Greenhouse Environment Regulation Method and Regulation System Based on Fuzzy Control

Abstract

The present invention provides a greenhouse environment control method and control system based on fuzzy control, relating to the field of greenhouse control. The method comprises: setting multiple sampling areas in a greenhouse environment; performing random sampling in each sampling area to determine multiple sample plants; obtaining leaf surface temperatures of the sample plants; obtaining regional environmental data for each sampling area; determining the leaf surface saturated vapor pressure difference of the sample plants based on the leaf surface temperatures and the regional environmental data; determining a leaf growth state coefficient based on the leaf surface saturated vapor pressure difference; determining a regional leaf state determination parameter based on the leaf growth state coefficient; determining a regional leaf state determination result based on the regional leaf state determination parameter; determining the regional environmental determination parameter based on the regional environmental data; and determining a control scheme based on the regional leaf state determination result and the regional environmental determination parameter. According to the present invention, the accuracy and applicability of greenhouse environment control can be improved.

Description

Technical Field

[0001] The present invention relates to the field of greenhouse control, and in particular to a greenhouse environment control method and control system based on fuzzy control. Background Art

[0002] In related technologies, CN104656617A discloses a greenhouse environment control system based on the Internet of Things and cloud computing technologies, including: a greenhouse environment sensor module, an environmental data transmission module, a cloud platform server module, a greenhouse environment control module, and a greenhouse environment actuator; as well as a greenhouse environment control method based on the Internet of Things and cloud computing technologies. This solution combines the needs of the crops themselves with environmental control, rather than a single threshold control, to achieve more precise greenhouse environmental control; using automatically monitored environmental data, it avoids the complex modeling process of crop-environment interaction and directly treats the environment as an input; using crop models, it can predict the harvest and market time and yield of crops; this is of great significance for improving the intelligence and automation of greenhouse management and improving the economic benefits of greenhouse growth.

[0003] CN107728473B discloses a greenhouse environment multi-parameter coordinated control system and control method. The system includes a sensor unit, a data processing and control unit, a fill light, and an electric heating unit. The system features multi-parameter coordinated regulation. Unlike the simple superposition and combination of single target parameters, this multi-parameter regulation makes control decisions based on the crop's multi-parameter coupled response to the environment. It automatically turns the fill light and electric heating unit on and off, matching and coordinating the greenhouse's light, temperature, and humidity parameters. This system, based on crop growth and taking into account the interaction of multiple factors, can minimize control costs and achieve energy-saving and high-efficiency technical results. This relates to the field of greenhouse control systems.

[0004] Based on the above-mentioned related technologies, the intelligence and automation of greenhouse management can be improved. However, the related technologies do not take into account the importance of the saturated vapor pressure difference of the plant's leaf surface in the greenhouse environment regulation process, that is, the greenhouse environment cannot be regulated according to the saturated vapor pressure difference of the plant's leaf surface and the greenhouse environment conditions.

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

[0006] The present invention provides a greenhouse environment control method and control system based on fuzzy control, which can solve the technical problem that related technologies cannot control the greenhouse environment according to the saturated vapor pressure difference of plant leaves and the greenhouse environmental conditions.

[0007] According to a first aspect of the present invention, a greenhouse environment control method based on fuzzy control is provided, comprising:

[0008] In a greenhouse environment, multiple sampling areas were set up;

[0009] Random sampling was carried out in each sampling area to determine multiple sample plants;

[0010] Obtaining the leaf surface temperature of the sample plants at multiple moments during the regulation cycle;

[0011] At multiple moments in the control cycle, regional environmental data for each sampling area is obtained;

[0012] determining the leaf surface saturated vapor pressure difference of the sample plant according to the leaf surface temperature and the regional environmental data;

[0013] determining a leaf growth state coefficient according to the leaf surface saturated vapor pressure difference;

[0014] Determining regional leaf state determination parameters based on the leaf growth state coefficient;

[0015] Determining a regional blade state determination result according to the regional blade state determination parameter;

[0016] Determining regional environment determination parameters based on the regional environment data;

[0017] A control scheme is determined based on the regional blade status determination result and the regional environment determination parameters.

[0018] According to the present invention, determining the leaf surface saturated vapor pressure difference of the sample plant based on the leaf surface temperature and the regional environmental data includes:

[0019] determining a saturated water vapor pressure according to the blade temperature;

[0020] determining regional humidity based on the regional environmental data;

[0021] Determine the actual water vapor pressure based on the humidity of the area;

[0022] The leaf surface saturated vapor pressure difference is determined according to the saturated water vapor pressure and the actual water vapor pressure.

[0023] According to the present invention, determining the leaf growth state coefficient based on the leaf surface saturated vapor pressure difference includes:

[0024] the plant species from which the sample plants were obtained;

[0025] determining a first leaf surface saturated vapor pressure difference threshold and a second leaf surface saturated vapor pressure difference threshold according to the plant species;

[0026] A leaf growth state coefficient is determined according to the first leaf surface saturated vapor pressure difference threshold, the second leaf surface saturated vapor pressure difference threshold, and the leaf surface saturated vapor pressure difference.

[0027] According to the present invention, determining the leaf growth state coefficient based on the first leaf surface saturated vapor pressure difference threshold, the second leaf surface saturated vapor pressure difference threshold, and the leaf surface saturated vapor pressure difference includes:

[0028] determining a first difference value according to the first blade surface saturated vapor pressure difference threshold and the blade surface saturated vapor pressure difference;

[0029] determining a second difference value according to the second blade surface saturated vapor pressure difference threshold and the blade surface saturated vapor pressure difference;

[0030] determining a first discrimination result according to the first difference and the second difference;

[0031] determining an average leaf surface saturated vapor pressure difference threshold value according to the first leaf surface saturated vapor pressure difference threshold value and the second leaf surface saturated vapor pressure difference threshold value;

[0032] A leaf growth state coefficient is determined according to the average leaf surface saturated vapor pressure difference threshold, the first discrimination result, and the leaf surface saturated vapor pressure difference.

[0033] According to the present invention, determining the leaf growth state coefficient based on the average leaf surface saturated vapor pressure difference threshold, the first discrimination result, and the leaf surface saturated vapor pressure difference includes:

[0034] According to the formula

[0035]

[0036] Determine the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle , where if is a conditional function, is the first discrimination result of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, is the leaf saturated vapor pressure difference of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, is the average leaf surface saturated vapor pressure difference threshold.

[0037] According to the present invention, determining the regional leaf state determination parameter based on the leaf growth state coefficient includes:

[0038] According to the formula

[0039]

[0040] Determine the regional leaf state judgment parameter of the i-th sampling area at the j-th moment of the control cycle , where if is the conditional function, max is the maximum value function, and min is the minimum value function. is the preset weight, is the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, K is the number of sample plants in the sampling area, k≤K, and both k and K are positive integers.

[0041] According to the present invention, determining a regional blade state determination result based on the regional blade state determination parameter includes:

[0042] If the regional blade state determination parameter is greater than the set first regional blade state determination parameter threshold, the regional blade state determination result is 1;

[0043] If the regional blade state determination parameter is less than the set second regional blade state determination parameter threshold, the regional blade state determination result is -1;

[0044] If the regional blade state determination parameter is smaller than a set first regional blade state determination parameter threshold and larger than a set second regional blade state determination parameter threshold, the regional blade state determination result is 0.

[0045] According to the present invention, determining regional environment determination parameters based on the regional environment data includes:

[0046] determining a regional temperature based on the regional environmental data;

[0047] determining a preset temperature threshold and a preset humidity threshold according to the plant species;

[0048] Fitting the regional temperature and the time in the regulation cycle to obtain a regional temperature function of the regional temperature in the regulation cycle;

[0049] Determining a regional temperature derivative function based on the regional temperature function;

[0050] Determining the regional temperature change rate at multiple moments in the control cycle based on the regional temperature derivative function;

[0051] Fitting the regional humidity and the time in the control cycle to obtain a regional humidity function of the regional humidity in the control cycle;

[0052] Determining a regional humidity derivative function based on the regional humidity function;

[0053] Determining the regional humidity change rate at multiple moments in the control cycle based on the regional humidity derivative function;

[0054] A regional environment determination parameter is determined according to the regional temperature, the regional humidity, the preset temperature threshold, the preset humidity threshold, the regional temperature change rate, and the regional humidity change rate.

[0055] According to the present invention, determining the regional environment judgment parameter according to the regional temperature, the regional humidity, the preset temperature threshold, the preset humidity threshold, the regional temperature change rate, and the regional humidity change rate includes:

[0056] Determining a regional temperature identification result according to the regional temperature and the preset temperature threshold;

[0057] Determining a regional humidity recognition result based on the regional humidity and the preset humidity threshold;

[0058] Determining a temperature change identification result based on the regional temperature change rate and a preset regional temperature change rate threshold;

[0059] Determining a humidity change recognition result based on the regional humidity change rate and a preset regional humidity change rate threshold;

[0060] A regional environment determination parameter is determined according to the regional temperature identification result, the regional humidity identification result, the temperature change identification result, and the humidity change identification result.

[0061] According to a second aspect of the present invention, there is provided a greenhouse environment control system based on fuzzy control, comprising:

[0062] The sampling area module is used to set up multiple sampling areas in a greenhouse environment;

[0063] A sample plant module is used to perform random sampling in each sampling area to determine multiple sample plants;

[0064] A leaf surface temperature module is used to obtain the leaf surface temperature of the sample plant at multiple moments in the control cycle;

[0065] The environmental data module is used to obtain regional environmental data of each sampling area at multiple moments in the control cycle;

[0066] a saturated vapor pressure module, configured to determine the saturated vapor pressure difference of the leaf surface of the sample plant according to the leaf surface temperature and the regional environmental data;

[0067] A leaf state module, configured to determine a leaf growth state coefficient based on the leaf surface saturated vapor pressure difference;

[0068] A state determination module, configured to determine a regional leaf state determination parameter based on the leaf growth state coefficient;

[0069] A determination result module, configured to determine a regional blade state determination result based on the regional blade state determination parameters;

[0070] An environment determination module, configured to determine regional environment determination parameters based on the regional environment data;

[0071] The control scheme module is used to determine the control scheme according to the regional blade status determination result and the regional environment determination parameter.

[0072] Technical Effect: According to the present invention, the leaf saturated vapor pressure difference of sample plants can be accurately determined, and the growth status of the sample plants and the overall growth status of all plants in the sampling area can be evaluated based on the leaf saturated vapor pressure difference. Furthermore, the control plan for each sampling area can be determined based on the overall growth status of the plants in the sampling area and the environmental conditions of the sampling area, thereby improving the accuracy and applicability of greenhouse environmental control. When determining the leaf growth state coefficient, the leaf growth state coefficient can be determined based on the average leaf saturated vapor pressure difference threshold, the first judgment result, and the leaf saturated vapor pressure difference. During the calculation process, it can be determined whether the leaf saturated vapor pressure difference is suitable for plant growth. If the leaf saturated vapor pressure difference is not suitable for plant growth, the degree of unsuitability can be determined based on the relative difference between the leaf saturated vapor pressure difference and the average leaf saturated vapor pressure difference threshold, thereby improving the comprehensiveness and accuracy of the leaf growth state coefficient. When determining the regional leaf state judgment parameter, the regional leaf state judgment parameter can be determined based on the leaf growth state coefficient. During the calculation process, the leaf growth state coefficient of a single sample plant in the sampling area can be used to determine the most severe case of overall unsuitability for all plants in the sampling area, thereby improving the comprehensiveness and accuracy of the regional leaf state judgment parameter.

[0073] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and not limiting of the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can derive other embodiments based on these drawings without inventive efforts.

[0075] Figure 1 A schematic flow chart of a greenhouse environment control method based on fuzzy control according to an embodiment of the present invention is exemplarily shown;

[0076] Figure 2The following is a flowchart showing the calculation of the leaf growth state coefficient according to an embodiment of the present invention;

[0077] Figure 3 The flowchart of regional environment determination parameter calculation according to an embodiment of the present invention is exemplarily shown;

[0078] Figure 4 A block diagram of a greenhouse environment control system based on fuzzy control according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings 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 making creative efforts shall fall within the scope of protection of the present invention.

[0080] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0081] Figure 1 The following is a flow chart of a greenhouse environment control method based on fuzzy control according to an embodiment of the present invention, wherein the method includes:

[0082] Step S1, setting multiple sampling areas in a greenhouse environment;

[0083] Step S2, performing random sampling in each sampling area to determine a plurality of sample plants;

[0084] Step S3, obtaining the leaf surface temperature of the sample plant at multiple moments in the control cycle;

[0085] Step S4, obtaining regional environmental data of each sampling area at multiple moments in the control cycle;

[0086] Step S5, determining the leaf surface saturated vapor pressure difference of the sample plant according to the leaf surface temperature and the regional environmental data;

[0087] Step S6, determining a leaf growth state coefficient according to the leaf surface saturated vapor pressure difference;

[0088] Step S7, determining a regional leaf state determination parameter based on the leaf growth state coefficient;

[0089] Step S8, determining a regional blade state determination result according to the regional blade state determination parameter;

[0090] Step S9, determining regional environment determination parameters based on the regional environment data;

[0091] Step S10: determining a control scheme based on the regional blade status determination result and the regional environment determination parameter.

[0092] According to the greenhouse environment control method based on fuzzy control in an embodiment of the present invention, the leaf surface saturated vapor pressure difference of the sample plant can be accurately determined, and the growth condition of the sample plant and the overall growth condition of all plants in the sampling area can be evaluated based on the leaf surface saturated vapor pressure difference. Furthermore, according to the overall growth condition of the plants in the sampling area and the environmental conditions of the sampling area, the control scheme for each sampling area is determined, thereby improving the accuracy and applicability of greenhouse environment control.

[0093] According to one embodiment of the present invention, in step S1, a plurality of sampling areas are set in a greenhouse environment.

[0094] For example, the areas are divided according to the plant species grown in the greenhouse environment and the natural light intensity. The growth light, growth temperature and growth soil environment in the same sampling area are relatively small, and the plant species grown in the same sampling area are the same. For example, the greenhouse environment is divided into areas according to the sunny side and the shady side, and the area where cucumbers are grown in the greenhouse environment is divided into one sampling area.

[0095] According to one embodiment of the present invention, in step S2, random sampling is performed in each sampling area to determine a plurality of sample plants.

[0096] For example, in the process of monitoring plant conditions, a large number of plants are involved, and the efficiency of plant condition monitoring can be improved through random sampling.

[0097] According to one embodiment of the present invention, in step S3, the leaf surface temperature of the sample plant is obtained at multiple moments in the regulation cycle.

[0098] For example, during the regulation cycle, the sample plants are monitored by an infrared thermometer to obtain the leaf surface temperature of the sample plants.

[0099] According to one embodiment of the present invention, in step S4, regional environmental data of each sampling area is acquired at multiple moments in the control cycle.

[0100] For example, during the control cycle, the temperature and humidity data of each sampling area are obtained by setting a temperature sensor and a humidity sensor in each sampling area.

[0101] According to an embodiment of the present invention, in step S5, the leaf surface saturated vapor pressure difference of the sample plant is determined based on the leaf surface temperature and the regional environmental data.

[0102] According to one embodiment of the present invention, step S105 includes:

[0103] Step S51, determining the saturated water vapor pressure according to the blade temperature;

[0104] Step S52, determining regional humidity based on the regional environmental data;

[0105] Step S53, determining the actual water vapor pressure according to the regional humidity;

[0106] Step S54: determining the leaf surface saturated vapor pressure difference according to the saturated vapor pressure and the actual vapor pressure.

[0107] For example, the Magnus-Tetens formula is used to calculate the saturated water vapor pressure at leaf surface temperature; the actual water vapor pressure in each area is calculated through the relative humidity of each area, that is, the regional humidity; and the leaf surface saturated vapor pressure difference of different sample plants in each sampling area is determined based on the difference between the saturated water vapor pressure and the actual water vapor pressure.

[0108] According to one embodiment of the present invention, in step S6, the leaf growth state coefficient is determined based on the leaf surface saturated vapor pressure difference.

[0109] According to one embodiment of the present invention, step S6 includes:

[0110] Step S61, obtaining the plant species of the sample plant;

[0111] Step S62, determining a first leaf surface saturated vapor pressure difference threshold and a second leaf surface saturated vapor pressure difference threshold according to the plant species;

[0112] Step S63 , determining a leaf growth state coefficient according to the first leaf surface saturated vapor pressure difference threshold, the second leaf surface saturated vapor pressure difference threshold, and the leaf surface saturated vapor pressure difference.

[0113] For example, the plant species grown in the greenhouse are obtained, such as wheat, tomato, and cucumber. Based on the plant species, the suitable leaf surface saturated vapor pressure difference of the plant species is determined. For example, the suitable leaf surface saturated vapor pressure difference of rice is between 0.8kPa and 1.5kPa, so the first leaf surface saturated vapor pressure difference threshold corresponding to rice is 0.8kPa, and the second leaf surface saturated vapor pressure difference threshold corresponding to rice is 1.5kPa. Based on the first leaf surface saturated vapor pressure difference threshold, the second leaf surface saturated vapor pressure difference threshold, and the leaf surface saturated vapor pressure difference, the growth status of the sample plant is evaluated to determine the leaf growth status coefficient.

[0114] Figure 2 The flowchart of calculating the leaf growth state coefficient according to an embodiment of the present invention is exemplarily shown.

[0115] According to one embodiment of the present invention, step S63 includes:

[0116] Step S631, determining a first difference value according to the first blade surface saturated vapor pressure difference threshold and the blade surface saturated vapor pressure difference;

[0117] Step S632, determining a second difference value according to the second blade surface saturated vapor pressure difference threshold and the blade surface saturated vapor pressure difference;

[0118] Step S633: determining a first discrimination result according to the first difference and the second difference;

[0119] Step S634, determining an average leaf surface saturated vapor pressure difference threshold value based on the first leaf surface saturated vapor pressure difference threshold value and the second leaf surface saturated vapor pressure difference threshold value;

[0120] Step S635 , determining a leaf growth state coefficient according to the average leaf surface saturated vapor pressure difference threshold, the first discrimination result, and the leaf surface saturated vapor pressure difference.

[0121] For example, a first difference is determined based on the leaf surface saturated vapor pressure difference minus the first leaf surface saturated vapor pressure difference threshold; a second difference is determined based on the leaf surface saturated vapor pressure difference minus the second leaf surface saturated vapor pressure difference threshold; if the product of the first difference and the second difference is less than or equal to 0, the first judgment result is -1, indicating that the leaf surface saturated vapor pressure difference is within the interval of the first leaf surface saturated vapor pressure difference threshold and the second leaf surface saturated vapor pressure difference threshold, and the leaf surface saturated vapor pressure difference is suitable for plant growth; if the product of the first difference and the second difference is greater than 0, the first judgment result is 1, indicating that the leaf surface saturated vapor pressure difference is not within the interval of the first leaf surface saturated vapor pressure difference threshold and the second leaf surface saturated vapor pressure difference threshold, and the leaf surface saturated vapor pressure difference is not suitable for plant growth; the average value of the first leaf surface saturated vapor pressure difference threshold and the second leaf surface saturated vapor pressure difference threshold is calculated to determine the average leaf surface saturated vapor pressure difference threshold; the leaf growth state coefficient is calculated based on the average leaf surface saturated vapor pressure difference threshold, the first judgment result and the leaf surface saturated vapor pressure difference.

[0122] According to one embodiment of the present invention, step S635 includes: determining the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle according to formula (1): ,

[0123] (1)

[0124] Among them, if is a conditional function, is the first discrimination result of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, is the leaf saturated vapor pressure difference of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, is the average leaf surface saturated vapor pressure difference threshold.

[0125] According to one embodiment of the present invention, in formula (1), the conditional function The value of includes the following two cases, when satisfying When the condition is met, it means that the leaf saturated vapor pressure difference of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle is suitable for plant growth, and the value of the condition function is 0. When the condition is met, it means that the leaf saturated vapor pressure difference of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle is not suitable for plant growth, and the value of the conditional function is , It is the relative difference between the leaf saturated vapor pressure difference and the average leaf saturated vapor pressure difference threshold of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, indicating the unsuitability of the leaf saturated vapor pressure difference. The larger the absolute value of the ratio, the greater the difference between the leaf saturated vapor pressure difference and the average leaf saturated vapor pressure difference threshold, and the less suitable it is for plant growth and development.

[0126] In this way, the leaf growth state coefficient can be determined based on the average leaf surface saturated vapor pressure difference threshold, the first judgment result and the leaf surface saturated vapor pressure difference. During the calculation process, it can be judged whether the leaf surface saturated vapor pressure difference is suitable for plant growth. If the leaf surface saturated vapor pressure difference is not suitable for plant growth, the degree of unsuitability can be determined based on the relative difference between the leaf surface saturated vapor pressure difference and the average leaf surface saturated vapor pressure difference threshold, thereby improving the comprehensiveness and accuracy of the leaf growth state coefficient.

[0127] According to one embodiment of the present invention, in step S7, a regional leaf state determination parameter is determined based on the leaf growth state coefficient.

[0128] According to one embodiment of the present invention, step S7 includes: determining the regional leaf state determination parameter of the i-th sampling area at the j-th moment of the control cycle according to formula (2): ,

[0129] (2)

[0130] Among them, if is the conditional function, max is the maximum value function, and min is the minimum value function. is the preset weight, is the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, K is the number of sample plants in the sampling area, k≤K, and both k and K are positive integers.

[0131] According to one embodiment of the present invention, in formula (2), the conditional function The value of includes the following two cases, when it satisfies When the condition is , it means that the leaf growth state of all sample plants in the i-th sampling area is large at the j-th moment of the regulation cycle, which is not suitable for plant growth. The value of the conditional function is , is the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, The larger the absolute value of , the worse the growth condition of the kth sample plant in the i-th sampling area at the jth moment of the regulation cycle. When there are more sample plants with poor growth conditions in the i-th sampling area, the preset weight corresponding to the sample plant is The larger the value of It represents the maximum value of the sum of the products of the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle and the corresponding weight. For example, when the leaf growth state coefficient of the first sample plant in the i-th sampling area is 0.2, the leaf growth state coefficient of the second sample plant is 0.1, and the leaf growth state coefficient of the third sample plant is 0.3, the preset weights are 、 and ,but ,but The value of , which means that when there are more sample plants with larger leaf growth coefficients in the i-th sampling area, the impact of the overall unsuitable growth of plants in the sampling area will become more serious as the number of sample plants with larger leaf growth coefficients increases. The change is in the form of a combination of exponential function and linear function. The part of the change in the form of a linear function indicates that the larger the leaf growth state coefficient of a single sample plant is, the more uniformly unsuitable the overall growth of plants in the sampling area will be. The exponential function part of the change indicates that as the number of sample plants with larger leaf growth coefficients increases, the impact on the overall growth unsuitability of plants in the sampling area increases rapidly and non-uniformly. The above method of assigning weight values to find the maximum value can be used to solve the situation where the overall growth unsuitability of plants in the sampling area is the most serious when all sample plants in the i-th sampling area have larger leaf growth states at the j-th moment of the regulation cycle. When it does not meet When the condition is , it means that the leaf growth state of all sample plants in the i-th sampling area is small at the j-th moment of the regulation cycle, which is not suitable for plant growth. The value of the conditional function is , similarly, It can be used to solve the situation where the overall growth unsuitability of plants in the sampling area is the most serious when the leaf growth state of all sample plants in the i-th sampling area is small at the j-th moment of the regulation cycle.

[0132] In this way, the regional leaf status judgment parameters can be determined based on the leaf growth status coefficient. During the calculation process, the most serious overall growth unsuitability of all plants in the sampling area can be determined based on the leaf growth status coefficient of a single sample plant in the sampling area, thereby improving the comprehensiveness and accuracy of the regional leaf status judgment parameters.

[0133] According to an embodiment of the present invention, in step S8, a regional blade state determination result is determined based on the regional blade state determination parameters.

[0134] According to one embodiment of the present invention, step S8 includes:

[0135] Step S81: If the regional blade state determination parameter is greater than a set first regional blade state determination parameter threshold, the regional blade state determination result is 1;

[0136] Step S82: If the regional blade state determination parameter is less than the set second regional blade state determination parameter threshold, the regional blade state determination result is -1;

[0137] Step S83: If the regional blade state determination parameter is less than the set first regional blade state determination parameter threshold and greater than the set second regional blade state determination parameter threshold, the regional blade state determination result is 0.

[0138] For example, the set first area leaf state judgment parameter threshold can be set to 0.5e. If the regional leaf state judgment parameter is greater than the set first area leaf state judgment parameter threshold, it means that the overall growth unsuitability of the sampling area is relatively large, and the overall leaf growth state coefficient is too large, and the environment of the area needs to be adjusted, and the regional leaf state judgment result is 1; the set second area leaf state judgment parameter threshold is set to -0.5e. If the regional leaf state judgment parameter is less than the set second area leaf state judgment parameter threshold, it means that the overall growth unsuitability of the sampling area is relatively large, and the overall leaf growth state coefficient is too small, and the environment of the area needs to be adjusted, and the regional leaf state judgment result is -1; if the regional leaf state judgment parameter is less than the set first area leaf state judgment parameter threshold and greater than the set second area leaf state judgment parameter threshold, it means that the overall growth unsuitability of the sampling area is relatively small, and the environment of the area does not need to be adjusted.

[0139] According to one embodiment of the present invention, in step S9, regional environment determination parameters are determined based on the regional environment data.

[0140] According to one embodiment of the present invention, step S9 includes:

[0141] Step S91, determining the regional temperature according to the regional environmental data;

[0142] Step S92, determining a preset temperature threshold and a preset humidity threshold according to the plant species;

[0143] Step S93, fitting the regional temperature and the time in the control cycle to obtain a regional temperature function of the regional temperature in the control cycle;

[0144] Step S94, determining a regional temperature derivative function based on the regional temperature function;

[0145] Step S95, determining the regional temperature change rate at multiple moments in the control cycle according to the regional temperature derivative function;

[0146] Step S96, fitting the regional humidity and the time in the control cycle to obtain a regional humidity function of the regional humidity in the control cycle;

[0147] Step S97, determining a regional humidity derivative function based on the regional humidity function;

[0148] Step S98, determining the regional humidity change rate at multiple moments in the control cycle based on the regional humidity derivative function;

[0149] Step S99 , determining a regional environment determination parameter according to the regional temperature, the regional humidity, the preset temperature threshold, the preset humidity threshold, the regional temperature change rate, and the regional humidity change rate.

[0150] For example, due to the influence of factors such as sunlight, the temperature of each area in the greenhouse may be different. The regional temperature of each sampling area is obtained through a temperature sensor; according to the plant species planted in each area, the temperature and humidity suitable for the growth of the plant species are determined, that is, the preset temperature threshold and the preset humidity threshold. The preset temperature threshold and the preset humidity threshold are in the form of intervals. For example, the preset temperature threshold corresponding to cucumber is 25 degrees Celsius-30 degrees Celsius, and the preset humidity threshold corresponding to pepper is 70%-85%; the regional temperature and the time in the control cycle are fitted to obtain the regional temperature function used to describe the law of regional temperature changes in the control cycle; the regional temperature function is differentiated , obtain the regional temperature derivative function; substitute multiple moments in the control cycle into the regional temperature derivative function to determine the regional temperature change rate at multiple moments in the control cycle; fit the regional humidity and the moments in the control cycle to obtain the regional humidity function used to describe the law of regional humidity change in the control cycle; differentiate the regional humidity function to obtain the regional humidity derivative function; substitute multiple moments in the control cycle into the regional humidity derivative function to determine the regional humidity change rate at multiple moments in the control cycle; evaluate the regional environmental conditions according to the regional temperature, regional humidity, preset temperature threshold, preset humidity threshold, regional temperature change rate and regional humidity change rate, and determine the regional environmental judgment parameters.

[0151] Figure 3 The flowchart of regional environment determination parameter calculation according to an embodiment of the present invention is exemplarily shown.

[0152] According to one embodiment of the present invention, step S99 includes:

[0153] Step S991, determining a regional temperature identification result according to the regional temperature and the preset temperature threshold;

[0154] Step S992, determining a regional humidity recognition result based on the regional humidity and the preset humidity threshold;

[0155] Step S993, determining a temperature change identification result according to the regional temperature change rate and a preset regional temperature change rate threshold;

[0156] Step S994, determining a humidity change recognition result based on the regional humidity change rate and a preset regional humidity change rate threshold;

[0157] Step S995 , determining regional environment determination parameters according to the regional temperature identification result, the regional humidity identification result, the temperature change identification result, and the humidity change identification result.

[0158] For example, when the regional temperature is within the preset temperature threshold range, it means that the regional temperature is suitable for plant growth, and the regional temperature identification result corresponding to the sampling area is 0; when the regional temperature is on the right side of the preset temperature threshold range, it means that the regional temperature is too high, and the regional temperature identification result corresponding to the sampling area is 1; when the regional temperature is on the left side of the preset temperature threshold range, it means that the regional temperature is too low, and the regional temperature identification result corresponding to the sampling area is -1; when the regional humidity is within the preset humidity threshold range, it means that the regional humidity is suitable for plant growth, and the regional humidity identification result corresponding to the sampling area is 0; when the regional humidity is on the right side of the preset humidity threshold range, it means that the regional humidity is too high, and the regional humidity identification result corresponding to the sampling area is 1; when the regional humidity is on the left side of the preset humidity threshold range, it means that the regional humidity is too low, and the regional humidity identification result corresponding to the sampling area is -1; if the regional temperature change rate is greater than or equal to the preset regional temperature change rate threshold, it means that the temperature in the area changes too fast, which will affect plant growth, and the temperature change identification result is 1. The preset regional temperature change rate threshold can be set to 3°C / h. If the regional temperature change rate is less than A preset regional temperature change rate threshold indicates that the temperature adjustment speed of the region is normal, and the temperature change recognition result is 0. If the regional humidity change rate is greater than or equal to the preset regional humidity change rate threshold, it indicates that the humidity in the region changes too quickly, which will affect plant growth, and the humidity change recognition result is 1. The preset regional humidity change rate threshold can be set to 15%RH / h. If the regional temperature change rate is less than the preset regional humidity change rate threshold, it indicates that the humidity adjustment speed of the region is normal, and the humidity change recognition result is 0. The regional environment determination parameter can be determined based on the regional temperature identification result, the regional humidity identification result, the temperature change identification result, and the humidity change identification result. The regional environment determination parameter is in the form of a set, where the first element in the set is the regional temperature identification result, the second element in the set is the regional humidity identification result, the third element in the set is the temperature change identification result, and the fourth element in the set is the humidity change identification result. For example, when the regional temperature identification result, the regional humidity identification result, the temperature change identification result, and the humidity change identification result are 1, -1, 0, and 0, respectively, the regional environment determination parameter is {1, -1, 0, 0}.

[0159] According to an embodiment of the present invention, in step S10, a control scheme is determined based on the regional blade status determination result and the regional environment determination parameter.

[0160] For example, when the regional leaf state judgment result is 1, it indicates that the overall growth unsuitability of the sampling area is relatively large, and the overall leaf growth state coefficient is relatively large. When the overall leaf growth state coefficient in the sampling area is relatively large, the regional humidity will not be relatively high, that is, the regional humidity identification result corresponding to the area is not equal to 1. At this time, it is necessary to further determine the control plan based on the elements of the corresponding regional temperature identification result and regional humidity identification result in the regional environment judgment parameter. For example, when the regional temperature identification result is 1, cooling treatment is required. When the regional temperature identification result is -1, heating treatment is required. When the regional humidity identification result is -1, humidification treatment is required. When the temperature change identification result is equal to 1, attention should be paid to the temperature adjustment amplitude. When the humidity change identification result is equal to 1, attention should be paid to the humidity adjustment amplitude. When the regional leaf state judgment result is -1, it indicates that the overall growth unsuitability of the sampling area is relatively large, and The overall leaf growth state coefficient is relatively small. When the overall leaf growth state coefficient in the sampling area is relatively small, the regional humidity will not be low, that is, the regional humidity identification result corresponding to the area is not equal to -1. When the regional temperature identification result is 1, cooling treatment is required. When the regional temperature identification result is -1, heating treatment is required. When the regional humidity identification result is 1, dehumidification treatment is required. When the temperature change identification result is equal to 1, attention should be paid to the temperature adjustment amplitude. When the humidity change identification result is equal to 1, attention should be paid to the humidity adjustment amplitude. When the regional leaf state judgment result is 0, it means that the overall growth unsuitability of the sampling area is small. At this time, the control plan needs to be determined according to the elements corresponding to the temperature change identification result and the humidity change identification result in the regional environment judgment parameter. When the temperature change identification result is equal to 1, attention should be paid to the temperature adjustment amplitude. When the humidity change identification result is equal to 1, attention should be paid to the humidity adjustment amplitude.

[0161] According to the greenhouse environment control method based on fuzzy control of an embodiment of the present invention, the leaf surface saturated vapor pressure difference of the sample plant can be accurately determined, and the growth status of the sample plant and the overall growth status of all plants in the sampling area can be evaluated based on the leaf surface saturated vapor pressure difference. Furthermore, the control scheme for each sampling area is determined based on the overall growth status of the plants in the sampling area and the environmental conditions of the sampling area, thereby improving the accuracy and applicability of greenhouse environment control. When determining the leaf growth state coefficient, the leaf growth state coefficient can be determined based on the average leaf surface saturated vapor pressure difference threshold, the first judgment result, and the leaf surface saturated vapor pressure difference. During the calculation process, it can be determined whether the leaf surface saturated vapor pressure difference is suitable for plant growth. If the leaf surface saturated vapor pressure difference is not suitable for plant growth, the degree of unsuitability can be determined based on the relative difference between the leaf surface saturated vapor pressure difference and the average leaf surface saturated vapor pressure difference threshold, thereby improving the comprehensiveness and accuracy of the leaf growth state coefficient. When determining the regional leaf status judgment parameters, the regional leaf status judgment parameters can be determined based on the leaf growth status coefficient. During the calculation process, the leaf growth status coefficient of a single sample plant in the sampling area can be used to determine the most serious overall growth unsuitability of all plants in the sampling area, thereby improving the comprehensiveness and accuracy of the regional leaf status judgment parameters.

[0162] Figure 4 The following is a block diagram of a greenhouse environment control system based on fuzzy control according to an embodiment of the present invention, wherein the system includes:

[0163] The sampling area module is used to set up multiple sampling areas in a greenhouse environment;

[0164] A sample plant module is used to perform random sampling in each sampling area to determine multiple sample plants;

[0165] A leaf surface temperature module is used to obtain the leaf surface temperature of the sample plant at multiple moments in the control cycle;

[0166] The environmental data module is used to obtain regional environmental data of each sampling area at multiple moments in the control cycle;

[0167] a saturated vapor pressure module, configured to determine the saturated vapor pressure difference of the leaf surface of the sample plant according to the leaf surface temperature and the regional environmental data;

[0168] A leaf state module, configured to determine a leaf growth state coefficient based on the leaf surface saturated vapor pressure difference;

[0169] A state determination module, configured to determine a regional leaf state determination parameter based on the leaf growth state coefficient;

[0170] A determination result module, configured to determine a regional blade state determination result based on the regional blade state determination parameters;

[0171] An environment determination module, configured to determine regional environment determination parameters based on the regional environment data;

[0172] The control scheme module is used to determine the control scheme according to the regional blade status determination result and the regional environment determination parameter.

[0173] 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 carrying computer-readable program instructions for executing various aspects of the present invention.

[0174] Those skilled in the art will appreciate that the embodiments of the present invention described above and shown in the accompanying drawings are intended to be illustrative only and are not intended to limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Any variations or modifications may be made to the embodiments of the present invention without departing from the principles described.

Claims

1. A greenhouse environment control method based on fuzzy control, characterized in that: include: In a greenhouse environment, multiple sampling areas were set up; Random sampling was carried out in each sampling area to determine multiple sample plants; Obtaining the leaf surface temperature of the sample plants at multiple moments during the regulation cycle; At multiple moments in the control cycle, regional environmental data for each sampling area is obtained; determining the leaf surface saturated vapor pressure difference of the sample plant according to the leaf surface temperature and the regional environmental data; the plant species from which the sample plants were obtained; determining a first leaf surface saturated vapor pressure difference threshold and a second leaf surface saturated vapor pressure difference threshold according to the plant species; determining a first difference value according to the first blade surface saturated vapor pressure difference threshold and the blade surface saturated vapor pressure difference; determining a second difference value according to the second blade surface saturated vapor pressure difference threshold and the blade surface saturated vapor pressure difference; determining a first discrimination result according to the first difference and the second difference; determining an average leaf surface saturated vapor pressure difference threshold value according to the first leaf surface saturated vapor pressure difference threshold value and the second leaf surface saturated vapor pressure difference threshold value; determining a leaf growth state coefficient according to the average leaf surface saturated vapor pressure difference threshold, the first discrimination result, and the leaf surface saturated vapor pressure difference; Determining regional leaf state determination parameters based on the leaf growth state coefficient; Determining a regional blade state determination result according to the regional blade state determination parameter; Determining regional environment determination parameters based on the regional environment data; Determining a control scheme based on the blade status determination result of the region and the environmental determination parameters of the region; Determining regional leaf state determination parameters based on the leaf growth state coefficient includes: According to the formula Determine the regional leaf state judgment parameter of the i-th sampling area at the j-th moment of the control cycle , where if is the conditional function, max is the maximum value function, and min is the minimum value function. is the preset weight, is the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, K is the number of sample plants in the sampling area, k≤K, and both k and K are positive integers.

2. The greenhouse environment control method based on fuzzy control according to claim 1, characterized in that: Determining the leaf surface saturated vapor pressure difference of the sample plant according to the leaf surface temperature and the regional environmental data, comprising: determining a saturated water vapor pressure according to the blade temperature; determining regional humidity based on the regional environmental data; Determine the actual water vapor pressure based on the humidity of the area; The leaf surface saturated vapor pressure difference is determined according to the saturated water vapor pressure and the actual water vapor pressure.

3. The greenhouse environment control method based on fuzzy control according to claim 1, characterized in that: Determining a leaf growth state coefficient according to the average leaf surface saturated vapor pressure difference threshold, the first discrimination result, and the leaf surface saturated vapor pressure difference includes: According to the formula Determine the leaf growth state coefficient of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle , where if is a conditional function, is the first discrimination result of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, is the leaf saturated vapor pressure difference of the kth sample plant in the i-th sampling area at the j-th moment of the regulation cycle, is the average leaf surface saturated vapor pressure difference threshold.

4. The greenhouse environment control method based on fuzzy control according to claim 1, characterized in that: Determining a regional blade state determination result according to the regional blade state determination parameter includes: If the regional blade state determination parameter is greater than the set first regional blade state determination parameter threshold, the regional blade state determination result is 1; If the regional blade state determination parameter is less than the set second regional blade state determination parameter threshold, the regional blade state determination result is -1; If the regional blade state determination parameter is smaller than a set first regional blade state determination parameter threshold and larger than a set second regional blade state determination parameter threshold, the regional blade state determination result is 0.

5. The greenhouse environment control method based on fuzzy control according to claim 2, characterized in that: Determining regional environment determination parameters based on the regional environment data includes: determining a regional temperature based on the regional environmental data; determining a preset temperature threshold and a preset humidity threshold according to the plant species; Fitting the regional temperature and the time in the regulation cycle to obtain a regional temperature function of the regional temperature in the regulation cycle; Determining a regional temperature derivative function based on the regional temperature function; Determining the regional temperature change rate at multiple moments in the control cycle based on the regional temperature derivative function; Fitting the regional humidity and the time in the control cycle to obtain a regional humidity function of the regional humidity in the control cycle; Determining a regional humidity derivative function based on the regional humidity function; Determining the regional humidity change rate at multiple moments in the control cycle based on the regional humidity derivative function; A regional environment determination parameter is determined according to the regional temperature, the regional humidity, the preset temperature threshold, the preset humidity threshold, the regional temperature change rate, and the regional humidity change rate.

6. The greenhouse environment control method based on fuzzy control according to claim 5, characterized in that: Determining a regional environment determination parameter according to the regional temperature, the regional humidity, the preset temperature threshold, the preset humidity threshold, the regional temperature change rate, and the regional humidity change rate includes: Determining a regional temperature identification result according to the regional temperature and the preset temperature threshold; Determining a regional humidity recognition result based on the regional humidity and the preset humidity threshold; Determining a temperature change identification result based on the regional temperature change rate and a preset regional temperature change rate threshold; Determining a humidity change recognition result based on the regional humidity change rate and a preset regional humidity change rate threshold; A regional environment determination parameter is determined according to the regional temperature identification result, the regional humidity identification result, the temperature change identification result, and the humidity change identification result.

7. A greenhouse environment control system based on fuzzy control for executing the method according to any one of claims 1 to 6, characterized in that: include: The sampling area module is used to set up multiple sampling areas in a greenhouse environment; A sample plant module is used to perform random sampling in each sampling area to determine multiple sample plants; A leaf surface temperature module is used to obtain the leaf surface temperature of the sample plant at multiple moments in the control cycle; The environmental data module is used to obtain regional environmental data of each sampling area at multiple moments in the control cycle; a saturated vapor pressure module, configured to determine the saturated vapor pressure difference of the leaf surface of the sample plant according to the leaf surface temperature and the regional environmental data; A leaf state module, configured to determine a leaf growth state coefficient based on the leaf surface saturated vapor pressure difference; A state determination module, configured to determine a regional leaf state determination parameter based on the leaf growth state coefficient; A determination result module, configured to determine a regional blade state determination result based on the regional blade state determination parameters; An environment determination module, configured to determine regional environment determination parameters based on the regional environment data; The control scheme module is used to determine the control scheme according to the regional blade status determination result and the regional environment determination parameter.

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