Intelligent greenhouse environment control method and system

Abstract

The invention discloses an intelligent greenhouse environment control method and system, particularly relates to the technical field of greenhouse control, and is used for solving the problem that the layering phenomenon is difficult to identify in the existing greenhouse carbon dioxide concentration monitoring. Modeling and identifying carbon dioxide concentration and gas flow distribution data in the greenhouse based on a clustering analysis algorithm to judge whether a carbon dioxide layering phenomenon exists or not; when a layering phenomenon exists, airflow velocity field characteristics are extracted through an optical flow method, a velocity field image is analyzed in combination with a convolutional neural network, and airflow vortex morphological characteristics of a local area of the greenhouse are identified; calculating the information entropy of the concentration change in combination with Shannon entropy, analyzing the diffusion path in combination with a dynamic programming algorithm, and evaluating the complexity of the diffusion path of the layered region; further identifying an eddy current risk area; the chlorophyll distribution characteristics of crops are analyzed through a spectral analysis algorithm, the growth abnormality is evaluated by combining carbon dioxide concentration data, and the abnormality is used as an auxiliary condition to precisely regulate and control ventilation and carbon dioxide supplementation.

Description

Technical Field

[0001] The present invention relates to the field of greenhouse control technology, and more specifically, to an intelligent greenhouse environment control method and system. Background Art

[0002] In large or high greenhouses, if the ventilation is uneven or the air inlet is not positioned properly, the carbon dioxide concentration may be stratified in the upper or lower layers, forming high-concentration or low-concentration air masses; if vortices or dead corners appear in the turbulent pattern of the internal airflow, long-term stagnation or depletion areas of carbon dioxide will occur.

[0003] In the existing technology, common arrangements may only monitor the average carbon dioxide concentration, which is difficult to reflect the actual conditions at different heights and locations. For crops that require precise carbon dioxide supplementation during their growth stages, if these eddies or dead corners cannot be discovered and regulated in a timely manner, this may cause overall crop growth differences and even lead to concentrated outbreaks of pests and diseases in specific microenvironments. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an intelligent greenhouse environment control method and system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An intelligent greenhouse environment control method comprises the following steps:

[0007] Model and identify the carbon dioxide concentration and airflow distribution data based on the cluster analysis algorithm to determine whether there is carbon dioxide stratification;

[0008] When there is carbon dioxide stratification, the optical flow method is used to extract the airflow velocity field characteristics and the convolutional neural network is used to analyze the velocity field image to identify the airflow vortex morphology characteristics in the local area of ​​the greenhouse. The Shannon entropy is used to calculate the information entropy of the concentration change and the dynamic programming algorithm is used to analyze the diffusion path to evaluate the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse.

[0009] Based on the vortex morphology characteristics of the airflow in the local area of ​​the greenhouse and the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse, the vortex risk area in the greenhouse is identified;

[0010] The spectral analysis algorithm was used to analyze the chlorophyll distribution characteristics of crop leaves in the vortex risk area, and the abnormal distribution of crop growth conditions was evaluated in combination with regional carbon dioxide concentration data;

[0011] The abnormal distribution of crop growth conditions is used as an auxiliary condition to perform local ventilation adjustment operations in the vortex risk area, and local carbon dioxide supplementation operations are enabled when predetermined conditions are met.

[0012] In a preferred embodiment, modeling and identifying the carbon dioxide concentration and airflow distribution data based on a cluster analysis algorithm to determine whether carbon dioxide stratification exists includes:

[0013] By arranging carbon dioxide concentration sensors and airflow monitoring devices at different heights and positions in the greenhouse, carbon dioxide concentration data and airflow distribution data at different points in the greenhouse are collected;

[0014] Preprocess the carbon dioxide concentration data and airflow distribution data, including data denoising and time series alignment;

[0015] Based on the pre-processed carbon dioxide concentration data and airflow distribution data, cluster analysis algorithm is used for grouping processing. The cluster analysis algorithm divides the data into multiple areas according to different spatial locations by setting grouping parameters that adapt to the environmental characteristics of the greenhouse.

[0016] Model the CO2 concentration distribution and airflow characteristics in each area to generate a hierarchical model of CO2 concentration and airflow distribution in the greenhouse;

[0017] Based on the stratification model, by calculating the carbon dioxide concentration gradient and airflow intensity difference in each area, it is determined whether there is carbon dioxide stratification in the greenhouse.

[0018] In a preferred embodiment, the airflow velocity field characteristics are extracted by optical flow method and the velocity field image is analyzed in combination with convolutional neural network to identify the airflow vortex morphological characteristics in the local area of ​​the greenhouse, including:

[0019] Collect air velocity data at different locations in the greenhouse to form a multi-point air velocity distribution sequence;

[0020] Based on the airflow velocity distribution sequence, the optical flow method is used to extract the airflow velocity field characteristics. The optical flow method generates a dynamic characteristic diagram of the airflow velocity field by calculating the change in airflow velocity at adjacent time points.

[0021] The dynamic characteristic map of the airflow velocity field is used as input data to import into the convolutional neural network model for feature extraction and analysis. The convolutional neural network extracts the local vortex morphological features in the dynamic characteristic map of the airflow velocity field through multi-layer convolution and pooling operations;

[0022] The output layer of the convolutional neural network classifies and labels the extracted local vortex morphological features, and generates a vortex morphological distribution model based on the vortex morphological characteristics at different positions in the dynamic characteristic map of the airflow velocity field;

[0023] The airflow vortex position and airflow vortex morphology characteristics in the local area of ​​the greenhouse are identified based on the vortex morphology distribution model. The airflow vortex morphology characteristics include vortex intensity, vortex radius and rotation direction.

[0024] In a preferred embodiment, the information entropy of the concentration change is calculated based on the Shannon entropy and the diffusion path is analyzed in combination with a dynamic programming algorithm to evaluate the complexity of the diffusion path of the carbon dioxide stratified area in the greenhouse, including:

[0025] Collect carbon dioxide concentration data in different areas of the greenhouse and construct a concentration distribution matrix in spatial and temporal dimensions;

[0026] Calculate the carbon dioxide concentration gradient in each area based on the constructed concentration distribution matrix and generate a concentration gradient change map;

[0027] Based on the concentration gradient change diagram, the Shannon entropy formula is used to calculate the concentration change information entropy of each region to indicate the complexity of the concentration change;

[0028] According to the spatial distribution results of the information entropy of concentration changes, the diffusion path is optimized and modeled in combination with the dynamic programming algorithm to generate a diffusion path complexity model;

[0029] The diffusion path complexity index was calculated using the diffusion path complexity model to evaluate the diffusion path complexity of the carbon dioxide stratification region.

[0030] In a preferred embodiment, the diffusion path complexity index is calculated by a diffusion path complexity model to evaluate the diffusion path complexity of the carbon dioxide stratification region, specifically:

[0031] The diffusion path complexity index is calculated according to the diffusion path complexity model: ;in, represents the diffusion path complexity index, is the total number of path points, Indicates the path The diffusion cost of a point is Indicates the index of the waypoint.

[0032] In a preferred embodiment, based on the airflow vortex morphology characteristics of the local area of ​​the greenhouse and the diffusion path complexity of the carbon dioxide stratification area in the greenhouse, the vortex risk area in the greenhouse is identified, including:

[0033] Extract the vortex morphological characteristics of the airflow, including vortex intensity, vortex radius and rotation direction;

[0034] Obtain the diffusion path complexity index of the carbon dioxide stratification area in the greenhouse and construct the diffusion complexity distribution map of the carbon dioxide stratification area;

[0035] Based on the airflow vortex morphological characteristics and the diffusion complexity distribution map, a spatial correlation analysis was performed to determine the intersection area between the vortex area and the stratified area, and the intersection area was marked as the vortex risk area.

[0036] In a preferred embodiment, the chlorophyll distribution characteristics of crop leaves in the vortex risk area are analyzed by a spectral analysis algorithm, and the abnormal distribution of crop growth conditions is evaluated in combination with regional carbon dioxide concentration data, including:

[0037] Collect real-time image data of crop leaves in the eddy current risk area, the image data includes reflectance spectrum information of crop leaves;

[0038] Preprocessing the reflectance spectrum information of crop leaves, including spectrum correction and background noise removal;

[0039] The chlorophyll content distribution of crop leaves is calculated based on the spectral analysis algorithm, and the chlorophyll content distribution data corresponds to the spatial position of the crop leaves;

[0040] Obtain carbon dioxide concentration data at different locations within the eddy risk area, and match the carbon dioxide concentration data with chlorophyll content distribution data according to spatial locations;

[0041] Based on the matching results, the correlation between the carbon dioxide concentration data and the chlorophyll content distribution data in the eddy risk area is analyzed, the abnormal distribution of crop growth conditions is evaluated, and the abnormal growth distribution areas are marked.

[0042] In a preferred embodiment, the abnormal distribution of crop growth conditions is used as an auxiliary condition to perform local ventilation adjustment operation on the vortex risk area, and the local carbon dioxide supplementation operation is enabled when the predetermined conditions are met, including:

[0043] Determine the specific spatial location of the abnormality based on the identified distribution area of ​​growth abnormality;

[0044] Control the operating range and air volume parameters of local ventilation equipment in the vortex risk area according to the specific spatial location of the anomaly;

[0045] During the local ventilation operation, the carbon dioxide concentration and airflow velocity data in the abnormal growth distribution area are monitored in real time to determine whether the carbon dioxide concentration reaches the preset supplementary conditions;

[0046] When the carbon dioxide concentration in the abnormal growth distribution area reaches the preset supplementary conditions, the local carbon dioxide supplementary equipment is activated to release carbon dioxide to the abnormal growth distribution area through the precise gas supply port.

[0047] On the other hand, the present invention provides an intelligent greenhouse environment control system, including a stratification phenomenon recognition module, a vortex characteristic analysis module, a diffusion complexity assessment module, an eddy current risk recognition module, an abnormal distribution assessment module, and an environment precision control module;

[0048] Stratification phenomenon identification module: Model and identify the carbon dioxide concentration and airflow distribution data based on the cluster analysis algorithm to determine whether there is carbon dioxide stratification phenomenon;

[0049] Vortex characteristic analysis module: When there is carbon dioxide stratification, the airflow velocity field characteristics are extracted by optical flow method and the velocity field image is analyzed in combination with convolutional neural network to identify the airflow vortex morphological characteristics in the local area of ​​the greenhouse;

[0050] Diffusion complexity assessment module: When there is carbon dioxide stratification, the information entropy of concentration changes is calculated based on Shannon entropy and the diffusion path is analyzed in combination with the dynamic programming algorithm to evaluate the complexity of the diffusion path of carbon dioxide stratification areas in the greenhouse;

[0051] Vortex risk identification module: Identify vortex risk areas in the greenhouse based on the airflow vortex morphology characteristics in the local area of ​​the greenhouse and the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse;

[0052] Abnormal distribution assessment module: Analyze the chlorophyll distribution characteristics of crop leaves in the vortex risk area through spectral analysis algorithms, and evaluate the abnormal distribution of crop growth conditions in combination with regional carbon dioxide concentration data;

[0053] Precision environmental control module: uses the abnormal distribution of crop growth conditions as an auxiliary condition to perform local ventilation adjustment operations in vortex risk areas, and enables local carbon dioxide supplementation operations when predetermined conditions are met.

[0054] The technical effects and advantages of the intelligent greenhouse environment control method and system of the present invention are as follows:

[0055] 1. Modeling and identifying carbon dioxide concentration and airflow distribution data through cluster analysis algorithm can not only reflect the distribution of carbon dioxide at different heights and positions, but also effectively discover the stratification phenomenon that may be caused by high-concentration or low-concentration areas, solving the defect of the existing technology that only relies on average concentration monitoring to find stratification problems; in addition, through the combination of optical flow method and convolutional neural network, the morphological characteristics of airflow vortex in local areas can be accurately extracted, which provides technical support for the identification of vortex risk areas and avoids the depletion or retention of carbon dioxide caused by airflow dead corners or vortex areas for a long time.

[0056] 2. The abnormal distribution of crop growth conditions in the vortex risk area is evaluated through spectral analysis algorithms, and combined with real-time monitoring data of carbon dioxide concentration in the area, a decision-making basis is provided for subsequent regulation. By taking the abnormal distribution of crop growth as an auxiliary condition, precise control of ventilation and carbon dioxide supplementation in the vortex risk area can be achieved, thereby optimizing the regional environment. This intelligent control method avoids crop growth differentiation caused by long-term imbalance of local carbon dioxide concentration and reduces the possibility of concentrated outbreaks of pests and diseases, thereby significantly improving the growth efficiency and yield stability of crops in the greenhouse, while reducing energy waste and operating costs, which has significant economic and environmental value. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A schematic diagram of an intelligent greenhouse environment control method of the present invention;

[0058] Figure 2 The figure is a schematic diagram of the structure of an intelligent greenhouse environment control system of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1: Figure 1 The present invention provides an intelligent greenhouse environment control method, comprising the following steps:

[0061] Based on the cluster analysis algorithm, the carbon dioxide concentration and airflow distribution data are modeled and identified to determine whether there is carbon dioxide stratification.

[0062] When carbon dioxide stratification exists, the optical flow method is used to extract the airflow velocity field characteristics and the velocity field image is analyzed in combination with a convolutional neural network to identify the airflow vortex morphology characteristics in the local area of ​​the greenhouse. The information entropy of the concentration change is calculated based on the Shannon entropy and the diffusion path is analyzed in combination with a dynamic programming algorithm to evaluate the complexity of the diffusion path in the carbon dioxide stratified area in the greenhouse.

[0063] Based on the airflow vortex morphology characteristics of local areas of the greenhouse and the complexity of the diffusion path of carbon dioxide stratified areas in the greenhouse, the vortex risk areas in the greenhouse are identified.

[0064] The chlorophyll distribution characteristics of crop leaves in the vortex risk area were analyzed through spectral analysis algorithms, and the abnormal distribution of crop growth conditions was evaluated in combination with regional carbon dioxide concentration data.

[0065] The abnormal distribution of crop growth conditions is used as an auxiliary condition to perform local ventilation adjustment operations in the vortex risk area, and local carbon dioxide supplementation operations are enabled when predetermined conditions are met.

[0066] Based on the cluster analysis algorithm, the carbon dioxide concentration and airflow distribution data are modeled and identified to determine whether there is carbon dioxide stratification, including:

[0067] The carbon dioxide concentration data and airflow distribution data at different points in the greenhouse are collected by placing carbon dioxide concentration sensors and airflow monitoring devices at different heights and positions in the greenhouse:

[0068] Equipment for monitoring carbon dioxide concentration and airflow distribution is set up in the greenhouse, where carbon dioxide concentration sensors are installed at different heights and locations in the greenhouse to collect carbon dioxide concentration data inside the greenhouse. The airflow monitoring device uses multi-point wind speed sensors, which are arranged at key locations in the greenhouse (such as near vents, above crops, and between crops) to capture the speed and direction data of airflow in the greenhouse.

[0069] Each carbon dioxide concentration sensor and airflow monitoring device is connected to the data acquisition system through wireless or wired communication, and the collected carbon dioxide concentration value and airflow velocity value are uploaded in real time. The data collection interval is set to once a minute to ensure sufficient spatiotemporal resolution while avoiding storage and computing pressure caused by excessive redundant data.

[0070] Preprocess the carbon dioxide concentration data and airflow distribution data, including data denoising and time series alignment:

[0071] Data denoising: A denoising method based on wavelet transform is used to decompose the collected data into multi-scale frequency components. The signal is smoothed by removing high-frequency noise components, thereby eliminating the interference of instantaneous abnormal data.

[0072] Time series alignment: Since there may be timestamp errors in the data collection of the CO2 concentration sensor and the airflow monitoring device, the dynamic time warping (DTW) algorithm is used to time align different data streams to ensure that the CO2 concentration data and the airflow distribution data have the same time reference.

[0073] Based on the pre-processed carbon dioxide concentration data and airflow distribution data, cluster analysis algorithm is used for grouping processing. The cluster analysis algorithm divides the data into multiple areas according to different spatial locations by setting grouping parameters that adapt to the environmental characteristics of the greenhouse:

[0074] A dynamic clustering algorithm based on K-means is selected. This algorithm can extract spatial location-related clustering features in high-dimensional data by iteratively updating cluster centers and cluster member distributions.

[0075] According to the structural characteristics of the greenhouse, the initial grouping parameters, including the number of groups and the cluster distance threshold, are set to ensure that the grouping results can adapt to the dynamic changes of the greenhouse environment.

[0076] The preprocessed carbon dioxide concentration data and airflow distribution data are used as algorithm inputs, and multiple iterative calculations are performed to generate spatial clustering results.

[0077] The CO2 concentration distribution and airflow characteristics of each area are modeled to generate a hierarchical model of CO2 concentration and airflow distribution in the greenhouse:

[0078] For each cluster area, the mean carbon dioxide concentration, concentration gradient value, and distribution characteristics of airflow speed and direction in the area are calculated respectively.

[0079] Using the grid interpolation method, the carbon dioxide concentration distribution and airflow velocity field in the area are reconstructed into a three-dimensional layered model. The three-dimensional layered model includes the carbon dioxide concentration value, concentration change gradient and airflow velocity vector at each spatial position.

[0080] Based on the stratification model, by calculating the carbon dioxide concentration gradient and airflow intensity difference in each area, it is determined whether there is carbon dioxide stratification in the greenhouse:

[0081] If the carbon dioxide concentration gradient in a certain area exceeds the set threshold (such as 10 ppm / m), or the airflow intensity difference exceeds the set value (such as 1 m / s), it is judged that carbon dioxide stratification exists in the area.

[0082] Output the identification results of the stratification phenomenon, including the location of the specific stratification area and its corresponding concentration gradient and airflow intensity information.

[0083] The optical flow method is used to extract the airflow velocity field characteristics and the convolutional neural network is used to analyze the velocity field image to identify the airflow vortex morphological characteristics in the local area of ​​the greenhouse, including:

[0084] Collect air velocity data at different locations in the greenhouse to form a multi-point air velocity distribution sequence:

[0085] Airflow monitoring devices are arranged at different heights, edge areas, central areas, and near ventilation openings and air supply openings inside the greenhouse to ensure that the spatial distribution of airflow data is comprehensive. Each airflow monitoring device contains a sensor that can record the airflow speed and direction in real time.

[0086] The airflow monitoring device records data at fixed time intervals. Each record includes the magnitude and direction of the airflow velocity. All data are stored in the form of time series. The data storage format includes location index, timestamp, airflow velocity magnitude and direction information.

[0087] Based on the airflow velocity distribution sequence, the optical flow method is used to extract the airflow velocity field characteristics. The optical flow method generates a dynamic characteristic diagram of the airflow velocity field by calculating the change in airflow velocity at adjacent time points:

[0088] Optical flow is a method that calculates the pixel motion vector field by analyzing the image changes between adjacent time points. In this scenario, the optical flow method is used to calculate the dynamic changes of the airflow velocity field.

[0089] Based on the airflow velocity data, a time-series image sequence of the velocity field is established, and each frame of the image represents the spatial distribution of the airflow velocity in the greenhouse.

[0090] Assume that the pixel motion of the airflow velocity field satisfies the optical flow constraint equation, the formula is as follows: ;in, Represents the horizontal gradient of the image. Represents the vertical gradient of the image. represents the temporal gradient of the image, and Represent the components of optical flow in the horizontal and vertical directions respectively.

[0091] By solving the above equations, the motion vector of the airflow velocity field at each pixel position is obtained.

[0092] The output of the optical flow method is a set of vector field images, which represent the dynamic changes of the airflow velocity field at each position, that is, the dynamic feature map of the airflow velocity field. This result is used as the input of the subsequent convolutional neural network.

[0093] The dynamic feature map of the airflow velocity field is used as input data to import into the convolutional neural network model for feature extraction and analysis. The convolutional neural network extracts the local vortex morphological features in the dynamic feature map of the airflow velocity field through multi-layer convolution and pooling operations:

[0094] The dynamic feature map of the airflow velocity field generated by the optical flow method is used as input and imported into the pre-trained convolutional neural network model for feature extraction and analysis.

[0095] The convolutional neural network model includes multiple convolutional layers, pooling layers and fully connected layers: the convolutional layer is used to extract the local features of the dynamic feature map of the airflow velocity field, and the size of the convolution kernel is set to 3×3 according to the characteristics of the greenhouse data; the pooling layer is used to reduce the dimension of the feature map and retain the main features; the fully connected layer is used to integrate the features extracted by each layer to generate the vortex morphology classification results.

[0096] The first convolution layer performs convolution operation on the input dynamic feature map of airflow velocity field. The formula is as follows: ;in, Indicates that in the convolutional neural network In the layer, the output feature map is at position The pixel value of Represents the row index of the feature map; Represents the column index of the feature map; Indicates the current layer number of the convolutional neural network, counting from the first layer; Represents the horizontal offset of the convolution kernel in the input feature map; Indicates the vertical offset of the convolution kernel in the input feature map; Indicates The convolution kernel of the layer is at position The weight value of is the row index of the convolution kernel, which is used to indicate the row position in the convolution kernel matrix; is the column index of the convolution kernel, which is used to indicate the column position in the convolution kernel matrix; Indicates The layer input feature map is at position The pixel value of Indicates the first Row plus convolution kernel horizontal offset The pixel position of Indicates the first Column plus the vertical offset of the convolution kernel The pixel position of Represents the feature map of the previous layer; Indicates The bias term of the layer convolution operation is a learnable parameter used to offset the convolution calculation result.

[0097] in, and The value range of is determined by the size of the convolution kernel (for example, the value range is {−1, 0, 1} for a 3×3 convolution kernel). The feature map is an abbreviation of the dynamic feature map of the airflow velocity field.

[0098] The last output layer classifies the extracted vortex morphological features and marks the vortex intensity, radius and rotation direction at different locations.

[0099] The output layer of the convolutional neural network classifies and labels the extracted local vortex morphological features, and generates a vortex morphological distribution model based on the vortex morphological characteristics at different positions in the dynamic characteristic map of the airflow velocity field:

[0100] The output layer of the convolutional neural network classifies the vortex morphological features extracted by the previous convolution and pooling layers through a fully connected layer. The classification is based on the intensity, radius, and rotation direction of the vortex.

[0101] The output classification labels include: vortex intensity level (such as weak, medium, strong); vortex radius range (divided in meters, divided into small vortex, medium vortex and large vortex); vortex rotation direction (divided into clockwise and counterclockwise).

[0102] The classification results are spatially annotated, and the morphological characteristics of the vortex are matched to its specific position in the dynamic characteristic diagram of the airflow velocity field.

[0103] The annotation results include: the spatial coordinates of the vortex center (determined by the point of maximum rotation intensity); the set of boundary points corresponding to the vortex radius; and the rotation direction of the vortex.

[0104] According to the classification and labeling results of all vortices, a three-dimensional vortex morphology distribution model is generated: the model is based on the three-dimensional spatial coordinates of the greenhouse; each vortex is described by its center coordinates, radius and rotation direction.

[0105] The vortex morphology distribution model is stored in a matrix form, where each cell of the matrix corresponds to a small area in the greenhouse space, marking whether the area belongs to a vortex and the morphological characteristics of the vortex.

[0106] Based on the vortex morphology distribution model, the airflow vortex position and airflow vortex morphology characteristics in the local area of ​​the greenhouse are identified. The airflow vortex morphology characteristics include vortex intensity, vortex radius and rotation direction:

[0107] The specific location of the airflow vortex in the greenhouse is identified through the vortex morphology distribution model: the location of the vortex center is determined by the point of maximum rotation intensity in the airflow velocity field; the vortex boundary is delineated by the position where the rotation intensity gradient is lower than the preset threshold.

[0108] The vortex strength is calculated based on the difference in airflow velocity between the vortex center and the boundary, indicating the severity of the velocity change inside the vortex. The vortex radius is calculated based on the maximum distance from the vortex center to the boundary point, in meters. The rotation direction is determined by analyzing the rotation pattern of the airflow vector in the vortex area to determine whether the rotation direction is clockwise or counterclockwise.

[0109] The identification results are output in the form of a list, including the center coordinates, intensity, radius and rotation direction of each vortex.

[0110] When there is carbon dioxide stratification, the information entropy of concentration changes is calculated based on Shannon entropy and the diffusion path is analyzed in combination with the dynamic programming algorithm to evaluate the complexity of the diffusion path of carbon dioxide stratification areas in the greenhouse, including:

[0111] Collect carbon dioxide concentration data from different areas of the greenhouse and construct a concentration distribution matrix in spatial and temporal dimensions:

[0112] Carbon dioxide concentration sensors are arranged at different heights and areas inside the greenhouse. The sensors are arranged in key locations, including air inlets, ventilation openings, central areas of the greenhouse, and edge areas.

[0113] The CO2 concentration data were collected at fixed time intervals (the sampling interval was set to 5 seconds), and the concentration values ​​and their corresponding spatial locations and timestamps were recorded.

[0114] Based on the collected carbon dioxide concentration data, a three-dimensional concentration distribution matrix is ​​constructed. The rows and columns of the matrix correspond to the spatial position indexes, and the third dimension corresponds to the time series.

[0115] Based on the constructed concentration distribution matrix, the carbon dioxide concentration gradient in each area is calculated to generate a concentration gradient change map:

[0116] According to the concentration distribution matrix, the concentration gradient in space and time dimensions is calculated: , ;in, Indicates location In time Horizontal carbon dioxide concentration gradient; Yes The partial derivative of the coordinates represents the rate of change of the lateral concentration; Indicates location In time The vertical carbon dioxide concentration gradient; represents the three-dimensional concentration distribution matrix; Yes The partial derivative of the coordinate represents the rate of change of the longitudinal concentration; Represents the lateral position coordinates in space; Represents the vertical position coordinate in space; and They represent the partial derivatives of the three-dimensional concentration distribution matrix in the horizontal and vertical directions respectively.

[0117] The concentration gradient values ​​at all positions are represented in the form of images to generate a concentration gradient change map, which serves as the basis for subsequent information entropy calculations.

[0118] Based on the concentration gradient change diagram, the Shannon entropy formula is used to calculate the concentration change information entropy of each region to indicate the complexity of the concentration change:

[0119] For each region, the probability distribution of its concentration change is calculated, and the information entropy is calculated based on the following formula: ;in, It represents information entropy and is used to quantify the complexity of the change in carbon dioxide concentration in a certain area; Indicates the concentration change state in a certain area Probability of occurrence; An index indicating the possible change state of carbon dioxide concentration in a certain area; For a state The ratio of the number of occurrences to the total number of occurrences of all states in the region.

[0120] The information entropy results are used to quantify the complexity of the concentration changes in each area. The results are stored in a table format, including the location index and its corresponding information entropy value.

[0121] According to the spatial distribution results of the information entropy of concentration changes, the diffusion path is optimized and modeled in combination with the dynamic programming algorithm to generate a diffusion path complexity model:

[0122] Based on the spatial distribution of information entropy, a diffusion path optimization model is established, and the path selection must meet the condition of minimizing the entropy value.

[0123] The state transition equation of dynamic programming is: ;in, Indicates that the diffusion path reaches the path point The minimum cost; represents the minimum cost of the diffusion path to reach the path point bbb; Indicates waypoints and waypoints The diffusion cost between Indicates that at all possible preceding path points Select a state to reach the path point The total cost is minimal; Indicates the index of the current target path point; Represents the leading index of the path point; Represents the set of all path points that can be selected for the current diffusion path.

[0124] The dynamic programming algorithm outputs a path complexity model, which includes the spatial coordinates of the diffusion path and the corresponding cost.

[0125] The diffusion path complexity index is calculated through the diffusion path complexity model to evaluate the diffusion path complexity of the carbon dioxide stratification area:

[0126] According to the diffusion path complexity model, the diffusion path complexity index is calculated: ;in, represents the diffusion path complexity index, which is used to quantify the overall complexity of the diffusion path; is the total number of path points, indicating the length of the diffusion path; Indicates the path The diffusion cost of each point; Indicates the index of the path point, from 1 to .

[0127] The output diffusion path complexity index and its corresponding stratified area position are used to characterize the complexity of the diffusion path in the area. The larger the diffusion path complexity index, the higher the diffusion path complexity of the carbon dioxide stratified area, reflecting that the path is more tortuous or blocked when carbon dioxide diffuses from the stratified area to the surrounding area. This may cause carbon dioxide to stay in the stratified area for a longer time and the local concentration changes unevenly, affecting the effective absorption of carbon dioxide by crops. Furthermore, areas with too high or too low concentrations may lead to differentiated crop growth, and in severe cases may even induce concentrated outbreaks of pests and diseases in specific microenvironments, reducing the overall crop growth efficiency and yield stability of the greenhouse.

[0128] Based on the vortex morphology characteristics of the local area of ​​the greenhouse and the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse, the vortex risk areas in the greenhouse are identified, including:

[0129] Extract the airflow vortex morphological characteristics, including vortex intensity, vortex radius and rotation direction.

[0130] Obtain the diffusion path complexity index of the carbon dioxide stratification area in the greenhouse and construct the diffusion complexity distribution map of the carbon dioxide stratification area:

[0131] According to the diffusion path complexity index, the complexity of the carbon dioxide stratification area is constructed in the form of spatial distribution as a diffusion complexity distribution map.

[0132] Each pixel point in the diffusion complexity distribution map represents the complexity index of the diffusion path at a specific location, and uses a color gradient to indicate the high and low indexes, for example, red represents high complexity and blue represents low complexity.

[0133] Based on the vortex morphological characteristics of the airflow and the diffusion complexity distribution map, a spatial correlation analysis is performed to determine the intersection area of ​​the vortex area and the stratified area; the intersection area is marked as the vortex risk area:

[0134] The extracted airflow vortex morphological characteristic data are matched with the diffusion path complexity index data of the carbon dioxide stratified area according to spatial location.

[0135] The matched data were spatially analyzed to determine the overlapping areas between the regions with significant eddy characteristics and the regions with high diffusion path complexity index.

[0136] The intersection area is a spatial location that meets the following conditions: the area has significant vortex intensity, a large vortex radius or a clear rotation direction; at the same time, the diffusion path complexity index of the area is higher than the preset threshold.

[0137] The specific location of the intersection area and its corresponding vortex morphological characteristics and diffusion path complexity index are output in the form of a list for use in subsequent steps.

[0138] The intersection area is marked as a vortex risk area, and the output includes detailed information such as spatial position, vortex intensity, vortex radius, rotation direction and diffusion path complexity index.

[0139] The significant vortex intensity is determined by calculating the change in the airflow velocity gradient, and the multiple exceeding the average velocity gradient is set as significant. The larger vortex radius is determined by detecting the distance from the vortex center to the boundary, and if it exceeds the average of the preset radius range, it is set as larger. The clear rotation direction is determined by analyzing the rotation mode of the airflow vector, using the curl of the vector field to calculate the positive and negative values, and determining whether it is clockwise or counterclockwise rotation. The judgment criteria are determined based on the upper and lower limits of statistical analysis.

[0140] The preset threshold of the diffusion path complexity index is determined according to the complexity statistics under normal diffusion conditions in the greenhouse, and the mean plus a certain multiple of the standard deviation is selected as the threshold. For example, the distribution range of the diffusion index is calculated through historical data, and the value above the 70% quantile is taken as the risk warning threshold of the complexity index.

[0141] The spectral analysis algorithm is used to analyze the chlorophyll distribution characteristics of crop leaves in the vortex risk area, and combined with the regional carbon dioxide concentration data to evaluate the abnormal distribution of crop growth conditions, including:

[0142] Collect real-time image data of crop leaves in the eddy current risk area, including the reflectance spectrum information of crop leaves:

[0143] Multispectral imaging equipment is deployed in the eddy current risk area to collect real-time image data of crop leaves. The equipment is installed at the main crop growth points in the area and the height is adjustable to adapt to different crop heights.

[0144] The collected image data contains the reflectance spectral information of crop leaves in different bands (such as red light, green light, and near-infrared light) for subsequent analysis.

[0145] The multispectral imaging device collects image data at fixed time intervals, and the sampling frequency is set to once every 10 minutes to ensure that the spectral changes of crop leaves are dynamically reflected.

[0146] The reflectance spectrum information of crop leaves is preprocessed, including spectrum correction and background noise removal:

[0147] A standard white plate is used for spectral correction to eliminate the impact of changes in ambient light intensity on the reflected spectrum data and ensure the accuracy of the data.

[0148] The filtering algorithm is used to remove the background noise in the image data, including the redundant spectral signals in the outer area of ​​the crop leaves. Each frame of image data is spatially filtered to retain the effective spectral information of the crop leaf area.

[0149] The chlorophyll content distribution of crop leaves is calculated based on the spectral analysis algorithm. The chlorophyll content distribution data corresponds to the spatial position of the crop leaves:

[0150] Based on the corrected reflectance spectrum information, the Normalized Difference Vegetation Index (NDVI) is used to calculate the chlorophyll content of crop leaves. According to the chlorophyll index value of each pixel point in the image data, a two-dimensional distribution map of chlorophyll content in the vortex risk area is generated, and each pixel point of the distribution map corresponds to the chlorophyll content of a specific spatial position.

[0151] Obtain the carbon dioxide concentration data at different locations within the eddy risk area, and match the carbon dioxide concentration data with the chlorophyll content distribution data according to spatial location:

[0152] Carbon dioxide concentration sensors are arranged in the vortex risk area, and the sensors are installed at key locations, including the air supply port, the vortex center and the area boundary. Carbon dioxide concentration data is collected at fixed time intervals, and each collected concentration value corresponds to the spatial position and timestamp.

[0153] The carbon dioxide concentration data are mapped one-to-one with the spatial coordinates of the eddy risk area and stored in a three-dimensional matrix format, with the matrix dimensions being spatial position and time.

[0154] Based on the matching results, the correlation between the carbon dioxide concentration data and the chlorophyll content distribution data in the vortex risk area is analyzed to evaluate the abnormal distribution of crop growth conditions and mark the abnormal growth distribution areas:

[0155] The chlorophyll content distribution map is matched with the carbon dioxide concentration data based on spatial location to ensure that the chlorophyll content at each location is associated with the corresponding carbon dioxide concentration data.

[0156] Statistical regression analysis methods were used to calculate the correlation strength between carbon dioxide concentration and chlorophyll content, and to determine the impact pattern of high-concentration areas on chlorophyll distribution.

[0157] Based on the results of the association analysis, areas with significantly lower chlorophyll content than expected were identified and marked as areas of abnormal growth distribution.

[0158] Taking the abnormal distribution of crop growth conditions as an auxiliary condition, local ventilation adjustment operations are performed in the vortex risk area, and local carbon dioxide supplementation operations are enabled when the predetermined conditions are met, including:

[0159] Determine the specific spatial location of the abnormality based on the identified distribution area of ​​growth abnormalities:

[0160] The specific spatial position index of the growth anomaly distribution area is extracted, and each position includes three-dimensional coordinates and corresponding anomaly type annotation (such as low chlorophyll content, low carbon dioxide concentration, etc.).

[0161] Generate a three-dimensional position table of growth abnormality distribution areas as input for subsequent regulation.

[0162] Control the operating range and air volume parameters of local ventilation equipment in the vortex risk area according to the specific spatial location of the abnormality:

[0163] Based on the three-dimensional location table of the growth abnormality distribution area, determine the local ventilation equipment that needs to be operated and its coverage range.

[0164] The coverage of each ventilation device is set by the equipment installation position and operation angle parameters to ensure that the influence area of ​​the ventilation device overlaps with the growth abnormality distribution area.

[0165] Set the air volume parameters of the ventilation equipment according to the size and type of the abnormal growth distribution area. For areas with abnormal chlorophyll content, set medium air volume to optimize air flow distribution. For areas with abnormal carbon dioxide concentration, set higher air volume to promote carbon dioxide concentration balance.

[0166] The air volume parameters are controlled by the fan speed and outlet air speed of the ventilation equipment. The specific parameter range is pre-calibrated according to the equipment model.

[0167] During the local ventilation operation, the carbon dioxide concentration and airflow velocity data in the abnormal growth distribution area are monitored in real time to determine whether the carbon dioxide concentration reaches the preset supplementary conditions:

[0168] Carbon dioxide concentration sensors and air flow velocity sensors are arranged in the growth abnormal distribution area to collect concentration and velocity values ​​at each location in real time. The sampling frequency can be set to once every 10 seconds, and the data is stored in the form of time series to ensure the continuity of monitoring data.

[0169] The preset replenishment conditions for CO2 concentration are determined based on the crop growth needs in the area, with a typical range of 400 to 800 ppm (specific values ​​are set based on crop type and growth stage). When the CO2 concentration in the monitored area is below the preset threshold and the airflow rate is stable, the replenishment condition is triggered.

[0170] When the carbon dioxide concentration in the abnormal growth distribution area reaches the preset supplementary condition, the local carbon dioxide supplement equipment is activated to release carbon dioxide to the abnormal growth distribution area through the precise gas supply port:

[0171] The carbon dioxide replenishment equipment installed near the abnormal growth distribution area is activated. The release direction of each device is precisely aimed at the abnormal area by adjusting the angle of the gas replenishment port to ensure that the carbon dioxide directly covers the target area.

[0172] The carbon dioxide release amount is calculated based on the area and concentration requirements of the abnormal growth distribution area. The specific formula is: ;in, Indicates the amount of carbon dioxide released (unit: liter); Indicates the area of ​​abnormal growth distribution (unit: square meters); Indicates the target concentration (unit: ppm); Indicates the current concentration (unit: ppm).

[0173] Embodiment 2: Figure 2 A structural schematic diagram of an intelligent greenhouse environmental control system of the present invention is given, which includes a stratified phenomenon recognition module, a vortex characteristic analysis module, a diffusion complexity assessment module, an eddy current risk recognition module, an abnormal distribution assessment module and an environmental precision control module.

[0174] Stratification phenomenon identification module: Based on the cluster analysis algorithm, the carbon dioxide concentration and airflow distribution data are modeled and identified to determine whether there is carbon dioxide stratification phenomenon.

[0175] Vortex characteristic analysis module: When there is carbon dioxide stratification, the airflow velocity field characteristics are extracted by the optical flow method and the velocity field image is analyzed in combination with the convolutional neural network to identify the airflow vortex morphological characteristics in the local area of ​​the greenhouse.

[0176] Diffusion complexity assessment module: When there is carbon dioxide stratification, the information entropy of concentration changes is calculated based on Shannon entropy and the diffusion path is analyzed in combination with the dynamic programming algorithm to evaluate the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse.

[0177] Vortex risk identification module: Identifies vortex risk areas in the greenhouse based on the airflow vortex morphology characteristics in local areas of the greenhouse and the complexity of the diffusion path of the carbon dioxide stratified areas in the greenhouse.

[0178] Abnormal distribution assessment module: Analyze the chlorophyll distribution characteristics of crop leaves in the vortex risk area through spectral analysis algorithm, and evaluate the abnormal distribution of crop growth conditions in combination with regional carbon dioxide concentration data.

[0179] Precision environmental control module: uses the abnormal distribution of crop growth conditions as an auxiliary condition to perform local ventilation adjustment operations in vortex risk areas, and enables local carbon dioxide supplementation operations when predetermined conditions are met.

[0180] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0181] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0182] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0183] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0184] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0185] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0186] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0187] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0188] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0189] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart greenhouse environment control method, characterized in that: The steps include: Model and identify the carbon dioxide concentration and airflow distribution data based on the cluster analysis algorithm to determine whether there is carbon dioxide stratification; When there is carbon dioxide stratification, the optical flow method is used to extract the airflow velocity field characteristics and the convolutional neural network is used to analyze the velocity field image to identify the airflow vortex morphology characteristics in the local area of ​​the greenhouse. The Shannon entropy is used to calculate the information entropy of the concentration change and the dynamic programming algorithm is used to analyze the diffusion path to evaluate the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse. Based on the vortex morphology characteristics of the airflow in the local area of ​​the greenhouse and the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse, the vortex risk area in the greenhouse is identified; The spectral analysis algorithm was used to analyze the chlorophyll distribution characteristics of crop leaves in the vortex risk area, and the abnormal distribution of crop growth conditions was evaluated in combination with regional carbon dioxide concentration data; The abnormal distribution of crop growth conditions is used as an auxiliary condition to perform local ventilation adjustment operations in the vortex risk area, and local carbon dioxide supplementation operations are enabled when predetermined conditions are met.

2. The intelligent greenhouse environment control method according to claim 1, characterized in that: Based on the cluster analysis algorithm, the carbon dioxide concentration and airflow distribution data are modeled and identified to determine whether there is carbon dioxide stratification, including: By arranging carbon dioxide concentration sensors and airflow monitoring devices at different heights and positions in the greenhouse, carbon dioxide concentration data and airflow distribution data at different points in the greenhouse are collected; Preprocess the carbon dioxide concentration data and airflow distribution data, including data denoising and time series alignment; Based on the pre-processed carbon dioxide concentration data and airflow distribution data, cluster analysis algorithm is used for grouping processing. The cluster analysis algorithm divides the data into multiple areas according to different spatial locations by setting grouping parameters that adapt to the environmental characteristics of the greenhouse. Model the CO2 concentration distribution and airflow characteristics in each area to generate a hierarchical model of CO2 concentration and airflow distribution in the greenhouse; Based on the stratification model, by calculating the carbon dioxide concentration gradient and airflow intensity difference in each area, it is determined whether there is carbon dioxide stratification in the greenhouse.

3. The intelligent greenhouse environment control method according to claim 1, characterized in that: The optical flow method is used to extract the airflow velocity field characteristics and the convolutional neural network is used to analyze the velocity field image to identify the airflow vortex morphological characteristics in the local area of ​​the greenhouse, including: Collect air velocity data at different locations in the greenhouse to form a multi-point air velocity distribution sequence; Based on the airflow velocity distribution sequence, the optical flow method is used to extract the airflow velocity field characteristics. The optical flow method generates a dynamic characteristic diagram of the airflow velocity field by calculating the change in airflow velocity at adjacent time points. The dynamic characteristic map of the airflow velocity field is used as input data to import into the convolutional neural network model for feature extraction and analysis. The convolutional neural network extracts the local vortex morphological features in the dynamic characteristic map of the airflow velocity field through multi-layer convolution and pooling operations; The output layer of the convolutional neural network classifies and labels the extracted local vortex morphological features, and generates a vortex morphological distribution model based on the vortex morphological characteristics at different positions in the dynamic characteristic map of the airflow velocity field; The airflow vortex position and airflow vortex morphology characteristics in the local area of ​​the greenhouse are identified based on the vortex morphology distribution model. The airflow vortex morphology characteristics include vortex intensity, vortex radius and rotation direction.

4. The intelligent greenhouse environment control method according to claim 1, characterized in that: The information entropy of concentration changes is calculated based on Shannon entropy and the diffusion path is analyzed in combination with the dynamic programming algorithm to evaluate the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse, including: Collect carbon dioxide concentration data in different areas of the greenhouse and construct a concentration distribution matrix in spatial and temporal dimensions; Calculate the carbon dioxide concentration gradient in each area based on the constructed concentration distribution matrix and generate a concentration gradient change map; Based on the concentration gradient change diagram, the Shannon entropy formula is used to calculate the concentration change information entropy of each region to indicate the complexity of the concentration change; According to the spatial distribution results of the information entropy of concentration changes, the diffusion path is optimized and modeled in combination with the dynamic programming algorithm to generate a diffusion path complexity model; The diffusion path complexity index was calculated using the diffusion path complexity model to evaluate the diffusion path complexity of the carbon dioxide stratification region.

5. The intelligent greenhouse environment control method according to claim 4, characterized in that: The diffusion path complexity index is calculated through the diffusion path complexity model to evaluate the diffusion path complexity of the carbon dioxide stratification area, specifically: The diffusion path complexity index is calculated according to the diffusion path complexity model: ;in, represents the diffusion path complexity index, is the total number of path points, Indicates the path The diffusion cost of a point is Indicates the index of the waypoint.

6. The intelligent greenhouse environment control method according to claim 1, characterized in that: Based on the vortex morphology characteristics of the local area of ​​the greenhouse and the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse, the vortex risk areas in the greenhouse are identified, including: Extract the vortex morphological characteristics of the airflow, including vortex intensity, vortex radius and rotation direction; Obtain the diffusion path complexity index of the carbon dioxide stratification area in the greenhouse and construct the diffusion complexity distribution map of the carbon dioxide stratification area; Based on the airflow vortex morphological characteristics and the diffusion complexity distribution map, a spatial correlation analysis was performed to determine the intersection area between the vortex area and the stratified area, and the intersection area was marked as the vortex risk area.

7. The intelligent greenhouse environment control method according to claim 1, characterized in that: The spectral analysis algorithm is used to analyze the chlorophyll distribution characteristics of crop leaves in the vortex risk area, and combined with the regional carbon dioxide concentration data to evaluate the abnormal distribution of crop growth conditions, including: Collect real-time image data of crop leaves in the eddy current risk area, the image data includes reflectance spectrum information of crop leaves; Preprocessing the reflectance spectrum information of crop leaves, including spectrum correction and background noise removal; The chlorophyll content distribution of crop leaves is calculated based on the spectral analysis algorithm, and the chlorophyll content distribution data corresponds to the spatial position of the crop leaves; Obtain carbon dioxide concentration data at different locations within the eddy risk area, and match the carbon dioxide concentration data with chlorophyll content distribution data according to spatial locations; Based on the matching results, the correlation between the carbon dioxide concentration data and the chlorophyll content distribution data in the eddy risk area is analyzed, the abnormal distribution of crop growth conditions is evaluated, and the abnormal growth distribution areas are marked.

8. The intelligent greenhouse environment control method according to claim 1, characterized in that: Taking the abnormal distribution of crop growth conditions as an auxiliary condition, local ventilation adjustment operations are performed in the vortex risk area, and local carbon dioxide supplementation operations are enabled when the predetermined conditions are met, including: Determine the specific spatial location of the abnormality based on the identified distribution area of ​​growth abnormality; Control the operating range and air volume parameters of local ventilation equipment in the vortex risk area according to the specific spatial location of the anomaly; During the local ventilation operation, the carbon dioxide concentration and airflow velocity data in the abnormal growth distribution area are monitored in real time to determine whether the carbon dioxide concentration reaches the preset supplementary conditions; When the carbon dioxide concentration in the abnormal growth distribution area reaches the preset supplementary conditions, the local carbon dioxide supplementary equipment is activated to release carbon dioxide to the abnormal growth distribution area through the precise gas supply port.

9. An intelligent greenhouse environment control system, used to implement an intelligent greenhouse environment control method according to any one of claims 1 to 8, characterized in that: It includes layered phenomenon identification module, vortex characteristic analysis module, diffusion complexity assessment module, eddy current risk identification module, abnormal distribution assessment module and environmental precision control module; Stratification phenomenon identification module: Model and identify the carbon dioxide concentration and airflow distribution data based on the cluster analysis algorithm to determine whether there is carbon dioxide stratification phenomenon; Vortex characteristic analysis module: When there is carbon dioxide stratification, the airflow velocity field characteristics are extracted by optical flow method and the velocity field image is analyzed in combination with convolutional neural network to identify the airflow vortex morphological characteristics in the local area of ​​the greenhouse; Diffusion complexity assessment module: When there is carbon dioxide stratification, the information entropy of concentration changes is calculated based on Shannon entropy and the diffusion path is analyzed in combination with the dynamic programming algorithm to evaluate the complexity of the diffusion path of carbon dioxide stratification areas in the greenhouse; Vortex risk identification module: Identify vortex risk areas in the greenhouse based on the airflow vortex morphology characteristics in the local area of ​​the greenhouse and the complexity of the diffusion path of the carbon dioxide stratification area in the greenhouse; Abnormal distribution assessment module: Analyze the chlorophyll distribution characteristics of crop leaves in the vortex risk area through spectral analysis algorithms, and evaluate the abnormal distribution of crop growth conditions in combination with regional carbon dioxide concentration data; Precision environmental control module: uses the abnormal distribution of crop growth conditions as an auxiliary condition to perform local ventilation adjustment operations in vortex risk areas, and enables local carbon dioxide supplementation operations when predetermined conditions are met.

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