Intelligent monitoring system for gas in plant factory

Abstract

The invention discloses an intelligent monitoring system for gas in a plant factory, and relates to the technical field of intelligent agriculture and Internet of Things, and the system comprises a gas monitoring module which is used for monitoring and obtaining gas information in a unit cultivation room of the plant factory; and the three-dimensional gas analysis mapping module is electrically connected with the gas monitoring module, and the three-dimensional gas analysis mapping module is used for constructing a three-dimensional gas distribution image based on the gas information. Through the functions of real-time accurate monitoring, three-dimensional visual analysis, scientific prediction and optimization, intelligent abnormal condition response, quantitative evaluation of influence, and disease prediction and response, the management efficiency, production efficiency and resource utilization rate of the plant factory are improved, the system ensures timeliness and accuracy of gas data, and the system is suitable for large-scale popularization and application. A manager is helped to visually master gas distribution through a visual means, the plant growth state is scientifically predicted, and economic benefits are improved.

Description

Technical Field

[0001] The present invention relates to the field of smart agriculture and Internet of Things technology, and in particular to an intelligent monitoring system for gases in a plant factory. Background Art

[0002] With the rapid development of modern agricultural technology, plant factories, as a highly integrated and controllable agricultural production method, have attracted more and more attention and application in the industry. Plant factories provide the best growth environment for plants by precisely controlling conditions such as light, temperature, humidity and gas environment, thereby achieving efficient, stable and sustainable agricultural production.

[0003] However, in the actual operation of plant factories, traditional plant factory gas monitoring methods mainly rely on single-point or multi-point gas sensors. Although these sensors can monitor the gas concentration at a specific location in real time, they are often unable to fully and dynamically reflect the gas distribution in the entire cultivation room. Since plants have different gas requirements at different growth stages, and gas distribution is affected by multiple factors such as airflow, temperature, and humidity, it is difficult to accurately evaluate the impact of the gas environment on plant growth by relying solely on single-point monitoring data. When the existing gas monitoring system detects gas anomalies, it can often only issue a simple alarm, but cannot comprehensively evaluate the specific impact on plant growth based on factors such as gas type, concentration, and diffusion rate, and cannot provide effective intervention measures to optimize the growth environment. In this regard, we propose an intelligent monitoring system for gas in plant factories. Summary of the invention

[0004] In order to solve the above technical problems, an intelligent monitoring system for gases in plant factories is provided. This technical solution solves the above problems.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] An intelligent monitoring system for gas in a plant factory, comprising:

[0007] A gas monitoring module, which is used to monitor and obtain gas information in the plant factory unit cultivation room;

[0008] A three-dimensional gas analysis and mapping module, which is electrically connected to the gas monitoring module and is used to construct a three-dimensional gas distribution image based on gas information and dynamically display the gas distribution within a time period;

[0009] A plant growth analysis and prediction module, which is electrically connected to the three-dimensional gas analysis and mapping module, and is used to analyze the effect of gas distribution in the current unit cultivation room on plant growth according to the three-dimensional gas distribution image, and predict whether the plant growth state has a pathological change;

[0010] A disease prediction module is electrically connected to the plant growth analysis and prediction module, and is used to predict the type of disease obtained after the plants in the unit cultivation room become diseased based on the plant growth prediction information.

[0011] Preferably, the gas monitoring module comprises: an oxygen monitoring unit, a carbon dioxide monitoring unit, an abnormal gas monitoring unit and a positioning unit, and the gas monitoring module calculates the number of gas monitoring modules in the incubation room according to the size of the space;

[0012] Among them, the calculation formula for the number of gas monitoring modules in the unit cultivation room is:

[0013]

[0014] Where N represents the number of gas monitoring modules, L, W, and H represent the length, width, and height of the unit cultivation room, respectively, V represents the detection range of the gas monitoring module, and R represents the redundancy coefficient obtained by the overlapping area of ​​the sensors.

[0015] Preferably, the three-dimensional gas analysis mapping module comprises:

[0016] The data preprocessing unit cleans the information data based on the gas information and aligns the data in time and space;

[0017] The three-dimensional space construction unit, based on the geometric data of the unit cultivation house, constructs several cubic grids, each of which represents a spatial unit and stores the gas concentration information in the area;

[0018] A three-dimensional gas image unit converts the gas information into a three-dimensional gas distribution image based on the processed gas information, distinguishes gases and gas concentration levels by different colors and color depths, and updates the dynamic three-dimensional gas distribution image according to the data changes within a time period;

[0019] In the three-dimensional gas image unit, green represents oxygen, yellow represents carbon dioxide, and red represents toxic gas.

[0020] Preferably, the plant growth analysis and prediction module comprises:

[0021] The plant database unit collects and organizes information data during the growth of cultivated plants to form a database according to the types of plants cultivated in the plant factory;

[0022] The growth analysis unit compares the gas dynamic change image information provided by the three-dimensional gas analysis mapping module with the gas information absorbed and released by the corresponding plants in the same cultivation environment and the ambient gas concentration in the database;

[0023] If the gas changes in the monitoring environment are within the data range in the database during the same time period, monitoring will continue;

[0024] If the gas changes in the monitored environment are outside the data range in the database or abnormal gas is detected during the same time period, it is judged to be abnormal and the abnormal situation is analyzed.

[0025] Preferably, the method for analyzing abnormal situations includes:

[0026] Analyze whether the abnormal gas is toxic or harmful based on the abnormal situation;

[0027] If the gas is toxic or harmful, its concentration information and diffusion distribution are obtained based on the dynamic change image information of the gas, and directly fed back to the disease prediction module to start the ventilation system to exhaust the gas. The degree of impact on the plants in the unit cultivation room and the probability of causing plant disease are evaluated through calculation based on the concentration and diffusion distribution.

[0028] If the gas is not toxic or harmful, its concentration information and diffusion distribution are obtained based on the dynamic change image information of the gas, and the impact degree on the plant and the probability of causing plant disease are calculated based on the concentration and diffusion distribution.

[0029] Preferably, the degree of impact on plants is calculated as follows:

[0030] I=C avg ×A affected ×S plant

[0031] In the formula, I represents the impact assessment score, C avg Represents the average concentration of gas in the affected area, A affectd It represents the ratio of the plant area affected by the gas to the total cultivated area, S plant Indicates the sensitivity coefficient of the plant to the gas.

[0032] Preferably, the probability of causing plant pathology is calculated as follows:

[0033] The calculation formula for toxic and harmful gases is:

[0034]

[0035] Where P toxicIndicates the probability of disease caused by toxic and harmful gases, C max Indicates the maximum concentration of gas in the affected area, C threshold The threshold concentration of gas causing significant pathological changes to plants, D recovery represents the time required for plants to recover from gas exposure, D exposure represents the duration of gas exposure, F stress represents the enhancement factor of other environmental stressors on the probability of plant disease;

[0036] The calculation formula for non-toxic and harmful gases is:

[0037]

[0038] Where P non-toxic represents the probability of disease caused by toxic and harmful gases and the probability of disease caused by non-toxic and harmful gases, K represents the threshold of the effect of gas concentration and exposure time on plants, A total Represents the total plant area within the unit cultivation house.

[0039] Preferably, the disease prediction module includes:

[0040] The gas and disease correlation analysis unit analyzes the situation with high probability of disease generation based on the analysis results of the plant growth analysis and prediction module, and predicts the type of disease obtained after the plant disease is detected through calculation;

[0041] The disease prediction output unit outputs the results based on the analysis results of the gas and disease correlation analysis unit.

[0042] Preferably, the method for calculating the disease type by the gas and disease association analysis unit is:

[0043] Based on the plant growth database, the continuous variable was determined to be the gas concentration and the binary variable was the disease type;

[0044] The diseases in the database are coded separately, with 1 indicating the presence of the disease and 0 indicating the absence of the disease.

[0045] Calculate the mean of the virus-present group and the mean of the disease-absent group;

[0046] Among them, the mean calculation formula of the virus presence group is:

[0047]

[0048] In the formula, μ1 represents the mean μ of disease presence group 1, x i represents the gas concentration value of the i-th observation in the disease existence group x, and n1 represents the number of observations n in the disease existence group 1;

[0049] Among them, the mean calculation formula for the disease-free group is:

[0050]

[0051] Where μ0 represents the mean μ of group 0 where the disease does not exist, y i represents the gas concentration value of the i-th observation in the disease-free group y, and n0 represents the number of observations n in the disease-free group 0;

[0052] Calculate the population standard deviation of gas concentrations;

[0053] Among them, the calculation formula for the overall standard deviation of gas concentration is:

[0054]

[0055] In the formula, σ represents the population standard deviation, n represents the total number of observations in the data set, and z i represents the i observation value z in the data set, and μ represents the overall mean of the data set;

[0056] The point-biserial correlation coefficient was used for calculation;

[0057] Among them, the point biserial correlation coefficient calculation formula is:

[0058]

[0059] Where r represents the coefficient value associated with the gas concentration value and the disease.

[0060] Preferably, the gas concentration value and the disease correlation coefficient range is between -1 and 1. A positive value indicates that when the gas concentration increases, the probability of the disease occurring increases accordingly. A negative value indicates that when the gas concentration decreases, the probability of the disease occurring decreases accordingly. 0 indicates that there is no correlation between the two.

[0061] Compared with the prior art, the beneficial effects of the present invention are: through real-time and precise monitoring, three-dimensional visual analysis, scientific prediction and optimization, intelligent response to abnormal situations, quantitative assessment of impact, disease prediction and response functions, the management efficiency, production efficiency and resource utilization of plant factories are significantly improved. The system ensures the timeliness and accuracy of gas data, and helps managers to intuitively grasp the gas distribution through visualization means, scientifically predict the growth status of plants, help effectively prevent diseases, reduce production costs, and improve economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is the structure diagram of the smart monitoring system;

[0063] Figure 2 This is the structural diagram of the gas monitoring module;

[0064] Figure 3 It is the structural diagram of the three-dimensional gas analysis mapping module;

[0065] Figure 4 Plant growth analysis and prediction module structure diagram

[0066] Figure 5 This is the structure diagram of the disease prediction module. DETAILED DESCRIPTION

[0067] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0068] Reference Figure 1 As shown, a smart monitoring system for gas in a plant factory includes:

[0069] A gas monitoring module, which is used to monitor and obtain gas information in the unit cultivation room of the plant factory, and can monitor and accurately collect key gas information in the unit cultivation room of the plant factory in real time, including but not limited to carbon dioxide concentration, oxygen content and concentration of harmful gases;

[0070] A three-dimensional gas analysis and mapping module, which is electrically connected to the gas monitoring module. The three-dimensional gas analysis and mapping module uses three-dimensional modeling and data analysis technology to convert the collected gas information into a three-dimensional gas distribution image. The image can display the gas distribution at the current moment and can also dynamically display the changing trend and distribution law of gas concentration in the past period of time;

[0071] A plant growth analysis and prediction module, which is electrically connected to the three-dimensional gas analysis and mapping module, and is used to analyze the influence of the gas distribution in the current unit cultivation room on the plant growth based on the three-dimensional gas distribution image and the gas distribution, and to predict whether the plant growth state has a pathological change, comprehensively consider the interaction of different gas concentrations, temperature, humidity and other factors, and evaluate their influence on the physiological processes of plant photosynthesis, respiration, nutrient absorption and the like;

[0072] A disease prediction module is electrically connected to the plant growth analysis and prediction module, and is used to predict the type of disease obtained after the plants in the unit cultivation room become diseased based on the plant growth prediction information.

[0073] Reference Figure 2 As shown, the gas monitoring module includes: an oxygen monitoring unit, a carbon dioxide detection unit, an abnormal gas monitoring unit and a positioning unit. The number of gas monitoring modules is changed according to the size of the space;

[0074] Oxygen is an indispensable element for plant respiration, and the stability of its concentration is crucial for the normal growth of plants. Through continuous monitoring, the oxygen monitoring unit can promptly detect and report any possible abnormal oxygen concentration, providing data support for the optimization of the growth environment;

[0075] Carbon dioxide is the main raw material for plant photosynthesis. Its concentration directly affects the growth rate and quality of plants. The carbon dioxide concentration in the cultivation room is accurately measured by the carbon dioxide detection unit, and the data is transmitted to the subsequent processing module for analysis;

[0076] In order to ensure the healthy growth of plants and avoid damage to plants by harmful gases, the abnormal gas monitoring unit is used to detect and identify potential harmful gases in the cultivation room, including ammonia, hydrogen sulfide, carbon monoxide, etc., and immediately issue an alarm when the concentration of harmful gases exceeds the standard so that timely intervention measures can be taken;

[0077] Among them, the calculation formula for the number of gas monitoring modules in the unit cultivation room is:

[0078]

[0079] Where N represents the number of gas monitoring modules, L, W, and H represent the length, width, and height of the unit cultivation room, respectively, V represents the detection range of the gas monitoring module, and R represents the redundancy coefficient obtained by the overlapping area of ​​the sensors.

[0080] Reference Figure 3 As shown, the stereo gas analysis mapping module includes:

[0081] The data preprocessing unit cleans the information data based on the gas information and aligns the data in time and space. The raw data contains noise, missing values ​​or inconsistent formats. Through the data cleaning step, the raw data is converted into a clean and tidy data set;

[0082] The three-dimensional space construction unit is based on the geometric data of the unit cultivation house to construct several cubic grids. Each unit represents a spatial unit and stores the gas concentration information in the area. The grid representation method not only facilitates data storage and retrieval, but also provides a solid foundation for subsequent three-dimensional visualization.

[0083] A three-dimensional gas image unit converts the gas information into a three-dimensional gas distribution image based on the processed gas information, distinguishes gases and gas concentration levels by different colors and color depths, and updates the dynamic three-dimensional gas distribution image according to the data changes within a time period;

[0084] In the three-dimensional gas image unit, green represents oxygen, yellow represents carbon dioxide, and red represents toxic gas.

[0085] Reference Figure 4 As shown, the plant growth analysis and prediction module includes:

[0086] The plant database unit collects and organizes information data on the growth process of the cultivated plants according to the plant species cultivated in the plant factory, including the growth cycle, required light intensity, temperature range, humidity requirements, and the absorption and release characteristics of gases at different growth stages, and creates a database;

[0087] The growth analysis unit compares the gas dynamic change image information provided by the three-dimensional gas analysis mapping module with the gas information absorbed and released by the corresponding plants in the same cultivation environment and the ambient gas concentration in the database. The comparative analysis includes the changes in gas types, the slight fluctuations in gas concentrations, and the distribution characteristics of these changes in time and space.

[0088] If the gas changes in the monitoring environment are within the data range in the database during the same time period, monitoring will continue;

[0089] If the gas changes in the monitored environment are outside the data range in the database or abnormal gas is detected during the same time period, it is judged to be abnormal and the abnormal situation is analyzed.

[0090] Methods for analyzing abnormal situations include:

[0091] Analyze whether the abnormal gas is toxic or harmful based on the abnormal situation;

[0092] If the gas is toxic or harmful, its concentration information and diffusion distribution are obtained based on the dynamic change image information of the gas, and directly fed back to the disease prediction module to start the ventilation system to exhaust the gas. The degree of impact on the plants in the unit cultivation room and the probability of causing plant disease are evaluated through calculation based on the concentration and diffusion distribution.

[0093] If the gas is not toxic or harmful, its concentration information and diffusion distribution are obtained based on the dynamic change image information of the gas. The degree of impact on the plants in the unit cultivation room and the probability of causing plant disease are evaluated through calculation based on the concentration and diffusion distribution.

[0094] The calculation method for evaluating the impact on plants is:

[0095] I=C avg ×A affected ×S plant

[0096] In the formula, I represents the impact assessment score, C avg Represents the average concentration of gas in the affected area, A affectdIt represents the ratio of the plant area affected by the gas to the total cultivated area, S plant Indicates the sensitivity coefficient of the plant to the gas.

[0097] The probability of causing plant disease is calculated as follows:

[0098] The calculation formula for toxic and harmful gases is:

[0099]

[0100] Where P toxic Indicates the probability of disease caused by toxic and harmful gases, C max Indicates the maximum concentration of gas in the affected area, C threshold The threshold concentration of gas causing significant pathological changes to plants, D recovery represents the time required for plants to recover from gas exposure, D exposure represents the duration of gas exposure, F stress represents the enhancement factor of other environmental stressors on the probability of plant disease;

[0101] The calculation formula for non-toxic and harmful gases is:

[0102]

[0103] Where P non-toxic represents the probability of disease caused by toxic and harmful gases and the probability of disease caused by non-toxic and harmful gases, K represents the threshold of the effect of gas concentration and exposure time on plants, A total Represents the total plant area within the unit cultivation house.

[0104] Reference Figure 5 As shown in Figure 1, the disease prediction module includes:

[0105] The gas and disease correlation analysis unit analyzes the situation with high probability of disease generation based on the analysis results of the plant growth analysis and prediction module, and predicts the type of disease obtained after the plant disease is detected through calculation;

[0106] The disease prediction output unit outputs the results based on the analysis results of the gas and disease correlation analysis unit.

[0107] The method for calculating the disease type by the gas and disease correlation analysis unit is:

[0108] Based on the plant growth database, the continuous variable was determined to be the gas concentration and the binary variable was the disease type;

[0109] The diseases in the database are coded separately, with 1 indicating the presence of the disease and 0 indicating the absence of the disease.

[0110] Calculate the mean of the gas concentration and the presence or absence of the disease when the values ​​are 1 and 0;

[0111] Among them, the mean calculation formula of the virus presence group is:

[0112]

[0113] In the formula, μ1 represents the mean of the disease presence group, x i represents the gas concentration value of each observation in the disease existence group, and n1 represents the number of observations in the disease existence group;

[0114] Among them, the mean calculation formula for the disease-free group is:

[0115]

[0116] In the formula, μ0 represents the mean of the disease-free group, y i represents the gas concentration value of each observation in the disease-free group, and n0 represents the number of observations in the disease-free group;

[0117] Calculate the population standard deviation of gas concentrations;

[0118] The formula for calculating the population standard deviation is:

[0119]

[0120] In the formula, σ represents the population standard deviation, n represents the total number of observations in the data set, and z i Represents each observation in the data set, and μ represents the overall mean of the data set;

[0121] The point-biserial correlation coefficient is used for calculation, and the calculation formula is:

[0122]

[0123] In the formula, r pb Represents the point-biserial correlation coefficient.

[0124] The value range of the point-binary correlation coefficient is between -1 and 1. A positive value indicates that when the gas concentration increases, the probability of disease occurrence increases accordingly. A negative value indicates that when the gas concentration decreases, the probability of disease occurrence decreases accordingly. 0 indicates that there is no correlation between the two. By calculating the correlation coefficient, the gas changes monitored by the gas monitoring system can predict the diseases obtained when the plants in the plant factory are diseased.

[0125] The use process of the present invention is:

[0126] Step 1: The gas monitoring module monitors the gas information and changes in the space in the plant cultivation room of the plant factory in real time;

[0127] Step 2: Based on the information collected by the gas monitoring module, the three-dimensional gas analysis and mapping module constructs a three-dimensional stereogram to dynamically monitor the gas distribution, concentration and change information in the space;

[0128] Step 3: Based on the information of the three-dimensional image, the plant growth analysis and prediction module compares the gas distribution, concentration and change information, identifies the abnormal points, analyzes the abnormal points, finds out the specific details of the abnormal changes, and predicts the impact of the abnormal changes on the plants and the probability of disease.

[0129] Step 4: Based on the probability of lesions, the disease prediction module is further used for analysis and judgment, and the type of disease after the lesion is predicted based on the information of gas changes in space.

[0130] To sum up, the advantages of the present invention are: improving the management efficiency, production efficiency and resource utilization of the plant factory, ensuring the timeliness and accuracy of gas data, helping managers to intuitively grasp the gas distribution, scientifically predict the growth status of plants, help effectively prevent diseases, reduce production costs, and improve economic benefits.

[0131] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent monitoring system for gas in a plant factory, characterized in that: include: A gas monitoring module, which is used to monitor and obtain gas information in the plant factory unit cultivation room; A three-dimensional gas analysis and mapping module, which is electrically connected to the gas monitoring module and is used to construct a three-dimensional gas distribution image based on gas information and dynamically display the gas distribution within a time period; A plant growth analysis and prediction module, which is electrically connected to the three-dimensional gas analysis and mapping module, and is used to analyze the effect of gas distribution in the current unit cultivation room on plant growth according to the three-dimensional gas distribution image, and predict whether the plant growth state has a pathological change; A disease prediction module is electrically connected to the plant growth analysis and prediction module, and is used to predict the type of disease obtained after the plants in the unit cultivation room become diseased based on the plant growth prediction information.

2. The intelligent monitoring system for gas in a plant factory according to claim 1, characterized in that: The gas monitoring module includes: an oxygen monitoring unit, a carbon dioxide monitoring unit, an abnormal gas monitoring unit and a positioning unit. The gas monitoring module calculates the number of gas monitoring modules in the incubation room according to the size of the space; Among them, the calculation formula for the number of gas monitoring modules in the unit cultivation room is: Where N represents the number of gas monitoring modules, L, W, and H represent the length, width, and height of the unit cultivation room, respectively, V represents the detection range of the gas monitoring module, and R represents the redundancy coefficient obtained by the overlapping area of ​​the sensors.

3. The intelligent monitoring system for gas in a plant factory according to claim 1, characterized in that: The three-dimensional gas analysis mapping module includes: The data preprocessing unit cleans the information data based on the gas information and aligns the data in time and space; The three-dimensional space construction unit, based on the geometric data of the unit cultivation house, constructs several cubic grids, each of which represents a spatial unit and stores the gas concentration information in the area; A three-dimensional gas image unit converts the gas information into a three-dimensional gas distribution image based on the processed gas information, distinguishes gases and gas concentration levels by different colors and color depths, and updates the dynamic three-dimensional gas distribution image according to the data changes within a time period; In the three-dimensional gas image unit, green represents oxygen, yellow represents carbon dioxide, and red represents toxic gas.

4. The intelligent monitoring system for gas in a plant factory according to claim 1, characterized in that: The plant growth analysis and prediction module comprises: The plant database unit collects and organizes information data during the growth of cultivated plants to form a database according to the types of plants cultivated in the plant factory; The growth analysis unit compares the gas dynamic change image information provided by the three-dimensional gas analysis mapping module with the gas information absorbed and released by the corresponding plants in the same cultivation environment and the ambient gas concentration in the database; If the gas changes in the monitoring environment are within the data range in the database during the same time period, monitoring will continue; If the gas changes in the monitored environment are outside the data range in the database or abnormal gas is detected during the same time period, it is judged to be abnormal and the abnormal situation is analyzed.

5. The intelligent monitoring system for gas in a plant factory according to claim 4, characterized in that: Methods for analyzing abnormal situations include: Analyze whether the abnormal gas is toxic or harmful based on the abnormal situation; If the gas is toxic or harmful, its concentration information and diffusion distribution are obtained based on the dynamic change image information of the gas, and directly fed back to the disease prediction module to start the ventilation system to exhaust the gas. The degree of impact on the plants in the unit cultivation room and the probability of causing plant disease are evaluated through calculation based on the concentration and diffusion distribution. If the gas is not toxic or harmful, its concentration information and diffusion distribution are obtained based on the dynamic change image information of the gas, and the impact degree on the plant and the probability of causing plant disease are calculated based on the concentration and diffusion distribution.

6. The intelligent monitoring system for gas in a plant factory according to claim 5, characterized in that: The degree of impact on plants is calculated as follows: I=C avg ×A affected ×S plant In the formula, I represents the impact assessment score, C avg Represents the average concentration of gas in the affected area, A affectd It represents the ratio of the plant area affected by the gas to the total cultivated area, S plant Indicates the sensitivity coefficient of the plant to the gas.

7. The intelligent monitoring system for gas in a plant factory according to claim 5, characterized in that: The probability of causing plant disease is calculated as follows: The calculation formula for toxic and harmful gases is: Where P toxic Indicates the probability of disease caused by toxic and harmful gases, C max Indicates the maximum concentration of gas in the affected area, C threshold The threshold concentration of gas causing significant pathological changes to plants, D recovery represents the time required for plants to recover from gas exposure, D exposure represents the duration of gas exposure, F stress represents the enhancement factor of other environmental stressors on the probability of plant disease; The calculation formula for non-toxic and harmful gases is: Where P non-toxic represents the probability of disease caused by toxic and harmful gases and the probability of disease caused by non-toxic and harmful gases, K represents the threshold of the effect of gas concentration and exposure time on plants, A total Represents the total plant area within the unit cultivation house.

8. The intelligent monitoring system for gas in a plant factory according to claim 1, characterized in that: The disease prediction module includes: The gas and disease correlation analysis unit analyzes the situation with high probability of disease generation based on the analysis results of the plant growth analysis and prediction module, and predicts the type of disease obtained after the plant disease is detected through calculation; The disease prediction output unit outputs the results based on the analysis results of the gas and disease correlation analysis unit.

9. The intelligent monitoring system for gas in a plant factory according to claim 8, characterized in that: The method for calculating the disease type by the gas and disease correlation analysis unit is: Based on the plant growth database, the continuous variable was determined to be the gas concentration and the binary variable was the disease type; The diseases in the database are coded separately, with 1 indicating the presence of the disease and 0 indicating the absence of the disease. Calculate the mean of the virus-present group and the mean of the disease-absent group; Among them, the mean calculation formula of the virus presence group is: In the formula, μ1 represents the mean μ of disease presence group 1, x i represents the gas concentration value of the i-th observation in the disease existence group x, and n1 represents the number of observations n in the disease existence group 1; Among them, the mean calculation formula for the disease-free group is: Where μ0 represents the mean μ of group 0 where the disease does not exist, y i represents the gas concentration value of the i-th observation in the disease-free group y, and n0 represents the number of observations n in the disease-free group 0; Calculate the population standard deviation of gas concentrations; Among them, the calculation formula for the overall standard deviation of gas concentration is: In the formula, σ represents the population standard deviation, n represents the total number of observations in the data set, and z i represents the i observation value z in the data set, and μ represents the overall mean of the data set; The point-biserial correlation coefficient was used for calculation; Among them, the point biserial correlation coefficient calculation formula is: Where r represents the coefficient value associated with the gas concentration value and the disease.

10. The intelligent monitoring system for gas in a plant factory according to claim 9, characterized in that: The correlation coefficient between gas concentration and disease ranges from -1 to 1. A positive value indicates that when the gas concentration increases, the probability of the disease increases accordingly. A negative value indicates that when the gas concentration decreases, the probability of the disease decreases accordingly. 0 indicates that there is no correlation between the two.

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