JP2021183891A5 - - Google Patents
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- JP2021183891A5 JP2021183891A5 JP2020089568A JP2020089568A JP2021183891A5 JP 2021183891 A5 JP2021183891 A5 JP 2021183891A5 JP 2020089568 A JP2020089568 A JP 2020089568A JP 2020089568 A JP2020089568 A JP 2020089568A JP 2021183891 A5 JP2021183891 A5 JP 2021183891A5
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- dust
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Claims (15)
前記データ取得部により過去に取得されたごみの表面の１つの領域に含まれる複数の点の計測データと、当該領域でのごみの特性を示す値またはごみの質を分類したラベルとの関係性を機械学習している学習済みモデルを用いて、前記データ取得部により新たに取得されたごみの表面の１つの領域に含まれる複数の点の計測データを入力として、当該領域でのごみの特性を示す値またはごみの質を分類したラベルを推定して出力する推定部と、
を備えたことを特徴とする情報処理装置。 a data acquisition unit that acquires measurement data from a laser type level sensor that irradiates an infrared laser onto the surface of the refuse stored in the refuse pit and measures the reflection intensity of the surface of the refuse;
The relationship between the measurement data of a plurality of points included in one area on the surface of the dust acquired in the past by the data acquisition unit, and the value indicating the characteristics of the dust in the area or the label classifying the quality of the dust. Using a learned model that machine-learns the characteristics of the dust in the area, the measurement data of a plurality of points included in one area of the surface of the dust newly acquired by the data acquisition unit is input. an estimating unit for estimating and outputting a value indicating or a label classifying the quality of garbage;
An information processing device comprising:
前記推定部は、前記データ取得部により過去に取得されたごみの表面の１つの領域に含まれる複数の点の反射強度、または反射強度および高さの計測データと、当該領域でのごみの特性を示す値またはごみの質を分類したラベルとの関係性を機械学習している学習済みモデルを用いて、前記データ取得部により新たに取得されたごみの表面の１つの領域に含まれる複数の点の反射強度、または反射強度および高さの計測データを入力として、当該領域でのごみの特性を示す値またはごみの質を分類したラベルを推定して出力する、
ことを特徴とする請求項１に記載の情報処理装置。 The laser level sensor measures the reflection intensity and height of the surface of the dust by irradiating the surface of the dust accumulated in the dust pit with an infrared laser,
The estimating unit acquires the measurement data of the reflection intensity or the reflection intensity and height of a plurality of points included in one region of the surface of the dust acquired in the past by the data acquisition unit, and the characteristics of the dust in the region. Using a learned model that machine-learns the relationship between the value indicating the or the label that classifies the quality of the garbage, a plurality of Taking as input the reflection intensity of a point, or the reflection intensity and height measurement data, estimating and outputting a value indicating the characteristics of the dust in the area or a label classifying the quality of the dust,
The information processing apparatus according to claim 1, characterized by:
ことを特徴とする請求項１または２に記載の情報処理装置。 The estimation unit divides the measurement data of the surface of the dust acquired by the data acquisition unit into a plurality of blocks , and measures a plurality of points included in one block of the measurement data in units of divided blocks. inputting data into the trained model, estimating and outputting a value indicating the characteristics of the garbage in the block or a label classifying the quality of the garbage in units of blocks;
3. The information processing apparatus according to claim 1, wherein:
をさらに備えたことを特徴とする請求項１～３のいずれかに記載の情報処理装置。 Based on the value indicating the characteristics of the garbage estimated by the estimation unit or the label classifying the quality of the garbage, an operation instruction is sent to a crane control device that controls a crane that agitates or conveys the garbage, or the garbage is sent. An instruction unit that transmits an operation instruction to a combustion control device that controls the combustion of
4. The information processing apparatus according to any one of claims 1 to 3, further comprising:
ことを特徴とする請求項１～４のいずれかに記載の情報処理装置。 The laser level sensor is installed on a crane girder,
The information processing apparatus according to any one of claims 1 to 4, characterized in that:
ことを特徴とする請求項１～５のいずれかに記載の情報処理装置。 The garbage property is at least one of weight, density, moisture content, and calorific value.
The information processing apparatus according to any one of claims 1 to 5, characterized in that:
ことを特徴とする請求項１～６のいずれかに記載の情報処理装置。 The machine learning algorithm is at least one of maximum likelihood classification, Boltzmann machine, neural network, support vector machine, Bayesian network, sparse regression, decision tree, statistical estimation using random forest, reinforcement learning, and deep learning. is one
The information processing apparatus according to any one of claims 1 to 6, characterized in that:
請求項１～７のいずれかに記載の情報処理装置と、
を備えたことを特徴とするごみ処理プラント。 a laser type level sensor that measures the reflection intensity of the surface of the dust by irradiating the surface of the dust accumulated in the dust pit with an infrared laser;
an information processing device according to any one of claims 1 to 7;
A waste treatment plant comprising:
ごみピット内に貯留されたごみの表面に赤外線レーザーを照射してごみの表面の反射強度を計測するレーザー式レベルセンサから計測データを取得するデータ取得部と、
前記データ取得部により過去に取得されたごみの表面の１つの領域に含まれる複数の点の計測データと、当該領域でのごみの特性を示す値またはごみの質を分類したラベルとの関係性を機械学習している学習済みモデルを用いて、前記データ取得部により新たに取得されたごみの表面の１つの領域に含まれる複数の点の計測データを入力として、当該領域でのごみの特性を示す値またはごみの質を分類したラベルを推定して出力する推定部と、
として機能させることを特徴とする情報処理プログラム。 the computer,
a data acquisition unit that acquires measurement data from a laser type level sensor that irradiates an infrared laser onto the surface of the refuse stored in the refuse pit and measures the reflection intensity of the surface of the refuse;
The relationship between the measurement data of a plurality of points included in one area on the surface of the dust acquired in the past by the data acquisition unit, and the value indicating the characteristics of the dust in the area or the label classifying the quality of the dust. Using a learned model that machine-learns the characteristics of the dust in the area, the measurement data of a plurality of points included in one area of the surface of the dust newly acquired by the data acquisition unit is input. an estimating unit for estimating and outputting a value indicating or a label classifying the quality of garbage;
An information processing program characterized by functioning as
ごみピット内に貯留されたごみの表面に赤外線レーザーを照射してごみの表面の反射強度を計測するレーザー式レベルセンサから計測データを取得するステップと、
前記レーザー式レベルセンサから過去に取得されたごみの表面の１つの領域に含まれる複数の点の計測データと、当該領域でのごみの特性を示す値またはごみの質を分類したラベルとの関係性を機械学習している学習済みモデルを用いて、前記レーザー式レベルセンサから新たに取得されたごみの表面の１つの領域に含まれる複数の点の計測データを入力として、当該領域でのごみの特性を示す値またはごみの質を分類したラベルを推定して出力するステップと、
を含むことを特徴とする情報処理方法。 A computer-executed information processing method comprising:
a step of irradiating an infrared laser onto the surface of the dust accumulated in the dust pit and acquiring measurement data from a laser level sensor that measures the reflection intensity of the surface of the dust;
The relationship between the measurement data of a plurality of points included in one area of the surface of the dust obtained in the past from the laser level sensor and the value indicating the characteristics of the dust in that area or the label classifying the quality of the dust Using a trained model that machine-learns the characteristics of the dust, the measurement data of a plurality of points included in one area of the surface of the dust newly acquired from the laser level sensor is input, and the dust in the area is measured. a step of estimating and outputting a value indicating the characteristics of or a label classifying the quality of the garbage;
An information processing method comprising:
を備えたことを特徴とする情報処理装置。 Measurement data of multiple points included in one area of the surface of the dust measured by a laser type level sensor that measures the reflection intensity of the surface of the dust by irradiating the surface of the dust accumulated in the dust pit with an infrared laser. (2) an information processing apparatus comprising: a training data generation unit that generates training data by associating a value indicating characteristics of dust in the area or a label classifying the quality of dust as a correct answer.
をさらに備えたことを特徴とする請求項１１に記載の情報処理装置。 12. The information processing apparatus according to claim 11, further comprising a model building section that builds a learned model by performing machine learning on the teacher data generated by the teacher data generating section.
前記データ取得部により過去に取得されたごみの表面の１つの領域に含まれる複数の点の計測データと、当該領域でのごみの特性を示す値またはごみの質を分類したラベルとの関係性を機械学習している学習済みモデルを用いて、前記データ取得部により新たに取得されたごみの表面の１つの領域に含まれる複数の点の計測データを入力として、当該領域でのごみの特性を示す値またはごみの質を分類したラベルを推定して出力する推定部と、
を備えたことを特徴とする情報処理装置。 a data acquisition unit that acquires measurement data from a laser type level sensor that irradiates an infrared laser onto the surface of the waste thrown into the feeding hopper and measures the reflection intensity of a plurality of points included in one region of the surface of the waste;
The relationship between the measurement data of a plurality of points included in one area on the surface of the dust acquired in the past by the data acquisition unit, and the value indicating the characteristics of the dust in the area or the label classifying the quality of the dust. Using a learned model that machine-learns the characteristics of the dust in the area, the measurement data of a plurality of points included in one area of the surface of the dust newly acquired by the data acquisition unit is input. an estimating unit for estimating and outputting a value indicating or a label classifying the quality of garbage;
An information processing device comprising:
前記データ取得部により過去に取得されたごみの表面の１つの領域に含まれる複数の点の計測データと、当該領域でのごみの特性を示す値またはごみの質を分類したラベルとの関係性を機械学習している学習済みモデルを用いて、前記データ取得部により新たに取得されたごみの表面の１つの領域に含まれる複数の点の計測データを入力として、当該領域でのごみの特性を示す値またはごみの質を分類したラベルを推定して出力する推定部と、
を備えたことを特徴とする情報処理装置。 a data acquisition unit that is installed near the input door and acquires measurement data from a laser level sensor that irradiates an infrared laser onto the surface of the garbage unloaded from the garbage delivery vehicle and measures the reflection intensity of the surface of the garbage;
The relationship between the measurement data of a plurality of points included in one area on the surface of the dust acquired in the past by the data acquisition unit, and the value indicating the characteristics of the dust in the area or the label classifying the quality of the dust. Using a learned model that machine-learns the characteristics of the dust in the area, the measurement data of a plurality of points included in one area of the surface of the dust newly acquired by the data acquisition unit is input. an estimating unit for estimating and outputting a value indicating or a label classifying the quality of garbage;
An information processing device comprising:
前記データ取得部により過去に取得された焼却灰の表面の１つの領域に含まれる複数の点の計測データと、当該領域での焼却灰の特性を示す値または焼却灰の質を分類したラベルとの関係性を機械学習している学習済みモデルを用いて、前記データ取得部により新たに取得された焼却灰の表面の１つの領域に含まれる複数の点の計測データを入力として、当該領域での焼却灰の特性を示す値または焼却灰の質を分類したラベルを推定して出力する推定部と、
を備えたことを特徴とする情報処理装置。 a data acquisition unit that acquires measurement data from a laser level sensor that irradiates an infrared laser onto the surface of the incineration ash stored in the ash pit and measures the reflection intensity of the surface of the incineration ash;
Measurement data of a plurality of points included in one region on the surface of the incinerated ash acquired in the past by the data acquisition unit, and a value indicating the characteristics of the incinerated ash in the region or a label classifying the quality of the incinerated ash Using a trained model that machine-learns the relationship of, the measurement data of a plurality of points included in one region of the surface of the incinerated ash newly acquired by the data acquisition unit is input, and in the region an estimating unit that estimates and outputs a value indicating the characteristics of the incinerated ash or a label that classifies the quality of the incinerated ash;
An information processing device comprising:
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP2020089568A JP2021183891A (en) | 2020-05-22 | 2020-05-22 | Information processing device, information processing program, and information processing method |
| PCT/JP2021/018895 WO2021235464A1 (en) | 2020-05-22 | 2021-05-19 | Information processing device, information processing program, and information processing method |
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| JP2020089568A JP2021183891A (en) | 2020-05-22 | 2020-05-22 | Information processing device, information processing program, and information processing method |
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| JP2021183891A JP2021183891A (en) | 2021-12-02 |
| JP2021183891A5 true JP2021183891A5 (en) | 2023-03-08 |
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| DE19919222C1 (en) * | 1999-04-28 | 2001-01-11 | Orfeus Comb Engineering Gmbh | Method for controlling the combustion of fuel with a variable calorific value |
| JP2006046680A (en) * | 2004-07-30 | 2006-02-16 | Kawasaki Heavy Ind Ltd | Pre-treatment device, method and program of waste treatment equipment |
| JP4701140B2 (en) * | 2006-09-06 | 2011-06-15 | 三菱重工環境・化学エンジニアリング株式会社 | Stoker-type incinerator and its combustion control method |
| JP6554148B2 (en) * | 2017-07-31 | 2019-07-31 | 荏原環境プラント株式会社 | Apparatus, system, program, method, and data structure for estimating waste quality |
| JP6457137B1 (en) * | 2018-02-27 | 2019-01-23 | 株式会社タクマ | Garbage mixing degree evaluation system |
| WO2020040110A1 (en) * | 2018-08-23 | 2020-02-27 | 荏原環境プラント株式会社 | Information processing device, information processing program, and information processing method |
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