CN114153252A

CN114153252A - Greenhouse ventilation method and system

Info

Publication number: CN114153252A
Application number: CN202111403234.7A
Authority: CN
Inventors: 魏育华
Original assignee: Individual
Current assignee: Guangzhou Huali Vocational College of Technology
Priority date: 2021-11-24
Filing date: 2021-11-24
Publication date: 2022-03-08
Anticipated expiration: 2041-11-24
Also published as: CN114153252B

Abstract

The embodiment of the specification provides a greenhouse ventilation method, which comprises the steps of obtaining environmental data in at least one greenhouse; determining whether to ventilate at least one greenhouse according to environmental data in the at least one greenhouse; based on the determination that ventilation is needed, an opening command is generated to instruct an electrically controlled valve in the ventilation device to open the ventilation port for ventilation.

Description

Greenhouse ventilation method and system

Technical Field

The specification relates to the technical field of agriculture, in particular to a greenhouse ventilation method and system.

Background

With the development of agricultural technology, greenhouse technology is gradually and widely applied to crop planting. Different environmental factors in the greenhouse can affect the growth of crops. The development of the internet of things provides a foundation for the intellectualization of the greenhouse. In order to enable the greenhouse to meet the growth requirements of crops, how to intelligently control ventilation of the greenhouse, labor is saved, and energy is saved becomes a problem which needs to be solved urgently at present.

Therefore, it is desirable to provide a greenhouse ventilation method and system, which can better intelligently control the ventilation of the greenhouse and improve the ventilation effect.

Disclosure of Invention

One embodiment of the present disclosure provides a method for ventilating a greenhouse. The method comprises the following steps: acquiring environmental data in at least one greenhouse; determining whether to ventilate at least one greenhouse according to environmental data in the at least one greenhouse; based on the determination that ventilation is needed, an opening command is generated to instruct an electrically controlled valve in the ventilation device to open the ventilation port for ventilation.

One of the embodiments of the present specification provides a greenhouse ventilation system, including: the device comprises an acquisition module, a judgment module and a control module; the acquisition module is used for acquiring environmental data in at least one greenhouse; the judgment module is used for determining whether to ventilate the at least one greenhouse according to the environmental data in the at least one greenhouse; the control module is used for generating an opening instruction based on a determination result that ventilation is needed so as to instruct an electric control valve in the ventilation device to open a ventilation port for ventilation.

One of the embodiments of the present disclosure provides a greenhouse ventilation device, which includes a processor, where the processor is configured to execute the greenhouse ventilation method.

One embodiment of the present disclosure provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the greenhouse ventilation method.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a schematic diagram of an application scenario of a greenhouse ventilation system according to some embodiments of the present disclosure;

FIG. 2 is an exemplary flow diagram of a method for ventilating a greenhouse, according to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram of a method of obtaining environmental data according to some embodiments of the present description;

FIG. 4 is an exemplary flow chart of a method of determining whether to ventilate a greenhouse according to some embodiments of the present disclosure;

fig. 5 is an exemplary block diagram of a greenhouse ventilation system according to some embodiments of the present disclosure.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

Fig. 1 is a schematic view of an application scenario of a greenhouse ventilation system according to some embodiments of the present disclosure. The ventilator 110, the detection device 120, the processor 130, the storage device 140, and the network 150 may be included in an application scenario.

The greenhouse ventilation system 100 can be used in greenhouse facilities that require ventilation. In some embodiments, the system may be used in an agricultural greenhouse. The greenhouse ventilation system 100 can implement the method and/or process disclosed in the present application to detect the environment of the agricultural greenhouse and automatically ventilate the greenhouse according to the result of the environment detection.

Ventilator 110, detection device 120, processor 130, and storage device 140 may exchange data and/or information via network 150 to implement an automatic ventilation function. The storage device 140 may store all information in performing the automatic ventilation function. In some embodiments, the detection device 120 may send the greenhouse environment detection result to the processor 130 and receive feedback information from the processor 130. Processor 130 may process greenhouse environment data, including current inspection data and historical environment data, where the historical environment data may be retrieved from a storage device via network 150. The processor 130 may process the greenhouse environment data, determine whether ventilation is required, generate an opening instruction based on a determination result that ventilation is required, and send the opening instruction to the ventilation device 110 through the network to instruct the ventilation device to perform ventilation. The above interaction relationship between the devices is only an example, and other interaction forms are possible according to actual situations.

The ventilation device 110 may be used to ventilate the agricultural greenhouse. In some embodiments, the ventilation device 110 may include an electrically controlled valve, a motor, a ventilation port, a ventilator, and the like. In response to an opening instruction sent by the processor 130, the electrically controlled valve and the motor are started to control the ventilation opening and the ventilation fan to be opened, and automatic ventilation is started.

The detection device 120 can be used to detect greenhouse environment information at different positions in at least one greenhouse. In some embodiments, the detection device may include a rail, a slide, a detection station, a sensor, and the like. The guide rails, the slide rails and the detection table are used for enabling the detection device 120 to move at different positions of different greenhouses, and the sensors are used for detecting greenhouse environment information such as temperature, humidity and carbon dioxide concentration in the greenhouses.

Processor 130 may process data and/or information obtained from other devices or system components. The processor may execute program instructions based on the data, information, and/or processing results to perform one or more of the functions described herein. In some embodiments, the processor 130 may include one or more sub-processing devices (e.g., single core processing devices or multi-core processing devices). Merely by way of example, the processor 120 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a programmable logic circuit (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like or any combination thereof.

Storage device 140 may be used to store data and/or instructions. Storage device 130 may include one or more storage components, each of which may be a separate device or part of another device. In some embodiments, storage 130 may include Random Access Memory (RAM), Read Only Memory (ROM), mass storage, removable storage, volatile read and write memory, and the like, or any combination thereof. Illustratively, mass storage may include magnetic disks, optical disks, solid state disks, and the like. In some embodiments, the storage device 130 may be implemented on a cloud platform.

The network 150 may connect the various components of the system and/or connect the system with external resource components. The network 150 enables communication between the various components and with other components outside the system to facilitate the exchange of data and/or information. In some embodiments, the network 150 may be any one or more of a wired network or a wireless network. For example, network 150 may include a cable network, a fiber optic network, a telecommunications network, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth network, a ZigBee network (ZigBee), Near Field Communication (NFC), an in-device bus, an in-device line, a cable connection, and the like, or any combination thereof. The network connection between the parts can be in one way or in multiple ways. In some embodiments, the network may be a point-to-point, shared, centralized, etc. variety of topologies or a combination of topologies. In some embodiments, the network 150 may include one or more network access points. For example, the network 150 may include wired or wireless network access points, such as base stations and/or network switching points, through which one or more components of the access point system 100 may connect to the network 150 to exchange data and/or information.

It should be noted that the description of the ventilation system and the modules thereof is only for convenience of description and should not be construed as limiting the scope of the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. In some embodiments, the obtaining module, the determining module, and the controlling module disclosed in fig. 5 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. For example, each module may share one memory module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.

Fig. 2 is an exemplary flow diagram of a method for ventilating a greenhouse according to some embodiments of the present disclosure. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, flow 200 may be performed by a processing device.

Step 210, obtaining environmental data in at least one greenhouse. In some embodiments, step 210 may be performed by acquisition module 510.

The environmental data refers to data related to the environmental conditions of the greenhouse, and includes all data information including the environment and factors which occur or may affect the environment. The environmental data may be comprised of at least one environmental indicator. The environmental indicators may include temperature, humidity, carbon dioxide concentration, and/or PM2.5 values, among others. The representation form of the environment data can be images, texts and the like, and the environment data can be acquired according to actual requirements. The environmental data may be time dependent. For example, the environmental data may be data relating to recent (e.g., recent 2 weeks, etc.) temperatures, humidity, carbon dioxide concentrations, etc. within the greenhouse.

In some embodiments, the environmental data may be obtained from the detection device 120 by the processor 130. For example, the greenhouse environment data is detected by the detection device 120, and the processor 130 may receive the environment data detected by the detection device 120.

In some embodiments, the environmental data detected by the detection apparatus 130 may be uploaded to the storage device 140 for saving.

In some embodiments, the environmental data may also be obtained through user input. For example, the user may upload environmental data to the ventilation system.

In some embodiments, the environmental data may be read by the processor 130 from the storage device 140 in some embodiments. The storage device 140 may be a storage device of the greenhouse ventilation system, or may be an external storage device that does not belong to the greenhouse ventilation system, for example, a hard disk, an optical disk, or the like. In some embodiments, the environmental data may be read through an interface including, but not limited to, a program interface, a data interface, a transmission interface, and the like. For example, the greenhouse ventilation system may automatically extract environmental data from the interface when operating. As another example, the greenhouse ventilation system may be invoked by an external device or system. The environmental data may also be acquired in any manner known to those skilled in the art.

In some embodiments, the greenhouse ventilation system may preprocess the environmental data. In some embodiments, the pre-processing may include, but is not limited to, one or a combination of data cleansing, data integration, and data transformation. For example, data cleaning, cleaning data by filling in missing values, smoothing out noisy data, identifying or deleting outliers, and resolving inconsistencies. In some embodiments, the pre-processing may also include any other reasonable processing steps, as may be determined by the circumstances.

In some embodiments, the environmental data within the at least one greenhouse may be acquired by a detection device. For a detailed description of the acquisition of the environmental data in the at least one greenhouse by the detection device, reference may be made to fig. 3 and the description thereof.

The sensing device 120 refers to a device that directly or indirectly measures sensed environmental data. The detection device 120 includes an environmental sensor 340. The environmental sensor may be used to detect various environmental indicators in the environmental data. For example, the environmental sensor may include a temperature sensor, a humidity sensor, a carbon dioxide concentration sensor, and the like. For example, the environmental sensors may include a soil temperature sensor, an air temperature and humidity sensor, an evaporation sensor, a light sensor, a wind speed and direction sensor, and the like.

In some embodiments, the detection device may include a detection station 330 provided with an environmental sensor. The test stand refers to a device capable of detecting an environment using a sensor. The environmental sensor may be disposed on the test platform. In some embodiments, the inspection station is an inspection station that is movable up and down.

In some embodiments, the detection device may be a patrol type detection device. The inspection type detection device is in wireless connection with the processor. For more details on the inspection type detection device, reference is made to fig. 3 and its associated description.

In some embodiments, if the detection device is of a patrol inspection type, the detection device can go to different positions of the greenhouse to detect the environmental data. For example, the opening of the greenhouse, the center position of the greenhouse, the farthest position of the greenhouse from the opening, and the like. Also for example, the bottom, middle, top, etc. of the greenhouse. In some embodiments, the final environmental data of the greenhouse can be obtained by processing the environmental data of different positions. For example, the processing device receives the environmental data of different positions of the greenhouse transmitted by the detection device, processes the data by a method such as an average value (for example, a weighted average value), and takes the processed result as the environmental data of the greenhouse. For another example, the processing device may directly take the worst environmental data from the environmental data at different locations as the environmental data of the greenhouse. The environmental data of the greenhouse can be determined based on the environmental data of different positions in the greenhouse in other modes.

Step 220, determining whether to ventilate the at least one greenhouse according to the environmental data in the at least one greenhouse. In some embodiments, step 220 may be performed by the decision module 520.

In some embodiments, for each of at least one greenhouse, it may be determined whether to ventilate the greenhouse based on whether environmental data within the greenhouse satisfies a preset condition. If not, ventilation is performed. The preset condition may be a relationship of one or more environmental indicators in the environmental data to a threshold or a threshold range. For example, the temperature is between a first temperature threshold and a second temperature threshold. As another example, the humidity is between a first humidity threshold and a second humidity threshold.

In some embodiments, the real-time judgment can be directly performed according to the environmental data of the greenhouse detected by the detection device, and whether a preset condition is met or not is determined to determine whether ventilation is performed or not.

In some embodiments, whether to ventilate may be determined according to whether the predicted environmental data of the greenhouse satisfies a preset condition. Further details regarding determining whether to ventilate based on the predicted environmental data are provided with reference to fig. 4 and its associated description.

In some embodiments, the determining module may determine whether one greenhouse needs to be ventilated or not, or may determine whether a plurality of greenhouses need to be ventilated or not at the same time. For example, each time the detection device detects the environmental data of a greenhouse, the determination module may determine whether the greenhouse needs to be ventilated based on the environmental data detected by the detection device.

Based on the determination that ventilation is needed, an opening command is generated to instruct an electronically controlled valve in the ventilation device to open the ventilation port for ventilation, step 230. In some embodiments, step 230 may be performed by control module 530.

The ventilation device is a device capable of exchanging gas in one space with gas in another space. In some embodiments, the air exchange device may include an electrically controlled valve, a motor, an air exchange port, a ventilator, or the like. In some embodiments, the electronic control valve and the motor enter the starting state in response to an opening command sent by the processor, the electronic control valve controls the ventilation opening to be opened, and the motor controls the ventilation fan to be opened to start automatic ventilation. In some embodiments, the ventilation device is installed at any position of the greenhouse. For example, the specific installation position of the opening of the greenhouse, the top of the greenhouse, the side of the greenhouse and the like can be determined according to actual requirements.

FIG. 3 is a schematic diagram of a method of obtaining environmental data according to some embodiments of the present description. As shown in FIG. 3, the obtain environment data method 300 may include the following. In some embodiments, the method may be performed by the obtaining module 510 for obtaining the environmental data of the greenhouse 350, wherein the greenhouse 350 includes at least one greenhouse requiring the obtaining of the environmental data.

In some embodiments, the inspection-type detection device may include a slide rail 310, and the monitoring device 120 may be moved by the slide rail 310. The slide rail is a connecting part which is fixed on the detection device and is used for the detection device to move along the guide rail. The slide rails may include roller type, ball type, gear type, damped slide rails, etc.

The guide rail is a groove or ridge made of metal or other materials, can bear, fix and guide the detection device and reduce the friction of the detection device, and can move in a designated direction with high precision. The guide rails may include T-shaped guide rails, L (or triangular) shaped guide rails, U-shaped guide rails, O-shaped guide rails, hollow guide rails, and the like.

In some embodiments, a track 320 is provided in each of the at least one shelter and a track 320 is provided between the at least one shelter, and the roving inspection device can be moved along the track 320 to different locations within the shelter and between different shelters. For example, the greenhouse 1 is provided with a guide rail 320 inside, and the guide rail 320 is arranged among the

greenhouses

1, 2 and 3.

In some embodiments, at least one greenhouse shares one inspection type detection device, wherein the at least one greenhouse is a plurality of greenhouses within a preset range. In some embodiments, acquiring the environmental data includes the processing device receiving the environmental data of the plurality of greenhouses sent by the detection apparatus. In some embodiments, the environmental data is detected and determined based on different positions of the detection device, which go to different greenhouses through the sliding rails and the guide rails installed between the greenhouses.

In some embodiments, it may be determined whether the detection device goes to the greenhouse for environmental data detection based on the prediction result of the environmental data of the greenhouse. Namely, whether the detection device goes to corresponding detection is determined based on the prediction result of the environment data, and whether ventilation is carried out finally or not is determined according to the data obtained after detection by the detection device. The predictions regarding the environmental data can be seen in fig. 4 and its associated description.

In some embodiments of the present description, whether to go to a corresponding greenhouse for detection is determined based on a prediction result, a time period that needs to be paid attention to can be effectively determined, stability of an environment in the greenhouse is ensured, a detection device is not required to continuously detect, the service life of the detection device can be prolonged, and unnecessary detection cost is avoided.

In some embodiments, when determining whether to detect the environmental data of the greenhouse based on the prediction result of the environmental data of the greenhouse, determining whether to go to detection based on a preset rule may be performed. For example, the prediction rule may be that when one or more environmental indicators in the predicted environmental data satisfy a preset condition, the prediction rule does not go to detection. For example, the preset condition may be a relationship of the predicted environmental data to a threshold or a threshold range.

In some embodiments, the determination of the preset rule may be time dependent. For example, the threshold value set in the preset condition is time-dependent. The environmental conditions suitable for crops during the day and night are different. Taking the temperature as an example, the crops are the green beans, and after planting, the temperature in the greenhouse is mainly maintained between 20 ℃ and 25 ℃ in the daytime and about 15 ℃ at night, so that the green beans can be effectively promoted to bloom and bear fruits. For another example, the crops are cucurbita pepo, after field planting, the temperature in the greenhouse is mainly maintained between 25 ℃ and 30 ℃ in the daytime in the seedling revival stage, the temperature is mainly maintained between 18 ℃ and 20 ℃ at night, the temperature is increased, and early rooting is promoted; after seedling delaying, the temperature in the greenhouse is mainly maintained between 20 ℃ and 25 ℃ in the daytime and between 12 ℃ and 15 ℃ at night, so that the growth of plant roots is promoted, and the like. Accordingly, the preset condition may set a corresponding threshold value for the time period to determine whether the prediction result is satisfied.

In some embodiments, the preset rules are further related to other planting-related factors, such as planting season, climate of the planting area, type of crop, and the like, and specifically, corresponding thresholds may be set for different seasons, thresholds may be set based on climate of the planting area, thresholds may be set according to suitable conditions for growth of different types of crop, and may be determined according to actual planting experience.

In some embodiments, the preset rule may be determined based on whether the confidence of the model result is below a preset threshold. For example, the preset threshold is 0.5, and the detection device can go to detection whether the detection device reaches the standard or not. The preset threshold value can be set according to actual conditions. A confidence model of the model results may be obtained through the model output. See figure 4 and its associated description for more on the model.

In some embodiments, the route for subsequent detection by the detection device may be determined based on the prediction results of the environmental data of the plurality of greenhouses in the plurality of future time periods in combination with the position of each greenhouse. For details of the prediction environment data, reference may be made to fig. 4 and its associated description.

In some embodiments, both time and distance factors are considered in determining the subsequent detection route by the detection device. For example, the current position of the detection device at the current time B is a, and the target greenhouse to which the detection device needs to go first is determined based on the distance between each greenhouse and the position a and the distance between the time required to be detected by each greenhouse and the time B. For example, the greenhouse with the smallest weighting result is the first greenhouse to go ahead. The weight of time may be greater than the weight of distance. It can be understood that when the detection device goes to the target greenhouse, a new target greenhouse is determined based on the position of the target greenhouse and the time after the greenhouse is detected.

For another example, the current time is 13:00, the environments of the greenhouse 1 and the greenhouse 2 need to be detected at 16:00-17:00, the environments of the greenhouse 2 and the greenhouse 3 need to be detected at 17:00-18:00, and as shown in fig. 3, the detection device 120 is closest to the greenhouse 3 at present, and then the greenhouse 2 and the greenhouse 1 are located. Therefore, when the environments of the greenhouses 1 to 3 are detected in the above two time periods, the detection device 120 firstly goes to the greenhouse needing to be detected firstly, namely the greenhouse 1 and the greenhouse 2 needing to be detected at 16:00 to 17:00, and because the detection device is closer to the greenhouse 2, the environment of the greenhouse 2 is detected firstly, and then the environment of the greenhouse 1 is detected. The environment of the greenhouse 2 and the environment of the greenhouse 3 are detected by the detection device 120 at a ratio of 17:00-18:00, and since the detection device is located in the greenhouse 1 after the last detection is finished and is closer to the greenhouse 2, the detection device 120 firstly detects the environment of the greenhouse 2 and then detects the environment of the greenhouse 3 in the time period.

In some embodiments of the description, the ventilation time, that is, the ventilation route, is determined based on the greenhouse environment information prediction result and the greenhouse position determination detection route, so that labor and energy can be effectively saved, the ventilation effect is improved, and ventilation intellectualization is realized.

Fig. 4 is an exemplary flow chart of a method of determining whether to ventilate a greenhouse according to some embodiments of the present disclosure.

And step 410, predicting at least one piece of predicted environment data of at least one time section in the future of the greenhouse based on at least one piece of historical environment data. In some embodiments, step 410 may be performed by decision module 520.

The historical environmental data refers to environmental data of the greenhouse in one or more past time periods. The historical environmental data is similar to the environmental data, and reference is made specifically to the description of the environmental data in step 210.

The predicted environment data refers to predicted environment data of one or more time periods in the future of the greenhouse. In some embodiments, the determination module 520 may predict one or more predicted environmental data for one or more future time periods of the greenhouse based on the one or more historical environmental data.

In some embodiments, the determining module 520 may predict one or more predicted environmental data for one or more future time periods of the greenhouse based on preset rules, and determine whether ventilation is required based on the result of the predicted environmental data. In some embodiments, the preset rules are associated with one or more future time periods. Taking the environmental factor of temperature as an example, the suitable temperature of crops in the greenhouse at night is usually lower than the suitable temperature in the daytime, and the greenhouse usually has good heat preservation capability, so the temperature in the greenhouse at the evening is higher than the suitable temperature of the crops, and at this time, the early evening environment prediction result is considered to be unqualified and ventilation is needed.

In some embodiments, the preset rules may also be related to other crop planting related factors, such as planting season, planting climate, crop type, and the like, and may be specifically set according to actual planting experience.

In some embodiments, the determination module 520 may predict one or more predicted environmental data for one or more time periods in the future via a machine learning model.

In some embodiments, the type of machine learning model may include Deep Neural Networks (DNNs), and the like.

In some embodiments, the input to the machine learning model may include one or more historical environmental data for one or more past time periods within the greenhouse. For example, temperature data, humidity data, carbon dioxide concentration data and the like of different time periods in the greenhouse.

In some embodiments, the inputs to the machine learning model may also include basic information of the greenhouse for the time period or time periods to be predicted in the future, environmental information outside the greenhouse for the time period or time periods to be predicted in the future, and the like. The environment information outside the greenhouse may include weather, temperature, humidity, etc. outside the greenhouse.

The basic information of the greenhouse refers to the information set of the greenhouse. For example, the type of vegetables or fruits to be planted in the greenhouse, the area of the greenhouse, the height of the greenhouse, and the like.

In some embodiments, the output of the machine learning model may include classification results for various environmental indicators. As previously mentioned, environmental indicators may include temperature, humidity, carbon dioxide concentration, and the like. The classification result may represent a range of environmental values in which the predicted environmental indicator is located. The presets may include different temperature levels, humidity levels, carbon dioxide concentration levels, etc. preset to a plurality of levels. For example, the temperature is preset to a plurality of levels, and different levels correspond to different temperature intervals. For example, grade 1 is 10 ℃ to 15 ℃; grade 2 is 15-20 ℃; grade 3 is 20-25 ℃; the 4-level is 25-30 ℃, and the output of the machine learning model is the temperature level corresponding to the greenhouse in different time periods. For another example, the carbon dioxide concentration is set in advance in a plurality of levels, and different levels correspond to different carbon dioxide concentrations. The 1 grade is 300-500 ml/cubic meter; the 2 level is 500-800 ml/cubic meter; grade 3 is 800-; the 4-level is 1000-1300 ml/cubic meter, and the output of the machine learning model is the carbon dioxide concentration levels corresponding to different time periods of the greenhouse. It should be noted that the output of the machine learning model may also be other types of classification results of other levels, which may be determined according to actual situations.

In some embodiments, the determining module 520 may predict one or more predicted environmental data for two or more greenhouses at the same time for one or more future time periods through the machine learning model. The input of the machine learning model is a matrix formed by two or more greenhouses together, the relevant characteristic information of one greenhouse can form a vector, and the vectors of different greenhouses form the matrix together. The output can also be a matrix, and different vectors in the matrix represent the predicted environment data of different greenhouses.

In some embodiments, the determining module 520 can predict a plurality of predicted environmental data for a plurality of time periods in the future of the greenhouse through the machine learning model. When predicting the predicted environment data of the next future time period, the predicted environment data of the previous future time period may be used for the prediction. For example, the historical environmental data of the past time periods are 5 a1, a2, A3, a4 and a5, the future time periods include B1, B2 and B3, and when the predicted environmental data of the future time period B1 is predicted, the predicted environmental data of the future time period B2 may be predicted based on the historical environmental data a1, a2, A3, a4 and a5, and the predicted environmental data of the future time period B2 may be predicted based on the predicted environmental data of a2, A3, a4, a5 and B1, and so on.

In some embodiments, the manner of training of the machine learning model may be supervised learning. A machine learning model may be derived based on a plurality of training samples and label training.

In some embodiments, the training samples include a plurality of historical environmental data for a plurality of time periods of the sample greenhouse, basic information of the greenhouse, environmental information outside the greenhouse, and the like. The label is a classification result for various types of environmental data set in advance for the sample. The training data can be obtained based on historical data, and the labels of the training data can be determined in a manual labeling mode or an automatic labeling mode.

In some embodiments of the description, the future environmental data of the greenhouse is predicted based on the historical environmental data through the machine learning model, and the predicted environmental data is further used for predicting the future environmental data, so that the greenhouse environment at the future time can be predicted by using richer data, and the prediction accuracy is improved; the relation between the historical environment and the future environment is fully considered, so that the predicted result has better credibility.

A determination is made, step 420, as to whether to ventilate the greenhouse based on the at least one predicted environmental data. In some embodiments, step 420 may be performed by decision module 520.

In some embodiments, the decision module 520 may determine whether to ventilate the greenhouse based on one or more predicted environmental data.

In some embodiments, the environmental data for each of the multiple time periods of the model input may correspond to different weights. The sum of the weights corresponding to the environmental data for different time periods may be 1. For example, the model receives environment data of 3 time periods, the environment data corresponding to the 3 time periods are I, II, and III, respectively, and the weight of I is 0.5, the weight of II is 0.3, and the weight of III is 0.2.

In some embodiments, the weights are higher for a plurality of time segments of the input, the closer the time segment is to the future time segment. For example, the time periods include 16:00-16:30, 16:30-17:00, 17:00-17:30, with future time periods of 17:30-18: 00. The corresponding weight of the environment data in different time periods of the time periods is 16:00-16:30, the weight of the environment data is less than that of the environment data in 16:30-17:00, and the weight of the environment data in 16:30-17:00 is less than that of the environment data in 17:00-17: 30.

In some embodiments, the environmental data of the input model may be weighted, and in particular, the weight of the environmental data may be determined according to the source of the environmental data. For example, environmental data for different time periods may be derived from the detection device, possibly from predictions. The weight of the environmental data derived from the detection means is greater than the weight of the environmental data derived from the prediction.

Determining weights by environmental data sources as referred to in some embodiments of the present description may improve the accuracy of predicting environmental data.

In some embodiments, whether the current time is to be ventilated may be determined in conjunction with a plurality of predictions for a one-to-one correspondence for a plurality of time periods in the future. The sum of the weights of the multiple predictors may be 1, with the closer the future time period is to the current time, the higher the weight of the corresponding predictor.

In some embodiments, the determination of ventilation may be determined by combining the weight of each prediction in at least one future time period and the determination of ventilation based on each prediction with a predetermined threshold. For example, the predetermined threshold may be 0.5, no ventilation at 0.5, greater than 0.5 ventilation. The future plurality of time periods includes B1, B2, and B3. B1 is closest to the current time, B2 times, B3 is least close to the current time, the prediction result of the predicted environment data of B1 is C1, and according to the prediction result C1, ventilation is determined not to be needed; the prediction result of the prediction environment data of B2 is C2, and ventilation is determined to be needed according to the prediction result C2; the prediction result of the predicted environment data of B3 is C3, and ventilation is determined to be required based on the prediction result C3. The weight of C1 is greater than the weight of C2 is greater than the weight of C3. Illustratively, C1 has a weight of 0.6, C2 has a weight of 0.3, and C3 has a weight of 0.1. The formula is 0.6 × 0+0.3 × 1+0.1 × 1 ═ 0.4, which is less than the preset threshold value of 0.5, and the current time may be unvented first.

It should be noted that the above descriptions regarding the

processes

200 and 400 are only for illustration and description, and do not limit the applicable scope of the present specification. Various modifications and changes to flow 200 and flow 400 will be apparent to those skilled in the art in light of this disclosure. However, such modifications and variations are intended to be within the scope of the present description.

In some embodiments, the greenhouse ventilation system 500 may include an acquisition module 510, a determination module 520, and a control module 530.

In some embodiments, the acquisition module 510 may be used to acquire environmental data within one or more greenhouses. In some embodiments, the obtaining module 510 may be further configured to obtain environmental data in one or more greenhouses through a detecting device, wherein an environmental sensor is disposed in the detecting device. In some embodiments, a rail is provided in each of the one or more greenhouses, a rail is provided between the one or more greenhouses, the detection device is provided with a slide rail, and the obtaining module 510 is further configured to determine environmental data within the one or more greenhouses based on the slide rail of the detection device moving on the rail, wherein for each of the one or more greenhouses, the environmental data within the greenhouse is determined based on the environmental data at different locations in the greenhouse. For more details on acquiring greenhouse environment data, refer to fig. 2, step 210 and related description, and fig. 3 and related description.

In some embodiments, the determination module 520 may be configured to determine whether to ventilate one or more greenhouses based on environmental data within the one or more greenhouses. In some embodiments, for each of the one or more greenhouses, the environmental data within the greenhouse comprises one or more historical environmental data for one or more time periods in the greenhouse past; the determining module 520 may be further configured to predict one or more predicted environmental data for one or more future time periods of the greenhouse based on the one or more historical environmental data; determining whether to ventilate the greenhouse based on the one or more predicted environmental data. For more details on the determination of whether to ventilate the greenhouse, refer to fig. 2, step 220 and related description, and to fig. 4 and related description.

In some embodiments, the control module 530 may be configured to generate an open command to instruct an electronically controlled valve in the ventilator to open the ventilation port for ventilation based on a determination that ventilation is needed. For more details regarding ventilating the greenhouse, reference may be made to step 230 of fig. 2 and its associated description.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A method of ventilating a greenhouse, performed by a processing device in a ventilating apparatus, the method comprising:

acquiring environmental data in at least one greenhouse;

determining whether to ventilate the at least one greenhouse according to the environmental data in the at least one greenhouse;

based on the determination that ventilation is needed, an opening command is generated to instruct an electrically controlled valve in the ventilation device to open the ventilation port for ventilation.

2. The greenhouse ventilation method of claim 1, wherein the acquiring environmental data within at least one greenhouse comprises:

and acquiring environmental data in the at least one greenhouse through a detection device, wherein an environmental sensor is arranged in the detection device.

3. A method of ventilating a greenhouse as claimed in claim 2, wherein a rail is provided in each of said at least one greenhouse, and a rail is provided between said at least one greenhouse, said detecting means is provided with a rail,

the acquiring of the environmental data in the at least one greenhouse by the detection device includes:

determining environmental data within the at least one greenhouse based on the movement of the rail of the detection device on the rail, wherein,

for each of the at least one greenhouse, environmental data within the greenhouse is determined based on environmental data for different locations in the greenhouse.

4. A greenhouse ventilation method as claimed in claim 1, for each of said at least one greenhouse,

the environmental data in the greenhouse comprises at least one historical environmental data of at least one past time period in the greenhouse;

the determining whether to ventilate the greenhouse according to the environmental data in the greenhouse comprises:

predicting at least one predicted environmental data for at least one future time period of the greenhouse based on the at least one historical environmental data;

determining whether to ventilate the greenhouse based on the at least one predicted environmental data.

5. A greenhouse ventilation system comprises an acquisition module, a judgment module and a control module;

the acquisition module is used for acquiring environmental data in at least one greenhouse;

the judgment module is used for determining whether to ventilate the at least one greenhouse according to the environmental data in the at least one greenhouse;

the control module is used for generating an opening instruction based on a determination result that ventilation is needed so as to instruct an electric control valve in the ventilation device to open a ventilation port for ventilation.

6. The greenhouse ventilation system of claim 5, the acquisition module further to:

7. A greenhouse ventilation system as claimed in claim 6, wherein a rail is provided in each of the at least one greenhouse and a rail is provided between the at least one greenhouse, the detection means is provided with a rail,

the acquisition module is further configured to:

8. A greenhouse ventilation system as claimed in claim 5, for each of the at least one greenhouse,

the judging module is further configured to:

9. A greenhouse ventilation device, comprising a processor, wherein the processor is used for executing the greenhouse ventilation method as claimed in any one of claims 1 to 4.

10. A computer-readable storage medium storing computer instructions, wherein when the computer instructions in the storage medium are read by a computer, the computer executes the greenhouse ventilation method as claimed in any one of claims 1 to 4.