JP7123970B2 - スマート学習を用いた付加価値のある有害生物防除システム - Google Patents
スマート学習を用いた付加価値のある有害生物防除システム Download PDFInfo
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- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
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- A—HUMAN NECESSITIES
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- A01M1/00—Stationary means for catching or killing insects
- A01M1/20—Poisoning, narcotising, or burning insects
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- A—HUMAN NECESSITIES
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Description
本願は、2017年4月12日に出願された米国特許出願第15/485,838号の利益を請求し、その内容は参照により全体が本願に援用される。
図7に表される実施例1は、有害生物の発生の監視を最適化するためにシステムがどのように配備されるかを示す、一般化された略図を示す。
監視される各アカウントは、ビジネスの性質、ビジネスの場所、施設の大きさ、考慮される有害生物の種類、配備される監視装置の種類といった、施設の記述的特徴でタグ付けされる。そして、2つの場所特有の調査が完了する。ある人は、有害生物が入ってくる外部開口部(例えば、ドア、ローディングドック、窓)の数などの、施設内での有害生物の発生を可能としてしまう非生物的要因に目を向ける。別の人は、覆われていない食料や開放水源などの、施設内での有害生物の発生を持続させてしまう生物的要因に注目する。こうした調査の結果は、有害生物監視装置の数および配置の決定のためにアルゴリズム(方式)で処理される。例えば、1つの監視装置は有害生物の侵入地点となっているドアの片側に置かれ、1つの監視装置は有害生物が頻繁に向かう食料および水源から規定の距離以内に置かれる。こうして監視場所の基本的な数が決められ、施設における最近の有害生物の活動履歴が相互参照されて、有害生物の過去の活動履歴の記録のある場所における監視装置の数が増やされる。そして、ユーザは2つのリスク指数のうちの1つを適用して、過去の監査または査察に反映された過去の有害生物の事件の記録を考慮して配備される監視装置の数をさらに変更することができて、特定のビジネスのリスク許容度に関連するより主観的な指数を適用することもできる。
前述のプロセスは、有害生物の存在を検知するために、施設内での監視装置の推奨される配置方式を規定する。システムが稼働するにつれて個別の有害生物の報告書が生じ、システムは「学習」を行って、施設自体の内部における進行中の有害生物の活動に応じて、または監視の「ライブラリ」から類似のビジネス、施設の大きさ、場所などを有する他の施設における配備を引き出すことで、有害生物監視装置の配置をさらに最適化することができる。このようにして、監視装置の数および配置を最適化して(増やされる、減らされる、または再配置される)、有害生物の発生を施設に対して設定された許容限界以内に維持しながらも有害生物監視ハードウェアの費用を最小化することができる。
図8に表される実施例2は、有害生物管理、特に施設内の有害生物の発生を軽減するための殺虫剤製剤の塗布、を最適化するためにシステムがどのように配備されるかを示す、一般化された略図を示す。
サービスを受ける各アカウントは、ビジネスの性質、ビジネスの場所、施設の大きさ、遭遇する有害生物の種類といった、施設の記述的特徴でタグ付けされる。あらゆるサービスにおいて、何時にどの殺虫剤がどんな濃度でどれだけの量を塗布されたか、といった、殺虫剤塗布イベントの特徴が記録される。この殺虫剤塗布の処置イベントの記録が分析のためにデータベースへ記録される。積み重ねられた処置イベントに対する分析方法は多様であるが、任意の数の例でいくつかのアイデアを得ることができる。一例では、データベースはその場所での温度および降水イベントの外部データベースにアクセスできて、これらのデータは処置イベントの頻度に相関性を持つことがあり、使用される特定の殺虫剤の持続性/有効性に気候がどんな影響を及ぼすかを学習することができる。また、システムは、いつどこで特定の殺虫剤を使用することができるかについての政府の規制または民間の規制を要約した様々な「ライブラリ」を参照するように設定することもできて、技術者に警告を(接続された塗布装置を介して)送って、意図しない違反により有害生物管理会社の法令順守記録に悪影響を与えるのを防ぐことができる。有害生物管理会社は演繹的にしきい値を規定する、または処置イベントの帰納的分析を行って、是正措置が必要な処置イベントにおける外れ値にフラグを立てることができる。例えば、そのすべての技術者による塗布を追跡することで、会社は塗布量やさらにサービス時間の閾値を、例えば
実施例1で説明された、最適化された有害生物監視とのインタフェースに応じて、ユーザは施設における処置の必要性を示唆する、有害生物発生のしきい値を適切な演算式で規定することができる。有害生物検知システムがそのようなしきい値を超える有害生物の発生を報告した場合はいつでも、最適化された有害生物管理システムは施設において追加のサービスが必要であることを塗布器に通知する警告を生成することができる。分析は、サービス上の警告の必要性を次のように修正するためになされる。特定の製品が、例えば前回のサービスからの時間が予想よりも短い場合に、推奨されることがある。
単一の会社または数多くの会社の記録に基づき、処置後の予想される有害生物抑止持続時間を、例えば
各化学薬品の割合Rに対して、有害生物抑止期間がある。それゆえ、Std(x)と
経時的に有害生物抑止が測定され、許容範囲より下まで下落した場合、必要に応じて警告が与えられる。
さらに、有害生物集団における特定の殺虫剤に対する生理学的抵抗が進展するのを未然に防ぐため、ユーザは異なる殺虫剤の間での計画的なローテーションを規定することができて、分析により施設内での一つの殺虫剤の逐次使用が記録されて、管理方式の中で次に使用される製品が推薦される。また、標準的な結果から外れた悪い結果も、抵抗の進展またはシステム全体の有効性を悪化させる他の条件に関するリスクについて、ユーザへ警告するよう作用する。
これらの実施例は、頻度または質に関して、配送データ知見によって殺虫剤塗布イベントを制御するのに、システムをどのように使うことができるかを示す。さらに、有害生物監視システムで生成された有害生物に関する警告への統合との相互作用によって、施設に対して設定された許容限界以内に有害生物の発生を維持しながら殺虫剤による処置に関連する費用およびリスクを最小化することで、随意で分析を望むとおりに変更し、施設における有害生物管理を最適化することができる。
Claims (2)
- ある場所用のデジタル化された総合的有害生物防除管理システムであって、前記システムは、
複数のデータソースから場所特有のデータを受信し、前記場所特有のデータを処理して、データベースを作成するよう構成されている、コンピュータ、クラウドベースのネットワーク、またはその両方と、
前記場所特有のデータを送受信するよう構成されている、前記場所に特有の有害生物防除プラットフォームであって、前記有害生物防除プラットフォームは、前記場所における有害生物防除のための2つ以上の仕組みを含み、前記2つ以上の仕組みは、有害生物防除用薬剤放出プロトコルと、前記場所特有のデータを生成および受信する内部センサを有する遠隔監視装置とを含む、有害生物防除プラットフォームと、
前記有害生物防除プラットフォームの外部のデータを収集する外部センサであって、前記外部データは、環境条件、有害生物の個体数、前記場所での人的交流、または前記場所に有害生物がいる可能性を予測することと相関性があるサードパーティデータ、またはこれらの組合せを含む、外部センサと、
を含み、
前記コンピュータ、前記クラウドベースのネットワーク、またはその両方は、前記場所特有のデータと、前記外部データと、過去データと、前記場所を含む構造的な配置と、を分析するアルゴリズムを利用した機械学習を使用して、有害生物管理のための前記2つ以上の仕組みを配置するための推奨プロトコルを生成するよう構成され、前記推奨プロトコルは、リアルタイムで更新されることが可能であり、
前記場所特有のデータは、有害生物の種類および有害生物の数を含み、
前記環境条件は、気候、地理的場所、植生、入手可能な水源、または入手可能な食糧源、またはこれらの組合せを含み、
前記場所特有のデータおよび前記外部データは、生データを構成し、
前記過去データは、過去の有害生物の事件を含み、
前記コンピュータ、前記クラウドベースのネットワーク、またはその両方は、前記場所での有害生物のリスクレベルを予測するよう構成され、
前記システムは、前記システムの1つ以上の側面をモバイルアプリに表示することを許す、
システム。 - 請求項1のシステムを使用して、前記場所の監視と、前記場所における有害生物の処置と、を同時に行う方法であって、前記方法は、
前記コンピュータ、前記クラウドベースのネットワーク、またはその両方により、前記有害生物防除プラットフォームから前記場所特有のデータを受信することと、
前記外部センサから前記外部データを受信することと、
前記推奨プロトコルを生成することと、
を含む方法。
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/485,838 | 2017-04-12 | ||
US15/485,838 US10152035B2 (en) | 2017-04-12 | 2017-04-12 | Value added pest control system with smart learning |
PCT/EP2018/059393 WO2018189293A1 (en) | 2017-04-12 | 2018-04-12 | Value added pest control system with smart learning |
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JP2020519238A JP2020519238A (ja) | 2020-07-02 |
JP2020519238A5 JP2020519238A5 (ja) | 2021-05-13 |
JP7123970B2 true JP7123970B2 (ja) | 2022-08-23 |
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US (2) | US10152035B2 (ja) |
EP (1) | EP3609321A1 (ja) |
JP (1) | JP7123970B2 (ja) |
CN (1) | CN110519984B (ja) |
CA (1) | CA3059523A1 (ja) |
WO (1) | WO2018189293A1 (ja) |
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EP3565407B8 (en) | 2017-01-06 | 2023-10-18 | Discovery Purchaser Corporation | Animal trap activation detection system |
EP3378308A1 (en) * | 2017-03-24 | 2018-09-26 | Skehøjgaard Development ApS | System and method for detecting rats and mice infestations |
US10798541B2 (en) | 2017-11-07 | 2020-10-06 | Pica Product Development, Llc | Systems, methods and devices for remote trap monitoring |
US10694338B2 (en) | 2017-11-07 | 2020-06-23 | Pica Product Development, Llc | Cellular automated external defibrillator (AED) tracker |
US10909830B1 (en) | 2017-11-07 | 2021-02-02 | Pica Product Development, Llc | Personal emergency alert system, method and device |
WO2019099785A1 (en) * | 2017-11-16 | 2019-05-23 | Verily Life Sciences Llc | Systems and methods for dynamic release planning for insect release |
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US10152035B2 (en) | 2018-12-11 |
CN110519984A (zh) | 2019-11-29 |
CA3059523A1 (en) | 2018-10-18 |
BR112019021298A2 (pt) | 2020-05-19 |
JP2020519238A (ja) | 2020-07-02 |
WO2018189293A1 (en) | 2018-10-18 |
US20180299842A1 (en) | 2018-10-18 |
CN110519984B (zh) | 2022-05-27 |
EP3609321A1 (en) | 2020-02-19 |
US11073801B2 (en) | 2021-07-27 |
US20190121302A1 (en) | 2019-04-25 |
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