Intelligent control method and system for agricultural planting greenhouse

Abstract

The invention provides an intelligent control method and system for an agricultural planting greenhouse, and relates to the technical field of intelligent control of the greenhouse, and the method comprises the steps: obtaining environment data in the greenhouse at a plurality of moments in a control period through sensors disposed at sampling sites, the environment data comprises temperature data, humidity data and illumination data; obtaining types and crop data of crops planted in the greenhouse; determining a first control discrimination parameter according to the crop type and the illumination data; determining a second control discrimination parameter according to the crop variety and the temperature data; determining a third control discrimination parameter according to the environment data and the crop data; and determining whether to control the greenhouse or not according to the first control discrimination parameter, the second control discrimination parameter and the third control discrimination parameter. According to the invention, the accuracy and effectiveness of intelligent control of the greenhouse can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of greenhouse, and particularly to an intelligent control method and system for agricultural planting greenhouse. Background Art

[0002] In the related art, CN108445943A discloses an intelligent control system for greenhouse, including: an environment acquisition unit, an intelligent control unit and a terminal processing system. The intelligent control unit and the environment acquisition unit are both arranged inside the greenhouse. A plurality of sensor acquisition units and a plurality of control units are arranged in the greenhouse. The environment inside the greenhouse is monitored in real time by using a plurality of sensors. When a certain environmental monitoring value inside the greenhouse is higher or lower than the set value, the terminal processing system opens or closes the corresponding environmental control unit through manual operation or CPU instruction, so that the environment inside the greenhouse is always in an environment beneficial to the healthy growth of plants, and it is avoided that when the traditional manual monitoring and operation of the environment inside the greenhouse, the environment inside the greenhouse is not conducive to the growth of plants due to manual monitoring errors or operation mistakes.

[0003] CN108594909B discloses a temperature control system for planting greenhouse. Aiming at the problem that the greenhouse has difficulty in dissipating heat during high temperature in the prior art, the following scheme is proposed: the temperature control system for planting greenhouse includes a greenhouse body. The greenhouse body includes a greenhouse wall, and ventilation holes are opened on the greenhouse wall. A fan that can rotate when powered on is installed in the ventilation holes; the fan is connected with a horizontal shaft. A first pulley is arranged at the left end of the horizontal shaft. A water tank is arranged below the first fan. A rotatable second pulley is arranged on the side wall of the water tank. The second pulley is connected with the first pulley by a belt. A disc fixedly connected to the end of the second pulley is circumferentially provided with blades. When in use, a part of the blades is located below the liquid level of the water tank. A swingable sieve plate is arranged on the water tank. The free end of the sieve plate is eccentrically ball-jointed with the second pulley. A medicine box is arranged above the sieve plate.

[0004] Based on the above related technologies, the environment inside the greenhouse can be always in an environment beneficial to the healthy growth of plants. However, the related technologies do not consider the health status of crops, that is, the greenhouse cannot be intelligently controlled according to the light condition, temperature condition and crop health status.

[0005] The information disclosed in the background art part of the present application is only intended to deepen the understanding of the general background art of the present application, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] The present invention provides an intelligent control method and system for an agricultural planting greenhouse, which can solve the technical problem that the related art cannot perform intelligent control on the greenhouse according to the light conditions, temperature conditions, and crop health conditions.

[0007] According to a first aspect of the present invention, there is provided an intelligent control method for an agricultural planting greenhouse, including:

[0008] At multiple moments in a control period, environmental data inside the greenhouse is obtained through sensors arranged at the sampling locations, where the environmental data includes: temperature data, humidity data, and light data;

[0009] Obtain the crop types and crop data planted inside the greenhouse;

[0010] Determine a first control discrimination parameter according to the crop type and the light data;

[0011] Determine a second control discrimination parameter according to the crop type and the temperature data;

[0012] Determine a third control discrimination parameter according to the environmental data and the crop data;

[0013] Determine whether to control the greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter.

[0014] According to a second aspect of the present invention, there is provided an intelligent control system for an agricultural planting greenhouse, including:

[0015] An environmental data module for obtaining environmental data inside the greenhouse through sensors arranged at the sampling locations at multiple moments in a control period, where the environmental data includes: temperature data, humidity data, and light data;

[0016] A crop data module for obtaining the crop types and crop data planted inside the greenhouse;

[0017] A first control module for determining a first control discrimination parameter according to the crop type and the light data;

[0018] A second control module for determining a second control discrimination parameter according to the crop type and the temperature data;

[0019] A third control module for determining a third control discrimination parameter according to the environmental data and the crop data;

[0020] A determination control module for determining whether to control the greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter.

[0021] Technical effects: According to the present invention, environmental data in a greenhouse and crop data of crops can be accurately collected. Based on the crop variety and light data, it can be determined whether light control is required. Based on the crop variety and temperature data, it can be determined whether temperature control is required. Based on the environmental data and crop data, it can be determined whether the greenhouse needs to be disinfected, improving the accuracy and effectiveness of intelligent control of the greenhouse. When determining the first control discrimination parameter, the first control discrimination parameter can be determined according to a preset light threshold, a preset light duration, and light data. During the calculation process, the influence of light intensity and light duration on crop growth is fully considered, and the first control discrimination parameter is determined based on this influence, improving the comprehensiveness and accuracy of the first control discrimination parameter. When determining the second control discrimination parameter, the second control discrimination parameter is determined according to the temperature change rate, a first preset temperature threshold, a second preset temperature threshold, temperature data, and a time condition vector. The temperature requirements of crops in different time periods are fully considered, and the second control discrimination parameter is determined based on both temperature and temperature change rate, improving the comprehensiveness and accuracy of the second control discrimination parameter. When determining the third control discrimination parameter, the third control discrimination parameter can be determined according to the crop state vector and sample coordinates. During the calculation process, the health status of the crop can be judged based on the transpiration rate, chlorophyll fluorescence value, and leaf surface temperature of the crop, and it can be judged whether disinfection is required based on the health status of the plant, improving the scientificity and accuracy of the third control discrimination parameter.

[0022] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present invention. Other features and aspects of the present invention will become clearer according to the following detailed description of exemplary embodiments with reference to the accompanying drawings. Description of the Drawings

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other embodiments can be obtained based on these drawings.

[0024] Figure 1 Exemplarily shows a flowchart of an intelligent control method for an agricultural planting greenhouse according to an embodiment of the present invention;

[0025] Figure 2 Exemplarily shows a flowchart of calculating a third discrimination parameter according to an embodiment of the present invention;

[0026] Figure 3 Exemplarily shows a block diagram of an intelligent control system for an agricultural planting greenhouse according to an embodiment of the present invention. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] The technical solutions of the present invention will be described in detail below with specific embodiments. These specific embodiments may be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0029] Figure 1 The flowchart of the intelligent control method for an agricultural planting greenhouse according to an embodiment of the present invention is exemplarily shown. The method includes:

[0030] Step S1, at multiple moments in a control period, obtain the environmental data in the greenhouse through sensors arranged at the sampling locations, where the environmental data includes: temperature data, humidity data, and light data;

[0031] Step S2, obtain the types of crops planted in the greenhouse and the crop data;

[0032] Step S3, determine a first control discrimination parameter according to the types of crops and the light data;

[0033] Step S4, determine a second control discrimination parameter according to the types of crops and the temperature data;

[0034] Step S5, determine a third control discrimination parameter according to the environmental data and the crop data;

[0035] Step S6, determine whether to control the greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter.

[0036] The intelligent control method for an agricultural planting greenhouse according to an embodiment of the present invention can accurately collect the environmental data in the greenhouse and the crop data of the crops, and judge whether it is necessary to control the light according to the types of crops and the light data, judge whether it is necessary to control the temperature according to the types of crops and the temperature data, and judge whether it is necessary to disinfect the greenhouse according to the environmental data and the crop data, improving the accuracy and effectiveness of the intelligent control of the greenhouse.

[0037] According to an embodiment of the present invention, in step S1, at multiple moments in a control period, environmental data inside the greenhouse is obtained through sensors arranged at the sampling location, where the environmental data includes: temperature data, humidity data, and light data.

[0038] For example, the sampling location is not affected by the temperature and humidity of the crops planted inside the greenhouse. For instance, the sampling location is the position with the lowest crop planting density inside the greenhouse. Obtaining the environmental data at the sampling location can accurately reflect the overall environmental conditions inside the greenhouse.

[0039] According to an embodiment of the present invention, in step S2, the types of crops planted inside the greenhouse and crop data are obtained.

[0040] For example, determine the types of crops planted inside the greenhouse; monitor crop data (such as transpiration rate) through detection devices (such as sap flow sensors).

[0041] According to an embodiment of the present invention, in step S3, a first control discrimination parameter is determined based on the types of crops and the light data.

[0042] According to an embodiment of the present invention, step S3 includes:

[0043] Step S31, determine a preset light threshold and a preset light duration based on the types of crops;

[0044] Step S32, determine a first control discrimination parameter based on the preset light threshold, the preset light duration, and the light data.

[0045] For example, based on the types of crops, determine the required light intensity and light duration for the crops, that is, the preset light threshold and the preset light duration. For instance, the preset light threshold for tomatoes is 8000 lux, and the preset light duration is 13 h; based on the preset light threshold, the preset light duration, and the light data, determine whether adjustment of the light data is required to determine the first control discrimination parameter.

[0046] According to an embodiment of the present invention, in step S32, determining a first control discrimination parameter based on the preset light threshold, the preset light duration, and the light data includes: determining the first control discrimination parameter Fdp at the i-th moment of the control period according to formula (1) i ,

[0047]

[0048] where if is a conditional function, Doi T is the preset light duration, Ld i is the light data at the i-th moment of the control period, Lt Tis a preset light threshold.

[0049] According to an embodiment of the present invention, in formula (1), the inner conditional function if {Ld i ≥Lt T , 1, 0} has the following two cases. When the condition of Ld i ≥Lt T is satisfied, the light data at the i-th moment of the control period is greater than or equal to the preset light threshold, indicating that the light data at the i-th moment of the control period meets the growth requirements of the crop, and the value of the inner conditional function is 1. When the condition of Ld i ≥Lt T is not satisfied, the value of the inner conditional function is 0. represents the actual light duration that meets the growth requirements of the crop from the start moment of the control period to the i-th moment of the control period.

[0050] According to an embodiment of the present invention, in formula (1), the conditional function has the following two cases. When the condition of is satisfied, the actual light duration that meets the growth requirements of the crop from the start moment of the control period to the i-th moment of the control period is less than the preset light duration, indicating that it is necessary to continue to provide sufficient-intensity light to the crop, and the value of the conditional function is When the condition of is not satisfied, it indicates that the light requirement of the plant has been met and there is no need to continue lighting. Instead, the light intensity should be appropriately reduced, as excessive light may interfere with the biological clock of the crop and cause abnormal flowering, and the value of the conditional function is -1.

[0051] According to an embodiment of the present invention, the value of the first control discrimination parameter Fdp i is or -1. When it is necessary to continue to provide sufficient-intensity light to the crop, the value of the first control discrimination parameter Fdp i is is the relative difference between the light data at the i-th moment of the control period and the preset light threshold. The larger this ratio is, the greater the light data at the i-th moment of the control period is either too large or too small, affecting the growth of the crop and requiring adjustment of the light. When the light requirement of the plant has been met and there is no need to continue lighting, the value of the first control discrimination parameter Fdp i is -1, and the light intensity needs to be reduced.

[0052] In this way, the first control discrimination parameter can be determined according to the preset light intensity threshold, the preset light duration, and the light data. During the calculation process, the influence of light intensity and light duration on crop growth is fully considered, and the first control discrimination parameter is determined based on this influence, improving the comprehensiveness and accuracy of the first control discrimination parameter.

[0053] According to an embodiment of the present invention, in step S4, the second control discrimination parameter is determined according to the crop type and the temperature data.

[0054] According to an embodiment of the present invention, step S4 includes:

[0055] Step S41, determining a first preset temperature threshold and a second preset temperature threshold according to the crop type;

[0056] Step S42, determining a time condition vector according to the time of the current control period;

[0057] Step S43, fitting the temperature data and the time in the current control period to obtain a temperature data function of the temperature data in the current control period;

[0058] Step S44, determining a temperature data derivative function according to the temperature data function;

[0059] Step S45, determining the temperature change rates at multiple times in the current control period according to the temperature data derivative function;

[0060] Step S46, determining the second control discrimination parameter according to the temperature change rate, the first preset temperature threshold, the second preset temperature threshold, the temperature data, and the time condition vector.

[0061] For example, according to the type of the planted crop, determine the suitable temperature of the crop during the day, i.e., the first preset temperature threshold, and the suitable temperature of the crop at night, i.e., the second preset temperature threshold. For example, the suitable temperature of tomatoes during the day is 26 degrees Celsius, and the suitable temperature of tomatoes at night is 19 degrees Celsius. If the time of the current control period is in the interval of 6:00 - 19:00, then the current time belongs to the day, and the time condition vector is (1, 0). If the time of the current control period is in the interval of 19:01 - 5:59, then the current time belongs to the night, and the time condition vector is (0, 1). Fit the temperature data and the time in the current control period to obtain a temperature data function for describing the change of the temperature data with time in the current control period. Take the derivative of the temperature data function to obtain a temperature data derivative function. Substitute multiple times in the current control period into the temperature data derivative function to obtain the temperature change rates at multiple times in the current control period. According to the temperature change rates, the first preset temperature threshold, the second preset temperature threshold, the temperature data, and the time condition vector, determine whether it is necessary to control the temperature in the greenhouse and determine a second control discrimination parameter.

[0062] According to an embodiment of the present invention, in step S46, determining the second control discrimination parameter according to the temperature change rate, the first preset temperature threshold, the second preset temperature threshold, the temperature data, and the time condition vector includes: determining the second control discrimination parameter Sdp at the i-th moment of the control period according to formula (2) i ,

[0063]

[0064] where α1 and α2 are preset weights, T i is the temperature data at the i-th moment of the current control period, (TC i ) is the time condition vector at the i-th moment of the current control period, T1 T is the first preset temperature threshold, T2 T is the second preset temperature threshold, (T1 T , T2 T ) is a vector determined according to the first preset temperature threshold and the second preset temperature threshold, (T1 T , T2 T ) T is the transposed vector of (T1 T , T2 T ), t i is the i-th moment of the control period, T'1(t i ) is the temperature change rate at the i-th moment of the control period, T' T is a preset temperature change rate threshold.

[0065] According to an embodiment of the present invention, (TC i )(T1 T , T2 T ) T is the product of the time condition vector at the i-th moment of the current control period and the transposed vector of the vector determined according to the first preset temperature threshold and the second preset temperature threshold. When the i-th moment of the control period belongs to the day, (TC i ) is equal to (1, 0), and the value of (TC i )(T1 T , T2 T ) T is the first preset temperature threshold. When the i-th moment of the control period belongs to the night, (TC i ) is equal to (0, 1), and the value of (TC i )(T1 T , T2 T ) T is the second preset temperature threshold. (TC i )T1 T , T2 T ) T is the adaptive temperature threshold determined according to time, is the relative difference between the temperature data at the i-th moment of the current control period and the adaptive temperature threshold determined according to time. The larger this ratio is, the less suitable the current temperature is for crop growth.

[0066] According to an embodiment of the present invention, is the relative difference between the temperature change rate at the i-th moment of the control period and the preset temperature change rate threshold. The larger this ratio is, the greater the temperature change rate at the i-th moment of the control period is, indicating that the temperature fluctuation at the i-th moment of the control period is greater, which is likely to cause diseases and is not conducive to crop growth. Among them, the preset temperature change rate threshold can be set to 10%.

[0067] According to an embodiment of the present invention, represents determining the second control discrimination parameter at the i-th moment of the control period according to the temperature condition and the temperature change condition.

[0068] In this way, the second control discrimination parameter can be determined according to the temperature change rate, the first preset temperature threshold, the second preset temperature threshold, the temperature data, and the time condition vector, fully considering the temperature requirements of the crop in different time periods, and determining the second control discrimination parameter based on both the temperature and the temperature change rate, improving the comprehensiveness and accuracy of the second control discrimination parameter.

[0069] According to an embodiment of the present invention, in step S5, a third control discrimination parameter is determined based on the environmental data and the crop data.

[0070] According to an embodiment of the present invention, step S5 includes:

[0071] Step S51, determining the leaf surface temperature according to the crop data;

[0072] Step S52, sampling the crops in the greenhouse by planting area to obtain sample crops;

[0073] Step S53, determining the sample coordinates of the sample crops in a preset coordinate system, where the preset coordinate system is a coordinate system established based on a preset origin in the greenhouse;

[0074] Step S54, determining the sample leaf surface temperature, transpiration rate, and chlorophyll fluorescence value of the sample crops;

[0075] Step S55, determining the water vapor pressure according to the temperature data and the humidity data;

[0076] Step S56, determining the environmental dew point temperature according to the water vapor pressure;

[0077] Step S56, determining the third control discrimination parameter according to the sample leaf surface temperature, the transpiration rate, the chlorophyll fluorescence value, the sample coordinates, and the environmental dew point temperature.

[0078] For example, the leaf surface temperature of the crops is detected by an infrared thermometer; the crops are sampled according to the planting area. For example, the greenhouse is a square with a side length of 30 m, and the area where the temperature greenhouse is located is divided into 100 small squares with an area of 9 square meters, and a sample crop is taken from each small square; the centroid of the area where the greenhouse is located is used as the coordinate origin, and a coordinate system is established with the ground as the xoy plane to determine the sample coordinates of each sample crop in the preset coordinate system; the transpiration rate of the sample crops is obtained by a stem flow sensor, and the chlorophyll fluorescence value of the sample crops is obtained by a pulse modulation fluorometer; the saturated water vapor pressure is calculated using the Tetens formula through the temperature and humidity, and then the water vapor pressure is obtained; the environmental dew point temperature is calculated according to the water vapor pressure and the dew point formula; according to the sample leaf surface temperature, transpiration rate, chlorophyll fluorescence value, sample coordinates, and environmental dew point temperature, it is judged whether disinfection is required in the greenhouse, and the third control discrimination parameter is determined.

[0079] Figure 2 Exemplarily, a flowchart of calculating the third discrimination parameter according to an embodiment of the present invention is shown.

[0080] According to an embodiment of the present invention, step S56 includes:

[0081] Step S561, determine the standard transpiration rate and the standard chlorophyll fluorescence value;

[0082] Step S562, determine the crop state vector according to the standard transpiration rate, the standard chlorophyll fluorescence value, the surface temperature of the sample leaf and the environmental dew point temperature;

[0083] Step S563, determine the third control discrimination parameter according to the crop state vector and the sample coordinates.

[0084] For example, according to the type of the crop, determine the standard transpiration rate and the standard chlorophyll fluorescence value. For example, the standard transpiration rate can be set to 0.5 mmol / (m²·s), and the standard chlorophyll fluorescence value can be set to 0.75; determine the crop state vector according to the standard transpiration rate, the standard chlorophyll fluorescence value, the surface temperature of the sample leaf and the environmental dew point temperature; determine whether disinfection is required in the greenhouse according to the crop state vector and the sample coordinates, and determine the third control discrimination parameter.

[0085] According to an embodiment of the present invention, in S563, determining the third control discrimination parameter according to the crop state vector and the sample coordinates includes: determining the third control discrimination parameter Tdp of the k-th sample crop at the i-th moment of the control period according to formula (3) i ,

[0086]

[0087] wherein, if is a conditional function, Trr k,i is the transpiration rate of the k-th sample crop at the i-th moment of the control period, Trr T is the standard transpiration rate, Cfv k,i is the chlorophyll fluorescence value of the k-th sample crop at the i-th moment of the control period, Cfv T is the standard chlorophyll fluorescence value, Lt k,i is the surface temperature of the sample leaf of the k-th sample crop at the i-th moment of the control period, Edt i is the environmental dew point temperature at the i-th moment of the control period, is the crop state vector of the k-th sample crop at the i-th moment of the control period, is the transposed vector of, SC k is the sample coordinate of the k-th sample crop.

[0088] According to an embodiment of the present invention, is the ratio of the difference between the transpiration rate of the k-th sample crop at the i-th moment in the control period and the standard transpiration rate to the absolute value of the difference between the transpiration rate of the k-th sample crop at the i-th moment in the control period and the standard transpiration rate. When the transpiration rate of the k-th sample crop at the i-th moment in the control period is greater than the standard transpiration rate, this ratio is 1. When the transpiration rate of the k-th sample crop at the i-th moment in the control period is less than the standard transpiration rate, it indicates that the stomata of the k-th sample crop are closed at the i-th moment in the control period, the disease resistance decreases, and there is a possibility of disease occurrence. This ratio is -1; is the ratio of the difference between the chlorophyll fluorescence value of the k-th sample crop at the i-th moment in the control period and the standard chlorophyll fluorescence value to the absolute value of the difference between the chlorophyll fluorescence value of the k-th sample crop at the i-th moment in the control period and the standard chlorophyll fluorescence value. When the chlorophyll fluorescence value of the k-th sample crop at the i-th moment in the control period is greater than the standard chlorophyll fluorescence value, the health condition of the crop is normal, and this ratio is 1. When the chlorophyll fluorescence value of the k-th sample crop at the i-th moment in the control period is less than the standard chlorophyll fluorescence value, it indicates that the crop is in a stress state and there is a possibility of disease occurrence. This ratio is -1; is the ratio of the difference between the surface temperature of the sample leaf of the k-th sample crop at the i-th moment in the control period and the environmental dew point temperature at the i-th moment in the control period to the absolute value of the difference between the surface temperature of the sample leaf of the k-th sample crop at the i-th moment in the control period and the environmental dew point temperature at the i-th moment in the control period. When the surface temperature of the sample leaf of the k-th sample crop at the i-th moment in the control period is greater than the environmental dew point temperature at the i-th moment in the control period, this ratio is 1. When the surface temperature of the sample leaf of the k-th sample crop at the i-th moment in the control period is less than the environmental dew point temperature at the i-th moment in the control period, water vapor in the air will condense into a water film on the leaf surface. Spores of most air-borne diseases (such as powdery mildew and downy mildew) need to complete germination and invasion in the liquid water film, resulting in an increased possibility of disease occurrence. This ratio is -1.

[0089] According to an embodiment of the present invention, is the cosine similarity between the crop state vector of the k-th sample crop at the i-th moment in the control period and the vector. When and both have a value of 1, that is, when the possibility of plant disease occurrence is relatively large, the value of this cosine similarity is 1.

[0090] According to an embodiment of the present invention, in formula (3), the conditional function 1,SC k ,0} has the following two cases. When When the condition is met, it indicates that the k-th sample crop is likely to be diseased at the i-th moment of the control period, and the value of the condition function is the sample coordinate SC of the k-th sample crop. k When the condition is not met, it indicates that the k-th sample crop is less likely to be diseased at the i-th moment of the control period, and the value of the condition function is 0.

[0091] In this way, the third control discrimination parameter can be determined according to the crop state vector and the sample coordinate. During the calculation process, the health status of the crop can be judged based on the transpiration rate, chlorophyll fluorescence value, and leaf surface temperature of the crop, and whether disinfection is required can be judged according to the health status of the plant, improving the scientificity and accuracy of the third control discrimination parameter.

[0092] According to an embodiment of the present invention, in step S6, it is determined whether to control the greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter.

[0093] According to an embodiment of the present invention, step S6 includes:

[0094] Step S61, if the first control discrimination parameter is greater than the set first control discrimination parameter threshold or the first control discrimination parameter is less than 0, it is determined to control the light of the greenhouse;

[0095] Step S62, if the second control discrimination parameter is greater than the set second control discrimination parameter threshold, it is determined to control the temperature of the greenhouse;

[0096] Step S63, if the third control discrimination parameter is not equal to 0, it is determined to trigger ozone disinfection.

[0097] For example, if the first control discrimination parameter is greater than the set first control discrimination parameter threshold (such as 0.1), it indicates that the light is not suitable for crop growth. The light is adjusted to the preset light threshold. If the first control discrimination parameter is less than 0, it indicates that the light requirement of the crop has been met, and the light is gradually reduced to prevent abnormal flowering of the crop and reduce energy consumption. If the second control discrimination parameter is greater than the set second control discrimination parameter threshold (such as, 0.1α1 + 0.1α2), it indicates that the temperature fluctuates too much or the temperature is not suitable for plant growth. Check the heat preservation device in the greenhouse and gradually adjust the temperature to a temperature suitable for crop growth to reduce temperature fluctuations. Obtain the third control discrimination parameters of all sample crops. If there is a third control discrimination parameter that is not equal to 0, it indicates that there is an area in the greenhouse with a high crop incidence rate. According to the coordinates corresponding to the third control discrimination parameter, ozone disinfection is carried out on this area, and the ozone concentration is less than 0.3 ppm for 30 minutes.

[0098] The intelligent control method for an agricultural planting greenhouse according to an embodiment of the present invention can accurately collect the environmental data in the greenhouse and the crop data of the crops, and determine whether it is necessary to control the light according to the crop type and the light data, determine whether it is necessary to control the temperature according to the crop type and the temperature data, and determine whether it is necessary to disinfect the greenhouse according to the environmental data and the crop data, improving the accuracy and effectiveness of the intelligent control of the greenhouse. When determining the first control discrimination parameter, the first control discrimination parameter can be determined according to a preset light threshold, a preset light duration, and the light data. During the calculation process, the influence of light intensity and light duration on crop growth is fully considered, and the first control discrimination parameter is determined based on this influence, improving the comprehensiveness and accuracy of the first control discrimination parameter. When determining the second control discrimination parameter, the second control discrimination parameter is determined according to the temperature change rate, a first preset temperature threshold, a second preset temperature threshold, the temperature data, and the time condition vector. The temperature requirements of the crops at different time periods are fully considered, and the second control discrimination parameter is determined based on both the temperature and the temperature change rate, improving the comprehensiveness and accuracy of the second control discrimination parameter. When determining the third control discrimination parameter, the third control discrimination parameter can be determined according to the crop state vector and the sample coordinates. During the calculation process, the health status of the crops can be judged according to the transpiration rate, chlorophyll fluorescence value, and leaf surface temperature of the crops, and it is judged whether disinfection is required according to the health status of the plants, improving the scientificity and accuracy of the third control discrimination parameter.

[0099] Figure 3 Exemplarily, a block diagram of an intelligent control system for an agricultural planting greenhouse according to an embodiment of the present invention is shown. The system includes:

[0100] An environmental data module, configured to obtain the environmental data in the greenhouse through sensors arranged at the sampling locations at multiple moments during a control period, where the environmental data includes: temperature data, humidity data, and light data;

[0101] A crop data module, configured to obtain the crop types and crop data planted in the greenhouse;

[0102] A first control module, configured to determine a first control discrimination parameter according to the crop type and the light data;

[0103] A second control module, configured to determine a second control discrimination parameter according to the crop type and the temperature data;

[0104] A third control module, configured to determine a third control discrimination parameter according to the environmental data and the crop data;

[0105] A determination control module is configured to determine whether to control a greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter.

[0106] The present invention may be a method, an apparatus, a system, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for performing various aspects of the present invention.

[0107] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been shown and described in the embodiments, and the embodiments of the present invention may have any variations or modifications without departing from the principles.

Claims

1. An intelligent control method for an agricultural planting greenhouse, characterized in that, Including: At multiple moments in a control period, environmental data inside the greenhouse is obtained through sensors set at the sampling location, where the environmental data includes: temperature data, humidity data, and light data; Obtain the types of crops planted inside the greenhouse and crop data; Determine a first control discrimination parameter according to the type of crop and the light data; Determine a second control discrimination parameter according to the type of crop and the temperature data; Determine a third control discrimination parameter according to the environmental data and the crop data; Determine whether to control the greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter.

2. The intelligent control method for an agricultural planting greenhouse as claimed in claim 1, wherein Determine a first control discrimination parameter according to the type of crop and the light data, including: Determine a preset light threshold and a preset light duration according to the type of crop; Determine a first control discrimination parameter according to the preset light threshold, the preset light duration, and the light data.

3. The intelligent control method for an agricultural planting greenhouse as claimed in claim 2, wherein Determine a first control discrimination parameter according to the preset light threshold, the preset light duration, and the light data, including: According to the formula Determine the first control discrimination parameter Fdp at the i-th moment of the control period i , where if is a conditional function, Doi T is the preset illumination duration, Ld i is the illumination data at the i-th moment of the control period, Lt T is the preset illumination threshold.

4. The intelligent control method for an agricultural planting greenhouse as claimed in claim 1, wherein Determine a second control discrimination parameter according to the type of crop and the temperature data, including: Determine a first preset temperature threshold and a second preset temperature threshold according to the type of crop; Determine a time condition vector according to the moment of the current control period; Fit the temperature data and the moment in the current control period to obtain a temperature data function of the temperature data in the current control period; Determine a temperature data derivative function according to the temperature data function; Determine the temperature change rate at multiple moments in the current control period according to the temperature data derivative function; Determine a second control discrimination parameter according to the temperature change rate, the first preset temperature threshold, the second preset temperature threshold, the temperature data, and the time condition vector.

5. The intelligent control method for an agricultural planting greenhouse as claimed in claim 4, wherein, Determine a second control discrimination parameter according to the temperature change rate, the first preset temperature threshold, the second preset temperature threshold, the temperature data, and the time condition vector, including: According to the formula Determine the second control discrimination parameter Sdp at the i-th moment of the control period i , where α1 and α2 are preset weights, T i is the temperature data at the i-th moment of the current control period, (TC i ) is the time condition vector at the i-th moment of the current control period, T1 T is the first preset temperature threshold, T2 T is the second preset temperature threshold, (T1 T , T2 T ) is the vector determined according to the first preset temperature threshold and the second preset temperature threshold, (T1 T , T2 T ) T is the transposed vector of (T1 T , T2 T ), t i is the i-th moment of the control period, T’1(t i ) is the temperature change rate at the i-th moment of the control period, T’ T is the preset temperature change rate threshold.

6. The intelligent control method for an agricultural planting greenhouse as claimed in claim 1, wherein Determine a third control discrimination parameter according to the environmental data and the crop data, including: Determine the leaf surface temperature according to the crop data; Sample the crops inside the greenhouse according to the planting area to obtain sample crops; Determine the sample coordinates of the sample crops in a preset coordinate system, where the preset coordinate system is a coordinate system established based on a preset origin inside the greenhouse; Determine the sample leaf surface temperature, transpiration rate, and chlorophyll fluorescence value of the sample crops; Determine the water vapor pressure according to the temperature data and the humidity data; Determine the environmental dew point temperature according to the water vapor pressure; Determine a third control discrimination parameter according to the sample leaf surface temperature, the transpiration rate, the chlorophyll fluorescence value, the sample coordinates, and the environmental dew point temperature.

7. The intelligent control method for an agricultural planting greenhouse as claimed in claim 6, wherein Determine a third control discrimination parameter according to the sample leaf surface temperature, the transpiration rate, the chlorophyll fluorescence value, the sample coordinates, and the environmental dew point temperature, including: Determine the standard transpiration rate and the standard chlorophyll fluorescence value; Determine a crop state vector according to the standard transpiration rate, the standard chlorophyll fluorescence value, the surface temperature of the sample leaf, and the environmental dew point temperature; Determine a third control discrimination parameter according to the crop state vector and the sample coordinates.

8. The intelligent control method for an agricultural planting greenhouse as claimed in claim 7, wherein Determine a third control discrimination parameter according to the crop state vector and the sample coordinates, including: According to the formula Determine the third control discrimination parameter Tdp of the k-th sample crop at the i-th moment of the control period i , where if is a conditional function, Trr k,i is the transpiration rate of the k-th sample crop at the i-th moment of the control period, Trr T is the standard transpiration rate, Cfv k,i is the chlorophyll fluorescence value of the k-th sample crop at the i-th moment of the control period, Cfv T is the standard chlorophyll fluorescence value, Lt k,i is the surface temperature of the sample leaf of the k-th sample crop at the i-th moment of the control period, Edt i is the environmental dew point temperature at the i-th moment of the control period, is the crop state vector of the k-th sample crop at the i-th moment of the control period, is the transposed vector of, SC k is the sample coordinate of the k-th sample crop.

9. The intelligent control method for an agricultural planting greenhouse as claimed in claim 1, wherein Determine whether to control the greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter, including: If the first control discrimination parameter is greater than a set first control discrimination parameter threshold or the first control discrimination parameter is less than 0, determine to control the lighting of the greenhouse; If the second control discrimination parameter is greater than a set second control discrimination parameter threshold, determine to control the temperature of the greenhouse; If the third control discrimination parameter is not equal to 0, determine to trigger ozone disinfection.

10. An intelligent control system for an agricultural planting greenhouse, characterized in that, Including: An environmental data module, configured to obtain environmental data in the greenhouse at multiple moments in a control period through sensors arranged at the sampling location, where the environmental data includes: temperature data, humidity data, and lighting data; A crop data module, configured to obtain the crop types and crop data planted in the greenhouse; A first control module, configured to determine a first control discrimination parameter according to the crop types and the lighting data; A second control module, configured to determine a second control discrimination parameter according to the crop types and the temperature data; A third control module, configured to determine a third control discrimination parameter according to the environmental data and the crop data; A determination control module, configured to determine whether to control the greenhouse according to the first control discrimination parameter, the second control discrimination parameter, and the third control discrimination parameter.

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