Intelligent Greenhouse Crop Growth Status Monitoring and Management System Based on the Internet of Things

Abstract

The present invention relates to the field of intelligent agriculture technology, and specifically relates to an intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things, which includes a multi-source perception module, a spatio-temporal calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation decision-making module, and a closed-loop execution module; among which: The multi-source perception module: collects canopy spectral data, stem pressure fluctuation data, and root conductivity data; The spatio-temporal calibration module: is used for spatio-temporal alignment; The physiological parameter calculation module: calculates the effective root activity in combination with the root conductivity data; The growth coupling analysis module: dynamically calculates the growth coordination index value; The regulation decision-making module: is used to generate a regulation instruction set; The closed-loop execution module: is used to dynamically correct the parameters of the regulation instructions. The present invention realizes precise environmental regulation based on the real-time growth status of crops through multi-source perception, dynamic calculation, and a closed-loop feedback mechanism, and improves the resource utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent agriculture, and particularly to an intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things. Background Art

[0002] With the development of intelligent agriculture and precision agriculture, the application of Internet of Things technology in the agricultural field is becoming increasingly widespread. Especially in greenhouse cultivation, the importance of environmental regulation for crop growth is becoming more and more prominent. Traditional greenhouse environmental regulation mostly relies on artificial experience or fixed control parameters, and it is difficult to accurately adjust according to the real-time growth status of crops, resulting in waste of resources and low crop growth efficiency. At the same time, the growth status of crops is closely related to environmental parameters such as light, temperature, humidity, and moisture. How to timely adjust the greenhouse environment according to the changes of these factors is an important challenge in greenhouse management.

[0003] However, the existing technologies often have the following problems: First, most greenhouse environmental regulation systems adjust the environment based on single-sensor data, and do not fully consider the comprehensive impact of multi-dimensional physiological data (such as photosynthetically active radiation absorption rate, transpiration intensity index, root activity, etc.) on crop growth. Second, the existing control systems cannot dynamically respond to the real-time growth needs of crops, resulting in the lag and inaccuracy of environmental control. Therefore, how to dynamically optimize the regulation parameters based on the real-time growth status of crops, improve crop growth efficiency, reduce energy waste, and achieve more intelligent greenhouse environment management is an urgent problem to be solved currently. Summary of the Invention

[0004] Based on the above purpose, the present invention provides an intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things.

[0005] The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things includes a multi-source perception module, a spatio-temporal calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation decision module, and a closed-loop execution module; wherein:

[0006] The multi-source perception module: synchronously collects canopy spectral data, stem pressure fluctuation data, and root zone conductivity data through a canopy multi-spectral camera, a stem hydraulic sensor, and a root zone dielectric probe.

[0007] The spatio-temporal calibration module: takes the stem bifurcation point as the reference point to perform spatio-temporal alignment on the collected canopy spectral data and stem pressure fluctuation data, and generates a time-synchronized canopy-stem joint dataset.

[0008] The physiological parameter calculation module: is used to extract the photosynthetically active radiation absorption rate and the transpiration intensity index from the canopy-stem joint dataset, and calculate the effective root activity in combination with the root zone conductivity data.

[0009] Growth coupling analysis module: used to dynamically calculate the growth coordination index value according to the coupling relationship between the photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity;

[0010] Regulation decision-making module: generates a set of regulation instructions for light supplement intensity, irrigation frequency, and ventilation duration based on the deviation degree between the growth coordination index value and a preset threshold;

[0011] Closed-loop execution module: after inputting the set of regulation instructions into the execution device for regulation, dynamically corrects the parameters of the regulation instructions according to the new growth coordination index value in the next acquisition cycle.

[0012] Optionally, the multi-source perception module includes a canopy spectrum acquisition unit, a stem pressure monitoring unit, and a root conductivity detection unit; among them:

[0013] Canopy spectrum acquisition unit: synchronously acquires the spectral data of the crop canopy through a canopy multispectral camera set in the greenhouse at an interval of 10 minutes;

[0014] Stem pressure monitoring unit: real-time acquires the pressure fluctuation data inside the stem through a hydraulic sensor installed on the crop stem;

[0015] Root conductivity detection unit: real-time monitors the root conductivity data through a dielectric probe buried in the root zone.

[0016] Optionally, the spatio-temporal calibration module includes a data reception unit, a reference point calibration unit, a spatio-temporal alignment unit, and a combined data generation unit; among them:

[0017] Data reception unit: used to receive the canopy spectral data and stem pressure fluctuation data from the multi-source perception module;

[0018] Reference point calibration unit: takes the stem bifurcation point as the reference point, and determines the corresponding positions of the stem bifurcation point in the two datasets by analyzing the timestamps in the canopy spectral data and stem pressure fluctuation data;

[0019] Spatio-temporal alignment unit: according to the reference position determined by the reference point calibration unit, uses the interpolation algorithm to perform spatio-temporal alignment on the canopy spectral data and stem pressure fluctuation data to ensure that the two datasets match in time and space;

[0020] Combined data generation unit: generates a time-synchronized canopy-stem combined dataset based on the spatio-temporally aligned canopy spectral data and stem pressure fluctuation data.

[0021] Optionally, the spatio-temporal alignment unit includes:

[0022] Determine the spatio-temporal offset: Calculate the time offset between the two sets of data based on the reference positions in the canopy spectral data and the stem pressure fluctuation data determined by the reference point calibration unit. ;

[0023] Data alignment: Based on the calculated time offset , perform time alignment on the canopy spectral data and the stem pressure fluctuation data by linear interpolation; specifically, for the th data point in the canopy spectral data, whose timestamp is , use the following linear interpolation formula to adjust it to the position corresponding to the timestamp of the stem pressure fluctuation data: , where is the adjusted data point, is the time offset, and are the values of two adjacent data points in the canopy spectral data, and are their corresponding timestamps respectively;

[0024] Spatial alignment: Calibrate the stem pressure fluctuation data through spatial interpolation method. Let the sampling point of the stem pressure fluctuation data correspond to the time , and its spatial position is . Adjust it to the position corresponding to the canopy spectral data according to the spatial interpolation algorithm. The spatial interpolation formula is: , where is the adjusted stem pressure fluctuation data, and are the spatial positions of the stem pressure fluctuation data and the canopy spectral data respectively, and are the two adjacent data values in the stem pressure fluctuation data, and are their corresponding spatial positions respectively.

[0025] Optionally, the physiological parameter calculation module includes a photosynthetically active radiation absorption rate calculation unit, a transpiration intensity index calculation unit, and an effective root activity calculation unit; where:

[0026] Photosynthetically active radiation absorption rate calculation unit: Used to extract the canopy spectral data from the canopy-stem combined dataset and calculate the photosynthetically active radiation absorption rate using the following formula: , where is the crop canopy reflectance, is the wavelength of The spectral radiation intensity, is the incident light intensity, represents the absorption rate of photosynthetically active radiation;

[0027] Transpiration intensity index calculation unit: used to extract spectral data related to transpiration from the leaf - stem combined dataset, and combine with the environmental data in the greenhouse to calculate the transpiration intensity index through the following formula: , where, is the environmental temperature, is the air humidity, represents the transpiration intensity index;

[0028] Effective root activity calculation unit: receives root conductivity data, and evaluates the health and activity of the roots by analyzing the relationship between root conductivity and crop growth status, thereby calculating the effective root activity.

[0029] Optionally, the effective root activity calculation unit includes:

[0030] Root conductivity data extraction: obtains root conductivity data from the multi - source sensing module;

[0031] Analysis of the correlation between root conductivity and growth status: Based on factors such as soil moisture and nutrient supply of the crop, analyze the relationship between root conductivity and crop growth status. The relationship between root conductivity and crop root activity is calculated through the following empirical formula: , where, is the root conductivity, is the nutrient concentration in the soil, is the soil temperature, and are fitting coefficients;

[0032] Evaluating the health of the roots: According to the correlation analysis between root conductivity and growth status, determine whether the roots are in a healthy state. If the root conductivity value is higher than , it indicates that the roots are healthy; if the root conductivity value is lower than , it indicates poor root activity;

[0033] Calculating the effective root activity: Combining root conductivity data with crop growth status, calculate the effective root activity through the following formula: , where, is the maximum root conductivity of the crop under optimal growth conditions, is the effective root activity.

[0034] Optionally, the growth coupling analysis module includes a data standardization unit and a coupling calculation unit; where:

[0035] Data normalization unit: It is used to receive data of photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity from the physiological parameter calculation module, and normalize the data to provide a unified input for subsequent coupling calculations;

[0036] Coupling calculation unit: It is used to calculate the growth coordination index value dynamically according to the normalized photosynthetically active radiation absorption rate , transpiration intensity index and effective root activity data, using the following coupling formula:

[0037] , where represents the growth coordination index value, are the weighting coefficients of the photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity respectively, is the maximum value of the corresponding parameter.

[0038] Optionally, the regulation and decision-making module includes a growth coordination index deviation calculation unit, a supplementary light intensity regulation unit, an irrigation frequency regulation unit, a ventilation duration regulation unit, and a regulation instruction generation unit; among them:

[0039] Growth coordination index deviation calculation unit: It is used to receive the growth coordination index value from the growth coupling analysis module, compare it with the preset growth coordination index threshold, and calculate its deviation degree;

[0040] Supplementary light intensity regulation unit: It is used to generate a supplementary light intensity regulation instruction according to the deviation amount calculated by the growth coordination index deviation calculation unit; specifically, when the deviation amount is negative, it means that the photosynthesis of the crop is insufficient, so the supplementary light intensity is increased; when the deviation amount is positive, it means that the photosynthesis of the crop is too strong, so the supplementary light intensity is reduced;

[0041] Irrigation frequency regulation unit: It is used to generate an irrigation frequency regulation instruction according to the growth coordination index deviation; specifically, when the deviation amount is negative, it means that the water demand of the crop increases, so the irrigation frequency is increased; when the deviation amount is positive, it means that the crop has too much water, so the irrigation frequency is reduced;

[0042] Ventilation duration regulation unit: It is used to generate a ventilation duration regulation instruction according to the growth coordination index deviation; specifically, when the deviation amount is negative, it means that the growth environment of the crop is too hot, so the ventilation duration is increased; when the deviation amount is positive, it means that the growth environment of the crop is suitable, so the ventilation duration is reduced;

[0043] Regulation instruction generation unit: It is used to integrate the regulation instructions of supplementary light intensity, irrigation frequency, and ventilation duration generated by each regulation unit, generate a complete regulation instruction set, and output it to the closed-loop execution module for actual adjustment.

[0044] Optionally, the calculation formula for the degree of deviation is: , where represents the deviation amount between the growth coordination index and the preset threshold, is the threshold value of the crop under the optimal growth conditions.

[0045] Optionally, the closed-loop execution module includes a regulation instruction input unit, a new growth coordination index acquisition unit, a deviation calculation unit, a regulation parameter correction unit, and a feedback adjustment unit; where:

[0046] Regulation instruction input unit: used to input the regulation instruction set of the light supplement intensity, irrigation frequency, and ventilation duration generated by the regulation decision module into the execution device, and the instruction set is used to control the environmental parameters in the greenhouse to ensure that the crops grow under suitable environmental conditions;

[0047] New growth coordination index acquisition unit: used to obtain the new growth coordination index value from the growth coupling analysis module at the end of each acquisition cycle;

[0048] Deviation calculation unit: used to calculate a new deviation value based on the deviation between the new growth coordination index value and the current growth coordination index threshold;

[0049] Regulation parameter correction unit: used to dynamically correct the regulation instructions of the execution device according to the calculated new deviation value;

[0050] Feedback adjustment unit: used to adjust the operating state of the execution device in real time according to the corrected regulation instructions calculated by the regulation parameter correction unit.

[0051] Advantages of the present invention:

[0052] In the present invention, through the comprehensive application of the multi-source perception module, dynamic calculation, and closed-loop feedback mechanism, precise regulation of the greenhouse environment is achieved. By collecting multi-dimensional physiological data such as photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity, combined with the real-time growth state of the crops, the growth coordination index can be dynamically calculated, and environmental parameters such as light supplement intensity, irrigation frequency, and ventilation duration can be adjusted according to this index, thereby precisely optimizing the greenhouse environment. This system can automatically adjust the environment according to the real-time needs of the crops, avoiding the limitations of manual adjustment and fixed parameter settings in traditional systems, improving the utilization efficiency of resources, and reducing energy consumption.

[0053] In the present invention, through the real-time correction and feedback of the regulation instructions by the closed-loop execution module, the continuous matching of the environmental conditions and the crop growth state is ensured. This intelligent regulation method not only optimizes the growth environment of the crops but also significantly improves the crop yield and quality. Description of the Drawings

[0054] To more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 Schematic diagram of the intelligent greenhouse crop growth status monitoring and management system according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of the space-time calibration module according to an embodiment of the present invention. Detailed implementation manners

[0057] The present invention will be described in detail below with reference to the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0058] It should be pointed out that in the specification, when referring to "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc., it indicates that the described embodiment may include specific features, structures or characteristics, but not necessarily every embodiment includes the specific feature, structure or characteristic. In addition, when combining an embodiment to describe a specific feature, structure or characteristic, implementing such a feature, structure or characteristic in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0059] Generally, the terms can be understood at least in part from their use in the context. For example, at least in part depending on the context, the term "one or more" used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. In addition, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but instead, at least in part depending on the context, allowing for the existence of other factors that may not be explicitly described.

[0060] As Figure 1 - Figure 2 shown, the intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things includes a multi-source perception module, a space-time calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation and decision-making module, and a closed-loop execution module; where:

[0061] Multi-source perception module: Synchronously collect canopy spectral data, stem pressure fluctuation data, and root zone conductivity data through a canopy multispectral camera, a stem hydraulic sensor, and a root zone dielectric probe;

[0062] Spatio-temporal calibration module: Using the stem bifurcation point as a reference point, spatio-temporally align the collected canopy spectral data and stem pressure fluctuation data to generate a time-synchronized canopy-stem joint dataset;

[0063] Physiological parameter calculation module: Used to extract the photosynthetically active radiation absorption rate and transpiration intensity index from the canopy-stem joint dataset, and calculate the effective root activity in combination with the root zone conductivity data;

[0064] Growth coupling analysis module: Used to dynamically calculate the growth coordination index value according to the coupling relationship between the photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity;

[0065] Regulation decision-making module: Based on the deviation degree between the growth coordination index value and the preset threshold, generate a regulation instruction set for light supplement intensity, irrigation frequency, and ventilation duration;

[0066] Closed-loop execution module: After inputting the regulation instruction set into the execution device for regulation, dynamically correct the parameters of the regulation instruction according to the new growth coordination index value in the next acquisition cycle.

[0067] The multi-source perception module includes a canopy spectral acquisition unit, a stem pressure monitoring unit, and a root zone conductivity detection unit; among them:

[0068] Canopy spectral acquisition unit: Through the canopy multispectral camera set in the greenhouse, synchronously collect the spectral data of the crop canopy at an interval of 10 minutes;

[0069] Stem pressure monitoring unit: Through the hydraulic sensor installed on the crop stem, real-time collect the pressure fluctuation data inside the stem;

[0070] Root zone conductivity detection unit: Through the dielectric probe buried in the root zone, real-time monitor the root zone conductivity data; Through the synchronous operation of the above multiple units, accurate data collection and integration are realized to generate a set of time-synchronized multi-dimensional growth data, providing data support for subsequent physiological parameter calculation and growth state analysis.

[0071] The spatio-temporal calibration module includes a data receiving unit, a reference point calibration unit, a spatio-temporal alignment unit, and a joint data generation unit; among them:

[0072] Data receiving unit: Used to receive the canopy spectral data and stem pressure fluctuation data from the multi-source perception module, and the data contains spectral information and pressure fluctuation information at different time points;

[0073] Reference Point Calibration Unit: Taking the stem bifurcation point as the reference point, by analyzing the timestamps in the canopy spectral data and the stem pressure fluctuation data, determine the corresponding positions of the stem bifurcation point in the two datasets; specifically, assuming that the timestamp of the stem bifurcation point in the stem pressure fluctuation data is , and the timestamp in the canopy spectral data is , then calculate the corresponding positions in the two datasets through the following formula: , where is the time offset between the two datasets. Through this offset, the time difference between the two sets of data can be calibrated, and then the corresponding positions of the stem bifurcation point in the two datasets can be determined;

[0074] Spatio-Temporal Alignment Unit: According to the reference positions determined by the Reference Point Calibration Unit, use the interpolation algorithm to perform spatio-temporal alignment on the canopy spectral data and the stem pressure fluctuation data to ensure that the two sets of data match in time and space;

[0075] Joint Data Generation Unit: Based on the spatio-temporally aligned canopy spectral data and stem pressure fluctuation data, generate a time-synchronized canopy-stem joint dataset for use by the subsequent physiological parameter calculation module; Through the above units, the spatio-temporal calibration module can accurately match different types of collected data in time and space, ensuring that the joint analysis of canopy and stem data provides a reliable data basis for subsequent processing.

[0076] The Spatio-Temporal Alignment Unit includes:

[0077] Determine the spatio-temporal offset: According to the reference positions in the canopy spectral data and the stem pressure fluctuation data determined by the Reference Point Calibration Unit, calculate the time offset between the two sets of data ;

[0078] Data alignment: According to the calculated time offset , perform time alignment on the canopy spectral data and the stem pressure fluctuation data through the linear interpolation method; specifically, for the th data point in the canopy spectral data, whose timestamp is , use the following linear interpolation formula to adjust it to the position corresponding to the timestamp of the stem pressure fluctuation data: , where is the adjusted data point, is the time offset, and are the values of two adjacent data points in the canopy spectral data, and are their corresponding timestamps respectively;

[0079] Spatial alignment: Calibrate the stem pressure fluctuation data through spatial interpolation method. Let the sampling points of the stem pressure fluctuation data correspond to the time , and its spatial position is . Adjust it to correspond to the position of the canopy spectral data according to the spatial interpolation algorithm. The spatial interpolation formula is: where, is the adjusted stem pressure fluctuation data, and are the spatial positions of the stem pressure fluctuation data and the canopy spectral data respectively, and are two adjacent data values in the stem pressure fluctuation data respectively, and are their corresponding spatial positions respectively; After the spatio-temporal alignment, the canopy spectral data and the stem pressure fluctuation data are adjusted by interpolation to generate a spatio-temporally synchronized and spatially aligned canopy-stem joint dataset for use by the subsequent physiological parameter calculation module; Through the above interpolation algorithm, the spatio-temporal alignment unit can accurately adjust the time and spatial positions of the canopy spectral data and the stem pressure fluctuation data, eliminate time deviation and spatial error. This precise spatio-temporal alignment method ensures the consistency and accuracy of the data, providing reliable input for the subsequent joint data generation and physiological parameter calculation.

[0080] The physiological parameter calculation module includes a photosynthetically active radiation absorption rate calculation unit, a transpiration intensity index calculation unit, and an effective root activity calculation unit; Among them:

[0081] Photosynthetically active radiation absorption rate calculation unit: Used to extract the canopy spectral data from the canopy-stem joint dataset and calculate the photosynthetically active radiation absorption rate using the following formula: where, is the crop canopy reflectance, is the spectral radiation intensity at wavelength , is the incident light intensity, represents the absorption rate of photosynthetically active radiation, reflecting the efficiency of the crop in using light energy;

[0082] Transpiration intensity index calculation unit: Used to extract the transpiration-related spectral data from the meta-layer-stem joint dataset and calculate the transpiration intensity index in combination with the environmental data in the greenhouse using the following formula: where, is the environmental temperature, is the air humidity, represents the transpiration intensity index, which is used to measure the water evaporation intensity of the crop;

[0083] Effective root activity calculation unit: Receives root conductivity data, and by analyzing the relationship between root conductivity and crop growth status, evaluates the health and activity of the roots, thereby calculating the effective root activity, which reflects the absorption and utilization efficiency of water and nutrients by the crop roots; the above unit extracts the photosynthetically active radiation absorption rate and transpiration intensity index from the canopy-stem combined dataset, and combines with the root conductivity data to calculate the effective root activity, which can accurately evaluate the growth status and physiological activities of the crop. Among them, the photosynthetically active radiation absorption rate and transpiration intensity index reflect the intensity of crop photosynthesis and water transpiration, while the effective root activity reflects the health and water absorption ability of the crop roots, providing comprehensive physiological index data.

[0084] The effective root activity calculation unit includes:

[0085] Root conductivity data extraction: Obtains root conductivity data from the multi-source sensing module, and the root conductivity data is used to reflect the water absorption and nutrient transport capabilities of the crop roots;

[0086] Analysis of the correlation between root conductivity and growth status: Based on factors such as soil humidity and nutrient supply of the crop, analyzes the relationship between root conductivity and crop growth status. The relationship between root conductivity and crop root activity is calculated through the following empirical formula: , where is the root conductivity, is the nutrient concentration in the soil, is the soil temperature, and are coefficients obtained by fitting experimental data, reflecting the linear relationship between root conductivity and crop root activity;

[0087] Evaluating the health of the roots: According to the correlation analysis between root conductivity and growth status, determines whether the roots are in a healthy state. If the root conductivity value is higher than , it indicates that the roots are healthy and have strong water and nutrient absorption capabilities; if the root conductivity value is lower than , it indicates that the root activity is poor and the absorption ability is weak. When the conductivity value is between and , it indicates that the root activity is within the normal range, but the absorption ability and water utilization ability are relatively moderate;

[0088] Calculating the effective root activity: Combining the root conductivity data with the crop growth status, calculates the effective root activity through the following formula: , where is the maximum root conductivity of the crop under optimal growth conditions, is the effective root activity, representing the percentage of the actual water absorption and nutrient utilization ability of crop roots; by analyzing the relationship between root conductivity and crop growth status in the above steps, the health and activity of crop roots can be accurately evaluated. Combining with the effective root activity value calculated from root conductivity, it can comprehensively reflect the water and nutrient absorption ability of roots, providing key physiological parameter support for subsequent growth coupling analysis and environmental regulation.

[0089] The growth coupling analysis module includes a data standardization unit and a coupling calculation unit; among them:

[0090] The data standardization unit: is used to receive data of photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity from the physiological parameter calculation module, and perform standardization processing on the data, so as to provide a unified input for subsequent coupling calculations;

[0091] The coupling calculation unit: is used to calculate the growth coordination index value dynamically according to the standardized photosynthetically active radiation absorption rate , transpiration intensity index and effective root activity data, using the following coupling formula:

[0092] , where represents the growth coordination index value, are the weighted coefficients of photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity respectively, is the maximum value of the corresponding parameter, reflecting the maximum physiological efficiency of the crop under the best environmental conditions; through dynamically calculating the growth coordination index value by the above units, this solution can comprehensively evaluate the coupling relationship among photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity, thereby accurately measuring the growth coordination of the crop. This coupling calculation provides a comprehensive evaluation index for crop growth, can effectively reflect the growth status of the crop under the existing environmental conditions, and provides a scientific basis for subsequent environmental regulation.

[0093] The regulation decision-making module includes a growth coordination index deviation calculation unit, a supplementary light intensity regulation unit, an irrigation frequency regulation unit, a ventilation duration regulation unit, and a regulation instruction generation unit; among them:

[0094] The growth coordination index deviation calculation unit: is used to receive the growth coordination index value from the growth coupling analysis module, compare it with the preset growth coordination index threshold, and calculate its deviation degree;

[0095] Supplementary light intensity regulation unit: It is used to generate a supplementary light intensity regulation instruction according to the deviation calculated by the growth coordination index deviation calculation unit. Specifically, when the deviation is negative, it indicates that the photosynthesis of the crop is insufficient, and the supplementary light intensity is increased; when the deviation is positive, it indicates that the photosynthesis of the crop is too strong, and the supplementary light intensity is decreased.

[0096] Irrigation frequency regulation unit: It is used to generate an irrigation frequency regulation instruction according to the growth coordination index deviation. Specifically, when the deviation is negative, it indicates that the water demand of the crop increases, and the irrigation frequency is increased; when the deviation is positive, it indicates that the crop has too much water, and the irrigation frequency is decreased.

[0097] Ventilation duration regulation unit: It is used to generate a ventilation duration regulation instruction according to the growth coordination index deviation. Specifically, when the deviation is negative, it indicates that the growth environment of the crop is too hot, and the ventilation duration is increased; when the deviation is positive, it indicates that the growth environment of the crop is suitable, and the ventilation duration is decreased.

[0098] Regulation instruction generation unit: It is used to integrate the regulation instructions of supplementary light intensity, irrigation frequency, and ventilation duration generated by each regulation unit, generate a complete regulation instruction set, and output it to the closed-loop execution module for actual adjustment.

[0099] The calculation formula for the degree of deviation is: , where represents the deviation between the growth coordination index and the preset threshold, is the threshold value of the crop under the optimal growth conditions; by dynamically calculating the deviation degree between the growth coordination index and the preset threshold, the regulation decision-making module can accurately generate regulation instructions for supplementary light intensity, irrigation frequency, and ventilation duration. This module can automatically adjust the environmental conditions according to the real-time growth needs of the crop, ensure that the crop is always in the best growth state, improve the growth efficiency and resource utilization rate of greenhouse crops, and further enhance the automation and precision management level of intelligent greenhouses.

[0100] The closed-loop execution module includes a regulation instruction input unit, a new growth coordination index acquisition unit, a deviation calculation unit, a regulation parameter correction unit, and a feedback adjustment unit; among them:

[0101] Regulation instruction input unit: It is used to input the regulation instruction set of supplementary light intensity, irrigation frequency, and ventilation duration generated by the regulation decision-making module into the execution device. The instruction set is used to control the environmental parameters in the greenhouse, such as lights, irrigation systems, and ventilation systems, etc., to ensure that the crop grows under suitable environmental conditions.

[0102] New growth coordination index acquisition unit: It is used to obtain a new growth coordination index value from the growth coupling analysis module at the end of each acquisition cycle.

[0103] Deviation calculation unit: used to calculate a new deviation value based on the deviation between the newly grown coordination index value and the current growth coordination index threshold;

[0104] Regulation parameter correction unit: used to dynamically correct the regulation instructions of the execution device according to the calculated new deviation value. Specifically, adjust the parameter values of light supplement intensity, irrigation frequency, and ventilation duration according to the deviation value to ensure that the crops are always in the best growth environment;

[0105] Feedback adjustment unit: used to adjust the operating state of the execution device in real time according to the corrected regulation instructions calculated by the regulation parameter correction unit. Whenever a new round of growth coordination index values are collected and calculated, the system will automatically update the regulation instructions, so as to realize the continuous optimization and adjustment of environmental conditions; By promptly responding to the changes in the newly grown coordination index value, the above units enable the system to continuously optimize the light supplement intensity, irrigation frequency, and ventilation duration, keeping the crops always in the best growth environment, thereby improving the growth efficiency, resource utilization rate, and yield of the crops, while minimizing the energy consumption of the greenhouse.

[0106] This invention covers any alternatives, modifications, equivalent methods, and solutions made within the essence and scope of this invention. For the public to have a thorough understanding of this invention, specific details are elaborated in the following preferred embodiments of this invention. However, those skilled in the art can fully understand this invention even without the description of these details. Additionally, well-known methods, processes, procedures, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of this invention.

[0107] The above are only the preferred embodiments of this invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of this invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of this invention.

Claims

1. An intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things, characterized in that It includes a multi-source perception module, a spatio-temporal calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation decision-making module, and a closed-loop execution module; among which: Multi-source perception module: Synchronously collect canopy spectral data, stem pressure fluctuation data, and root zone conductivity data through a canopy multi-spectral camera, a stem hydraulic sensor, and a root zone dielectric probe; Spatio-temporal calibration module: Taking the stem bifurcation point as a reference point, perform spatio-temporal alignment on the collected canopy spectral data and stem pressure fluctuation data to generate a time-synchronized canopy-stem joint dataset; The spatio-temporal calibration module includes a data reception unit, a reference point calibration unit, a spatio-temporal alignment unit, and a joint data generation unit; among which: Data reception unit: Used to receive the canopy spectral data and stem pressure fluctuation data from the multi-source perception module; Reference point calibration unit: Taking the stem bifurcation point as a reference point, determine the corresponding positions of the stem bifurcation point in the two datasets by analyzing the timestamps in the canopy spectral data and stem pressure fluctuation data; Spatio-temporal alignment unit: According to the reference position determined by the reference point calibration unit, use the interpolation algorithm to perform spatio-temporal alignment on the canopy spectral data and stem pressure fluctuation data to ensure the matching of the two sets of data in time and space; Joint data generation unit: Based on the spatio-temporally aligned canopy spectral data and stem pressure fluctuation data, generate a time-synchronized canopy-stem joint dataset; The spatio-temporal alignment unit includes: Determine the spatio-temporal offset: Calculate the time offset between the two sets of data based on the reference positions in the canopy spectral data and the stem pressure fluctuation data determined by the reference point calibration unit. ; Data alignment: According to the calculated time offset , the canopy spectral data and the stem pressure fluctuation data are time-aligned by linear interpolation; specifically, for the th data point in the canopy spectral data, its timestamp is , and the following linear interpolation formula is used to adjust it to the position corresponding to the timestamp of the stem pressure fluctuation data: , where, is the adjusted data point, is the time offset, and are the values of two adjacent data points in the canopy spectral data, and are their corresponding timestamps respectively; Spatial alignment: Calibrate the stem pressure fluctuation data through spatial interpolation method. Assume the sampling points of the stem pressure fluctuation data correspond to the time , and its spatial position is . Adjust it to correspond to the position of the canopy spectral data according to the spatial interpolation algorithm. The spatial interpolation formula is: , where , is the adjusted stem pressure fluctuation data, and are the spatial positions of the stem pressure fluctuation data and the canopy spectral data respectively, and are two adjacent data values in the stem pressure fluctuation data respectively, and are their corresponding spatial positions respectively; Physiological parameter calculation module: Used to extract the photosynthetically active radiation absorption rate and transpiration intensity index from the canopy-stem joint dataset, and calculate the effective root activity in combination with the root zone conductivity data; Growth coupling analysis module: Used to dynamically calculate the growth coordination index value according to the coupling relationship between the photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity; Regulation decision-making module: Based on the deviation degree between the growth coordination index value and the preset threshold, generate a regulation instruction set for light supplement intensity, irrigation frequency, and ventilation duration; Closed-loop execution module: Used to input the regulation instruction set into the execution device for regulation, and then dynamically correct the parameters of the regulation instruction according to the new growth coordination index value in the next acquisition cycle.

2. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 1, characterized in that, The multi-source perception module includes a canopy spectral acquisition unit, a stem pressure monitoring unit, and a root zone conductivity detection unit; among which: Canopy spectral acquisition unit: Through the canopy multi-spectral camera set in the greenhouse, synchronously collect the spectral data of the crop canopy at an interval of every 10 minutes; Stem pressure monitoring unit: Through the hydraulic sensor installed on the crop stem, continuously collect the pressure fluctuation data inside the stem; Root zone conductivity detection unit: Through the dielectric probe buried in the root zone, continuously monitor the root zone conductivity data.

3. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 1, wherein The physiological parameter calculation module includes a photosynthetically active radiation absorption rate calculation unit, a transpiration intensity index calculation unit, and an effective root activity calculation unit; among which: Photosynthetically Active Radiation Absorption Rate Calculation Unit: used to extract canopy spectral data from the canopy-stem combined dataset and calculate the photosynthetically active radiation absorption rate using the following formula: , where is the reflectance of the crop canopy, is the spectral radiation intensity at wavelength , is the incident light intensity, represents the absorption rate of photosynthetically active radiation; Transpiration intensity index calculation unit: It is used to extract the spectral data related to transpiration from the meta-layer - stem combined dataset, and combine the environmental data in the greenhouse to calculate the transpiration intensity index through the following formula: , where is the environmental temperature, is the air humidity, represents the transpiration intensity index; Effective root activity calculation unit: Receive the root zone conductivity data, and evaluate the health and activity of the roots by analyzing the relationship between the root zone conductivity and the crop growth state, so as to calculate the effective root activity.

4. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 3, characterized in that, The effective root activity calculation unit includes: Root conductivity data extraction: Obtain root conductivity data from the multi-source perception module; Correlation analysis between root conductivity and growth status: Based on factors such as soil moisture and nutrient supply of crops, analyze the relationship between root conductivity and crop growth status. The relationship between root conductivity and crop root activity is calculated through the following empirical formula: , where is the root conductivity, is the nutrient concentration in the soil, is the soil temperature, and are fitting coefficients; Evaluate the root system health: Based on the correlation analysis between the root system conductivity and the growth state, determine whether the root system is in a healthy state. If the root system conductivity value is higher than , it indicates that the root system is healthy; if the root system conductivity value is lower than , it indicates poor root system activity. Calculate the effective root activity: Combine the root conductivity data with the crop growth status and calculate the effective root activity using the following formula: , where is the maximum root conductivity of the crop under optimal growth conditions, is the effective root activity.

5. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 4, characterized in that The growth coupling analysis module includes a data standardization unit and a coupling calculation unit; specifically: Data standardization unit: It is used to receive data on photosynthetically active radiation absorption rate, transpiration intensity index, and effective root activity from the physiological parameter calculation module, and perform standardization processing on the data, so as to provide a unified input for subsequent coupling calculations; Coupling calculation unit: used to dynamically calculate the growth coordination index value according to the data of the standardized photosynthetically active radiation absorption rate , transpiration intensity index and effective root activity by using the following coupling formula: , where represents the growth coordination index value, are the weighting coefficients of the photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity respectively, is the maximum value of the corresponding parameter.

6. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 1, characterized in that, The regulation and decision-making module includes a growth coordination index deviation calculation unit, a supplementary light intensity regulation unit, an irrigation frequency regulation unit, a ventilation duration regulation unit, and a regulation instruction generation unit; specifically: Growth coordination index deviation calculation unit: It is used to receive the growth coordination index value from the growth coupling analysis module, and compare it with the preset growth coordination index threshold to calculate its deviation degree; Supplementary light intensity regulation unit: It is used to generate a supplementary light intensity regulation instruction according to the deviation calculated by the growth coordination index deviation calculation unit; specifically, when the deviation is negative, it means that the photosynthesis of the crop is insufficient, so the supplementary light intensity is increased; when the deviation is positive, it means that the photosynthesis of the crop is too strong, so the supplementary light intensity is reduced; Irrigation frequency regulation unit: It is used to generate an irrigation frequency regulation instruction according to the growth coordination index deviation; specifically, when the deviation is negative, it means that the water demand of the crop increases, so the irrigation frequency is increased; when the deviation is positive, it means that the crop has too much water, so the irrigation frequency is reduced; Ventilation duration regulation unit: It is used to generate a ventilation duration regulation instruction according to the growth coordination index deviation; specifically, when the deviation is negative, it means that the growth environment of the crop is too hot, so the ventilation duration is increased; when the deviation is positive, it means that the growth environment of the crop is suitable, so the ventilation duration is reduced; Regulation instruction generation unit: It is used to integrate the regulation instructions for supplementary light intensity, irrigation frequency, and ventilation duration generated by each regulation unit, generate a complete regulation instruction set, and output it to the closed-loop execution module for actual adjustment.

7. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 6, characterized in that, The calculation formula for the degree of deviation is as follows: , where represents the deviation amount between the growth coordination index and the preset threshold value, is the threshold value of the crop under the optimal growth conditions.

8. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 1, characterized in that, The closed-loop execution module includes a regulation instruction input unit, a new growth coordination index acquisition unit, a deviation calculation unit, a regulation parameter correction unit, and a feedback adjustment unit; specifically: Regulation instruction input unit: It is used to input the regulation instruction set for supplementary light intensity, irrigation frequency, and ventilation duration generated by the regulation and decision-making module into the execution device, and the instruction set is used to control the environmental parameters in the greenhouse to ensure that the crop grows under suitable environmental conditions; New growth coordination index acquisition unit: It is used to obtain a new growth coordination index value from the growth coupling analysis module at the end of each acquisition cycle; Deviation calculation unit: It is used to calculate a new deviation value according to the deviation between the new growth coordination index value and the current growth coordination index threshold; Regulation parameter correction unit: It is used to dynamically correct the regulation instructions of the execution device according to the calculated new deviation value; Feedback adjustment unit: It is used to adjust the operating state of the execution device in real time according to the corrected regulation instructions calculated by the regulation parameter correction unit.

Images