garden environment monitoring and management method based on Internet of Things technology of the present invention; Figure 3 It is a technical roadmap of the garden environment monitoring and management based on Internet of Things technology of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with 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 the embodiments. 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.

[0017] Embodiment 1 Please refer to Figure 1 and Figure 3As shown in the figure, 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: In garden maintenance, the rational utilization of water resources has always been a difficult problem. For a long time, the traditional irrigation mode has relied heavily on manual experience, which makes it difficult to achieve precise irrigation when faced with 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 lead to 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 status in real time and accurately and implement precise irrigation regulation based on this has become an urgent need in the garden maintenance industry.

[0018] This demand coincides with the concept of pursuing refinement in modern garden management. 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 handled in time, 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 stretched 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.

[0019] 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 significant progress of sensor technology is particularly crucial. Now, various environmental parameter sensors have achieved the characteristics of smaller volume, higher precision, and lower power consumption, making it possible to deploy sensors on a large scale and in an all-round way in the garden environment. At the same time, the innovation of communication technology has also ensured that data can be transmitted at high speed and stably, so as to timely and accurately converge 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 has made it possible to deeply mine and scientifically model a large amount of environmental data, providing a scientific and reliable basis for garden management decisions.

[0020] In traditional garden environment monitoring and management, it mainly relies on fixed irrigation time and quantitative water replenishment. 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, and unable to accurately calculate the actual water demand of the vegetation, which is likely to cause excessive or insufficient water supply, resulting in water resource waste or vegetation water shortage; Traditional water volume calculation methods rely on fixed parameters and look-up tables, without introducing a carbon efficiency correction factor. They cannot compensate for the phenomenon of reduced photosynthetic capacity due to insufficient water, resulting in a large deviation between the theoretical water demand and the actual demand, and thus the irrigation decision-making 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 maximize the ecological value of gardens. 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 replenishment, the water content in the root layer and the vegetation carbon flux may have changed, but the system fails to respond and adjust promptly, 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. 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, and cannot balance water resource conservation and ecosystem function improvement. A single irrigation strategy may lead to the carbon absorption capacity of the garden ecosystem not meeting the standards.

[0021] In this context, the present invention proposes a garden environment monitoring and management system based on Internet of Things technology, including: 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. A carbon flux sensing module, which monitors the 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, and obtains the net carbon exchange amount based on the carbon flux data and photosynthetic efficiency parameters. 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 sink efficiency index, and determines whether to generate an initial irrigation demand signal. A dynamic irrigation decision-making module. If an initial irrigation demand signal is generated, it uses the transpiration-osmosis dynamic balance algorithm to obtain the actual water demand of the vegetation through the reference evapotranspiration, combined with the crop coefficient and the carbon efficiency correction factor, and corrects the initial irrigation demand signal to obtain an accurate irrigation signal. An intelligent drip irrigation module, which executes the water replenishment instruction through an intelligent drip irrigation control terminal based on the accurate irrigation signal, and regularly collects the updated water content in the root layer and the net carbon exchange amount after executing the accurate irrigation signal to form irrigation result data. The feedback optimization module obtains the inverted leaf area index, combines it with a 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 correlation model using the corrected extinction coefficient, and re-obtains the carbon sink efficiency index.

[0022] The method for obtaining the water content of the root layer of garden vegetation includes: According to the distribution of 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 probes are distributed in three dimensions 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 in 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 the entire spatial area of the vegetation root activities, obtaining the soil water content from the surface to the deep layer, and realizing the visualization of the water content at different positions and depths in the garden soil area; Use a TDR device to send electromagnetic pulse signals to each TDR probe, and measure the time delay for each TDR probe to receive the electromagnetic pulse signal and send back the reflected signal to the TDR device ; According to the time delay and the length of the TDR probe , calculate the actual propagation speed of the electromagnetic pulse signal in the soil area , and combine it with the speed of electromagnetic waves in a vacuum to calculate the dielectric constant of different soil areas ; Substitute the dielectric constants of different soil areas into the Topp equation to calculate the volumetric water content of different soil areas; perform weighted averaging on the volumetric water content of the soil areas at the same depth, and then obtain the water content of the root layer of garden vegetation.

[0023] The method for obtaining the net carbon exchange amount includes: Build an eddy covariance tower according to the height of garden vegetation, and synchronously install an anemometer and a gas analyzer on the eddy covariance tower to obtain the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration through measurement; calculate the average value of the product of the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration, and perform spectral correction to obtain the carbon flux data; according to the distribution of garden vegetation, install a fixed chlorophyll fluorometer, and preset a timed sampling program to perform periodic monitoring on the surrounding vegetation to obtain the photosynthetic efficiency parameters; By using the eddy covariance tower, the change of carbon dioxide concentration can be monitored in-situ without disturbance, with the response ability at the micrometeorological level. The carbon flux data calculated by combining the instantaneous deviation of wind speed and carbon dioxide concentration has high spatio-temporal representativeness and flux conservation. The chlorophyll fluorometer provides photosynthetic efficiency indicators, reflecting the response of vegetation to environmental stresses (such as drought, temperature, etc.). After being fused with the carbon flux data, it can dynamically monitor the change of carbon sink capacity of vegetation at different times (such as in the morning, afternoon) or weather (sunny or cloudy). Combining the micrometeorological monitoring technology with the means of plant physiological response, a complete set of processes from CO2 flux monitoring - photosynthetic efficiency assessment - respiration modeling - net exchange amount calculation is formed, realizing the refined assessment of the carbon efficiency of garden vegetation and the optimized management of the carbon sink function. Construct a night respiration model, construct a temperature response function through continuous night carbon flux data and night temperature, and calculate and obtain the total respiration amount of garden vegetation; subtract the total respiration amount of garden vegetation from the photosynthetic efficiency parameters to obtain the net carbon exchange amount.

[0024] The method for establishing the water-carbon fixation correlation model includes: Denote the water content of the root layer of garden vegetation as , denote the net carbon exchange amount 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 a water-carbon fixation correlation model to fit the functional relationship between water content and carbon fixation ability. The water-carbon fixation correlation model is ; Among them, 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, the carbon sink efficiency index is extracted.

[0025] Based on the Gaussian response model, construct the water-carbon exchange response curve and accurately fit the optimal water 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 water content interval; Through the least squares method, optimize the fitted Gaussian curve to obtain the central value of the optimal soil water content interval; The construction of the water-carbon fixation correlation model is based on the physiological mechanism that plant photosynthesis depends on water supply. It fits the response relationship between the root water content and the net carbon exchange, converts the plant's response mechanism to water into a mathematical function, realizes the explicit modeling of the carbon sink capacity with respect to water supply, thereby judging the marginal effect of the current water level on the carbon sink, finding the optimal irrigation interval, avoiding "inefficient irrigation" or "over-irrigation", and providing a quantitative basis and control means for realizing precise irrigation and ecological management intelligence. It is a key bridge module in the "Internet of Things + ecological feedback" system.

[0026] The method for judging 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 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.

[0027] The method for obtaining the actual water demand of vegetation includes: 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 during their growth cycles ; by calculating the product of the reference evapotranspiration and the crop coefficient, the potential water demand of the vegetation is further obtained , which is used to describe the theoretical water demand under no water stress; The "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 replenishment. 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. The reference evapotranspiration is calculated by combining meteorological factors through the FAO Penman-Monteith equation; this algorithm comprehensively considers the actual water demand of plant transpiration and the limitation on water supply. 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; Based on the photosynthetic efficiency parameters and the net carbon exchange, evaluate the impact of water stress on the photosynthesis of vegetation, and then obtain the carbon efficiency correction factor , this factor reflects the adjustment of vegetation's water demand under actual water stress conditions. Through this factor, the initially calculated water demand can be dynamically corrected; 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.

[0028] Principle: Since the soil water condition affects 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 vegetation photosynthesis; the carbon efficiency correction factor is used to measure the change in 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 current water stress on the physiological processes of vegetation; when the soil water is insufficient, the net carbon exchange obtained through actual measurement will be lower than the ideal value of the net carbon exchange under optimal water supply, resulting in the carbon efficiency correction factor being less than 1 (reflecting a decrease in water demand). Conversely, when the soil water is sufficient, the carbon efficiency correction factor will be greater than or equal to 1; The introduction of the carbon efficiency correction factor not only considers 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 coordinated intelligent irrigation", achieving a double breakthrough in water resource utilization efficiency and ecological benefits, providing new ideas for intelligent garden management; through adaptive control, water conservation is achieved and the vegetation needs are met, optimizing irrigation decisions 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", achieving water supply on demand, significantly improving irrigation accuracy and water conservation effects; through carbon efficiency feedback regulation of irrigation, effectively maintaining the optimal photosynthetic level and carbon sequestration capacity of plants, contributing to maximizing the ecological functions of garden green spaces.

[0029] The method for obtaining the precise irrigation signal includes: 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 the precise irrigation signal.

[0030] For example: The tree irrigation area A-12 (area 50m²) in an urban ecological park triggers an irrigation demand: The precise irrigation signal is: the target irrigation area number A-12, the minimum irrigation water volume 330L (6.6mm per minute), and the irrigation duration 110min.

[0031] A method for executing a water replenishment instruction by an 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 replenish water and irrigate the garden vegetation in the target irrigation area.

[0032] A method for dynamically correcting the extinction coefficient includes: Using 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 , and constructing a carbon storage estimation model based on the retrieved leaf area index and the extinction coefficient: ; where represents the estimated value of carbon storage; represents the carbon fixation ability per unit leaf area; Preset the target carbon storage, and use the preset target carbon storage as the expected value of carbon storage , and 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 , then it is judged that the carbon storage is insufficient and the extinction coefficient is increased; if , then it is judged that the carbon storage is redundant and the extinction coefficient is decreased.

[0033] Precision 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; Adaptive control: Through the mechanism of "physical model of light attenuation + target-driven feedback", dynamic self-calibration of carbon storage estimation is realized, with strong adaptability and robustness; Guiding management decisions: Accurate carbon storage estimation not only provides a basis for ecological compensation and green finance, but also can guide the optimal allocation and management measures of garden vegetation, so as to achieve a double improvement of ecological and economic benefits.

[0034] The method for re-acquiring the carbon sequestration 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 sequestration efficiency index.

[0035] The preset extinction coefficient is set by the staff based on the historical data analysis results. This historical analysis process includes systematically 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 sequestration 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.

[0036] In this embodiment, through the transpiration-osmosis dynamic balance algorithm, combined with the reference evapotranspiration, crop coefficient, and carbon efficiency correction factor, the influence of meteorological conditions, vegetation type, growth period, and water stress on vegetation photosynthesis is fully considered, and the actual water requirement of the vegetation is accurately calculated, avoiding 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 requirement of the vegetation but also takes into account ecological benefits such as carbon fixation, achieving a double breakthrough in water resource utilization efficiency and ecological benefits; The initial irrigation signal is triggered based on the carbon sequestration 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 by each drop of water; the updated root zone moisture content and net carbon exchange after irrigation are input into the carbon storage estimation model, combined with the inversion of the leaf area index (LAI) and the dynamic correction of the extinction coefficient, to achieve a positive feedback loop of "irrigation effect evaluation → model parameter optimization → carbon sequestration target approximation". The extinction coefficient is adjusted by deviation drive, making the carbon storage estimation value gradually converge to the preset target carbon storage, ensuring long-term ecological benefits; 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 sequestration; 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 and determining water demand based on water supply", achieving the triple goals of water resource conservation, carbon sequestration capacity improvement, and maximization of garden ecological value, providing a feasible technical solution for smart cities.

[0037] Embodiment 2 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 method for monitoring and managing the garden environment based on Internet of Things technology is provided, including: S1. Obtain the water content rate of the root layer of garden vegetation through an implanted TDR soil moisture sensor array; S2. Monitor the carbon dioxide flux and the photosynthetic efficiency of vegetation through an eddy covariance tower and a chlorophyll fluorometer respectively, obtain carbon flux data and photosynthetic efficiency parameters; based on the carbon flux data and the photosynthetic efficiency parameters, obtain the net carbon exchange amount; S3. Conduct a coupling analysis on the water content rate of the root layer 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; S4. If an initial irrigation demand signal is generated, then adopt a transpiration-osmosis dynamic balance algorithm, through the reference evapotranspiration, combined with the crop coefficient and the carbon efficiency correction factor, obtain the actual water requirement of the vegetation, and correct the initial irrigation demand signal to obtain an accurate irrigation signal; S5. Based on the accurate irrigation signal, execute the water supply instruction through an intelligent drip irrigation control terminal, and regularly collect 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; S6. Obtain the retrieved leaf area index, and combined with a preset extinction coefficient, establish a carbon storage estimation model; input the irrigation result data into the carbon storage estimation model, calculate the deviation between the carbon storage estimation value and the expected value, and dynamically correct the extinction coefficient; use the corrected extinction coefficient to update the water-carbon fixation correlation model and re-obtain the carbon sink efficiency index.

[0038] 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 forms of change 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 introduced 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 scope protected by the present application.

[0039] 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 that is 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.

[0040] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements 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 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 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; A data coupling module, which conducts a 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; A dynamic irrigation decision-making module, if an initial irrigation demand signal is generated, then uses the transpiration-osmosis dynamic balance algorithm, through the reference evapotranspiration, combined with the crop coefficient and the carbon efficiency correction factor, obtains the actual water demand of the vegetation, corrects the initial irrigation demand signal, and obtains an accurate irrigation signal; An intelligent drip irrigation module, based on the accurate irrigation signal, executes the water supply instruction through an intelligent drip irrigation control terminal, and regularly collects the updated moisture content 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, obtains the retrieved leaf area index, and combines it with a 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 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.

2. The garden environment monitoring and management system based on the Internet of Things technology according to claim 1, characterized in that, The method for obtaining the moisture content of the root layer of the garden vegetation includes: According to the distribution of the garden vegetation, bury n TDR probes in the soil area where the vegetation roots are distributed to form a three-dimensional monitoring network; use a TDR device to emit electromagnetic pulse signals to each TDR probe, measure the time delay for each TDR probe to receive the electromagnetic pulse signal and send back the reflected signal to the TDR device; according to the time delay and the length of the TDR probe, calculate the actual propagation speed of the electromagnetic pulse signal in the soil area, and combine it with the speed of electromagnetic waves in a vacuum to calculate the dielectric constant of different soil areas; substitute the dielectric constants of different soil areas into the Topp equation to calculate the volumetric water content of different soil areas; perform weighted averaging on the volumetric water content of the soil areas at the same depth, and then obtain the moisture content 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: Build an eddy covariance tower according to the height of the garden vegetation, and synchronously install an anemometer and a gas analyzer on the eddy covariance tower, and obtain the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration through measurement; calculate the average value of the product of the instantaneous deviation of the vertical wind speed and the instantaneous deviation of the carbon dioxide concentration, and perform spectral correction to obtain the carbon flux data; according to the distribution of the garden vegetation, install a fixed chlorophyll fluorometer, and preset a timing sampling program to conduct periodic monitoring on the surrounding vegetation to obtain the photosynthetic efficiency parameters; Construct a night respiration model, build a temperature response function through continuous night carbon flux data and night temperature, calculate and obtain the total respiration amount of the garden vegetation; subtract the total respiration amount of the garden vegetation from the photosynthetic efficiency parameters 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, characterized in that, The method for establishing the water-carbon fixation correlation model includes: Record the water content of the root layer of garden vegetation as , and record 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 a 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, the carbon sink efficiency index is extracted.

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 determining whether to generate an initial irrigation demand signal includes: Preset a threshold value for the carbon sink efficiency index, and compare the carbon sink efficiency index with the preset threshold value for the carbon sink efficiency index; if the carbon sink efficiency index is less than the preset threshold value for the carbon sink efficiency index, 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 threshold value for the carbon sink efficiency index, 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.

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 actual water requirement 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 during their growth cycles ; By calculating the product of the reference evapotranspiration and the crop coefficient, the potential water requirement of the vegetation is further obtained ; 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 by 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.

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 obtaining the precise irrigation signal includes: Based on the actual water requirement of the vegetation, the initial irrigation demand signal is corrected. The actual water requirement of the vegetation is set as the minimum irrigation water volume, and according to the preset flow value of the drip irrigation equipment, the irrigation duration required based on the minimum irrigation water volume is calculated; the initial irrigation demand signal, the minimum irrigation water volume, and the irrigation duration are integrated to obtain a precise irrigation signal.

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 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 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 to the garden vegetation in the target irrigation area.

9. The garden environment monitoring and management system based on the Internet of Things technology according to claim 8, wherein The method for dynamically correcting the extinction coefficient includes: Using ground instruments to directly observe the light transmittance of the vegetation canopy and inversely calculate the retrieved leaf area index LAL, with a preset extinction coefficient of , constructing a carbon storage estimation model based on the retrieved leaf area index and the 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 carbon storage value from the expected value of carbon storage to obtain the deviation of the estimated carbon storage value ; Dynamically correct the extinction coefficient through the extinction coefficient correction formula, and the extinction coefficient correction formula is: ; Wherein, 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.

10. The garden environment monitoring and management system based on the Internet of Things technology according to claim 9, wherein 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.

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