CN114637351B - Greenhouse environment regulation and control method and system for facility crops - Google Patents
Greenhouse environment regulation and control method and system for facility crops Download PDFInfo
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Abstract
The invention provides a greenhouse environment regulation method and a greenhouse environment regulation system for facility crops, wherein the greenhouse environment regulation method comprises the following steps: collecting crop variety information and crop state information of a first crop in a first greenhouse; obtaining environmental information of a first crop within the first greenhouse; predicting the growth condition of the first crop by adopting an unequal weight combination prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result; obtaining information on the expected growth condition of the first crop; judging whether the first prediction result reaches the expected growth condition information or not, if not, obtaining a first regulation instruction, and obtaining a standard matching degree training set of the environmental information in the first greenhouse and the crop variety training set; training a feedforward neural network and constructing an environment control model; and obtaining adjustment environment information in a first greenhouse corresponding to the first crop, wherein the adjustment environment information comprises adjustment illumination information and adjustment temperature and humidity information.
Description
Technical Field
The invention relates to the technical field of artificial intelligence correlation, in particular to a greenhouse environment regulation and control method and system for facility crops.
Background
The facility crops are characterized in that under the condition of controllable environment, a novel agricultural production mode for efficiently producing animals and plants is realized by adopting engineering technical means and controlling the environmental conditions, for example, plants such as vegetables are planted in a facility greenhouse, a production environment favorable for the plants is created, interference to environmental elements unfavorable for the plants is eliminated, and the purpose of improving the yield and the quality of the crops is further achieved.
The control of the growth environment of crops in the production of facility crops is the most important link for guaranteeing the healthy growth of the crops, and at present, two general environmental regulation modes are provided: firstly, facility environment is adaptively adjusted according to the growth state of crops, namely, environmental elements are adjusted after adverse effects are generated on the crops, and the purpose of advanced adjustment cannot be achieved; and secondly, the growth state of the facility crops is predicted, and then the adjustment in advance is realized, but the accuracy is difficult to guarantee due to the fact that the evaluation benchmark dimension of the prediction process of the crops is single.
However, in the process of implementing the technical solution of the invention in the embodiment of the present application, it is found that the above-mentioned technology has at least the following technical problems:
in the prior art, the evaluation benchmark dimension of the crop prediction process is single, so that the technical problem that the accuracy is difficult to guarantee exists.
Disclosure of Invention
The embodiment of the application provides a greenhouse environment regulation and control method and system for facility crops, and solves the technical problem that in the prior art, due to the fact that the evaluation benchmark dimension of the prediction process of the crops is single, the accuracy is difficult to guarantee. The method comprises the steps of collecting varieties and growth state information of facility crops, collecting environment information in the facility, performing multiple kinds of prediction and reintegration by using an unequal weight combined prediction method in combination with the varieties, growth state information and the environment information of the crops to obtain a growth state prediction result of the crops, constructing an environment control model to adjust the environment information in the facility when the growth state prediction result does not reach an expected growth condition, integrating the prediction results of multiple prediction methods based on the unequal weight combined prediction method, and achieving the technical effect of improving the greenhouse environment accuracy of the facility crops.
In view of the above problems, the embodiments of the present application provide a greenhouse environment control method and system for facility crops.
In a first aspect, the present application provides a greenhouse environment regulation method for facility crops, wherein the method includes: collecting crop variety information and crop state information of a first crop in a first greenhouse; obtaining environmental information of the first crop within the first greenhouse; predicting the growth condition of the first crop by adopting an unequal weight combined prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result; obtaining information on the expected growth condition of the first crop; judging whether the first prediction result reaches the expected growth condition information or not; if the first prediction result does not reach the expected growth condition information, obtaining a first adjusting instruction; according to the first adjusting instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set is obtained; training a feedforward neural network according to the standard matching degree training set and the crop variety training set to construct an environment control model; and inputting the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model to obtain adjustment environment information in the first greenhouse corresponding to the first crop, wherein the adjustment environment information comprises adjustment illumination information and adjustment temperature and humidity information.
In another aspect, the present application provides a greenhouse environment regulation system for a facility crop, where the system includes: the first collecting unit is used for collecting crop variety information and crop state information of a first crop in the first greenhouse; a first obtaining unit, configured to obtain environmental information of the first crop in the first greenhouse; the first processing unit is used for predicting the growth condition of the first crop by adopting an unequal weight combined prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result; a second obtaining unit, configured to obtain information on expected growth conditions of the first crop; a first judging unit, configured to judge whether the first prediction result reaches the expected growth condition information; a third obtaining unit, configured to obtain a first adjustment instruction if the first prediction result does not reach the expected growth condition information; a fourth obtaining unit, configured to obtain, according to the first adjustment instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set; a first construction unit, configured to train a feed-forward neural network according to the standard matching degree training set and the crop variety training set, and construct an environment control model; and the second processing unit is used for inputting the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model to obtain adjusted environment information in the first greenhouse corresponding to the first crop, wherein the adjusted environment information comprises adjusted illumination information and adjusted temperature and humidity information.
In a third aspect, embodiments of the present application provide a greenhouse environment regulation system for facility crops, including a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of any one of the methods of the first aspect when executing the program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method of any one of the first aspect.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of collecting crop variety information and crop state information of a first crop in a first greenhouse; obtaining environmental information of the first crop within the first greenhouse; predicting the growth condition of the first crop by adopting an unequal weight combined prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result; obtaining information on the expected growth of the first crop; judging whether the first prediction result reaches the expected growth condition information or not; if the first prediction result does not reach the expected growth condition information, obtaining a first adjusting instruction; according to the first adjusting instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set is obtained; constructing an environment control model according to the standard matching degree training set and the crop variety training set training feedforward neural network; inputting the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model to obtain adjusted environment information in the first greenhouse corresponding to the first crop, wherein the adjusted environment information comprises technical schemes of adjusting illumination information and adjusting temperature and humidity information, acquiring variety and growth state information of facility crops, acquiring environment information in the facility, performing multiple kinds of prediction and integration by using an unequal weight combined prediction method in combination with the variety, growth state information and environment information of the crops to obtain a growth state prediction result of the crops, and when the growth state prediction result does not reach an expected growth condition, establishing the environment control model to adjust the environment information in the facility, so that the prediction result of a multiple prediction method can be integrated based on the unequal weight combined prediction method, and the technical effect of improving the greenhouse environment accuracy of the facility crops is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a greenhouse environment control method for facility crops according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for obtaining an amount of light entering in a greenhouse environment control method for a facility crop according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of a method for obtaining light intensity in a greenhouse environment control method for facility crops according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a greenhouse environment regulation system for facility crops according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: the system comprises a first acquisition unit 11, a first obtaining unit 12, a first processing unit 13, a second obtaining unit 14, a first judging unit 15, a third obtaining unit 16, a fourth obtaining unit 17, a first constructing unit 18, a second processing unit 19, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The embodiment of the application provides a greenhouse environment regulation and control method and system for facility crops, and solves the technical problem that in the prior art, due to the fact that the evaluation benchmark dimension of the prediction process of the crops is single, the accuracy is difficult to guarantee. The method comprises the steps of collecting varieties and growth state information of facility crops, collecting environment information in the facility, performing multiple kinds of prediction and reintegration by using an unequal weight combined prediction method in combination with the varieties, growth state information and the environment information of the crops to obtain a growth state prediction result of the crops, constructing an environment control model to adjust the environment information in the facility when the growth state prediction result does not reach an expected growth condition, integrating the prediction results of multiple prediction methods based on the unequal weight combined prediction method, and achieving the technical effect of improving the greenhouse environment accuracy of the facility crops.
Summary of the application
Facility crops refer to under the controllable condition of environment, adopt engineering technology means, control environmental condition, realize that animals and plants produce a novel agricultural production mode with high efficiency, for example plant plants such as vegetables in the facility greenhouse, create the production environment favorable to the plant, get rid of the interference to the unfavorable environmental factor of plant, and then reach the purpose that improves crop output and quality, control to crop growth environment is the most important link of guarantee crop healthy growth in facility crop's production, generally there are two kinds to the regulation mode of environment at present: firstly, facility environment is adaptively adjusted according to the growth state of crops, namely, environmental elements are adjusted after adverse effects are generated on the crops, and the purpose of advanced adjustment cannot be achieved; the other is to predict the growth state of the facility crops and further realize the adjustment in advance, but the accuracy is difficult to guarantee due to the fact that the evaluation benchmark dimension of the prediction process of the crops is single, but in the prior art, the accuracy is difficult to guarantee due to the fact that the evaluation benchmark dimension of the prediction process of the crops is single.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a greenhouse environment regulation and control method for facility crops, wherein the method comprises the following steps: collecting crop variety information and crop state information of a first crop in a first greenhouse; obtaining environmental information of the first crop within the first greenhouse; predicting the growth condition of the first crop by adopting an unequal weight combination prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result; obtaining information on the expected growth condition of the first crop; judging whether the first prediction result reaches the expected growth condition information or not; if the first prediction result does not reach the expected growth condition information, obtaining a first adjusting instruction; according to the first adjusting instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set is obtained; training a feedforward neural network according to the standard matching degree training set and the crop variety training set to construct an environment control model; inputting the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model, and obtaining adjusted environment information in the first greenhouse corresponding to the first crop, wherein the adjusted environment information comprises adjusted illumination information and adjusted temperature and humidity information.
Having described the principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present application provides a greenhouse environment regulation method for facility crops, wherein the method includes:
s100: collecting crop variety information and crop state information of a first crop in a first greenhouse;
s200: obtaining environmental information of the first crop within the first greenhouse;
specifically, the facility crops include: facility planting, such as greenhouse vegetables, fruits, and the like; facility farming, such as animal farming; culturing edible fungi such as lactobacillus. Different facility crops have different dependence on growth environment, and in conclusion, in order to guarantee the growth environment elements of the crops: information such as temperature, humidity, oxygen concentration, carbon dioxide concentration, pH, salinity, illumination, etc. is fully controllable, and a large walk-in phytotron is generally used as the growing environment of crops.
The first greenhouse refers to an area for providing a growing environment for crops, and the elements of the growing environment in the first greenhouse must be completely controllable; the environmental information in the first greenhouse refers to various items of environmental element information in the first greenhouse, and exemplarily: such as large-scale walk-in artificial climate chamber, can control the indoor temperature, illumination, carbon dioxide concentration, pH value and other environmental factor information.
The first crop refers to a crop cultivated in a first greenhouse, and can be of the type of planting crop, breeding crop, beneficial bacteria crop, and the like, exemplarily: for example, grape crops cultured in large-scale walk-in artificial climates are not influenced by natural environment, and the grapes can be cultured out of season according to requirements, so that the grape cultivation method has high freedom degree. The crop variety information refers to a type of the first crop, illustratively: variety information of grapes, cabbages, oranges and the like; the crop status information refers to real-time growth status index information of the first crop, exemplarily: such as the growth state indexes of the grapes cultivated in the large walk-in artificial climate chamber, including but not limited to the size of the grapes, the color of the grape leaves, the chlorophyll content of the grapes, the net photosynthetic rate, the contents of superoxide dismutase and malondialdehyde, the cultivation duration and other index information.
By collecting the crop variety information and the crop state information, the ideal growth states of different crop varieties in the same environment are different, and the crop variety information and the crop state information are stored in a one-to-one correspondence manner, so that the defect of rapidly evaluating the real-time crop state information according to the different crop variety information in the later step is facilitated, and timely adjustment is facilitated.
S300: predicting the growth condition of the first crop by adopting an unequal weight combination prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result;
specifically, the unequal weight combined prediction method refers to integrating multiple prediction methods to process crop variety information, crop state information and environment information to predict the growth state of a first crop, and illustratively, the regression prediction method is used to evaluate the crop state information at a certain time node in the future of the current crop variety information and the crop state information based on historical data; and predicting the growth state of the crop future preset time node by using a Kalman filtering prediction method based on the current crop variety information and environmental information.
Furthermore, the regression prediction method can obtain the trend relation of the crop state information along with the change of time by combining historical data, and further evaluate the crop state information at a certain time node in the future; the Kalman filtering prediction method can predict the crop state of the next time node only by considering the crop state of the previous time node aiming at the dynamically changed environmental information.
Furthermore, the first prediction result refers to information representing the growth state of the crop, which is obtained by integrating two prediction results with different weights, and the weight is given by way of example and not limitation: inputting the two prediction results into 6 weight distribution channels which are constructed based on the plant crop experts and have mutual information isolation, obtaining 6 weight distribution results, then respectively obtaining the mean value of the weights obtained by the two prediction results, obtaining the weights of the two prediction results, and then integrating to obtain a first prediction result.
The growth condition of the first crop is predicted through the unequal weight combination prediction method, and compared with the method depending on a single prediction means, the estimated growth state of the first crop can be represented more accurately and objectively by integrating the first prediction results of a plurality of prediction results.
S400: obtaining information on the expected growth of the first crop;
s500: judging whether the first prediction result reaches the expected growth condition information or not;
specifically, the expected growth condition information of the first crop refers to the optimal growth state of the first crop at different incubation time nodes in the first greenhouse artificially set on the basis of theory, and exemplarily: for example, the indexes of the expected growth condition information of grape crops cultured in a large-scale step-in type artificial climate chamber correspond to the growth state indexes one by one, specific values are given to the indexes of the expected growth condition information and are stored correspondingly with the nodes of the culture time of the grape crops, and the later steps can call and feed back conveniently in time.
Comparing the growth state indexes of the first crops corresponding to the first prediction results under the same time node with the expected growth condition information one by one, and storing the difference information of the predicted growth state indexes and the expected growth condition information of the first crops, wherein the storage form is exemplarily as follows: [ type of index, degree of failure ], if the index information is the malondialdehyde content of grape, the form is as follows: [ malonaldehyde content, predicted value-desired value ], and the like. And comparing the first prediction result with the expected growth condition information to obtain the difference degree information of the difference index information, and providing reference data for the subsequent environmental adjustment.
S600: if the first prediction result does not reach the expected growth condition information, obtaining a first adjusting instruction;
specifically, the first adjusting instruction refers to an instruction for generating and controlling environmental information in the first greenhouse in order to ensure that the growth state of the first crop meets the information of the expected growth condition after the first prediction result and the information of the expected growth condition are traversed and compared. By timely adjusting the environmental information in the first greenhouse, the occurrence of difference information between the first prediction result and the expected growth condition information is avoided, and the technical effect of guaranteeing the growth state of the first crop in advance is achieved.
S700: according to the first adjusting instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set is obtained;
specifically, the crop variety training set refers to a variety of crop variety information cultivated in the first greenhouse, illustratively: apple, orange, grape and other varieties cultivated in large-scale walk-in artificial climates; the standard matching degree training set refers to the difference degree information obtained by matching the optimal environmental information in the first greenhouse in the growth states of different crop varieties and comparing the optimal environmental information with the current environmental information, and the difference degree information is exemplarily shown as follows: if the controllable environmental elements of the large-scale walk-in artificial climate chamber comprise information such as temperature, illumination, pH value and humidity, the specific values of the information such as the temperature, the illumination, the pH value and the humidity of the three groups of the apples, the oranges and the grapes are corresponding to the same time node; and the same crop variety also corresponds to a plurality of groups of specific values of temperature, illumination, pH value, humidity and other information at different time nodes, and the matched environmental element information and the real-time environmental information are compared to obtain a standard matching degree training set.
When the first adjusting instruction is generated, a crop variety training set and a standard matching degree training set are immediately generated, training data are provided for the subsequent construction of an environment control model, and the output control parameters and the adaptability of the first crop and the first greenhouse are improved.
S800: training a feedforward neural network according to the standard matching degree training set and the crop variety training set to construct an environment control model;
specifically, the optimal environmental information in a plurality of groups of standard matching degree training sets is used as output identification information, and a plurality of groups of crop variety training sets and a plurality of groups of standard matching degree training sets are set as input training data sets; preferably, the multiple sets of output identification information and input training data sets corresponding to each other are divided into 9:1, the data set of 9 of which is used to construct the model, and the data set of 1 of which is used to measure the stability of the model.
The environment control model is an intelligent model constructed based on feedforward neural network training, after a neural network model framework is constructed based on the feedforward neural network, 9-proportion multiple groups of output identification information and input training data sets are called to train the neural network model framework constructed based on the feedforward neural network, when the model reaches preset accuracy, 1-proportion data set is used for evaluating the stability of model accuracy, the process is repeated to complete repeated iterative training, and further the construction of the environment control model is completed, and the constructed environment control model can be matched with the more accurate optimal environment information which is suitable for a first crop in the adjustment environment information in the first greenhouse when the standard matching degree of the crop variety and the first crop is input.
S900: inputting the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model, and obtaining adjusted environment information in the first greenhouse corresponding to the first crop, wherein the adjusted environment information comprises adjusted illumination information and adjusted temperature and humidity information.
Specifically, after the environment control model is constructed, the crop variety information of the first crop and the standard matching degree of the first crop are input into the environment control model, so that the adjustment information of the environmental elements capable of guiding the first crop to grow to the expected growth state can be obtained, and an exemplary output form is as follows: a = [ adjustment index, adjustment degree ], where a is a vector and is recorded as the adjustment environment information.
Further, adjusting the environment information is, for example, preferable: adjusting environmental indexes of grape crops: adjusting illumination information: a = [ illumination information, illumination intensity, illumination duration, etc. ]; adjusting temperature information: b = [ temperature information, temperature value ]; adjusting humidity information: c = [ humidity information, water content adjustment value ], and the like. The environmental information of the first greenhouse is adaptively adjusted by adjusting the environmental information, so that the growth state of the first crop is ensured to reach an expected growth state.
Further, based on the crop variety information, the crop state information, and the environmental information, an unequal weight combined prediction method is used to predict the growth condition of the first crop, so as to obtain a first prediction result, where step S300 includes:
s310: obtaining a second prediction result by a regression prediction method according to the crop variety information and the crop state information;
s320: obtaining a third prediction result by a Kalman filtering prediction method according to the crop variety information and the environment information;
s330: and predicting the growth condition of the first crop by an unequal combined prediction method based on the second prediction result and the third prediction result to obtain a first prediction result.
Specifically, the second prediction result refers to a prediction result obtained by using a regression prediction method to evaluate future crop state information based on crop variety information and crop state information, the evaluation mode preferably collects growth state information of different crop variety information in the first greenhouse at different cultivation time nodes through historical data, and regression evaluation can be performed on the change trend of the subsequent crop state information on the premise of current crop variety information and crop state information through multiple sets of crop variety information and growth state information stored in a simultaneous and time-sequential manner, so that the implementation mode is an example without limitation: the growth state information of different crop variety information in a plurality of groups of first greenhouses under different cultivation time nodes can be used for training an intelligent model constructed based on a neural network model, and then a model for evaluating the future crop state information based on the crop variety information and the crop state information is obtained.
The third prediction result refers to a result of evaluating the subsequent growth state of the crop by using a Kalman filtering prediction method to the crop variety information and the environmental information, the Kalman filtering prediction method is characterized in that the future state prediction result can be determined only by knowing the error of the prediction result of the previous state, and the Kalman filtering prediction method is suitable for the environmental information needing dynamic change.
After the second prediction result and the third prediction result are obtained, the second prediction result and the third prediction result are integrated by using the unequal weight combined prediction method in the step S300, so that the first prediction result is obtained, and the reliability of the prediction result is improved through a plurality of evaluation dimensions.
Further, after the inputting the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model, the step S900 further includes:
s910: constructing and training an identification network according to the standard matching degree training set and the crop variety training set and the generation data of the environment control model;
s920: obtaining the accuracy of the output data of the environment control model based on the identification network;
s930: and screening the output data of the environment control model according to the accuracy.
Specifically, in order to guarantee the accuracy of the output information of the environment control model, a training data parallel construction identification network is used for evaluating the accuracy of the output value when the subsequent environment control model construction is finished to work; the identification network is a system for evaluating the accuracy of the output information of the environment control model, which is constructed by using a standard matching degree training set and a crop variety training set as input training data sets and using generated data of the environment control model, which meet preset accuracy, as output identification data.
The screening of the output data of the environment control model can be realized through the identification network, namely, the environment adjustment information output by the environment control model does not completely meet the preset accuracy, only the environment adjustment information meeting the preset accuracy is adjusted, the environment adjustment information not meeting the preset accuracy is marked and sent to a worker for processing, and the fault tolerance of the model is improved.
Further, the method further includes S1000:
s1010: obtaining a greenhouse structure of the first greenhouse;
s1020: obtaining the light inlet quantity according to the greenhouse structure of the first greenhouse;
s1030: obtaining a light-transmitting material of the first greenhouse;
s1040: obtaining light entering intensity according to the light-transmitting material;
s1060: and obtaining first supplementary lighting information according to the light inlet amount and the light inlet intensity on the basis of the adjusted lighting information.
Specifically, the adjustment amount of the illumination information generally relates to the light entering amount and the light entering intensity, and the light transmittance of the first greenhouse, that is, the greenhouse structure needs to be changed, and a specific implementation manner thereof is, for example and without limitation:
the greenhouse structure of the first greenhouse refers to a first greenhouse internal construction feature, such as, for example: light entrance area, light entrance area, light entrance angle, greenhouse height, greenhouse light facing surface laying angle and other information; the light incoming amount refers to the light incoming amount of the first greenhouse collected within a preset time, the preferable light incoming time is used for indirect representation, and the preset time is preferably 24 hours; the light-transmitting material refers to material information of the light inlet surface of the first greenhouse, and exemplarily: information such as glass type, glass color, glass thickness, etc.; the light entering intensity refers to an acceptable light entering intensity interval obtained according to the material information of the light entering surface of the first greenhouse and a current light entering intensity, wherein the light entering intensity can be characterized by using brightness.
The first supplementary lighting information refers to adjusting the light inlet quantity and the light inlet intensity according to the adjustment lighting information so as to achieve the result after the preset adjustment quantity in the adjustment lighting information is adjusted, and further achieve the technical effect of guaranteeing the healthy growth of the first crop in the first greenhouse.
Further, as shown in fig. 2, the step S1020 of obtaining the amount of light entering based on the greenhouse structure according to the first greenhouse includes:
s1021: judging whether the greenhouse structure is a single-roof greenhouse or not;
s1022: if the greenhouse structure is a single-roof greenhouse, obtaining the elevation angle of the rear roof, the intersection angle of the front roof and the ground and the length of a rear slope of the first greenhouse;
s1023: and calculating according to the elevation angle of the rear roof, the intersection angle of the front roof and the ground and the length of the rear slope to obtain the light entering amount.
Specifically, in general, the greenhouse structure is set as a single-roof greenhouse, so that the illumination quantity can be adjusted conveniently, and aiming at the single-roof greenhouse: in order to ensure sufficient irradiation of sunlight, an inclined roof in the east-west direction is generally adopted; the elevation angle of the rear roof refers to the lifting angle of the sloping roof close to the west side relative to the ground, and the intersection angle of the front roof and the ground refers to the intersection angle of the sloping roof close to the east side relative to the ground; the back slope length refers to the roof length information of the back roof and the front roof. The light advance duration in the preset time in unit area can be determined through the elevation angle of the rear roof, the intersection angle of the front roof and the ground and the length of the rear slope, and the light advance duration is recorded as the light advance amount, so that the feedback processing of the backward step information is facilitated.
Further, as shown in fig. 3, based on the obtaining of the light entering intensity according to the light-transmitting material, the step S1040 includes:
s1041: obtaining the illumination attenuation intensity of the light-transmitting material according to the light-transmitting material;
s1042: obtaining real-time illumination intensity;
s1043: and obtaining the light incoming intensity according to the real-time illumination intensity and the illumination attenuation intensity.
Specifically, the illumination attenuation intensity of the light-transmitting material refers to the difference between the intensity of illumination transmitted when the illumination contacts the outer surface of the light-transmitting material and the inner surface of the light-transmitting material; the real-time illumination intensity refers to the real-time illumination intensity of the outer surface of the light-transmitting material in contact, and the light inlet intensity can be calculated by using the following formula: entrance light intensity = real-time illumination intensity-illumination attenuation intensity. And storing the calculation result to facilitate the feedback processing of the subsequent information.
Further, the method further includes step S1100:
s1110: performing anti-fouling grade analysis on the light-transmitting material to obtain a first anti-fouling grade;
s1120: determining a first cleaning period according to the first anti-fouling grade;
s1130: cleaning the first greenhouse according to the first cleaning period.
Specifically, the first anti-fouling rating refers to information characterizing the fouling retention capacity of the light-transmitting material, and is determined by an exemplary method: collecting the dirt remaining area of the light-transmitting material in the same environment within a preset time period, wherein the larger the area is, the higher the first dirt-resisting grade of the light-transmitting material is; the smaller the area, the lower the first anti-fouling grade of the light-transmitting material.
When the first anti-fouling grade meets the preset grade, the preset time period dirt retention area is indicated to have influence on light transmission, and the first cleaning period is required to be set for cleaning the first greenhouse, wherein the first cleaning period is less than or equal to one half of the preset time period. Thereby ensuring the performability of the environment adjustment information and the healthy growth of the first crop.
To sum up, the greenhouse environment regulation and control method and system for facility crops provided by the embodiment of the application have the following technical effects:
1. the embodiment of the application provides a greenhouse environment regulation and control method and system for facility crops, and solves the technical problem that in the prior art, due to the fact that the evaluation benchmark dimension of the prediction process of the crops is single, the accuracy is difficult to guarantee. The method comprises the steps of collecting varieties and growth state information of facility crops, collecting environment information in the facility, performing multiple kinds of prediction and reintegration by using an unequal weight combined prediction method in combination with the varieties, growth state information and the environment information of the crops to obtain a growth state prediction result of the crops, constructing an environment control model to adjust the environment information in the facility when the growth state prediction result does not reach an expected growth condition, integrating the prediction results of multiple prediction methods based on the unequal weight combined prediction method, and achieving the technical effect of improving the greenhouse environment accuracy of the facility crops.
2. The screening of the output data of the environment control model can be realized through the identification network, namely, the environment adjustment information output by the environment control model does not completely meet the preset accuracy, only the environment adjustment information meeting the preset accuracy is adjusted, the environment adjustment information not meeting the preset accuracy is marked and sent to workers for processing, and the fault tolerance rate of the model is improved.
Example two
Based on the same inventive concept as the greenhouse environment control method for facility crops in the previous embodiment, as shown in fig. 4, the present embodiment provides a greenhouse environment control system for facility crops, wherein the system comprises:
the first collecting unit 11 is used for collecting crop variety information and crop state information of a first crop in the first greenhouse;
a first obtaining unit 12, wherein the first obtaining unit 12 is configured to obtain environmental information of the first crop in the first greenhouse;
the first processing unit 13 is configured to predict a growth condition of the first crop by using an unequal weight combined prediction method according to the crop variety information, the crop state information and the environment information, and obtain a first prediction result;
a second obtaining unit 14, wherein the second obtaining unit 14 is used for obtaining the information of the expected growth condition of the first crop;
a first judging unit 15, where the first judging unit 15 is configured to judge whether the first prediction result reaches the expected growth condition information;
a third obtaining unit 16, wherein the third obtaining unit 16 is configured to obtain a first adjusting instruction if the first prediction result does not reach the expected growth condition information;
a fourth obtaining unit 17, where the fourth obtaining unit 17 is configured to obtain a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set according to the first adjustment instruction;
a first constructing unit 18, where the first constructing unit 18 is configured to construct an environment control model according to the standard matching degree training set and the crop variety training set training feed-forward neural network;
a second processing unit 19, where the second processing unit 19 is configured to input the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model, and obtain adjustment environment information in the first greenhouse corresponding to the first crop, where the adjustment environment information includes adjustment illumination information and adjustment humiture information.
Further, the method further comprises:
the second acquisition unit is used for acquiring crop variety information and crop state information of a first crop in the first greenhouse;
a fifth obtaining unit, configured to obtain environmental information of the first crop in the first greenhouse;
the third processing unit is used for predicting the growth condition of the first crop by adopting an unequal weight combined prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result;
a sixth obtaining unit, configured to obtain information about expected growth conditions of the first crop;
a second judging unit, configured to judge whether the first prediction result reaches the expected growth condition information;
a seventh obtaining unit, configured to obtain a first adjustment instruction if the first prediction result does not reach the expected growth condition information;
an eighth obtaining unit, configured to obtain, according to the first adjustment instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set;
the second construction unit is used for training the feedforward neural network according to the standard matching degree training set and the crop variety training set to construct an environment control model;
a fourth processing unit, configured to input the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model, and obtain adjustment environment information in the first greenhouse corresponding to the first crop, where the adjustment environment information includes adjustment illumination information and adjustment humiture information.
Further, the method further comprises:
a ninth obtaining unit, configured to obtain a second prediction result by a regression prediction method according to the crop variety information and the crop state information;
a tenth obtaining unit, configured to obtain a third prediction result by a kalman filter prediction method according to the crop variety information and the environment information;
and the first prediction unit is used for predicting the growth condition of the first crop by an unequal weight combined prediction method based on the second prediction result and the third prediction result to obtain a first prediction result.
Further, the method further comprises:
a third construction unit, configured to construct and train an authentication network according to the standard matching degree training set and the crop variety training set and the generation data of the environment control model;
an eleventh obtaining unit, configured to obtain an accuracy of the output data of the environment control model based on the discrimination network;
and the first screening unit is used for screening the output data of the environment control model according to the accuracy.
Further, the method further comprises:
a twelfth obtaining unit for obtaining a greenhouse structure of the first greenhouse;
a thirteenth obtaining unit for obtaining an amount of incoming light in accordance with a greenhouse structure of the first greenhouse;
a fourteenth obtaining unit, configured to obtain a light-transmitting material of the first greenhouse;
a fifteenth obtaining unit, configured to obtain light incoming intensity according to the light-transmitting material;
a sixteenth obtaining unit, configured to obtain first supplementary lighting information according to the light entering amount and the light entering intensity based on the adjusted lighting information.
Further, the method further comprises:
a third judging unit, configured to judge whether the greenhouse structure is a single-roof greenhouse;
a seventeenth obtaining unit configured to obtain a rear roof elevation angle, a front roof intersection angle with the ground, and a rear slope length of the first greenhouse if the greenhouse structure is a single-roof greenhouse;
and the eighteenth obtaining unit is used for calculating and obtaining the light incidence quantity according to the rear roof elevation angle, the intersection angle of the front roof and the ground and the back slope length.
Further, the method further comprises:
a nineteenth obtaining unit, configured to obtain, according to the light-transmitting material, an illumination attenuation intensity of the light-transmitting material;
a twentieth obtaining unit for obtaining a real-time illumination intensity;
a twenty-first obtaining unit, configured to obtain the light incoming intensity according to the illumination intensity and the illumination attenuation intensity.
Further, the method further comprises:
a twenty-second obtaining unit, configured to perform an anti-fouling grade analysis on the light-transmitting material to obtain a first anti-fouling grade;
a first determining unit for determining a first cleaning cycle according to the first anti-fouling grade;
a first execution unit for cleaning the first greenhouse according to the first cleaning cycle.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to figure 5,
based on the same inventive concept as the greenhouse environment control method for the facility crops in the previous embodiment, the embodiment of the present application further provides a greenhouse environment control system for the facility crops, comprising: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects.
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
The communication interface 303 is a system using any transceiver or the like, and is used for communicating with other devices or communication networks, such as ethernet, radio Access Network (RAN), wireless Local Area Network (WLAN), wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact-read-only-memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integrated with the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is used for executing the computer-executable instructions stored in the memory 301, so as to implement the greenhouse environment regulation method for facility crops provided by the above embodiments of the present application.
The embodiment of the present application provides a computer-readable storage medium, which is characterized in that a computer program is stored on the storage medium, and when the computer program is executed by a processor, the computer program implements the method of any one of the embodiments.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The embodiment of the application provides a greenhouse environment regulation and control method and system for facility crops, and solves the technical problem that in the prior art, due to the fact that the evaluation benchmark dimensionality of the prediction process of the crops is single, the accuracy is difficult to guarantee. The method comprises the steps of collecting varieties and growth state information of facility crops, collecting environment information in the facility, performing multiple kinds of prediction and reintegration by using an unequal weight combined prediction method in combination with the varieties, growth state information and the environment information of the crops to obtain a growth state prediction result of the crops, constructing an environment control model to adjust the environment information in the facility when the growth state prediction result does not reach an expected growth condition, integrating the prediction results of multiple prediction methods based on the unequal weight combined prediction method, and achieving the technical effect of improving the greenhouse environment accuracy of the facility crops.
Those of ordinary skill in the art will understand that: various numbers of the first, second, etc. mentioned in this application are only for convenience of description and distinction, and are not used to limit the scope of the embodiments of this application, nor to indicate a sequence order. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable system. The computer finger
The instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, where the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, including one or more integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by general purpose processors, digital signal processors, application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic systems, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing systems, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations may be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and its equivalent technology, the present application is intended to include such modifications and variations.
Claims (7)
1. A greenhouse environment regulation method for facility crops, which is characterized by comprising the following steps:
collecting crop variety information and crop state information of a first crop in a first greenhouse;
obtaining environmental information of the first crop within the first greenhouse;
predicting the growth condition of the first crop by adopting an unequal weight combined prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result;
obtaining information on the expected growth of the first crop;
judging whether the first prediction result reaches the expected growth condition information or not;
if the first prediction result does not reach the expected growth condition information, obtaining a first adjusting instruction;
according to the first adjusting instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set is obtained;
training a feedforward neural network according to the standard matching degree training set and the crop variety training set to construct an environment control model;
inputting the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model to obtain adjusted environment information in the first greenhouse corresponding to the first crop, wherein the adjusted environment information comprises adjusted illumination information and adjusted temperature and humidity information;
obtaining a greenhouse structure of the first greenhouse;
obtaining the light inlet quantity according to the greenhouse structure of the first greenhouse;
obtaining a light-transmitting material of the first greenhouse;
obtaining light entering intensity according to the light-transmitting material;
based on the adjusted illumination information, obtaining first supplementary illumination information according to the light incoming amount and the light incoming intensity;
the obtaining of the amount of incoming light according to the greenhouse structure of the first greenhouse includes:
judging whether the greenhouse structure is a single-roof greenhouse;
if the greenhouse structure is a single-roof greenhouse, obtaining the elevation angle of the rear roof, the intersection angle of the front roof and the ground and the length of a rear slope of the first greenhouse;
calculating according to the elevation angle of the rear roof, the intersection angle of the front roof and the ground and the length of the rear slope to obtain the light entering amount;
according to the printing opacity material, obtain into luminous intensity, include:
obtaining the illumination attenuation intensity of the light-transmitting material according to the light-transmitting material;
obtaining real-time illumination intensity;
and obtaining the light incoming intensity according to the illumination intensity and the illumination attenuation intensity.
2. The method of claim 1, wherein the predicting the growth of the first crop using the unequal weight combined prediction method according to the crop variety information, the crop state information, and the environment information to obtain a first prediction result comprises:
obtaining a second prediction result by a regression prediction method according to the crop variety information and the crop state information;
obtaining a third prediction result through a Kalman filtering prediction method according to the crop variety information and the environment information;
and predicting the growth condition of the first crop by adopting the unequal weight combined prediction method based on the second prediction result and the third prediction result to obtain a first prediction result.
3. The method of claim 1, wherein said entering said crop variety information for said first crop and said first crop's standard match into said environmental control model further comprises:
constructing and training an identification network according to the standard matching degree training set and the crop variety training set and the generation data of the environment control model;
obtaining the accuracy of the output data of the environment control model based on the identification network;
and screening the output data of the environment control model according to the accuracy.
4. The method of claim 1, wherein the method further comprises:
performing anti-fouling grade analysis on the light-transmitting material to obtain a first anti-fouling grade;
determining a first cleaning period according to the first anti-fouling grade;
cleaning the first greenhouse according to the first cleaning period.
5. A facility crop greenhouse environment regulation system, the system comprising:
the first acquisition unit is used for acquiring crop variety information and crop state information of a first crop in the first greenhouse;
a first obtaining unit, configured to obtain environmental information of the first crop in the first greenhouse;
the first processing unit is used for predicting the growth condition of the first crop by adopting an unequal weight combined prediction method according to the crop variety information, the crop state information and the environment information to obtain a first prediction result;
a second obtaining unit, configured to obtain information on expected growth conditions of the first crop;
a first judging unit, configured to judge whether the first prediction result reaches the expected growth condition information;
a third obtaining unit, configured to obtain a first adjustment instruction if the first prediction result does not reach the expected growth condition information;
a fourth obtaining unit, configured to obtain, according to the first adjustment instruction, a standard matching degree training set of the environmental information in the first greenhouse and a crop variety training set;
a first construction unit, configured to construct an environment control model according to the standard matching degree training set and the crop variety training set training feed-forward neural network;
a second processing unit, configured to input the crop variety information of the first crop and the standard matching degree of the first crop into the environment control model, and obtain adjusted environment information in the first greenhouse corresponding to the first crop, where the adjusted environment information includes adjusted illumination information and adjusted temperature and humidity information;
a twelfth obtaining unit for obtaining a greenhouse structure of the first greenhouse;
a thirteenth obtaining unit for obtaining an amount of incoming light in accordance with a greenhouse structure of the first greenhouse;
a fourteenth obtaining unit, configured to obtain a light-transmitting material of the first greenhouse;
a fifteenth obtaining unit, configured to obtain light incoming intensity according to the light-transmitting material;
a sixteenth obtaining unit, configured to obtain first supplementary lighting information according to the light incoming amount and the light incoming intensity based on the adjusted lighting information;
a third judging unit, configured to judge whether the greenhouse structure is a single-roof greenhouse;
a seventeenth obtaining unit configured to obtain a rear roof elevation angle, a front roof intersection angle with the ground, and a rear slope length of the first greenhouse if the greenhouse structure is a single-roof greenhouse;
an eighteenth obtaining unit, configured to obtain the light entering amount by calculation according to the rear roof elevation angle, the intersection angle of the front roof and the ground, and the rear slope length;
a nineteenth obtaining unit, configured to obtain, according to the light-transmitting material, an illumination attenuation intensity of the light-transmitting material;
a twentieth obtaining unit for obtaining a real-time illumination intensity;
a twenty-first obtaining unit, configured to obtain the light incoming intensity according to the illumination intensity and the illumination attenuation intensity.
6. A facility crop greenhouse environment regulation system, comprising: a processor coupled to a memory, the memory for storing a program, wherein the program, when executed by the processor, causes a system to perform the method of any of claims 1 to 4.
7. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1 to 4.
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| CN116452358B (en) * | 2023-03-07 | 2024-06-07 | 东莞市众冠网络科技有限公司 | Intelligent agriculture management system based on Internet of things |
| CN116310815B (en) * | 2023-03-08 | 2024-06-14 | 重庆市农业科学院 | Disease and pest early warning and self-learning method suitable for intelligent tea garden |
| CN116486253A (en) * | 2023-03-08 | 2023-07-25 | 重庆市农业科学院 | Monitoring and management system for tea garden diseases and insect pests and growth conditions |
| CN118466303A (en) * | 2024-05-20 | 2024-08-09 | 福建保丰数智农业发展有限公司 | Plant growth light environment control system based on deep learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101782698A (en) * | 2009-01-20 | 2010-07-21 | 欧睿控股股份有限公司 | Ambient light-assisted liquid crystal display and illuminating device |
| CN103065055A (en) * | 2013-01-15 | 2013-04-24 | 北京农业信息技术研究中心 | Calculating method for light distribution in sunlight greenhouse |
| CN204968730U (en) * | 2015-06-05 | 2016-01-20 | 寿光蔬菜产业控股集团有限公司 | Short adverse grade type sunlight greenhouse of high back wall earthing heat preservation |
| CN110377961A (en) * | 2019-06-25 | 2019-10-25 | 北京百度网讯科技有限公司 | Crop growth environment control method, device, computer equipment and storage medium |
| CN114016870A (en) * | 2021-10-29 | 2022-02-08 | 珠海华发人居生活研究院有限公司 | Intelligent window capable of automatically adjusting luminous flux |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106373022B (en) * | 2016-09-13 | 2021-12-24 | 重庆科技学院 | BP-GA-based greenhouse crop planting efficiency condition optimization method and system |
| WO2019049048A1 (en) * | 2017-09-08 | 2019-03-14 | 9337-4791 Quebec, Inc. | System and method for controlling a growth environment of a crop |
| CN108388291A (en) * | 2018-01-17 | 2018-08-10 | 中国农业大学 | A kind of greenhouse cluster environment regulation and control method and system |
| CN109146093B (en) * | 2018-08-08 | 2020-08-11 | 成都保源酷码科技有限公司 | Power equipment field investigation method based on learning |
| CN109856973A (en) * | 2019-02-25 | 2019-06-07 | 山东省农业机械科学研究院 | A kind of Technique for Controlling Greenhouse Environment and system based on fuzzy neural network |
| CN112166885B (en) * | 2019-07-03 | 2022-05-17 | 青岛农业大学 | Novel sunlight greenhouse with variable front roof angle |
| CN110503253A (en) * | 2019-08-12 | 2019-11-26 | 北京环丁环保大数据研究院 | A kind of planting environment self-adaptation control method and device |
| CN111524023A (en) * | 2020-04-07 | 2020-08-11 | 中国农业大学 | Greenhouse adjusting method and system |
-
2022
- 2022-03-14 CN CN202210245327.XA patent/CN114637351B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101782698A (en) * | 2009-01-20 | 2010-07-21 | 欧睿控股股份有限公司 | Ambient light-assisted liquid crystal display and illuminating device |
| CN103065055A (en) * | 2013-01-15 | 2013-04-24 | 北京农业信息技术研究中心 | Calculating method for light distribution in sunlight greenhouse |
| CN204968730U (en) * | 2015-06-05 | 2016-01-20 | 寿光蔬菜产业控股集团有限公司 | Short adverse grade type sunlight greenhouse of high back wall earthing heat preservation |
| CN110377961A (en) * | 2019-06-25 | 2019-10-25 | 北京百度网讯科技有限公司 | Crop growth environment control method, device, computer equipment and storage medium |
| CN114016870A (en) * | 2021-10-29 | 2022-02-08 | 珠海华发人居生活研究院有限公司 | Intelligent window capable of automatically adjusting luminous flux |
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