Intelligent greenhouse crop growth state monitoring and management system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent greenhouse crop growth state monitoring and management system based on the Internet of Things, which comprises a multi-source sensing module, a space-time calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation and control decision module and a closed-loop execution module. Wherein the multi-source sensing module is used for acquiring canopy spectral data, stem pressure fluctuation data and root conductivity data; the space-time calibration module is used for carrying out space-time alignment; the physiological parameter calculation module is used for calculating effective root activity in combination with the conductivity data of the root system; the growth coupling analysis module is used for dynamically calculating a growth coordination index value; the regulation and control decision module is used for generating a regulation and control instruction set; and the closed-loop execution module is used for dynamically correcting parameters of the regulation and control instruction. According to the invention, through multi-source sensing, dynamic calculation and closed-loop feedback mechanisms, precise environment regulation and control based on the real-time growth state of crops are realized, and the resource utilization efficiency is improved.

Description

Technical Field

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

[0002] With the development of smart agriculture and precision agriculture, the application of Internet of Things technology in the agricultural field is becoming more and more extensive, especially in greenhouse cultivation, where the importance of environmental control to crop growth is becoming more and more significant. Traditional greenhouse environmental control mostly relies on manual experience or fixed control parameters, which 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 adjust the greenhouse environment in time according to changes in these factors is an important challenge in greenhouse management. However, existing technologies often have the following problems: First, greenhouse environmental control systems are mostly based on single sensor data for environmental regulation, and fail to 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, existing control systems cannot dynamically respond to the real-time growth needs of crops, resulting in lag and inaccuracy in environmental control. Therefore, how to dynamically optimize control parameters based on the real-time growth status of crops, improve crop growth efficiency, reduce energy waste, and achieve smarter greenhouse environmental management is an urgent problem to be solved. Summary of the invention

[0003] Based on the above objectives, the present invention provides an intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things.

[0004] The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things includes a multi-source perception module, a time-space calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation and decision-making module, and a closed-loop execution module; among which: Multi-source sensing module: canopy spectral data, stem pressure fluctuation data and root conductivity data are collected synchronously through canopy multispectral camera, stem hydraulic pressure sensor and root zone dielectric probe; Spatiotemporal calibration module: Using the stem bifurcation point as the reference point, the collected canopy spectral data and stem pressure fluctuation data are spatiotemporally aligned to generate a time-synchronized canopy-stem joint data set; Physiological parameter calculation module: used to extract photosynthetically active radiation absorption rate and transpiration intensity index from the canopy-stem joint data set, and calculate effective root activity in combination with root conductivity data; Growth coupling analysis module: used to dynamically calculate the growth coordination index value based on the coupling relationship between photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity; Control decision module: Generates a control instruction set for supplementary light intensity, irrigation frequency and ventilation duration based on the degree of deviation between the growth coordination index value and the preset threshold; Closed-loop execution module: It is used to input the control instruction set into the execution device for control, and dynamically correct the parameters of the control instruction according to the new growth coordination index value of the next acquisition cycle.

[0005] Optionally, the multi-source sensing module includes a canopy spectrum acquisition unit, a stem pressure monitoring unit and a root conductivity detection unit; wherein: Canopy spectrum acquisition unit: Through the canopy multispectral camera set in the greenhouse, the spectral data of the crop canopy is synchronously collected at intervals of 10 minutes; Stem pressure monitoring unit: Through the hydraulic sensor installed on the crop stem, the pressure fluctuation data inside the stem is collected in real time; Root conductivity detection unit: Real-time monitoring of root conductivity data through dielectric probes buried in the root zone.

[0006] Optionally, the spatiotemporal calibration module includes a data receiving unit, a reference point calibration unit, a spatiotemporal alignment unit, and a joint data generation unit; wherein: Data receiving unit: used to receive canopy spectrum data and stem pressure fluctuation data from the multi-source sensing module; Benchmark point calibration unit: Taking the stem bifurcation point as the benchmark point, the corresponding position of the stem bifurcation point in the two data sets is determined by analyzing the time stamps in the canopy spectrum data and the stem pressure fluctuation data; Spatiotemporal alignment unit: Based on the reference position determined by the reference point calibration unit, the interpolation algorithm is used to align the canopy spectral data and the stem pressure fluctuation data in time and space to ensure that the two sets of data match in time and space; Joint data generation unit: Based on the spatiotemporally aligned canopy spectral data and stem pressure fluctuation data, a time-synchronized canopy-stem joint dataset is generated.

[0007] Optionally, the spatiotemporal alignment unit includes: Determine the temporal and spatial offset: Calculate the temporal offset of 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: based on the calculated time offset The canopy spectral data and the stem pressure fluctuation data are time-aligned by linear interpolation. Specifically, for the first Data points , whose timestamp is , use the following linear interpolation formula to adjust it to the timestamp of the stem pressure fluctuation data Corresponding position: ,in, is the adjusted data point, is the time offset, and are the values ​​of two adjacent data points in the canopy spectral data, and They are their corresponding timestamps respectively; Spatial alignment: The stem pressure fluctuation data is calibrated by spatial interpolation method, and the sampling points of the stem pressure fluctuation data are set. Corresponding time , whose spatial position is , and adjust it to the position of the canopy spectral data using the spatial interpolation algorithm Correspondingly, the spatial interpolation formula is: ,in, is the adjusted stem pressure fluctuation data, and are the spatial locations of the stem pressure fluctuation data and the canopy spectral data, and are two adjacent data values ​​in the stem pressure fluctuation data, and are their corresponding spatial positions respectively.

[0008] 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; wherein: Photosynthetically active radiation absorption rate calculation unit: used to extract canopy spectral data from the canopy-stem joint data set and calculate the photosynthetically active radiation absorption rate using the following formula: ,in, is the crop canopy reflectance, The wavelength is The spectral radiation intensity, is the incident light intensity, It represents the absorption rate of photosynthetically active radiation; Transpiration intensity index calculation unit: used to extract transpiration-related spectral data from the meta-layer-stem joint data set, and combined with the environmental data in the greenhouse, calculate the transpiration intensity index through the following formula: ,in, is the ambient temperature, is the air humidity, represents the transpiration intensity index; Effective root activity calculation unit: Receives root conductivity data, and evaluates the health and activity of the root system by analyzing the relationship between root conductivity and crop growth status, thereby calculating the effective root activity.

[0009] Optionally, 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 the factors of soil moisture and nutrient supply of crops, the relationship between root conductivity and crop growth status is analyzed. The relationship between root conductivity and crop root activity is calculated using the following empirical formula: ,in, is the root conductivity, is the nutrient concentration in the soil, is the soil temperature, and is the fitting coefficient; Assess the health of the root system: Based on the correlation analysis between the root conductivity and the growth status, determine whether the root system is in a healthy state. If the root conductivity value is higher than , it indicates that the root system is healthy; if the root conductivity value is lower than , it indicates that the root activity is poor; Calculate effective root activity: Combine root conductivity data with crop growth status and calculate effective root activity using the following formula: ,in, is the maximum root conductivity of the crop under optimal growth conditions, is the effective root activity.

[0010] Optionally, the growth coupling analysis module includes a data standardization unit and a coupling calculation unit; wherein: Data standardization unit: used to receive data of photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity from the physiological parameter calculation module, and standardize the data to provide a unified input for subsequent coupling calculations; Coupled calculation unit: used to calculate the photosynthetically active radiation absorption rate according to the standardized , transpiration intensity index and effective root activity The growth coordination index value is dynamically calculated using the following coupling formula: ,in, represents the growth coordination index value, are the weighted coefficients of photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity, is the maximum value of the corresponding parameter.

[0011] Optionally, the control decision module includes a growth coordination index deviation calculation unit, a supplementary light intensity control unit, an irrigation frequency control unit, a ventilation duration control unit, and a control instruction generation unit; wherein: Growth coordination index deviation calculation unit: used for receiving the growth coordination index value from the growth coupling analysis module, and comparing it with the preset growth coordination index threshold value to calculate its deviation degree; The fill light intensity control unit is used to generate a fill light intensity control instruction according to the deviation calculated by the growth coordination index deviation calculation unit; specifically, when the deviation is a negative value, it means that the crop photosynthesis is insufficient, and the fill light intensity is increased; when the deviation is a positive value, it means that the crop photosynthesis is too strong, and the fill light intensity is reduced; Irrigation frequency control unit: used to generate irrigation frequency control instructions according to the deviation of the growth coordination index; specifically, when the deviation is a negative value, it means that the crop water demand increases, so the irrigation frequency is increased; when the deviation is a positive value, it means that the crop has too much water, so the irrigation frequency is reduced; Ventilation duration control unit: used to generate ventilation duration control instructions according to the growth coordination index deviation; specifically, when the deviation is a negative value, it means that the crop growth environment is overheated, so the ventilation duration is increased; when the deviation is a positive value, it means that the crop growth environment is suitable, so the ventilation duration is reduced; Control instruction generation unit: used to integrate the control instructions of fill light intensity, irrigation frequency and ventilation duration generated by each control unit, generate a complete control instruction set, and output it to the closed-loop execution module for actual adjustment.

[0012] Optionally, the calculation formula for the deviation degree is: ,in, Indicates the deviation of the growth coordination index from the preset threshold. It is the threshold value for crops under optimal growth conditions.

[0013] Optionally, the closed-loop execution module includes a control instruction input unit, a new growth coordination index acquisition unit, a deviation calculation unit, a control parameter correction unit and a feedback adjustment unit; wherein: Control instruction input unit: used to input the control instruction set of supplementary light intensity, irrigation frequency and ventilation duration generated by the control decision module into the execution device. The instruction set is used to control the environmental parameters in the greenhouse to ensure that crops grow under suitable environmental conditions; New growth coordination index acquisition unit: 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: 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 value; Control parameter correction unit: used to dynamically correct the control instructions of the execution device according to the calculated new deviation value; Feedback adjustment unit: used to adjust the operating state of the execution device in real time according to the corrected control instructions calculated by the control parameter correction unit.

[0014] Beneficial effects of the present invention: The present invention realizes precise control of greenhouse environment through the comprehensive application of multi-source sensing module, dynamic calculation and closed-loop feedback mechanism. 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 status of crops, the growth coordination index can be dynamically calculated, and environmental parameters such as supplementary light intensity, irrigation frequency and ventilation duration can be adjusted according to this index, so as to accurately optimize the greenhouse environment. The system can automatically adjust the environment according to the real-time needs of crops, avoiding the limitations of manual adjustment and fixed parameter settings in traditional systems, improving resource utilization efficiency and reducing energy consumption.

[0015] The present invention ensures the continuous matching of environmental conditions with crop growth status through real-time correction and feedback of control instructions by a closed-loop execution module. This intelligent control method not only optimizes the growth environment of crops, but also significantly improves crop yield and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 Schematic diagram of an intelligent greenhouse crop growth status monitoring and management system according to an embodiment of the present invention; Figure 2 Schematic diagram of a spatiotemporal calibration module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0019] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0020] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0021] like Figure 1-Figure 2 As shown, the intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things includes a multi-source perception module, a spatiotemporal calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation and decision-making module, and a closed-loop execution module; among which: Multi-source sensing module: canopy spectral data, stem pressure fluctuation data and root conductivity data are collected synchronously through canopy multispectral camera, stem hydraulic pressure sensor and root zone dielectric probe; Spatiotemporal calibration module: Using the stem bifurcation point as the reference point, the collected canopy spectral data and stem pressure fluctuation data are spatiotemporally aligned to generate a time-synchronized canopy-stem joint data set; Physiological parameter calculation module: used to extract photosynthetically active radiation absorption rate and transpiration intensity index from the canopy-stem joint data set, and calculate effective root activity in combination with root conductivity data; Growth coupling analysis module: used to dynamically calculate the growth coordination index value based on the coupling relationship between photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity; Control decision module: Generates a control instruction set for supplementary light intensity, irrigation frequency and ventilation duration based on the degree of deviation between the growth coordination index value and the preset threshold; Closed-loop execution module: It is used to input the control instruction set into the execution device for control, and dynamically correct the parameters of the control instruction according to the new growth coordination index value of the next acquisition cycle.

[0022] The multi-source sensing module includes a canopy spectrum acquisition unit, a stem pressure monitoring unit, and a root conductivity detection unit; among which: Canopy spectrum acquisition unit: Through the canopy multispectral camera set in the greenhouse, the spectral data of the crop canopy is synchronously collected at intervals of 10 minutes; Stem pressure monitoring unit: Through the hydraulic sensor installed on the crop stem, the pressure fluctuation data inside the stem is collected in real time; Root conductivity detection unit: Real-time monitoring of root conductivity data through dielectric probes buried in the root zone; through the synchronous operation of the above multiple units, accurate data collection and integration are achieved to generate a set of time-synchronized multi-dimensional growth data, providing data support for subsequent physiological parameter calculations and growth status analysis.

[0023] The spatiotemporal calibration module includes a data receiving unit, a reference point calibration unit, a spatiotemporal alignment unit, and a joint data generation unit; wherein: Data receiving unit: used to receive canopy spectral data and stem pressure fluctuation data from the multi-source sensing module, the data includes spectral information and pressure fluctuation information at different time points; Benchmark point calibration unit: Taking the stem bifurcation point as the benchmark point, the corresponding position of the stem bifurcation point in the two data sets is determined by analyzing the timestamps in the canopy spectral data and the stem pressure fluctuation data; specifically, assuming that the timestamp of the stem bifurcation point in the stem pressure fluctuation data is , the timestamp in the canopy spectral data is , the corresponding positions in the two data sets are calculated by the following formula: ,in, is the time offset of the two data sets, through which the time difference of the two sets of data can be calibrated, and then the corresponding position of the stem bifurcation point in the two data sets can be determined; Spatiotemporal alignment unit: Based on the reference position determined by the reference point calibration unit, the interpolation algorithm is used to align the canopy spectral data and the stem pressure fluctuation data in time and space to ensure that the two sets of data match in time and space; Joint data generation unit: Based on the spatiotemporally aligned canopy spectral data and stem pressure fluctuation data, a time-synchronized canopy-stem joint data set is generated for use by the subsequent physiological parameter calculation module. Through the above unit, the spatiotemporal calibration module can accurately match the 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.

[0024] The spatiotemporal alignment unit includes: Determine the temporal and spatial offset: Calculate the temporal offset of 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: based on the calculated time offset The canopy spectral data and the stem pressure fluctuation data are time-aligned by linear interpolation. Specifically, for the first Data points , whose timestamp is , use the following linear interpolation formula to adjust it to the timestamp of the stem pressure fluctuation data Corresponding position: ,in, is the adjusted data point, is the time offset, and are the values ​​of two adjacent data points in the canopy spectral data, and They are their corresponding timestamps respectively; Spatial alignment: The stem pressure fluctuation data is calibrated by spatial interpolation method, and the sampling points of the stem pressure fluctuation data are set. Corresponding time , whose spatial position is , and adjust it to the position of the canopy spectral data using the spatial interpolation algorithm Correspondingly, the spatial interpolation formula is: ,in, is the adjusted stem pressure fluctuation data, and are the spatial locations of the stem pressure fluctuation data and the canopy spectral data, and are two adjacent data values ​​in the stem pressure fluctuation data, and are their corresponding spatial positions respectively; after completing the spatiotemporal alignment, the canopy spectral data and the stem pressure fluctuation data are adjusted through interpolation to generate a time-synchronized and spatially aligned canopy-stem joint data set for use by the subsequent physiological parameter calculation module; through the above interpolation algorithm, the spatiotemporal alignment unit can accurately adjust the time and spatial positions of the canopy spectral data and the stem pressure fluctuation data, eliminating time deviation and spatial error. This precise spatiotemporal alignment method ensures the consistency and accuracy of the data, and provides reliable input for the subsequent joint data generation and physiological parameter calculation.

[0025] 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; wherein: Photosynthetically active radiation absorption rate calculation unit: used to extract canopy spectral data from the canopy-stem joint data set and calculate the photosynthetically active radiation absorption rate using the following formula: ,in, is the crop canopy reflectance, The wavelength is The spectral radiation intensity, is the incident light intensity, It indicates the absorption rate of photosynthetically active radiation, reflecting the efficiency of crops in utilizing light energy; Transpiration intensity index calculation unit: used to extract transpiration-related spectral data from the meta-layer-stem joint data set, and combined with the environmental data in the greenhouse, calculate the transpiration intensity index through the following formula: ,in, is the ambient temperature, is the air humidity, It stands for transpiration intensity index, which is used to measure the water evaporation intensity of crops; Effective root activity calculation unit: Receives root conductivity data, and evaluates the health and activity of the root system by analyzing the relationship between root conductivity and crop growth status, thereby calculating effective root activity, which reflects the absorption and utilization efficiency of water and nutrients by the crop root system; the above unit extracts the photosynthetically active radiation absorption rate and transpiration intensity index from the canopy-stem joint data set, and calculates the effective root activity in combination with the root conductivity data, which can accurately evaluate the growth status and physiological activities of crops. 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 capacity of the crop root system, providing comprehensive physiological indicator data.

[0026] The effective root activity calculation unit includes: Root conductivity data extraction: Root conductivity data is obtained from the multi-source sensing module. Root conductivity data is used to reflect the water absorption and nutrient transport capacity of crop roots; Correlation analysis between root conductivity and growth status: Based on the factors of soil moisture and nutrient supply of crops, the relationship between root conductivity and crop growth status is analyzed. The relationship between root conductivity and crop root activity is calculated using the following empirical formula: ,in, is the root conductivity, is the nutrient concentration in the soil, is the soil temperature, and is the coefficient obtained by fitting the experimental data, reflecting the linear relationship between root conductivity and crop root activity; Assess the health of the root system: Based on the correlation analysis between the root conductivity and the growth status, determine whether the root system is in a healthy state. If the root conductivity value is higher than , it means that the root system is healthy and has a strong ability to absorb water and nutrients; if the root conductivity value is lower than , indicating that the root activity is poor, the absorption capacity is weak, and the conductivity value is between and If the value is between 0.1 and 0.2, it indicates that the root activity is in the normal range, but the absorption capacity and water utilization capacity are relatively moderate; Calculate effective root activity: Combine root conductivity data with crop growth status and calculate effective root activity using the following formula: ,in, is the maximum root conductivity of the crop under optimal growth conditions, is the effective root activity, which represents the percentage of the actual water absorption and nutrient utilization capacity of the crop root system. The above steps can accurately evaluate the health and activity of the crop root system by analyzing the relationship between root conductivity and crop growth status. The effective root activity value calculated in combination with the root conductivity can comprehensively reflect the water and nutrient absorption capacity of the root system, and provide key physiological parameter support for subsequent growth coupling analysis and environmental regulation.

[0027] The growth coupling analysis module includes a data standardization unit and a coupling calculation unit; wherein: Data standardization unit: used to receive data of photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity from the physiological parameter calculation module, and standardize the data to provide a unified input for subsequent coupling calculations; Coupled calculation unit: used to calculate the photosynthetically active radiation absorption rate according to the standardized , transpiration intensity index and effective root activity The growth coordination index value is dynamically calculated using the following coupling formula: ,in, represents the growth coordination index value, are the weighted coefficients of photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity, is the maximum value of the corresponding parameter, reflecting the maximum physiological efficiency of the crop under the optimal environmental conditions; the above-mentioned unit dynamically calculates the growth coordination index value. This scheme can comprehensively evaluate the coupling relationship between photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity, so as to accurately measure the growth coordination of crops. This coupling calculation provides a comprehensive evaluation index for crop growth, which can effectively reflect the growth status of crops under existing environmental conditions and provide a scientific basis for subsequent environmental regulation.

[0028] The control decision module includes a growth coordination index deviation calculation unit, a supplementary light intensity control unit, an irrigation frequency control unit, a ventilation duration control unit, and a control instruction generation unit; wherein: Growth coordination index deviation calculation unit: used for receiving the growth coordination index value from the growth coupling analysis module, and comparing it with the preset growth coordination index threshold value to calculate its deviation degree; The fill light intensity control unit is used to generate a fill light intensity control instruction according to the deviation calculated by the growth coordination index deviation calculation unit; specifically, when the deviation is a negative value, it means that the crop photosynthesis is insufficient, and the fill light intensity is increased; when the deviation is a positive value, it means that the crop photosynthesis is too strong, and the fill light intensity is reduced; Irrigation frequency control unit: used to generate irrigation frequency control instructions according to the deviation of the growth coordination index; specifically, when the deviation is a negative value, it means that the crop water demand increases, so the irrigation frequency is increased; when the deviation is a positive value, it means that the crop has too much water, so the irrigation frequency is reduced; Ventilation duration control unit: used to generate ventilation duration control instructions according to the growth coordination index deviation; specifically, when the deviation is a negative value, it means that the crop growth environment is overheated, so the ventilation duration is increased; when the deviation is a positive value, it means that the crop growth environment is suitable, so the ventilation duration is reduced; Control instruction generation unit: used to integrate the control instructions of fill light intensity, irrigation frequency and ventilation duration generated by each control unit, generate a complete control instruction set, and output it to the closed-loop execution module for actual adjustment.

[0029] The calculation formula for the degree of deviation is: ,in, Indicates the deviation of the growth coordination index from the preset threshold. It is the threshold for crops under optimal growth conditions; by dynamically calculating the degree of deviation between the growth coordination index and the preset threshold, the control decision module can accurately generate control instructions for fill light intensity, irrigation frequency and ventilation duration; this module can automatically adjust environmental conditions according to the real-time growth needs of crops to ensure that crops are always in the best growth state, thereby improving the growth efficiency and resource utilization of greenhouse crops, and further enhancing the automation and precision management level of smart greenhouses.

[0030] The closed-loop execution module includes a control instruction input unit, a new growth coordination index acquisition unit, a deviation calculation unit, a control parameter correction unit and a feedback adjustment unit; wherein: Control instruction input unit: used to input the control instruction set of supplementary light intensity, irrigation frequency and ventilation duration generated by the control decision module into the execution device. The instruction set is used to control the environmental parameters in the greenhouse, such as lighting, irrigation system and ventilation system, to ensure that crops grow under suitable environmental conditions; New growth coordination index acquisition unit: 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: 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 value; Control parameter correction unit: used to dynamically correct the control instructions of the execution equipment according to the calculated new deviation value. Specifically, the parameter values ​​of the fill light intensity, irrigation frequency and ventilation duration are adjusted according to the deviation value to ensure that the crops are always in the best growth environment; Feedback adjustment unit: used to adjust the operating status of the execution equipment in real time according to the corrected control instructions calculated by the control parameter correction unit. Whenever a new round of growth coordination index values ​​is collected and calculated, the system will automatically update the control instructions to achieve continuous optimization and adjustment of environmental conditions. By responding to changes in the new growth coordination index values ​​in a timely manner, the above-mentioned unit can continuously optimize the supplementary lighting intensity, irrigation frequency and ventilation duration, so that the crops are always in the best growth environment, thereby improving the growth efficiency, resource utilization and yield of the crops, while minimizing the energy consumption of the greenhouse.

[0031] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0032] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present 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 spatiotemporal calibration module, a physiological parameter calculation module, a growth coupling analysis module, a regulation and decision-making module, and a closed-loop execution module; among which: Multi-source sensing module: canopy spectral data, stem pressure fluctuation data and root conductivity data are collected synchronously through canopy multispectral camera, stem hydraulic pressure sensor and root zone dielectric probe; Spatiotemporal calibration module: Using the stem bifurcation point as the reference point, the collected canopy spectral data and stem pressure fluctuation data are spatiotemporally aligned to generate a time-synchronized canopy-stem joint data set; Physiological parameter calculation module: used to extract photosynthetically active radiation absorption rate and transpiration intensity index from the canopy-stem joint data set, and calculate effective root activity in combination with root conductivity data; Growth coupling analysis module: used to dynamically calculate the growth coordination index value based on the coupling relationship between photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity; Control decision module: Generates a control instruction set for supplementary light intensity, irrigation frequency and ventilation duration based on the degree of deviation between the growth coordination index value and the preset threshold; Closed-loop execution module: It is used to input the control instruction set into the execution device for control, and dynamically correct the parameters of the control instruction according to the new growth coordination index value of 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 is characterized in that: The multi-source sensing module includes a canopy spectrum acquisition unit, a stem pressure monitoring unit and a root conductivity detection unit; wherein: Canopy spectrum acquisition unit: Through the canopy multispectral camera set in the greenhouse, the spectral data of the crop canopy is synchronously collected at intervals of 10 minutes; Stem pressure monitoring unit: Through the hydraulic sensor installed on the crop stem, the pressure fluctuation data inside the stem is collected in real time; Root conductivity detection unit: Real-time monitoring of root conductivity data through dielectric probes buried in the root zone.

3. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: The spatiotemporal calibration module includes a data receiving unit, a reference point calibration unit, a spatiotemporal alignment unit and a joint data generation unit; wherein: Data receiving unit: used to receive canopy spectrum data and stem pressure fluctuation data from the multi-source sensing module; Benchmark point calibration unit: Taking the stem bifurcation point as the benchmark point, the corresponding position of the stem bifurcation point in the two data sets is determined by analyzing the time stamps in the canopy spectrum data and the stem pressure fluctuation data; Spatiotemporal alignment unit: Based on the reference position determined by the reference point calibration unit, the interpolation algorithm is used to align the canopy spectral data and the stem pressure fluctuation data in time and space to ensure that the two sets of data match in time and space; Joint data generation unit: Based on the spatiotemporally aligned canopy spectral data and stem pressure fluctuation data, a time-synchronized canopy-stem joint dataset is generated.

4. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 3 is characterized in that: The spatiotemporal alignment unit comprises: Determine the temporal and spatial offset: Calculate the temporal offset of 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: based on the calculated time offset The canopy spectral data and the stem pressure fluctuation data are time-aligned by linear interpolation. Specifically, for the first Data points , whose timestamp is , use the following linear interpolation formula to adjust it to the timestamp of the stem pressure fluctuation data Corresponding position: ,in, is the adjusted data point, is the time offset, and are the values ​​of two adjacent data points in the canopy spectral data, and They are their corresponding timestamps respectively; Spatial alignment: The stem pressure fluctuation data is calibrated by spatial interpolation method, and the sampling points of the stem pressure fluctuation data are set. Corresponding time , whose spatial position is , and adjust it to the position of the canopy spectral data using the spatial interpolation algorithm Correspondingly, the spatial interpolation formula is: ,in, is the adjusted stem pressure fluctuation data, and are the spatial locations of the stem pressure fluctuation data and the canopy spectral data, and are two adjacent data values ​​in the stem pressure fluctuation data, and are their corresponding spatial positions respectively.

5. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 1 is characterized in that: 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; wherein: Photosynthetically active radiation absorption rate calculation unit: used to extract canopy spectral data from the canopy-stem joint data set and calculate the photosynthetically active radiation absorption rate using the following formula: ,in, is the crop canopy reflectance, The wavelength is The spectral radiation intensity, is the incident light intensity, It represents the absorption rate of photosynthetically active radiation; Transpiration intensity index calculation unit: used to extract transpiration-related spectral data from the meta-layer-stem joint data set, and combined with the environmental data in the greenhouse, calculate the transpiration intensity index through the following formula: ,in, is the ambient temperature, is the air humidity, represents the transpiration intensity index; Effective root activity calculation unit: Receives root conductivity data, and evaluates the health and activity of the root system by analyzing the relationship between root conductivity and crop growth status, thereby calculating the effective root activity.

6. The intelligent greenhouse crop growth status monitoring and management system based on the Internet of Things according to claim 5 is characterized in that: The effective root activity calculation unit comprises: Root conductivity data extraction: Obtain root conductivity data from the multi-source perception module; Correlation analysis between root conductivity and growth status: Based on the factors of soil moisture and nutrient supply of crops, the relationship between root conductivity and crop growth status is analyzed. The relationship between root conductivity and crop root activity is calculated using the following empirical formula: ,in, is the root conductivity, is the nutrient concentration in the soil, is the soil temperature, and is the fitting coefficient; Assess the health of the root system: Based on the correlation analysis between the root conductivity and the growth status, determine whether the root system is in a healthy state. If the root conductivity value is higher than , it indicates that the root system is healthy; if the root conductivity value is lower than , it indicates that the root activity is poor; Calculate effective root activity: Combine root conductivity data with crop growth status and calculate effective root activity using the following formula: ,in, is the maximum root conductivity of the crop under optimal growth conditions, is the effective root activity.

7. The Internet of Things-based intelligent greenhouse crop growth status monitoring and management system according to claim 6, characterized in that: The growth coupling analysis module includes a data standardization unit and a coupling calculation unit; wherein: Data standardization unit: used to receive data of photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity from the physiological parameter calculation module, and standardize the data to provide a unified input for subsequent coupling calculations; Coupled calculation unit: used to calculate the photosynthetically active radiation absorption rate according to the standardized , transpiration intensity index and effective root activity The growth coordination index value is dynamically calculated using the following coupling formula: ,in, represents the growth coordination index value, are the weighted coefficients of photosynthetically active radiation absorption rate, transpiration intensity index and effective root activity, is the maximum value of the corresponding parameter.

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

9. The Internet of Things-based intelligent greenhouse crop growth status monitoring and management system according to claim 8, characterized in that: The calculation formula of the deviation degree is: ,in, Indicates the deviation of the growth coordination index from the preset threshold. It is the threshold value for crops under optimal growth conditions.

10. 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 control instruction input unit, a new growth coordination index acquisition unit, a deviation calculation unit, a control parameter correction unit and a feedback adjustment unit; wherein: Control instruction input unit: used to input the control instruction set of supplementary light intensity, irrigation frequency and ventilation duration generated by the control decision module into the execution device. The instruction set is used to control the environmental parameters in the greenhouse to ensure that crops grow under suitable environmental conditions; New growth coordination index acquisition unit: 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: 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 value; Control parameter correction unit: used to dynamically correct the control instructions of the execution device according to the calculated new deviation value; Feedback adjustment unit: used to adjust the operating state of the execution device in real time according to the corrected control instructions calculated by the control parameter correction unit.

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