WO2018016512A1 - 穀物処理施設の運転補助システム、および、サテライト施設の自動運転制御方法 - Google Patents
穀物処理施設の運転補助システム、および、サテライト施設の自動運転制御方法 Download PDFInfo
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Images
Classifications
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- F26B17/12—Machines or apparatus for drying materials in loose, plastic, or fluidised form, e.g. granules, staple fibres, with progressive movement with movement performed solely by gravity, i.e. the material moving through a substantially vertical drying enclosure, e.g. shaft
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Definitions
- the present invention relates to operation control technology for a grain processing facility.
- Patent Document 1 discloses a crop management system for quickly transporting a harvested product to an appropriate transport destination after harvesting with a harvesting machine.
- a plurality of satellite facilities that receive the straw brought from the farmer and perform semi-drying, and a terminal facility that is installed in the center of the plurality of satellite facilities and receives the semi-dried firewood and performs the main drying. are known (for example, Patent Document 1 below).
- Patent Document 1 Japanese Patent Document 1 below.
- the terminal facility and the satellite facility are connected via some kind of communication network, they do not perform autonomous group behavior that functions independently for each facility. For this reason, none of the facilities could be unmanned.
- IoT Internet of Things
- Non-Patent Document 1 “DENSO introduces“ IoT ”that connects everything to the Internet in every factory around the world. Improve productivity by 30% by making it possible to immediately grasp and analyze the operational status of equipment. ... Omitted ... We want to connect factories in Japan and overseas that make the same products as model factories, and in 18 years we want to connect all 130 factories. Is described.
- the non-patent document 1 describes that a model factory that serves as a standard and another factory that produces the same product as this model factory are linked via the Internet, and the model factory is set to “follow right” as a group action. Monitor. As a result, judgment at other factories becomes unnecessary, and unmanned work is possible.
- the present invention allows unmanned operation of a satellite facility even in a grain processing facility (for example, a grain joint drying preparation facility, a rice mill factory, a food factory, etc.) in which the properties of the starting material change every day.
- a grain processing facility for example, a grain joint drying preparation facility, a rice mill factory, a food factory, etc.
- a driving assistance system for a grain processing facility includes a first characteristic of a first grain brought into the model facility, a first operating parameter of the model facility used when the first grain is processed at the model facility, a model A database in which index values actually measured for the taste of the first grain after processing at the facility are stored in association with each other, and a reception for receiving the second characteristic of the second grain carried into the satellite facility And a calculation unit for calculating a second operating parameter used when processing the second crop at the satellite facility based on the received second characteristic and the information accumulated in the database.
- a providing unit that provides the calculated second operating parameter to the satellite facility via the network.
- the optimum operating parameter (second operating parameter) for the satellite facility is calculated based on the processing results in the model facility. Therefore, by providing the second operation parameter to the satellite facility, the satellite facility can be suitably unmanned operation according to the second parameter. In other words, the satellite facility can be driven unattended by following (simulating) a standard model facility.
- the satellite facility includes a plurality of satellite facilities.
- the calculation unit calculates an optimum operation parameter when the second grain is processed in the model facility, and corrects the optimum operation parameter according to each condition of the plurality of satellite facilities, thereby obtaining the second operation parameter. Calculate the parameters. According to such a form, even if there are conditions of each of the plurality of satellite facilities (for example, special circumstances different from the model facilities such as regional differences, product types, environmental conditions, equipment specifications, etc.) By performing correction according to the conditions, each satellite facility can be suitably and automatically operated.
- the accepting unit accepts the second characteristic via the network.
- the operator of the model facility or the farmer who uses the satellite facility can input the second characteristic remotely using the communication terminal. For this reason, user convenience is improved.
- the driving assistance system performs control for automatically operating the satellite facility based on the second parameter provided by the providing unit. A part. According to this mode, the same effect as any one of the first to third modes can be obtained.
- a grain processing facility includes a model facility, a satellite facility, and any one of the first to fourth driving assistance systems. According to such a grain processing facility, the same effects as any one of the first to fourth embodiments can be obtained.
- the model facility includes a data recording device that records various data of equipment provided in the model facility. According to such a form, it is possible to acquire processing data when each processing (for example, rough selection, drying, hulling, sorting) is performed in association with the receipt data, and can be used for calculating the second operation parameter.
- processing for example, rough selection, drying, hulling, sorting
- the model facility and the satellite facility are a grain joint drying preparation facility.
- the model facility and the satellite facility are a rice mill.
- a method for automatically controlling satellite facilities in a wide-area distributed grain processing facility comprising a modeling facility and a satellite facility.
- the method includes the steps of recording a first characteristic of a first grain that is brought into the model facility, processing the first grain at the model facility, and an indicator for the taste of the processed first grain.
- the process of measuring the value, the first characteristic, the first operating parameter of the model facility used when the first grain is processed in the model facility, and the index value are stored in association with each other.
- a satellite based on the step of preparing the database, the step of accepting the second characteristic of the second grain carried into the satellite facility, the received second characteristic, and the information accumulated in the database Providing a second operating parameter for use in processing the second crop at the facility, and providing the calculated second operating parameter to the satellite facility via the network; Based on the second parameter, and a step of performing automatic operation of the satellite site, the. According to this method, the same effect as that of the first embodiment is obtained.
- FIG. 1 is a conceptual diagram of a grain processing facility 1 according to an embodiment of the present invention.
- a grain processing facility 1 (hereinafter also simply referred to as a facility 1) according to an embodiment of the present invention includes a standard model facility 2 that serves as a model or norm, and a plurality of facilities installed in each production area.
- n satellite facilities also referred to as field facilities, field facilities, farm facilities or site facilities
- 3-1 to 3-n satellite facilities
- satellite facilities also referred to as satellite facilities
- satellite facilities also referred to as field facilities, field facilities, farm facilities or site facilities
- Satellite facilities are also referred to as field facilities, field facilities, farm facilities, or site facilities.
- the model facility 2, the satellite facility 3, and the cloud server 5 are connected to be communicable with each other via a network (here, the Internet 4).
- a network here, the Internet 4
- any network such as a dedicated line can be adopted.
- the model facility 2 and the satellite facility 3 are each realized as a grain joint drying preparation facility 100a, 100b in which operations from drying of grains to packaging shipment are performed.
- the grain co-drying facility is jointly used by several to hundreds of farmers.
- the model facility 2 is operated and managed by personnel, while the satellite facility 3 is operated unattended.
- the cloud server 5 acts as a driving assistance system for automatically unattended operation of the satellite facility 3.
- the cloud server 5 includes a database 5a, a reception unit 5b, a calculation unit 5c, a provision unit 5d, and a weather information acquisition unit 5e. These functional units are realized by executing a predetermined program stored in the memory.
- the database 5a the characteristics of the grains (here, rice) carried into the model facility 2, the operating parameters used when the rice was processed in the model facility 2, and the processed in the model facility 2
- the index values actually measured for the rice taste are stored in association with each other.
- the accepting unit 5 b accepts the characteristics of rice that is carried into the satellite facility 3.
- the calculation unit 5c calculates an operation parameter used when processing the rice at the satellite facility 3 based on the characteristics received by the reception unit 5b and the information stored in the database 5a.
- the providing unit 5 d provides the operating parameters calculated by the calculating unit 5 c to the satellite facility 3 via the Internet 4.
- the weather information acquisition unit 5e acquires the weather information of the area where the satellite facility 3 is installed via the Internet 4. Such weather information is provided on the Internet 4 from, for example, the Japan Meteorological Agency or a private weather information company. Details of the functions of these cloud servers 5 will be described later.
- FIG. 2 is a diagram showing a schematic configuration of the grain joint drying preparation facility 100a, 100b as the model facility 2 and the satellite facility 3.
- the model facility 2 (the grain joint drying preparation facility 100a) includes a load receiving hopper 101, a load receiving unit 103, a ventilation drying unit 105, a thermal power drying unit 107, a silo unit 109, and a huller 110.
- a sorter 111 and a preparation unit 113 are provided.
- the consignment hopper 101 receives a ginger mainly brought from a farmer.
- the load receiving unit 103 includes a coarse selection machine 118 and a load receiving weighing machine 102.
- the ventilation drying unit 105 includes a plurality of ventilation dryers 104.
- the thermal drying unit 107 includes a thermal dryer 106 that dries to a predetermined moisture while circulating the grains.
- the silo unit 109 includes a plurality of silos 108 for storing dried grains.
- the huller 110 takes out the dried grain from the silo 108 and hulls it.
- the sorter 111 performs fine grain selection after graining.
- the preparation unit 113 measures and packages the selected grain.
- the model facility 2 further includes a test dryer 114, a self-test apparatus 115, a grain discriminator 116, and a taste measuring device 117 as incidental facilities.
- the test dryer 114 dries the sample basket collected by the consignment receiving unit 103 to a predetermined moisture value.
- the self-inspection apparatus 115 removes the dried sample cake and separates it into sized particles and waste particles, and calculates the yield rate from the respective weight values of the sized particles and waste particles.
- the grain discriminator 116 takes out the grain from the self-test device 115 and optically calculates the quality and the like.
- the taste measuring device 117 optically calculates a taste value and the like.
- Grade is an index that is calculated by actually measuring the proportion of sized particles, the moisture content, the proportion of damaged particles such as colored and immature grains, and so on. It can be understood as one of the indicators.
- the taste value is an index of taste calculated based on amylose, protein, moisture, and degree of fatty acid measurement measured with a near-infrared analyzer. The taste value may be obtained by a sensory test instead of or in addition to the analytical test.
- the data recording device D for acquiring various data is electrically connected to each device constituting the model facility 2.
- the coarse selection data grain weight (branch to which the rice cob is attached)
- grain weight fine grain weight
- ratio of branch leaf grain straw weight
- a data recording device D1 is connected to acquire the straw ratio, etc.
- the receiving data grain weight value, receiving water value, variety, farm owner code, production location (field location), etc.
- the thermal dryer 106 obtains drying data (initial moisture value of straw before drying, finish moisture value of straw after drying, drying rate, total drying time, fuel consumption rate, total electric energy, etc.).
- a data recording device D2 is connected.
- the hulling machine 110 is provided with hulling data (dehulling capacity, dehulling rate, immature grain mixing rate, dehulling roll replacement frequency, maximum current value, average current value, minimum current value, total electric energy, etc.).
- the data recording device D3 to be acquired is connected.
- the sorting machine 111 includes sorting data (defective particle removal rate (sorting rate), rotation speed of the sorting cylinder of the rotary sorting machine, number of ejectors of the color sorting machine, maximum current value, average current value, minimum current value, and total power.
- a data recording device D4 for acquiring the quantity etc. is connected.
- the weighing / packaging machine 112 is connected to a data recording device D5 for obtaining weighing / packaging data (measurement count, cumulative (shipped) measured value, cumulative (shipped) packaged number, total electric energy, etc.).
- the silo 108 is connected to a data recording device D6 that acquires storage data (storage period, number of rotations, maximum grain temperature, minimum grain temperature, average grain temperature, etc.).
- the self-verification device 115, the grain discriminator 116 and the taste measuring device 117 include self-verification data (weight value of rice bran, brown rice, sizing and waste grains, moisture, yield, variety, farm owner code). And production location (field location, etc.), quality data (size, particle size, colored particle weight, etc.) and taste data (protein content, amylose content, taste sensory evaluation, taste value, etc.) A data recording device D7 to be acquired is connected.
- the satellite facility 3 does not include various instruments such as the test dryer 114, the self-test device 115, and the various data recording devices D1 to D7.
- the operation management in the facility 1 described above will be described below.
- consignment data such as a producer, a variety, and a production location is acquired by the data recording device D1. This is done by the importer or the operator of the model facility 2 using the user interface for input.
- the received rice is sorted and dried in various facilities, and the data recording devices D2 to D7 acquire the preparation processing data in which the processing state of the preparation machine at that time is quantified.
- the data recording device D7 measures the index values (here, the quality data and the taste data) about the processed rice taste.
- Information acquired by the data recording devices D1 to D7 is associated with each other and recorded in the database 5a via the Internet 4.
- FIG. 3 and 4 show an example of a basic database configuration of the model facility.
- FIG. 3 shows information acquired by the data recording devices D1 and D7.
- FIG. 4 shows preparation processing data acquired by the data recording devices D1 to D4, for example, rough selection data, drying data, hulling data, sorting data, etc. (the weighing / packaging data and storage data are not shown in the figure). Omitted.)
- the information shown in FIG. 4 is stored in association with each “receipt No.” shown in FIG.
- the database 5a may also store production location information for various parts of Japan. From this database, an operation parameter (for example, in FIG. 3, “good” or higher quality and 80 or higher taste value are associated with each other for each specific variety in a specific production area.
- a reference operation parameter that is an operation parameter) may be set. For example, in the model facility 2, preparation processing data when processing rice corresponding to this condition is set as a reference operation parameter corresponding to “rice produced in A district of A prefecture and cultivar Koshihikari” May be.
- the weather information acquired by the weather information acquisition unit 5e may be recorded in the database 5a.
- This meteorological information is meteorological data corresponding to the rice production area and the year of harvest, which are the basis of each data group stored in the database 5a.
- the weather information may be, for example, accumulated sunshine hours or accumulated temperatures (accumulated values of daily average temperatures) during a period from sowing to harvesting. Accumulated sunshine hours affect rice maturity, and cumulative air temperature affects protein content, so these are important factors in producing good-tasting rice.
- the operation parameters of the satellite facility 3 are calculated according to the characteristics of the rice carried into the satellite facility 3.
- FIG. 5 is a conceptual diagram of a process for calculating the operation parameters of the satellite facility 3.
- this process is started when the operator of the model facility 2 or the farmer (importer) who uses the satellite facility 3 inputs the characteristics of the rice that is carried into the satellite facility 3 (steps). S1).
- This characteristic is an arbitrary factor that affects the quality of rice, and such factors include, for example, the production area, variety, weather conditions, soil conditions, pattern, and the like.
- This input may be performed via the Internet 4 using an information terminal such as a smartphone, or may be performed via the user interface of the model facility 2 or the satellite facility 3.
- Such input content is received by the receiving unit 5 b of the cloud server 5.
- the calculation unit 5c (FIG. 1) of the cloud server 5 searches the various data files 119 to 125 (FIG. 5) stored in the database 5a based on the various factors received by the reception unit 5b.
- the weather information acquisition unit 5e of the cloud server 5 acquires the weather information of the area to which the satellite facility 3 belongs via the Internet 4.
- the calculation part 5c is based on the characteristic of the rice received by the reception part 5b (The weather information acquired by the weather information acquisition part 5e may be included), and the information accumulate
- each condition of the satellite facility 3 that is, the model facility 2 and the like (for example, regional difference, product type difference, environmental condition difference, equipment specification difference, etc.) Corrections are made according to different special circumstances.
- the content of this correction is experimentally measured by measuring the taste index value of the rice processed at the satellite facility 3 using the above-mentioned optimum operation parameter, and empirically and experimentally from the relationship between the optimum operation parameter and the taste index value. It is set in advance.
- This correction may be preset for each combination of rice characteristics before and after the correction.
- the correction coefficient in the correspondence relationship between “rice produced in A district and produced in A district and the variety is Koshihikari rice” and “produced in B district and produced in B district and the variety is Hinohikari rice” is stored in the table. May be.
- the correction value (also referred to as a correction operation parameter) is calculated in this way, the correction value is stored in the master table 126 (FIG. 5) of the cloud server 5 and is provided to the satellite facility 3 via the Internet 4 by the providing unit 5d. Provided.
- control unit 33 receives the corrected operation parameter provided by the providing unit 5d, and automatically operates the various facilities of the satellite facility 3 based on the correction operation parameter.
- AI artificial intelligence
- logical inference based on the data files 119 to 125 and artificial intelligence (“AI”) learning from past experience are used. May be. Even if various known methods and algorithms are used, such as experimental design, neural network, deep learning, fuzzy reasoning, multivariate analysis (Mahalanobis distance, multiple regression analysis, etc.), sparse modeling, support vector machine, etc. Good. Moreover, even if AI learns by periodically measuring the taste index value of the rice processed at the satellite facility 3 and associating the taste index value with the characteristic of the rice and feeding back to the cloud server 5. Good.
- the satellite facility 3 can be operated with optimum parameters based on the results of the model facility 2. Therefore, even in the grain common drying preparation facility where the properties of the starting material change every day, the group behavior is monitored while following the model facility 2, and thereby, the satellite facility 3 can also be unmanned.
- the satellite facility 3 can basically be operated following the standard model facility 2, various instruments such as the test dryer 114, the self-test device 115, and the various data recording devices D1 to D7 are not necessarily required. Therefore, the satellite facility 3 can have a simplified equipment configuration than the model facility 2.
- Second embodiment A second embodiment of the present invention will be described.
- the model facility 2 and the satellite facility 3 are, in this embodiment, milled rice mills 200a and 200b, each of which selects and weighs the selected brown rice, and then sorts and weighs it and packs it for shipping. Realized.
- description of the same points as in the first embodiment will be omitted, and only differences from the first embodiment will be described.
- the rice milling plant 200a includes a cargo receiving unit 203, a rice milling unit 208, a selection unit 211, and a weighing packaging unit 213.
- the load receiving unit 203 includes a load receiving hopper 201 that receives brown rice brought in from the market, and a coarse sorter 202.
- the rice milling unit 208 includes a plurality of rice milling machines 204, 205, 206 and a stone remover 207.
- the selection unit 211 includes a color sorter 209 and a sieving machine 210.
- the weighing and packaging unit 213 includes a weighing and packaging machine 212.
- a data recording device D for acquiring various data is electrically connected to each device.
- the consignment hopper 201 and the coarse selection machine 202 include consignment data (grain weight value, consignment moisture value, variety, farm owner code, production location (field location), etc.) and coarse selection data (string-like).
- a data recording device D10 for acquiring a mixing ratio of foreign matters such as objects and a sizing weight) is connected.
- the rice milling machine (first machine) 204 is connected to a data recording device D11 that obtains the most sophisticated data (current value, yield, whiteness, total driving time, total electric energy, etc.).
- a data recording device D12 for acquiring second machine milling data is connected to the rice milling machine (second machine) 205, and data for obtaining third machine milling data is connected to the rice milling machine (third machine) 206.
- a recording device D13 is connected.
- the stone remover 207 is connected to a data recording device D14 that obtains stone removal data (the weight of stone particles, the weight of sized particles, the mixing rate of stones, the total amount of electric power, etc.).
- the color sorter 209 includes a data recording device for obtaining sorting data (defective grain removal rate (sorting rate), number of ejectors of the color sorter, maximum current value, average current value, minimum current value, total electric energy, etc.). D15 is connected.
- the sieving machine 210 is connected to a data recording device D16 that acquires sieving data (sieving machine rotation speed, crushed grain ratio, sizing ratio, total electric energy, etc.).
- the weighing / packing machine 212 is connected to a data recording device D17 for obtaining weighing / packaging data (measurement count, accumulated (shipped) measured value, accumulated (shipped) wrapped bag number, total electric energy, etc.).
- the calculation unit 5c processes rice at the satellite facility 3 based on the characteristics received by the receiving unit 5b and the information stored in the database 5a. Calculate the operating parameters to be used. As a result, the same effect as in the first embodiment can be obtained.
- Modification 2 In addition to the above-described embodiment, when the model facility 2 receives the grain, the operating parameters for processing the grain at the model facility 2 are determined by the above-described method, that is, the characteristics of the imported grain and the database 5a. And may be calculated based on the information stored in.
- Modification 3 At least a part of the functions of the cloud server 5 described above may be arranged at a location on another network.
- all of the functions of the cloud server 5 may be provided in the model facility 2 or may be provided in an information processing terminal connected to the model facility 2 via a LAN.
- Modification 4 The embodiments described above can be applied to any grain processing facility, for example, a food factory that handles grains.
- the characteristics of the grains that are brought into the grain processing facility, the operating parameters that were used when the grains were processed in the grain processing facility, and the measured indicators of the taste of the grains after they were processed in the grain processing facility Based on a database in which values are stored in association with each other, a reception unit that receives characteristics of a grain newly brought into a grain processing facility, received characteristics, and information accumulated in the database, Operation of a grain processing facility comprising: a calculation unit that calculates an operation parameter used when processing newly introduced grain; and a control unit that controls operation of the grain processing facility based on the calculated operation parameter A control system may be provided.
- Input means 8 Meteorological information acquisition means 100 Grain joint drying preparation facility 101 Load receiving hopper 102 Load receiving weighing machine 103 Load receiving section 104 Ventilation dryer 105 Ventilation drying section 106 Thermal dryer 107 Thermal Power Drying Unit 108 Silo 109 Silo Unit 110 Peeling Machine 111 Sorting Machine 112 Weighing / Packing Machine 113 Preparation Unit 114 Test Dryer 115 Self-Test Device 116 Grain Discriminator 117 Taste Measuring Instrument 118 Coarse Selector 119 Receipt Data File 120 Selection data file 121 Drying data file 122 Rice hull data file 123 Selection data file 124 Weighing / packaging data file 125 Storage data file 126 Master table 200 Rice mill 201 Receiving hot 202 roughing machine 203 goods receptacle 204 rice mill 205 rice mill 206 rice mill 207 Stone cutting machine 208 rice polishing section 209 color sorter
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Abstract
Description
以下、本発明の第1実施形態を図面に基づいて説明する。図1は、本発明の一実施形態による穀物処理施設1の概念図である。図1に示すように、本発明の一実施形態による穀物処理施設1(以下、単に施設1とも呼ぶ)は、模範または規範となる標準的なモデル施設2と、各生産地に設置される複数(ここでは、n個)のサテライト施設(現場施設、フィールド施設、圃場施設またはサイト施設とも称される)3-1~3-n(サテライト施設3-1~3-nを総称して、サテライト施設3とも呼ぶ)と、クラウドサーバ5と、を備えている。サテライト施設は、現場施設、フィールド施設、圃場施設またはサイト施設とも称される。モデル施設2とサテライト施設3とクラウドサーバ5とは、ネットワーク(ここでは、インターネット4)を介して互いに通信可能に接続されている。インターネット4に代えて、専用回線など、任意のネットワークが採用され得る。
本発明の第2実施形態について説明する。第2実施形態では、モデル施設2およびサテライト施設3は、本実施形態では、それぞれ、精選された玄米を精米した後、選別・計量するとともに袋詰めをして出荷を行う精米工場200a,200bとして実現される。以下、第1実施形態と同一の点については説明を省略し、第1実施形態と異なる点についてのみ説明する。
C-1.変形例1:
モデル施設2が1つのサテライト施設3-1に対して学習を行えば、学習を終えたサテライト施設3-1を基準にして、他のサテライト施設3-2~3-nに順次伝達する(サテライト施設3-1…3-nどうしの相互学習)ようにしてもよい。この処理は、クラウドサーバ5の算出部5cによって実行されてもよい。
上述の実施形態に加えて、モデル施設2は、穀物の搬入を受けた場合、その穀物をモデル施設2で処理するための運転パラメータを上述の手法によって、つまり、搬入穀物の特性と、データベース5aに蓄積された情報と、に基づいて算出してもよい。
上述したクラウドサーバ5の機能の少なくとも一部は、他のネットワーク上の場所に配置されてもよい。例えば、クラウドサーバ5の機能のすべては、モデル施設2に備えられてもよいし、モデル施設2とLANを介して接続された情報処理端末に備えられてもよい。
上述の実施形態は、任意の穀物処理施設に適用することができ、例えば、穀物を扱う食品工場に適用されてもよい。
2 モデル施設
3 サテライト施設
4 インターネット
5 クラウドサーバ
6 補正手段
7 入力手段
8 気象情報取得手段
100 穀物共同乾燥調製施設
101 荷受ホッパ
102 荷受計量機
103 荷受部
104 通風乾燥機
105 通風乾燥部
106 火力乾燥機
107 火力乾燥部
108 サイロ
109 サイロ部
110 籾摺機
111 選別機
112 計量・包装機
113 調製部
114 テストドライヤー
115 自主検定装置
116 穀粒判別器
117 食味測定器
118 粗選機
119 荷受データファイル
120 粗選データファイル
121 乾燥データファイル
122 籾摺データファイル
123 選別データファイル
124 計量・包装データファイル
125 貯蔵データファイル
126 マスタテーブル
200 精米工場
201 荷受ホッパ
202 粗選機
203 荷受部
204 精米機
205 精米機
206 精米機
207 石抜き機
208 精米部
209 色彩選別機
210 篩分け機
211 精選部
212 計量・包装機
213 計量包装部
D データ収録装置
Claims (9)
- 穀物処理施設の運転補助システムであって、
モデル施設に搬入される第1の穀物の第1の特性と、前記第1の穀物が前記モデル施設で処理された際に使用された該モデル施設の第1の運転パラメータと、前記モデル施設で処理された後の前記第1の穀物の味覚について実測された指標値と、が対応付けられて記憶されるデータベースと、
サテライト施設に搬入される第2の穀物の第2の特性を受け付ける受付部と、
前記受け付けられた第2の特性と、前記データベースに蓄積された情報と、に基づいて、前記サテライト施設で前記第2の穀物を処理する際に使用される第2の運転パラメータを算出する算出部と、
前記算出された第2の運転パラメータを、ネットワークを介して前記サテライト施設に提供する提供部と
を備える運転補助システム。 - 請求項1に記載の運転補助システムであって、
前記サテライト施設は、複数のサテライト施設を備え、
前記算出部は、前記第2の穀物を前記モデル施設で処理する場合の最適運転パラメータを算出し、該最適運転パラメータに対して、前記複数のサテライト施設の各々の条件に応じた補正を行うことによって、前記第2のパラメータを算出する
運転補助システム。 - 請求項1または請求項2に記載の運転補助システムであって、
前記受付部は、ネットワークを介して前記第2の特性を受け付ける
運転補助システム。 - 請求項1ないし請求項3のいずれか一項に記載の運転補助システムであって、
前記提供部によって提供される前記第2のパラメータに基づいて、前記サテライト施設の自動運転を行う制御部を備える
運転補助システム。 - 穀物処理施設であって、
前記モデル施設と、
前記サテライト施設と、
請求項1ないし請求項4のいずれか一項に記載の運転補助システムと
を備える穀物処理施設。 - 請求項5に記載の穀物処理施設であって、
前記モデル施設は、該モデル施設が備える設備の各種データを収録するデータ収録装置を備える
穀物処理施設。 - 請求項5または請求項6の穀物処理施設であって、
前記モデル施設および前記サテライト施設は、穀物共同乾燥調製施設である
穀物処理施設。 - 請求項5または請求項6の穀物処理施設であって、
前記モデル施設および前記サテライト施設は、精米工場である
穀物処理施設。 - モデル化施設とサテライト施設とを備える広域分散配置型穀物処理施設において、前記サテライト施設を自動運転制御する方法であって、
モデル施設に搬入される第1の穀物の第1の特性を記録する工程と、
前記モデル施設において前記第1の穀物を処理する工程と、
前記処理された第1の穀物の味覚についての指標値を実測する工程と、
前記第1の特性と、前記第1の穀物が前記モデル施設で処理された際に使用された該モデル施設の第1の運転パラメータと、前記指標値と、が対応付けられて記憶されるデータベースを用意する工程と、
前記サテライト施設に搬入される第2の穀物の第2の特性を受け付ける工程と、
前記受け付けられた第2の特性と、前記データベースに蓄積された情報と、に基づいて、前記サテライト施設で前記第2の穀物を処理する際に使用される第2の運転パラメータを算出する工程と、
前記算出された第2の運転パラメータを、ネットワークを介して前記サテライト施設に提供する工程と、
前記提供された前記第2のパラメータに基づいて、前記サテライト施設の自動運転を行う工程と
を備える方法。
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BR112019000774-1A BR112019000774A2 (pt) | 2016-07-22 | 2017-07-19 | sistema de assistência operacional para instalação de processamento de grãos e método de controle de operação automática para instalação por satélite |
EP17831028.0A EP3489884A4 (en) | 2016-07-22 | 2017-07-19 | ASSISTANCE SYSTEM FOR THE OPERATION OF A GRAIN PROCESSING SITE AND METHOD FOR CONTROLLING THE AUTOMATIC OPERATION OF A RELATED SITE |
CN201780045460.8A CN109564670A (zh) | 2016-07-22 | 2017-07-19 | 谷物处理设施的运转辅助系统、以及卫星设施的自动运转控制方法 |
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YUSUKE YOKOTA: "We want to know expanding IoT, and how will factories change?", 3 July 2016, NIHON KEIZAI SHINBUN |
Also Published As
Publication number | Publication date |
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EP3489884A1 (en) | 2019-05-29 |
TW201816695A (zh) | 2018-05-01 |
CN109564670A (zh) | 2019-04-02 |
KR20190034249A (ko) | 2019-04-01 |
JP6681048B2 (ja) | 2020-04-15 |
BR112019000774A2 (pt) | 2019-04-24 |
JP2018014028A (ja) | 2018-01-25 |
EP3489884A4 (en) | 2020-02-19 |
PH12019500150A1 (en) | 2019-10-14 |
US20210283618A1 (en) | 2021-09-16 |
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