TW202213249A - Intelligent environmental control method for agricultural field - Google Patents

Abstract

The present invention relates to an intelligent environmental control method, which includes: storing reference data in an artificial intelligence system, analyzing the reference data, and establishing a prediction system. The reference data includes environmental data, environmental control equipment set, and biological properties. The artificial intelligence system analyzes the reference data with algorithm and performs machine learning to establish the prediction system. Environmental parameters of a to-be-controlled agricultural field are input to the prediction system, wherein the environmental parameters include a predicted environmental parameter within an interval, an environmental control equipment parameter, and a biological parameter. The prediction system calculates the environmental parameters to obtain a recommended data and accordingly outputs a recommended equipment list. The artificial intelligence system automatically controls an environmental control equipment module of the agricultural field according to the recommended equipment list, thereby maintaining an optimized environment for agriculture.

Description

Intelligent environmental control method in agricultural field

The present invention relates to an optimal agricultural field control method which can automatically adjust environmental control by means of machine learning.

At present, in general poultry farms, orchid gardens or greenhouses for breeding or planting, the environmental conditions in the agricultural field are closely related to the external environment. The environmental conditions in the agricultural field are often controlled by many environmental control equipment, such as: Fans, water curtain walls, humidifiers, heaters, air conditioners, etc. are used to adjust the environment to suit the growth of various organisms in agricultural fields, such as white broiler chickens, phalaenopsis, etc. When the environmental changes in the agricultural field exceed the suitable range for the cultivated organisms, the environmental control equipment in the agricultural field must be activated to ensure that the environmental conditions can be maintained within the suitable range. When there is a biological demand, the manager must set the control mode of starting or closing each environmental control equipment or facility according to the previous practical experience, but the outdoor environment changes drastically with the four seasons, and the equipment in the agricultural field may not be able to. The regulation is within the suitable range of biological growth. For example, in different stages such as chick period, brooding period, moulting period, fattening period, meat changing period, etc., the allowable temperature, humidity, carbon dioxide concentration or feed amount and other environmental ranges are also not allowed. In the same way, it is necessary to conduct long-term inductive analysis by managers to obtain the set control mode of environmental control equipment or facilities, so as to establish an environmental range suitable for white broiler chickens at different growth stages, and manually adjust accordingly. Therefore, managers need long-term practical experience in order to set a control mode suitable for the environment. However, each manager has different practical experience, and sets different control modes suitable for the environment. Moreover, it takes a long time to accumulate practical experience, and each manager has different learning ability. The best control mode cannot be obtained, so the culling rate and mortality rate of chickens will be too high, the breeding cost will increase, and effective management cannot be achieved.

Therefore, there is a new patent case No. M523165 "Automated Agricultural Management and Control Equipment and System" announced on June 1, 2005 by the Republic of China, which mainly discloses: For the control of a farming area, the automatic agricultural management and control equipment includes sensing devices and adjustment devices. And the control device, the sensing device senses the farming information of the farming area and transmits it wirelessly, and the adjustment device receives the wireless control signal and performs the farming adjustment control including temperature adjustment, humidity adjustment and illumination adjustment to the farming area accordingly, the management and control device It includes a wireless radio frequency transmission component and an automatic processing component. The automatic processing component automatically receives the farming information of the farming area sensed by the sensing device through the wireless radio frequency transmission component, and automatically generates a wireless control signal according to the farming information and transmits it through the wireless radio frequency. The transmission member controls the adjustment device, thereby performing farming adjustment control for the farming area.

Although the above-mentioned patent case has the function of automatic regulation, the setting values of the control modes of various environments are manually input, so the setting values are all fixed and cannot be adjusted and changed at any time according to the changes of different equipment and environments. There are still shortcomings in use.

In this regard, in view of the above-mentioned shortcomings in the environmental control of the conventional agricultural field. Therefore, the present invention provides an intelligent environmental control method for agricultural fields, which includes: inputting a reference data through artificial intelligence, and after analyzing the reference data, a prediction system is established, and the reference data includes a combination of environmental data and environmental control equipment and a biological property, the environmental data, the combination of environmental control equipment and the biological property are analyzed through the artificial intelligence machine learning and algorithm to establish the prediction system; the prediction system inputs an agricultural field to be controlled an environmental parameter of the , use the artificial intelligence to automatically control an environmental control equipment module in the agricultural field, and transmit the execution result of controlling the environmental control equipment module to the artificial intelligence, so as to adjust the control mode of the environmental control equipment module at any time.

The above-mentioned artificial intelligence includes a server and a database, and the reference data is big data obtained according to previous practical experience in various agricultural fields, and is stored in the database. The agricultural field is a poultry farm , orchid garden or greenhouse.

The above environmental data includes one or any combination of ambient temperature, ambient humidity, and carbon dioxide concentration, and the combination of environmental control equipment includes one or any combination of dehumidifiers, water curtain walls, fans, heaters, and air conditioners. Biological properties include the different growth stages of the organism to be farmed or grown.

The above-mentioned prediction system includes an outdoor environment prediction system, a multi-variable environmental control system and a biological property prediction system. After the environmental data is analyzed by the artificial intelligence in the way of outdoor environment prediction and learning, the outdoor environment prediction system is established, and the outdoor environment prediction system is established. The outdoor environment prediction system correspondingly calculates the environmental parameters of the prediction interval, the combination of the environment control equipment is analyzed by the artificial intelligence in the way of multivariate environment control learning, and the multivariate environment control system is established, and the multivariate environment control system corresponds to the calculation The parameters of the environmental control equipment and the biological properties are analyzed by artificial intelligence in the manner of biological properties learning, and then the biological properties prediction system is established, and the biological properties prediction system correspondingly calculates the biological parameters.

The environmental parameters in the above prediction interval include one or any combination of the actual temperature, actual humidity, and actual carbon dioxide concentration of the agricultural field, and the environmental control equipment parameters include the dehumidifiers, water curtain walls, fans, etc. in the agricultural field. One or any combination of the actual number, model, and specification of heaters and air conditioners, and the biological parameter includes one or any combination of the period, species, and number of organisms raised in the agricultural field.

The above prediction system generates a plurality of sets of the equipment combination menu, and the content of the suggested data includes one of the best temperature, the best humidity, the best carbon dioxide concentration, the best feed amount, the biological elimination rate, and the biological mortality rate or any combination thereof.

The above suggested data are functional-oriented, including promoting the rapid growth of organisms, saving energy and saving electricity, or saving feed costs.

After selecting the above equipment combination menu, the controller work flow will be set according to the equipment combination menu, and the environmental control equipment module will be automatically controlled. The environmental control equipment module includes dehumidifier, water curtain wall, fan, heater, air conditioner one or any combination of them.

The process data of the above execution result will be fed back to the artificial intelligence, so as to update the reference data and store it, and then through the machine learning of the artificial intelligence, and then use the algorithm analysis to modify the prediction system, and recalculate the environment parameters to obtain new suggested data to update the equipment combination menu, and then automatically adjust the control mode of the environmental control equipment module.

Further, a sensing module is used to continuously detect the environment of the agricultural field, and the sensing module includes one or any combination of a hygrometer, a thermometer, a carbon dioxide sensor, an air particle detector, and an odor detector, and When the sensing module detects an abnormal condition of the hygrometer, the thermometer, the carbon dioxide sensor, the air particle detector or the odor detector, the sensing module will issue an abnormal warning.

The above technical features have the following advantages:

1. According to the big data obtained from the practical experience of different agricultural fields, through machine learning, the best control mode of environmental control in agricultural fields can be found, and the best environment suitable for breeding or planting can be established accordingly.

2. According to the execution result of automatic control, the controller work flow can be adjusted at any time, so that the control mode of the environmental control equipment module can be changed in real time.

3. The execution result of automatic control will be fed back to the artificial intelligence to update the reference data and store it, and then through machine learning, algorithm analysis and calculation, to obtain new suggested data, update the equipment combination menu, and automatically adjust the loop. The control mode of the control equipment module to maintain the agricultural field as the most suitable agricultural environment for breeding or planting.

4. There is also a sensing module, so that when abnormal conditions are detected in the hygrometer, thermometer, carbon dioxide sensor, air particle detector or odor detector, an abnormal warning can be issued immediately to notify the administrator to repair immediately. process and maintain the entire system to function properly.

Please refer to the first figure and the second figure, the embodiment of the present invention includes the following steps:

A. Input a reference data through artificial intelligence, and after analyzing the reference data, establish a prediction system. The artificial intelligence system includes a server 1 and a database 11 . The reference data are big data previously obtained by relying on managers based on practical experience in different agricultural fields, and are stored in the database 11 . The reference data includes environmental data, environmental control equipment combinations and biological properties. Wherein, the environmental data includes one of environmental temperature, environmental humidity, carbon dioxide concentration or any combination thereof. The environmental control equipment combination includes one of a dehumidifier, a water curtain wall, a fan, a heater, and an air conditioner or any combination thereof. The biological properties include the different growth periods of the organisms to be bred or planted. For example, the biological properties of white broiler chickens include the chick period, the brooding period, the moulting period, the fattening period and the meat changing period and the corresponding requirements for each growth period. amount of feed. If it is to plant orchids, it includes bottle seedling stage, small seedling stage, medium and large seedling stage and flowering plant stage. The reference data of the above-mentioned environmental data, combination of environmental control equipment and biological properties are input into a computer, and analyzed through artificial intelligence machine learning and various algorithms to establish a prediction system 2 . The algorithm system includes Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Lightweight Gradient Boosting Model (LightGBM) or Multilayer Perceptron ( MLP). The prediction system 2 includes an outdoor environment prediction system 21 , a multi-variable environmental control system 22 and a biological property prediction system 23 . The outdoor environment prediction system 21 is established after the environmental data is analyzed by artificial intelligence by means of outdoor environment prediction and learning, so as to predict changes in the outdoor environment, such as changes in ambient temperature, ambient humidity and carbon dioxide concentration. The environmental control equipment combination is analyzed by artificial intelligence in the way of multi-variable environmental control learning, and the multi-variable environmental control system 22 is established to control environmental equipment such as dehumidifiers, water curtain walls, fans, heaters and air conditioners. The biological properties are analyzed by artificial intelligence in the manner of biological property learning, and the biological properties prediction system 23 is established to predict changes in the amount of feed, biological elimination rate and biological mortality of the organisms in different growth periods.

B. Input an environmental parameter of the agricultural field to be controlled into the prediction system. The agricultural field to be controlled in the embodiment of the present invention is illustrated by raising white broiler chickens on a poultry farm, and the environmental parameters are manually input. The environmental parameters include a prediction interval environmental parameter, an environmental control equipment parameter and a biological parameter. The environmental parameters in the prediction interval include the actual temperature, actual humidity and actual carbon dioxide concentration of the poultry farm. The parameters of the environmental control equipment include the actual quantity, model and specification of the dehumidifiers, water curtain walls, fans, heaters and air conditioners in the poultry farm. The biological parameters include the period, species and quantity of chickens raised in the poultry farm.

C. The prediction system calculates the environmental parameters to obtain a suggestion data, and outputs at least one equipment combination menu. The prediction system 2 performs calculations according to the environmental parameters input by the poultry farm in the agricultural field, wherein the outdoor environment prediction system 21 corresponds to the calculation of the environmental parameters in the prediction interval, and the multi-variable environmental control system 22 corresponds to the calculation of the environmental parameters Equipment parameters, the biological property prediction system 23 calculates the biological parameters correspondingly, so as to generate and output the equipment combination menu with the suggested data. The prediction system 2 can generate a plurality of sets of the equipment combination menus, for example, the content of the suggested data of the equipment combination menus includes the optimal temperature, optimal humidity, and optimal carbon dioxide concentration of chickens in different growth periods. , optimal feed amount, biological elimination rate and biological mortality for managers to choose. Further, the proposed data is functional-oriented, for example, to promote the rapid growth of chickens, or to save energy and electricity, or to save feed costs, so that managers can choose to use them according to their needs.

D. According to the equipment combination menu, use the artificial intelligence to automatically control an environmental control equipment module of the agricultural field. When the administrator selects the device combination menu to be set, the server 1 will set the controller workflow according to the device combination menu to automatically control the environmental control device module 3, the environmental control device module Series 3 includes one of a dehumidifier 31 , a water curtain wall 32 , a fan 33 , a heater 34 and an air conditioner 35 or any combination thereof. For example, activating the water curtain wall 32 can reduce the temperature, increase the humidity and reduce the carbon dioxide concentration. Activating the heater 34 can increase the temperature, decrease the humidity and increase the carbon dioxide concentration. Activating the air conditioner 35 can lower the temperature and reduce the humidity, thereby making the agricultural field an optimal environment for raising chickens.

E. Continuously detect the environment of the agricultural field with a perception module, and transmit the execution result to the artificial intelligence, so as to adjust the control mode of the environmental control equipment module at any time. The sensing module 4 includes one of a hygrometer 41 , a thermometer 42 , a carbon dioxide sensor 43 , an air particle detector 44 , and an odor detector 45 or any combination thereof.

The server 1 adjusts the work flow of the controller according to the execution result, so that the control mode of the environmental control device module 3 can be changed in real time. For example, when it is detected that the humidity is too high, the dehumidifier 31 will be automatically controlled to start to reduce the humidity. If it is detected that the temperature is too high, the fan 33 will be automatically controlled to start to reduce the temperature. Thereby, the agricultural field can be maintained in an environment suitable for raising chickens.

And the process data of the execution result will be fed back to the server 1 of the artificial intelligence, so as to update the reference data in the database 11 and store it, and then use the algorithm through the machine learning of the artificial intelligence. Analysis, to modify the prediction system 2, recalculate the environmental parameters to obtain new suggested data, update the equipment combination menu, and automatically adjust the control mode of the environmental control equipment module to maintain the agricultural field as the best An agricultural environment suitable for raising chickens.

When the sensing module 4 detects an abnormal condition of the hygrometer 41, the thermometer 42, the carbon dioxide sensor 43, the air particle detector 44 or the odor detector 45, the sensing module 4 An abnormal warning will be issued, such as a warning sound or a warning light signal, to notify the administrator to repair and deal with it immediately, so as to ensure the normal operation of the entire system.

Based on the descriptions of the above embodiments, one can fully understand the operation, use and effects of the present invention, but the above-mentioned embodiments are only preferred embodiments of the present invention, which should not limit the implementation of the present invention. Scope, that is, simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the description of the invention, all fall within the scope of the present invention.

1: Server 11:Database 2: Prediction system 21: Outdoor Environment Prediction System 22: Multi-variable loop control system 23: Biological property prediction system 3: Environmental control equipment module 31: Dehumidifier 32: Water Curtain Wall 33: Fan 34: Heater 35: Air conditioner 4: Perception module 41: Hygrometer 42: Thermometer 43: CO2 Sensor 44: Air Particle Detector 45: Odor detector

[Figure 1] is an operation block diagram of an embodiment of the present invention.

[Figure 2] is a flow chart of the steps of the embodiment of the present invention.

1: Server

11:Database

2: Prediction system

21: Outdoor Environment Prediction System

22: Multi-variable loop control system

23: Biological property prediction system

3: Environmental control equipment module

31: Dehumidifier

32: Water Curtain Wall

33: Fan

34: Heater

35: Air conditioner

4: Perception module

41: Hygrometer

42: Thermometer

43: CO2 Sensor

44: Air Particle Detector

45: Odor detector

Claims (10)

An intelligent environmental control method for agricultural fields, including: A reference data is input through artificial intelligence, and after analyzing the reference data, a prediction system is established. The reference data includes an environmental data, a combination of environmental control equipment and a biological property, and the environmental data, the combination of environmental control equipment and the The biological properties are analyzed through the machine learning of the artificial intelligence using algorithms to establish the prediction system; An environmental parameter of an agricultural field to be controlled is input into the prediction system, and the environmental parameter includes a prediction interval environmental parameter, an environmental control equipment parameter and a biological parameter; The prediction system calculates the environmental parameter to obtain a suggestion data, and outputs at least one equipment combination menu; According to the equipment combination menu, use the artificial intelligence to automatically control an environmental control equipment module in the agricultural field, and transmit the execution result of controlling the environmental control equipment module to the artificial intelligence, so as to adjust the environmental control equipment model at any time. Group control mode. As claimed in claim 1, the intelligent environmental control method for agricultural fields, wherein the artificial intelligence includes a server and a database, and the reference data is big data obtained according to previous practical experience in different agricultural fields, And stored in the database, the agricultural field is a poultry farm, an orchid garden or a greenhouse. The intelligent environmental control method for agricultural fields as claimed in claim 1, wherein the environmental data includes one or any combination of ambient temperature, ambient humidity, and carbon dioxide concentration, and the environmental control equipment combination includes dehumidifiers, water curtain walls, One or any combination of fans, heaters, air conditioners, and the biological properties include different growth stages of the organisms to be cultivated or grown. The intelligent environmental control method for agricultural fields as claimed in claim 1, wherein the prediction system comprises an outdoor environment prediction system, a multi-variable environmental control system and a biological property prediction system, and the environmental data is obtained through the artificial intelligence to outdoor After analyzing the method of environmental prediction and learning, the outdoor environment prediction system is established. The outdoor environment prediction system correspondingly calculates the environmental parameters of the prediction interval. The multi-variable loop control system, the multi-variable loop control system correspondingly calculates the parameters of the loop control equipment, and the biological properties are analyzed by artificial intelligence in the manner of biological properties learning to establish the biological properties prediction system, the biological properties prediction system Then the biological parameter is calculated accordingly. The intelligent environmental control method for an agricultural field as claimed in claim 1, wherein the environmental parameters in the prediction interval include one or any combination of the actual temperature, actual humidity, and actual carbon dioxide concentration of the agricultural field, and the environmental control equipment parameters It includes the actual number, model, and specification of the dehumidifiers, water curtain walls, fans, heaters and air conditioners in the agricultural field or any combination thereof. The biological parameters include the biological parameters raised in the agricultural field. period, species, quantity or any combination thereof. The intelligent environmental control method for agricultural fields as claimed in claim 1, wherein the prediction system generates multiple sets of the equipment combination menu, and the content of the suggested data includes optimal temperature, optimal humidity, optimal carbon dioxide concentration, One or any combination of optimal feed amount, biological culling rate, biological mortality rate. For example, the intelligent environmental control method in the agricultural field of claim 6, wherein the proposed data is oriented by functionality, including promoting the rapid growth of organisms, focusing on energy saving and power saving, or saving feed costs. For example, the intelligent environmental control method in the agricultural field of claim 1, wherein, after selecting the equipment combination menu, the controller workflow will be set according to the equipment combination menu to automatically control the environmental control equipment module, the environmental control equipment The modules include one of a dehumidifier, a water curtain wall, a fan, a heater, and an air conditioner or any combination thereof. For the intelligent environmental control method in the agricultural field of claim 1, the process data of the execution result will be fed back to the artificial intelligence, so as to update the reference data and store it, and then through the machine learning of the artificial intelligence, the Then use algorithm analysis to modify the prediction system, re-calculate the environmental parameters to obtain new suggested data, update the equipment combination menu, and then automatically adjust the control mode of the environmental control equipment module. According to the intelligent environmental control method of the agricultural field of claim 1, a sensing module is used to continuously detect the environment of the agricultural field, and the sensing module includes a hygrometer, a thermometer, a carbon dioxide sensor, and an air particle detector. , one of the odor detectors or any combination thereof, and when the sensing module detects an abnormal condition of the hygrometer, the thermometer, the carbon dioxide sensor, the air particle detector or the odor detector, The perception module will issue an abnormal warning.

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