WO2020255678A1 - 情報処理装置及び方法 - Google Patents

情報処理装置及び方法 Download PDF

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Publication number
WO2020255678A1
WO2020255678A1 PCT/JP2020/021367 JP2020021367W WO2020255678A1 WO 2020255678 A1 WO2020255678 A1 WO 2020255678A1 JP 2020021367 W JP2020021367 W JP 2020021367W WO 2020255678 A1 WO2020255678 A1 WO 2020255678A1
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WO
WIPO (PCT)
Prior art keywords
information
house
prediction
cultivation
environmental conditions
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Ceased
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PCT/JP2020/021367
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English (en)
French (fr)
Japanese (ja)
Inventor
聖 伊藤
スマート ビクトリア
メフル バンサル
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Bosch Corp
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Bosch Corp
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Publication date
Application filed by Bosch Corp filed Critical Bosch Corp
Priority to CA3143485A priority Critical patent/CA3143485A1/en
Priority to US17/620,647 priority patent/US20220357481A1/en
Priority to MX2021015702A priority patent/MX2021015702A/es
Priority to CN202080044415.2A priority patent/CN114008644A/zh
Priority to JP2021527530A priority patent/JPWO2020255678A1/ja
Priority to EP20826202.2A priority patent/EP3984349A4/en
Priority to AU2020298380A priority patent/AU2020298380A1/en
Priority to KR1020217040362A priority patent/KR20220020814A/ko
Publication of WO2020255678A1 publication Critical patent/WO2020255678A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G7/00—Botany in general
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
    • A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G9/00—Cultivation in receptacles, forcing-frames or greenhouses; Edging for beds, lawn or the like
    • A01G9/24—Devices or systems for heating, ventilating, regulating temperature, illuminating, or watering, in greenhouses, forcing-frames, or the like
    • A01G9/26—Electric devices
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
    • Y02A40/25—Greenhouse technology, e.g. cooling systems therefor

Definitions

  • the present invention relates to an information processing device and a method.
  • the environmental conditions inside the house are predicted.
  • By predicting environmental conditions it is possible to predict crop yields and the risk of pests.
  • In order to avoid the predicted risk of pests it is possible to take measures to adjust the environmental conditions in the house by air conditioning or the like.
  • the state of the house is predicted based on the past performance of the control device installed in the house and the sensor information obtained by the sensor in the house (see Patent Document 1). Further, the temperature of the greenhouse is controlled based on the input from the outside air temperature sensor, the room temperature sensor, the sunlight sensor, etc. (see Patent Document 2).
  • An object of the present invention is to improve the prediction accuracy of environmental conditions with a simple configuration.
  • An information processing device (40) including a prediction unit (423) for predicting environmental conditions in the house is provided.
  • the method of predicting the environmental conditions in the house the step of acquiring the prediction information of the weather conditions outside the house and the cultivation information of the crops in the house, and the above.
  • a method including a step of predicting environmental conditions in the house based on the weather condition prediction information and the cultivation information is provided.
  • FIG. 1 It is a figure which shows the structure of the information providing system including the information processing server of this embodiment. It is a figure which shows the structure of an information processing server. It is a flowchart which shows the processing procedure which an information processing server generates a prediction model. It is a flowchart which shows the processing procedure which the information processing server predicts the environmental condition in a house.
  • FIG. 1 shows an information providing system 1 according to an embodiment of the present invention.
  • the information providing system 1 predicts the environmental conditions in the houses 10a to 10c based on the prediction information of the weather conditions outside the houses 10a to 10c and the cultivation information of the crops in the houses 10a to 10c, and provides the prediction information.
  • FIG. 1 shows an example in which the prediction information of the three houses 10a to 10c is provided, but the number of houses that can provide the prediction information is not particularly limited, and the prediction information of one or a plurality of houses can be provided.
  • the information providing system 1 includes a plurality of sensors 21 to 23, a communication device 26, a weather server 30, an information processing server 40, and a user terminal 50.
  • the communication device 26, the weather server 30, the information processing server 40, and the user terminal 50 are connected to each other so as to be able to communicate with each other via the network 12.
  • the network 12 can include the Internet, a telephone line network, a LAN (Local Area Network), and the like.
  • Each sensor 21 to 23 is provided in the houses 10a to 10c, and measures the environmental conditions in the houses 10a to 10c at regular intervals such as 10 minutes.
  • environmental conditions include temperature, relative humidity, amount of solar radiation, carbon dioxide concentration, wind speed, geothermal heat, and amount of soil moisture.
  • the sensor 21 measures the temperature
  • the sensor 22 measures the relative humidity
  • the sensor 23 measures the amount of solar radiation, but even if a sensor for measuring other environmental conditions such as carbon dioxide concentration is provided. Good.
  • the communication device 26 transmits the temperature, relative humidity, solar radiation, etc. measured by the sensors 21 to 23 to the information processing server 40 as measurement information of the environmental conditions in the houses 10a to 10c. Further, a control device 20 for adjusting environmental conditions may be provided in the houses 10a to 10c. The communication device 26 can acquire necessary information from the control device 20, generate operation information of the control device 20, and transmit it to the information processing server 40. Examples of the control device 20 include an air conductor, a sprinkler, a sunshade curtain, a window opening / closing control device, and the like.
  • the communication between the communication device 26 and the sensors 21 to 23 and the control device 20 is wireless communication such as BLE (Bluetooth (registered trademark) Low Energy), Wi-Fi (registered trademark), but even wired communication. Good.
  • BLE Bluetooth (registered trademark) Low Energy
  • Wi-Fi registered trademark
  • the meteorological server 30 transmits forecast information of meteorological conditions outside each house 10a to 10c to the information processing server 40.
  • meteorological conditions include temperature, relative humidity, solar radiation, rainfall, wind speed, etc. for each region.
  • the forecast information may be a weather forecast.
  • the meteorological server 30 can transmit not only the prediction information but also the measurement information of the meteorological conditions measured around the houses 10a to 10c to the information processing server 40.
  • the information processing server 40 acquires the prediction information of the weather conditions outside the houses 10a to 10c and the cultivation information of the crops in the houses 10a to 10c, and based on the acquired prediction information and the cultivation information, the information processing server 40 inside the houses 10a to 10c. It is an information processing device that predicts environmental conditions. The information processing server 40 can generate and output prediction information of environmental conditions based on the prediction result.
  • the user terminal 50 is, for example, a mobile phone, a tablet, a PC (Personal Computer), or the like.
  • the user terminal 50 is used by a user such as a farmer who manages the houses 10a to 10c, and displays the prediction information transmitted from the information processing server 40.
  • FIG. 2 shows a configuration example of the information processing server 40.
  • the information processing server 40 includes a communication unit 410, a control unit 420, and a storage unit 430.
  • the communication unit 410 is an interface for communicating with an external device on the network 12, such as the communication device 26, the weather server 30, and the user terminal 50.
  • the control unit 420 controls the operation of the information processing server 40.
  • the control unit 420 predicts the environmental conditions in the houses 10a to 10c.
  • the control unit 420 includes an information acquisition unit 421, a learning unit 422, a prediction unit 423, and an information estimation unit 424 as shown in FIG.
  • a processor such as a CPU (Central Processing Unit) executes a program stored in a storage medium such as the above-mentioned storage unit 430 or other memory. It may be realized by the software processing to be performed, or it may be realized by hardware such as ASIC (Application Specific Integrated Circuit).
  • ASIC Application Specific Integrated Circuit
  • the information acquisition unit 421 acquires the forecast information of the weather conditions from the weather server 30 via the communication unit 410, and acquires the cultivation information of the crops of each house 10a to 10c estimated by the information estimation unit 424. Cultivation information is information on the cultivation conditions of crops.
  • the information acquisition unit 421 stores each acquired information in the storage unit 430.
  • the information acquisition unit 421 can acquire the measurement information of the environmental conditions in the houses 10a to 10c and the operation information of the control device 20 from the communication device 26, and acquire the measurement information of the weather conditions from the weather server 30. Can be done.
  • the information acquisition unit 421 may acquire measurement information at predetermined intervals such as 10-minute intervals.
  • the learning unit 422 When the weather condition prediction information and cultivation information stored in the storage unit 430 are input, the learning unit 422 generates a prediction model that outputs the prediction result of the environmental conditions in the house.
  • the generated prediction model is stored in the storage unit 430.
  • the prediction unit 423 predicts the environmental conditions in the houses 10a to 10c based on the weather condition prediction information and the cultivation information acquired by the information acquisition unit 421. Specifically, the prediction unit 423 can acquire the prediction result of the environmental conditions in the houses 10a to 10c by inputting the weather condition prediction information and the cultivation information into the prediction model generated by the learning unit 422. it can.
  • the information estimation unit 424 estimates the cultivation status of the crops in the greenhouse and generates cultivation information.
  • the information estimation unit 424 can generate cultivation information at an arbitrary observation time in response to a request from the information acquisition unit 421 and provide it to the information acquisition unit 421.
  • the storage unit 430 stores each information acquired by the information acquisition unit 421, specifically, weather condition prediction information, measurement information, environmental condition measurement information in each house 10a to 10c, crop cultivation information, and the like. .. Further, the storage unit 430 stores the prediction model generated by the learning unit 422. As the storage unit 430, a storage medium such as a large-capacity hard disk can be used.
  • the information processing server 40 generates a prediction model from the measurement information and cultivation information of the past weather conditions, and predicts the environmental conditions in the houses 10a to 10c by the generated prediction model.
  • FIG. 3 shows a processing procedure in which the information processing server 40 generates a prediction model.
  • the information acquisition unit 421 acquires the information necessary for generating the prediction model. Specifically, the information acquisition unit 421 acquires measurement information of meteorological conditions outside the houses 10a to 10c from the meteorological server 30. Further, the information acquisition unit 421 acquires the cultivation information of the crops in the houses 10a to 10c from the information estimation unit 424, and acquires the measurement information of the environmental conditions in the houses 10a to 10c from the communication device 26 (step S11).
  • Cultivation information is information on cultivation conditions of crops, and includes, for example, at least one information on crop type, cultivation amount, growth status, and medium.
  • the type of crop is, for example, a classification of cucumber, tomato, and the like.
  • Examples of the amount of cultivation include the land area of the houses 10a to 10c, the number of planted plants, and the planting density. The planting density may be calculated by dividing the number of planted plants by the planted area. Examples of the growth situation include the number of days after planting after the planting date, the growth stage estimated from the number of days after planting, and the like.
  • the medium is classified into, for example, soil cultivation and hydroponics.
  • the information input from the user terminal 50 is stored in advance in the storage unit 430 of the information processing server 40.
  • the information estimation unit 424 generates cultivation information at an arbitrary observation time by estimating the subsequent cultivation situation from the first stored cultivation information. Specifically, the information estimation unit 424 counts the number of days elapsed from the planting date to the observation time in the cultivation information as the number of days after planting, and determines the growth stage by comparing the number of days after planting with the threshold value.
  • the learning unit 22 generates a prediction model that outputs the prediction result of the environmental conditions in the house (step S12).
  • the prediction model may be a prediction formula that outputs predicted values such as temperature and humidity in the houses 10a to 10c by using the prediction information of the weather conditions and cultivation information as variables, or the predicted values may be set in advance for the variables. It may be a defined table.
  • each variable is defined as follows.
  • T in Predicted value of temperature inside the house
  • S Predicted value of solar radiation outside the house
  • Z 1 (t) and Z 2 (t) Predict environmental conditions Factors corresponding to time
  • a 1 , A 2 , B 1 and B 2 Coefficient H in : Predicted relative humidity inside the house H out : Predicted relative humidity outside the house
  • M (t) Environmental conditions Coefficients corresponding to the month to which the predicted time belongs
  • a 1 , A 2 , B 1 , B 2 , Z 1 (t), Z 2 (t) and M (t) are, for example, values determined by machine learning. Can be used.
  • the prediction model may be a model generated by machine learning using the measurement information of the weather conditions and the cultivation information as input data and the measurement information of the environmental conditions in the houses 10a to 10c as the teacher data. ..
  • a prediction model by machine learning is preferable because the prediction accuracy is further improved.
  • Examples of machine learning for generating a prediction model include linear regression, filters such as Kalman filter, support vector machine, decision tree such as random forest, neighbor method, neural network such as deep learning, and Bayesian network. These machine learnings can be used alone or in combination of two or more.
  • the type of machine learning can be appropriately selected according to its characteristics.
  • the Kalman filter can adjust the parameters that make up the prediction model so that the difference between the prediction data and the measurement information becomes small, and it is easy to respond to changes in environmental conditions over time.
  • the neural network can easily cope with non-linear changes as compared with the linear model and the Kalman filter, and can easily cope with a sudden change in the temperature setting of the control device 20 and the like.
  • the learning unit 422 uses the measurement information of the weather condition and the time information indicating the observation time of the cultivation information as one of the input data, and uses the time information indicating the observation time of the measurement information of the environmental condition as one of the teacher data. Can be done. For example, when the information acquisition unit 421 acquires the measurement information of the weather conditions measured at 17:00 on April 5, the learning unit 422 uses the time information at 17:00 on April 5 as one of the input data. The learning unit 422 can also use the time information of the time measured similarly for the environmental conditions as one of the input data.
  • the observation time of the cultivation information is the time when the cultivation status is estimated by the information estimation unit 424.
  • the time information includes not only the time but also the month and day as described above, it can be predicted by the pattern of time-series changes in environmental conditions over a longer span.
  • the time information is not limited to the time, and may be information representing a time zone such as 17:00 to 19:00.
  • the learning unit 422 can generate a prediction model by using information that affects the environmental conditions in the house as input data in addition to the measurement information and cultivation information of the above weather conditions. By using a plurality of pieces of information, multifaceted prediction becomes possible and the prediction accuracy is further improved.
  • the information acquisition unit 421 can further acquire the identification information of the houses 10a to 10c, and the learning unit 422 can further use the identification information of the house as one of the input data.
  • the identification information of the house 10a is also acquired. Since the environmental conditions inside the house differ depending on the installation position and structure of each house, the presence or absence of control equipment installed inside, the performance, etc., the prediction accuracy of the environmental conditions of each house 10a to 10c can be determined by using the identification information of the house. Can be enhanced.
  • each house 10a to 10c may be grouped according to the installation conditions. For example, when the houses 10a and 10c are installed in the same area and the sizes are within a certain range, the houses 10a and 10c are classified into the same group and the identification information of the same group is given.
  • the grouping and the assignment of the identification information may be performed by, for example, the learning unit 422 by machine learning, or may be manually performed by the administrator of the information processing server 40.
  • the information acquisition unit 421 may further acquire the identification information of the group to which each house 10a to 10c belongs, and the learning unit 422 may further use the group identification information as one of the input data. it can.
  • the group identification information By using the group identification information, the prediction accuracy of the environmental conditions of the houses 10a to 10c belonging to each group can be further improved. Further, even if the measurement information includes noise, the influence of noise can be reduced because the prediction is performed according to the tendency of the group.
  • the information acquisition unit 421 can further acquire the operation information of the control device 20 from the communication device 26, and the learning unit 422 can further use the operation information as one of the input data.
  • the operation information include the presence / absence of installation of the control device 20, the type of the control device 20, the operation status indicating whether the control device 20 is stopped or in operation, the operation content such as the target temperature and the target humidity, and the like. Since the operation of the control device 20 changes the environmental conditions in the houses 10a to 10c, the prediction accuracy of the environmental conditions in each house 10a to 10c can be further improved by using the operation information to generate the prediction model. ..
  • the learning unit 422 periodically or arbitrarily performs the above-described processing to update the prediction model stored in the storage unit 430. This makes it possible to make predictions according to the latest trends.
  • the information acquisition unit 421 acquires the prediction information of the weather conditions and the cultivation information at the time of prediction described later, then acquires the measurement information of the environmental conditions at the prediction time of the weather conditions, and uses the prediction information as input data.
  • the prediction model can be updated by using the measurement information as teacher data. In this case, the prediction model can be modified so that the discrepancy between the prediction data and the measurement information is small.
  • FIG. 4 shows a processing procedure in which the information processing server 40 predicts the environmental conditions in the houses 10a to 10c.
  • the prediction unit 423 accepts the designation of the house to be predicted.
  • the prediction unit 423 can also accept the designation of the time to be predicted (step S21).
  • the designation may be accepted from the user terminal 50, or when predicting at a fixed time interval, the time to be predicted may be changed at a fixed time interval and automatically accepted.
  • the information acquisition unit 421 acquires the forecast information of the weather conditions from the weather server 30.
  • the forecast information of the weather condition is the predicted information, for example, the weather forecast, which is not actually measured.
  • the information acquisition unit 421 acquires cultivation information at the time to be predicted in the designated house from the information estimation unit 424 (step S22).
  • the prediction unit 423 acquires the prediction result of the environmental condition in the house at the time of the prediction target by inputting the prediction information of the acquired weather condition, the cultivation information and the time information indicating the time of the prediction target into the prediction model (Ste S23).
  • the prediction unit 423 generates and outputs prediction information of environmental conditions based on the prediction result (step S24).
  • Examples of the predicted information include predicted values such as temperature, relative humidity, solar radiation, carbon dioxide concentration, and soil water content in the house at a specified time, and a graph of the predicted values.
  • the prediction unit 423 may generate a graph of each prediction value obtained by shifting the prediction time by a fixed time unit such as one day as prediction information. Further, the prediction information may be transmitted to the predicted house control device 20 and used for controlling the environmental conditions in the house, or may be transmitted to the user terminal 50 and displayed so that the user can view it. Good.
  • the weather condition prediction information and the cultivation information are used for the prediction of the environmental conditions in the houses 10a to 10c.
  • Meteorological conditions may be forecast information such as weather forecasts, and measuring devices such as sensors are not required. Since the cultivation information is estimated, once the basic information is input, it is not necessary to input each time the prediction is made, so that the prediction can be made with a simple configuration.
  • the prediction is made based on the prediction information of the weather conditions, not the current weather conditions, and the prediction accuracy of the environmental conditions is improved by making the prediction based on the cultivation information having a large influence on the environmental conditions in the houses 10a to 10c. Can be enhanced.
  • the learning unit 422 is provided not in the information processing server 40 but in an external device such as another server, and the information processing server 40 acquires the prediction model generated in the external device and stores it in the storage unit 430. It may be.

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PCT/JP2020/021367 2019-06-17 2020-05-29 情報処理装置及び方法 Ceased WO2020255678A1 (ja)

Priority Applications (8)

Application Number Priority Date Filing Date Title
CA3143485A CA3143485A1 (en) 2019-06-17 2020-05-29 Information processing device and method
US17/620,647 US20220357481A1 (en) 2019-06-17 2020-05-29 Information processing device and method
MX2021015702A MX2021015702A (es) 2019-06-17 2020-05-29 Dispositivo y metodo de procesamiento de informacion.
CN202080044415.2A CN114008644A (zh) 2019-06-17 2020-05-29 信息处理设备以及方法
JP2021527530A JPWO2020255678A1 (https=) 2019-06-17 2020-05-29
EP20826202.2A EP3984349A4 (en) 2019-06-17 2020-05-29 INFORMATION PROCESSING DEVICE AND METHOD
AU2020298380A AU2020298380A1 (en) 2019-06-17 2020-05-29 Information processing device and method
KR1020217040362A KR20220020814A (ko) 2019-06-17 2020-05-29 정보 처리 장치 및 방법

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JP2019-111699 2019-06-17

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CN (1) CN114008644A (https=)
AU (1) AU2020298380A1 (https=)
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JP2022124198A (ja) * 2021-02-15 2022-08-25 国立大学法人東海国立大学機構 情報処理装置、情報処理システム、情報処理方法、および、コンピュータプログラム

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CN117267905A (zh) * 2023-09-12 2023-12-22 珠海格力电器股份有限公司 一种空调的控制方法、装置、空调和存储介质
CN119376468A (zh) * 2024-10-16 2025-01-28 山东优百氏农业科技发展有限公司 一种种植大棚的作物生长控制方法及系统

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