以下，一面參照圖式一面對本發明之實施形態之運轉計畫擬訂裝置進行說明。 (第1實施形態) 對本發明之第1實施形態之運轉計畫擬訂裝置進行說明。圖1係顯示包含第1實施形態之運轉計畫擬訂裝置之運轉計畫擬訂系統之概略構成之一例的方塊圖。 如圖1所示，運轉計畫擬訂系統具備運轉計畫擬訂裝置100、輸入輸出裝置200、發電機特性資料管理裝置300、定檢制約資料管理裝置400、運轉制約資料管理裝置500、及電力需求預測裝置600。 運轉計畫擬訂裝置100係擬訂發電單元之定期檢查之實施計畫。另，發電單元包含發電機、及其周邊裝置(渦輪機、鍋爐等)。又，發電單元之發電機例如為火力發電機，但並未限於此，亦可為利用水力、核能、或可再生能源等之發電機。 於本實施形態中，運轉計畫擬訂裝置100以複數個發電單元為對象，擬訂作為運轉計畫之一部分之定期檢查計畫。定檢計畫為對發電單元定期檢查之計畫。對該運轉計畫擬訂裝置100之細節予以後述。 輸入輸出裝置200係指定運轉計畫擬訂裝置100擬訂運轉計畫所需之資料。另，擬訂運轉計畫所需之資料包含定檢制約資料、發電機特性資料、電力需求資料、及運轉制約資料。此處，對各資料說明概要。 定檢制約資料為表示定期檢查之特性之資料。定檢制約資料包含定期檢查之種類(定檢類型)、及定期檢查之詳細資訊。定期檢查之詳細資訊例如為上一次定期檢查之結束日、容許間隔、所需期間(所需天數)、實施定期檢查所需之費用(總費用)等相關之資料。此處，容許間隔為定期檢查之間容許之時間間隔，例如為由法律規定之定檢間隔之上限(即最大期間)。例如容許間隔若為4年，必須於上一次定期檢查結束之日起4年以內實施下一次定期檢查。定檢制約資料用於後述之最佳化問題之制約條件及目的函數之擬訂。 定檢制約資料亦可包含實施定期檢查所需之機材、零件、人工(工日)相關之資訊。機材相關之資訊例如包含所需機材之種類、數量、持有數等。又，零件相關之資訊例如包含所需零件之種類、數量、庫存數、入庫時程等。 發電機特性資料為表示發電機之特性之資料。該資料包含發電機之輸出電力、基於輸出電力之運算值。例如，亦可包含輸出電力之最小值、最大值、平均值、發熱量、每單位發熱量之運轉成本、每單位時間之發熱量等。發電機特性資料係用於擬定後述之最佳化問題之制約條件及目的函數。 電力需求資料係表示發電單元或發電單元群所需之發電量(電力需求)之資料。此處，所謂發電單元群是指由複數個發電單元構成之組。另，1個發電單元可屬於複數個發電單元群。電力需求資料用作後述之最佳化問題之制約條件。 運轉制約資料係表示對於發電單元之運轉之制約之資料。例如，發電單元停止後至可再啟動所花費之時間(參照圖16之停止期間)為運轉制約之一。另，加諸於發電單元群之運轉制約係加諸於屬於該發電單元群之所有發電單元。運轉制約資料用作後述之最佳化問題之制約條件。 發電機特性資料管理裝置300、定檢制約資料管理裝置400、運轉制約資料管理裝置500及電力需求預測裝置600可通信地連接於運轉計畫擬訂裝置100及輸入輸出裝置200。發電機特性資料管理裝置300係管理發電機特性資料之裝置。定檢制約資料管理裝置400係管理定檢制約資料之裝置。運轉制約資料管理裝置500係管理運轉制約資料之裝置。電力需求預測裝置600預測電力需求，且產生電力需求資料。 接著，參照圖2～圖10，對記憶定檢制約資料之資料庫之具體例進行說明。 圖2顯示將定檢類型及上一次定檢結束日與發電單元ID建立關聯而記憶之資料庫之一例。例如，登錄ID=1之資料表示對於發電單元ID為“1”之發電單元之定檢類型A之上一次定期檢查於2010年10月7日結束。 圖3顯示記憶每個定檢類型之容許間隔之資料庫之一例。圖4顯示將定檢類型、所需期間及總費用與發電單元ID建立關聯而記憶之資料庫之一例。 圖5顯示記憶定期檢查所需之機材之資訊之資料庫之一例。於該例中，針對發電單元ID為“1”之發電單元，記憶有定檢類型A之每經過天數所需之機材之種類與數量。另，亦可針對其他發電單元及其他定檢類型設置此種資料庫。圖6顯示記憶機材之每個種類之持有數、即當前持有之數量之資料庫之一例。 圖7顯示記憶定期檢查所需之零件之資訊之資料庫之一例。於該例中，對發電單元ID為“1”之發電單元，記憶有定檢類型A之每經過天數所需之零件之種類與數量。圖8顯示記憶零件之每個種類之庫存數之資料庫之一例。圖9顯示記憶零件之每個種類之入庫日(到貨日)與入庫量之資料庫之一例。 圖10顯示將定期檢查所需之人工資訊與發電單元ID及定檢類型建立關聯而記憶之資料庫之一例。於該例中，記憶有每經過天數所需之人工。另，亦可對定期檢查所需之每項技能設定人工類型，依人工類型別而將所需之人工記憶於資料庫。 ＜運轉計畫擬訂裝置100＞ 接著，對運轉計畫擬訂裝置100之詳細構成進行說明。 運轉計畫擬訂裝置100如圖1所示，具備輸入部101、記憶部102、輸出部103、及定檢計畫擬訂部104。 輸入部101自發電機特性資料管理裝置300、定檢制約資料管理裝置400、運轉制約資料管理裝置500及電力需求預測裝置600取得運轉計畫擬訂處理所需之資料，使取得之資料記憶於記憶部102。用於擬訂運轉計畫所需之資料係自藉由輸入輸出裝置200指定之外部裝置或系統(未圖示)取得。於本實施形態中，輸入部101自發電機特性資料管理裝置300取得發電機特性資料，自定檢制約資料管理裝置400取得定檢制約資料，自運轉制約資料管理裝置500取得運轉制約資料，自電力需求預測裝置600取得電力需求資料。 另，輸入部101亦可受理對於運轉計畫擬訂裝置100之各構成要素之指令等運轉計畫擬訂處理所需之資料以外之資訊。該情形時，輸入部101將受理之資訊發送至需要該資訊之構成要素。例如，當輸入部101自外部接收到定檢計畫擬訂指令時，將該指令自輸入部101送交至定檢計畫擬訂部104。 記憶部102記憶輸入部101接收到之各種資料。將記憶之資料作為資料庫(DB)進行管理。於本實施形態中，如圖1所示，記憶部102具有記憶定檢制約資料之資料庫DB1、記憶發電機特性資料之資料庫DB2、記憶電力需求資料之資料庫DB3、及記憶運轉制約資料之資料庫DB4。 另，記憶部102亦可根據記憶之資訊而區分記憶目的地。記憶部102亦可記憶運轉計畫擬訂裝置100之各構成要素之處理結果，例如藉由定檢計畫擬訂部104擬訂之定檢計畫。 記憶部102係由快閃記憶體等記憶體、或硬碟等儲存器構成。另，記憶部102可由1個記憶體或1個儲存器構成，亦可由複數個記憶體或複數個儲存器構成，抑或可由記憶體與儲存器之組合構成。 輸出部103輸出擬訂之定檢計畫等運轉計畫。於本實施形態中，輸出部103將輸入輸出裝置200作為資訊之輸出目的地。另，輸出部103亦可對輸入輸出裝置200以外之裝置輸出資訊。又，輸出部103亦可輸出運轉計畫以外之資訊。例如，亦可輸出用於擬定運轉計畫之資料、或直到擬定運轉計畫為止之中間處理結果等。又，輸出部103可自定檢計畫擬訂部104等資訊處理部取得所要輸出之資訊，亦可自記憶部102取得。 輸出部103輸出之資訊之輸出形式並非特別限定。例如，輸出部103可將定檢計畫等資訊作為用以顯示於外部顯示器之圖像資訊而輸出，或，亦可作為用以保存於外部裝置之檔案資訊而輸出。 定檢計畫擬訂部104係擬訂發電單元之定檢計畫。更詳細而言，定檢計畫擬訂部104對每個發電單元至少擬訂表示何時至何時實施何種定檢類型之定期檢查的定期計畫。細節予以後述，定檢計畫擬訂部104藉由基於制約條件及目的函數解決最佳化問題，而擬訂定檢計畫。例如，擬訂每單位期間之定期檢查之計畫。此處，所謂定期檢查之單位期間是指將定檢計畫之計畫期間劃分為複數個期間時之最小期間。單位期間例如為一天(24小時)。 定檢計畫擬訂部104如圖1所示，具有目的函數設定部1041、制約條件設定部1042、及最佳化問題求解部1043。以下對各構成要素詳細說明。 目的函數設定部1041係將最佳化問題之目的函數定式化。更詳細而言，目的函數設定部1041設定表示與定期檢查關聯之成本之目的函數。本實施形態之目的函數係表示複數個發電單元之運轉成本及定期檢查成本之和之函數(參照後述之式(1))。 運轉成本係發電單元之運轉所需之費用。運轉成本例如包含發電單元之運轉所需之物品、人力、服務之費用。發電單元運轉所需之物品例如包含發電單元之動力源(燃料等)、動力源以外者(冷卻水、催化劑、消耗品、藥劑等)。動力源之種類未特別限定，例如可為化石燃料、木質燃料、核燃料、儲存於水壩等之揚水、氫發電所使用之甲基環己烷等化學物質。又，於運轉成本，亦可包含隨附於發電單元之運轉而產生之費用。例如，運轉成本亦可包含為了去除由發電產生之排放氣體中所含之化學物質而使用之石灰石、液氨之費用。 定檢成本係實施發電單元之定期檢查所需之費用。定檢成本例如包含實施發電單元之定期檢查所需之機材(卡車、起重機等)、零件(更換零件、消耗零件等)、人力、服務、其他物品所花費之費用。 下式顯示表示運轉成本與定檢成本之和之目的函數之一例。式(1)之右邊第一項表示複數個發電單元之運轉成本之和，右邊第二項表示複數個發電單元之定檢成本之和。此處，u：發電單元，U：擬訂定檢計畫之發電單元之集合，d：天，D：日之集合，αu_d
：運轉成本，Uu_d
：發電單元啟動旗標，k：定檢類型，K：定檢類型之集合，βu_k
：定檢成本，n：定期檢查數，N：對象定期檢查數，Iu_k_n_d
：定檢實施旗標。 運轉成本αu_d
表示發電單元u之日d之運轉成本。另，αu_d
例如對於變數u及d可為固定值，抑或可為基於發電單元之輸出電力或發電單價而決定之值。 定檢成本βu_k
表示對發電單元u實施定檢類型k之定期檢查所需之費用。另，βu_k
例如可為依每個定檢類型決定之固定值，抑或可為基於定期檢查之實施時期等參數而決定之值。若βu_k
為固定值，例如亦可將圖4之總費用作為定檢成本。 定期檢查數n表示定期檢查之序號，若為最初之定期檢查則取“1”之值，若為第2次定期檢查則取“2”之值。 發電單元啟動旗標Uu_d
表示發電單元u於日d啟動或停止。若發電單元為啟動狀態則Uu_d
取“1”之值，若為停止狀態則取“0”之值。 定檢實施旗標Iu_k_n_d
表示是否於日d實施對於發電單元u之定檢類型k之第n次定期檢查。如要實施定期檢查，Iu_k_n_d
取“1”之值，如不實施定期檢查則取“0”之值。 另，式(1)之目的函數為一例，並非限於此。例如，目的函數亦可僅為式(1)之第2項(定檢成本)。又，目的函數可如上述般將複數個發電單元作為對象，抑或可僅將一個發電單元作為對象。 制約條件設定部1042係將最佳化問題之制約條件定式化。更詳細而言，制約條件設定部1042至少基於定檢制約資料、發電機特性資料、及電力需求資料而設定制約條件。另，制約條件設定部1042亦可使用運轉制約資料而設定制約條件。 以下顯示定式化之制約條件之具體例。 於式(2)～式(5)表示定期檢查之開始日應滿足之制約條件。式(2)表示對於最初之定期檢查之開始日Su_k_ 1
之制約條件。式(3)表示對於第2次以後之定期檢查之開始日Su_k_n
(n=2、3、…)之制約條件。式(3)例如顯示於後述之圖14中，第2次定期檢查之開始日Su_k_2
必須在時刻T1
與時刻T2
之間之制約條件。另，式(3)使用儲存於資料庫DB1之定檢制約資料而設定。例如，使用圖2～圖4所示之定檢制約資料。此處，DAYfirst
係可實施第1次定期檢查之開始候補日中最早之日，DAYend
係可實施第1次定期檢查之開始候補日中最晚之日。此處，Su_k_n
係對於發電單元u之定檢類型k之第n個定期檢查之開始日。LTu_k
係對於發電單元u之定檢類型k之定期檢查所需之期間(定期檢查期間、所需期間)。Wu_k
係就對於發電單元u之定檢類型k之定期檢查，自上一次定期檢查結束至開始下一次定期檢查之前最低限度確保之期間(不可實施定檢之期間)。CTu_k
係對於發電單元u之定檢類型k之定期檢查之容許間隔。 式(4)表示定期檢查之開始日Su_k_n
為實施定期檢查之日所用之制約條件。式(5)表示用以將屬於集合D之日中之任一日作為定檢實施日之制約條件。式(6)表示定期檢查對象之發電單元於定期檢查之實施日為停止狀態所用之制約條件。此處，D’為實施定期檢查之日之集合。 式(7)表示為滿足電力需求之制約條件。另，式(7)使用儲存於資料庫DB3之電力需求資料而設定。此處，Xu_d
係日d之發電單元u之輸出電力。DMDd
係於日d對於集合U所含之發電單元之電力需求。DMDd
例如為電力需求之最大值。 式(8)表示發電單元之輸出電力成為輸出上限以下所用之制約條件。另，式(8)使用儲存於資料庫DB2之發電機特性資料而設定。此處，PMPMUu_d
係日d之發電單元u之輸出電力之上限值。 式(9)表示發電單元之輸出電力成為輸出下限以上所用之制約條件。另，式(9)使用儲存於資料庫DB2之發電機特性資料而設定。此處，PMPMLu_d
係日d之發電單元u之輸出電力之下限值。 藉由使用上述式(2)～式(9)之制約條件式解決目的函數之最佳化問題，決定定期檢查之開始日及實施期間。再者，如以下所示，亦可考量實施定期檢查所需之機材、零件及人工。 ＜實施定期檢查所需之機材之考量＞ 制約條件設定部1042亦可考量實施定期檢查所需之機材而設定制約條件。式(10)與式(11)表示為能使用實施定期檢查所需之機材所用之制約條件。另，式(10)使用圖5所示之定檢制約資料而設定，式(11)使用圖6所示之定檢制約資料而設定。此處，Mu_ k _ n _ t _ x d
係實施對於發電單元u之定檢類型k之第n個定期檢查時所需之機材t之總數。xd為滿足式(5)之日d，表示實施定期檢查之日。t為機材之種類。mu_ k _ n _d_ t
係實施對於發電單元u之定檢類型k之第n個定期檢查時於日d所需之機材t之數量。此處，Machinet
為機材t之持有數。 ＜實施定期檢查所需之零件之考量＞ 制約條件設定部1042亦可考量實施定期檢查所需之零件而設定制約條件。式(12)與式(13)表示為能使用實施定期檢查所需之零件所用之制約條件。另，式(12)使用圖7所示之定檢制約資料而設定，式(13)使用圖7、圖8及圖9所示之定檢制約資料而設定。此處，Pu_ k _ n _c_ x d
係實施對於發電單元u之定檢類型k之第n個定期檢查時所需之零件c之總數。xd為滿足式(5)之日d，表示實施定期檢查之日。c為零件之種類。pu_ k _ n _d_ c
係實施對於發電單元u之定檢類型k之第n個定期檢查時於日d所需之零件c之數量。此處，Partsc_d
為零件c之日d之庫存數，Rc_d
為於日d入庫之零件c之數量。 式(13)之右邊第一項表示前一天之零件c之庫存數，右邊第2項表示當天之零件c之使用量(消耗量)，右邊第3項表示當天入庫之零件c之數量。以式(13)求出之Partsc_d
之值必須為0以上。 ＜實施定期檢查所需之人工之考量＞ 制約條件設定部1042亦可考量實施定期檢查所需之人工而設定制約條件。式(14)與式(15)表示為能確保實施定期檢查所需之人工所用之制約條件。另，式(14)使用圖10所示之定檢制約資料而設定。式(14)表示於定期檢查之實施日所需之人工之數量，式(15)表示於日d實施之定期檢查所需之人工之數量為於日d可確保之人工之數量以下所用之制約條件。此處，MHu_ k _ n _ x d
係實施對於發電單元u之定檢類型k之第n個定期檢查時所需之人工之總數。xd為滿足式(5)之日d，表示實施定期檢查之日。mhu_ k _ n _d
係實施對於發電單元u之定檢類型k之第n個定期檢查時於日d所需之人工之數量。此處，ManHoursd
為於日d可動員之人工之數量。 以上，對藉由制約條件設定部1042定式化之制約條件式之例進行了說明。 上述制約條件式為一例，制約條件設定部1042亦可使用周知之方法設定制約條件。另，設定之制約條件可為對發電單元單體之制約條件，抑或可為對發電單元群之制約條件。對發電單元群之制約條件可為發電單元群全體之發電量或燃料使用量等之對發電單元群全體之制約條件。或，對發電單元群之制約條件亦可為對屬於發電單元群之各發電單元之制約條件。 接著，對最佳化問題求解部1043進行說明。 最佳化問題求解部1043係求解基於藉由目的函數設定部1041設定之目的函數、與藉由制約條件設定部1042設定之制約條件的最佳化問題。 即，最佳化問題求解部1043構成為於藉由制約條件設定部1042設定之制約條件下，解決藉由目的函數設定部1041設定之目的函數之最佳化問題。例如，最佳化問題求解部1043係解決將式(1)之目的函數之值設為最小之最佳化問題。作為求解方法，例如可使用2次計畫法、線形計畫法等周知之最佳化問題之解決方法。又，為進行求解處理，可使用專用之程式，亦可使用周知之解算器。 另，最佳化問題求解部1043可以使運轉成本或定檢成本為特定值之方式解決最佳化問題，抑或可以使定檢成本為最小之方式解決最佳化問題。 最佳化問題求解部1043藉由解決上述最佳化問題，而擬訂定期檢查之計畫(定期檢查之定檢類型、開始日及期間等)。 於本實施形態中，最佳化問題求解部1043係以集合U所含之複數個發電單元之運轉成本與定檢成本之和為最小之方式解決最佳化問題。藉此，針對集合U所含之發電單元各者，求出定檢類型、定檢開始日及期間等。圖13及圖14係藉由定檢計畫擬訂部104擬訂之定期檢查之一例。圖13係針對ID為1及2之發電單元，顯示依各定檢類型A、B、C之實施時程。於圖13中，橫向延伸之各條表示定期檢查。條之長度表示定期檢查之實施期間。另，圖13之橫軸表示時間，但亦可於該橫軸之附近顯示日期等資訊。 圖14顯示某發電單元之某定檢類型之定期檢查之時程。圖14中，定期檢查期間意指自定期檢查之開始日至結束日之期間。又，定期檢查間隔為上述之容許間隔，意指自上一次定期檢查結束至執行下一次定期檢查為止之最大期間。發電單元必須自定期檢查結束至定期檢查間隔期滿為止之期間接受下一次定期檢查。 於圖14之例中，自第1次(n=1)定期檢查結束至開始第2次(n=2)定期檢查之前確保不可實施定檢之期間Wu_ k
。該不可實施定檢之期間係決定定期檢查之實施間隔之最小期間。另，於圖14中，時刻T1
係實施第2次定期檢查之最早時序，時刻T2
係實施第2次定期檢查之最晚時序。 圖15顯示考量定期檢查所需之機材、零件及人工而擬訂之定檢計畫之一例。如圖15所示，自定期檢查之開始日至結束日，依各實施日顯示定期檢查所需之機材之種類與數量、零件之種類與數量及人工。 ＜定檢計畫擬訂處理＞ 參照圖12，說明定檢計畫擬訂部104之定檢計畫擬訂處理之處理流程。另，以下之處理流程中所需之資料(即，定檢制約資料、發電機特性資料、電力需求資料及運轉制約資料)係藉由輸入部101預先取得，且記憶於記憶部102。 首先，目的函數設定部1041設定最佳化問題之目的函數(步驟S101)。於本實施形態中，將上述式(1)所示之成本函數作為目的函數而定式化。 接著，制約條件設定部1042設定最佳化問題之制約條件(步驟S102)。於本實施形態中，將上述式(2)～式(9)作為表示制約條件之式而定式化。 另，於本步驟中，制約條件設定部1042亦可根據需要而產生式(10)～式(15)。例如，若考量實施定期檢查所需之機材，產生式(10)及式(11)。若考量實施定期檢查所需之零件，產生式(12)及式(13)。若考量實施定期檢查所需之人工，產生式(14)及式(15)。 接著，最佳化問題求解部1043於步驟S102中設定之制約條件下，解決步驟S101中設定之目的函數之最佳化問題(步驟S103)，藉此擬訂定檢計畫。於本實施形態中，最佳化問題求解部1043藉由解決將式(1)之目的函數之值設為最小之最佳化問題，而訂立如運轉成本與定檢成本之合計成本為最小之定檢計畫。其後，輸出部103將擬訂之定檢計畫輸出至外部裝置。 如以上所說明，於第1實施形態中，目的函數設定部1041設定表示與定期檢查關聯之成本之目的函數，制約條件設定部1042至少基於定檢制約資料、發電機特性資料及電力需求資料而設定制約條件，最佳化問題求解部1043於藉由目的函數設定部1041設定之制約條件下，解決藉由目的函數設定部1041設定之目的函數之最佳化問題。藉此，根據第1實施形態，可擬訂不僅考量到發電機特性資料及電力需求資料、且亦考量到定檢制約資料之定檢計畫。其結果，可擬訂精度高且最佳化之定檢計畫。 於上述實施形態之說明中，自完全不存在定檢計畫之狀態擬訂對複數個發電單元之定檢計畫，但並未限於此，運轉計畫擬訂裝置100亦可進行已擬訂之定檢計畫之變更，而擬訂新的定檢計畫。運轉計畫擬訂裝置100亦可變更屬於已擬訂定檢計畫之集合U之發電單元中之一個或複數個發電單元(以下，稱為「指定發電單元」)之定檢計畫。作為變更已擬訂之定檢計畫之狀況，例如考量因第1發電單元發生故障，為滿足電力需求而無法實施第2發電單元之定期檢查之情形等。 例如，以對於指定發電單元之定檢計畫之時間移動量(即，定檢實施開始日之遷移量)為最小之方式，擬訂指定發電單元之定檢計畫。另，亦可以對於指定發電單元之定檢計畫之開始日為期望日之方式擬訂指定發電單元之定檢計畫。 將對於指定發電單元之定檢計畫之時間移動量最佳化之情形，目的函數成為表示已擬訂之定檢計畫之時間移動成本之函數。該時間移動成本係基於自當初計畫之定檢實施開始日之遷移量而有所變化之成本，該成本例如包含定檢成本、運轉成本等。最佳化問題求解部1043以該目的函數為最小之方式，即，以已擬訂之定檢計畫之移位量為最小之方式解決最佳化問題。藉此，可盡可能不對已擬訂定檢計畫之全體成本賦予影響，而變更指定發電單元之定檢計畫。 另，於變更已擬訂之複數個發電單元之定檢計畫之一部分之情形時，目的函數不限於時間移動成本，亦可為表示運轉成本、定檢成本等費用之函數。例如，運轉計畫擬訂裝置100(最佳化問題求解部1043)亦可針對指定發電單元以外之發電單元，以滿足已擬訂之定檢計畫之制約條件(機材、零件、人工等條件)、且發電單元之定期檢查所需之總成本為最小之方式，擬訂對指定發電單元之定檢計畫。 例如，針對已擬訂之10台發電單元之定檢計畫，變更其中2台指定發電單元之定檢計畫之情形，運轉計畫擬訂裝置100以滿足10台發電單元中不變更定檢計畫之8台發電單元之定檢計畫之制約條件、且10台發電單元之定期檢查所需之總成本為最小之方式，擬訂2台指定發電單元之定檢計畫。 (第2實施形態) 對本發明之第2實施形態之運轉計畫擬訂裝置進行說明。圖17係顯示包含第2實施形態之運轉計畫擬訂裝置之運轉計畫擬訂系統之概略構成之一例的方塊圖。於圖17中對具有與圖1同等之功能之構成要素標註相同符號。第2實施形態與第1實施形態之間之不同點在於，第2實施形態之運轉計畫擬訂裝置進而具備運轉狀態決定部與輸出電力決定部，且基於定檢計畫擬訂部擬訂之定檢計畫而擬訂發電單元之運轉計畫。 於對第2實施形態之運轉計畫擬訂裝置進行說明之前，參照圖16，對發電單元之輸出電力之特性進行說明。圖16係用以說明發電單元之輸出電力之特性之圖。發電單元係根據擬訂之運轉計畫，進行停止及啟動等處理。自接收停止指示至發電單元實際停止為止需要時間。同樣地，自接收啟動指示至發電單元之輸出實際到達特定值為止亦需要時間。將接收到停止指示之發電單元實際自電力系統切斷稱為「解除並聯」。又，將接收啟動指示而將發電單元連接於電力系統稱為「並聯」。將解除並聯至並聯之時間稱為發電單元之「停止期間」。將發電單元之停止期間之狀態稱為「停止狀態」，將該停止狀態以外之運轉狀態稱為「啟動狀態」。如此，於本實施形態中，發電單元之運轉狀態有啟動狀態及停止狀態2種。 如圖16所示，當接收到停止指示時，發電單元之輸出電力自某輸出值持續下降而成為0。輸出位準下降之部分稱為停止曲線。又，並聯後，發電單元之輸出電力逐漸上升，達到一定之輸出值。輸出位準上升之部分稱為啟動曲線。 ＜運轉計畫擬訂裝置100A＞ 接著，對第2實施形態之運轉計畫擬訂裝置100A進行說明。運轉計畫擬訂裝置100A如圖17所示，具備輸入部101、記憶部102、輸出部103、定檢計畫擬訂部104、運轉狀態決定部105、及輸出電力決定部106。由於運轉狀態決定部105及輸出電力決定部106以外之構成要素與第1實施形態相同，故省略詳細說明。 運轉狀態決定部105進行決定發電單元之運轉狀態(啟動狀態或停止狀態)之運轉狀態決定處理。 運轉狀態決定部105基於發電機特性資料、運轉制約資料、電力需求資料、及藉由定檢計畫擬訂部104擬訂之定檢計畫，決定發電單元之運轉狀態。具體而言，運轉狀態決定部105藉由解決基於制約條件及目的函數之最佳化問題，而決定解除並聯及並聯之時序。且，基於決定之發電單元之解除並聯及並聯之時序，決定發電單元之運轉狀態。 輸出電力決定部106進行決定發電單元之每單位期間之輸出電力之值之輸出電力決定處理。此處，所謂「單位期間」是指將運轉計畫之計畫期間劃分成複數個期間時之最小期間。單位期間亦稱為網格。 輸出電力決定部106基於發電機特性資料、運轉制約資料、電力需求資料、及藉由運轉狀態決定部105決定之發電單元之運轉狀態，決定發電單元之輸出電力。具體而言，輸出電力決定部106藉由解決基於制約條件及目的函數之最佳化問題，決定各發電單元之輸出電力。 接著，參照圖18，對運轉計畫擬訂裝置100A之運轉狀態決定部105之處理流程進行說明。 運轉狀態決定部105自記憶部102取得必要之資料(S201)。更詳細而言，運轉狀態決定部105除發電機特性資料、電力需求資料及運轉制約資料外，亦自記憶部102取得顯示藉由定檢計畫擬訂部104擬訂之定檢計畫之資料。 取得必要之資料後，運轉狀態決定部105執行目的函數設定處理(S202)。於該目的函數設定處理中，與第1實施形態所說明之目的函數設定部1041之處理同樣地，將最佳化問題之目的函數定式化。目的函數設定處理使用周知之方法即可，目的函數可任意決定。例如，可將使1個發電單元之運轉成本、或包含複數個發電單元之發電單元群之運轉成本最小化作為目的。抑或可將使運轉成本接近於任意決定之目標值作為目的。 進行目的函數設定處理後，運轉狀態決定部105執行制約條件設定處理(S203)。於制約條件設定處理中，與第1實施形態所說明之制約條件設定部1042之處理同樣地，將最佳化問題之制約條件定式化。制約條件設定處理使用周知之方法即可，擬訂之制約條件可為對1個發電單元之制約條件，抑或可為對發電單元群之制約條件。另，對發電單元群之制約條件可為發電單元群全體之發電量或燃料使用量等之對發電單元群全體之制約條件，抑或可為對屬於發電單元群之各發電單元之制約條件。 接著，運轉狀態決定部105求解基於步驟S202之目的函數設定處理中設定之目的函數、與步驟S203之制約條件設定處理中設定之制約條件的最佳化問題。作為求解方法，與第1實施形態所說明之最佳化問題求解部1043之處理同樣地，可使用2次計畫法、線形計畫法等周知之最佳化問題解決方法。又，為進行求解處理，可使用專用之程式，亦可使用周知之解算器。 藉由上述處理流程，決定發電單元之解除並聯及並聯之時序。 且，運轉狀態決定部105基於算出之解除並聯及並聯之時序，擬訂於每小時顯示發電單元之運轉狀態之資訊。例如，擬訂於計畫期間內依決定之複數個區間各者顯示運轉狀態之資訊。 接著，參照圖19，對運轉計畫擬訂裝置100A之輸出電力決定部106之處理流程進行說明。 輸出電力決定部106自記憶部102取得必要之資料(S301)。更詳細而言，輸出電力決定部106除發電機特性資料、電力需求資料及運轉制約資料外，亦自記憶部102取得表示藉由運轉狀態決定部105決定之各發電單元之運轉狀態之資料。另，表示藉由運轉狀態決定部105決定之發電單元之運轉狀態之資料係與本步驟中取得之運轉制約資料為不同之資料，但並未限於此，亦可含在運轉制約資料內。 接著，輸出電力決定部106執行目的函數設定處理(S302)。於該目的函數設定處理中，與運轉狀態決定部105之處理同樣地，將最佳化問題之目的函數定式化。目的函數設定處理使用周知之方法即可，目的函數可任意決定。 接著，輸出電力決定部106執行制約條件設定處理(S303)。於制約條件設定處理中，與運轉狀態決定部105之處理同樣地，將最佳化問題之制約條件定式化。制約條件設定處理使用周知之方法即可，擬訂之制約條件可為對1個發電單元之制約條件，抑或可為對發電單元群之制約條件。另，對發電單元群之制約條件可為發電單元群全體之發電量或燃料使用量等之對發電單元群全體之制約條件，抑或可為對屬於發電單元群之各發電單元之制約條件。 接著，輸出電力決定部106求解基於步驟S302之目的函數設定處理中設定之目的函數、與步驟S303之制約條件設定處理中設定之運轉制約的最佳化問題。作為求解方法，與運轉狀態決定部105之處理同樣地，可使用2次計畫法、線形計畫法等周知之最佳化問題解決方法。又，為進行求解處理，可使用專用之程式，亦可使用周知之解算器。 根據上述處理流程，決定計畫期間之各發電單元之輸出電力。 如以上所說明，於第2實施形態中，運轉狀態決定部105基於藉由定檢計畫擬訂部104擬訂之定檢計畫，決定各發電單元之運轉狀態。且，輸出電力決定部106基於藉由運轉狀態決定部105決定之各發電單元之運轉狀態，決定各發電單元之輸出電力。藉此，根據第2實施形態，可擬訂考量到發電單元之定檢計畫之運轉計畫。因此，例如，可擬訂用以使電力供給者之收益最大化之運轉計畫。 另，上述實施形態所說明之處理流程僅為一例，只要能夠獲得所需之處理結果，亦可進行其他處理，抑或可更換處理順序等。 又，對處理結果之輸出方法亦未特別限定。例如，針對定檢計畫擬訂部104、運轉狀態決定部105及輸出電力決定部106各者，輸出部103亦可於完成各處理後逐次輸出處理結果。即，輸出部103可於每當自各構成要素接收到處理結果時，向外部裝置(輸入輸出裝置200等)發送處理結果。或者，可將各構成要素之處理結果逐次記憶於記憶部102。且，輸出部103亦可於輸入部101自外部裝置受理要求時，參照記憶部102，取得所要求之資訊。 上述實施形態僅為一例，實施形態之構成要素之一部分亦可位於外部裝置。例如，第2實施形態之運轉計畫擬訂裝置100A具有定檢計畫擬訂部104，但該定檢計畫擬訂部104亦可設置於外部裝置。該情形時，輸入部101自具有定檢計畫擬訂部104之外部裝置取得定檢計畫，送交至運轉狀態決定部105。 此外，亦可假定運轉計畫擬訂裝置內之構成要素分別設置於不同裝置之實施形態。例如，亦可為具有定檢計畫擬訂部104之第1裝置、具有運轉狀態決定部105之第2裝置、及具有輸出電力決定部106之第3裝置彼此可通信地連接，且與運轉計畫擬訂裝置100A同樣發揮功能。 上述第1及第2實施形態之處理可藉由軟體(程式)而實現。因此，上述各實施形態例如可藉由將泛用之電腦裝置用作基本硬體，且使搭載於電腦裝置之中央處理裝置(CPU：Central Processing Unit)等處理器執行程式而實現。 圖20係顯示運轉計畫擬訂裝置100、100A之硬體構成之一例的方塊圖。運轉計畫擬訂裝置100、100A具備處理器701、主記憶裝置702、輔助記憶裝置703、網路介面704、及器件介面705，且可作為將該等經由匯流排706連接之電腦裝置700而實現。又，運轉計畫擬訂裝置100、100A亦可具備泛用之輸入裝置及輸出裝置，作為輸入輸出裝置200。 運轉計畫擬訂裝置100、100A可藉由將各裝置所執行之程式預先安裝於電腦裝置700而實現，亦可藉由將程式記憶於CD-ROM(Compact Disc-Read Only Memory：唯讀光碟)等記憶媒體，或經由網路發佈，適當安裝於電腦裝置700而實現。 另，於圖20中，電腦裝置700具備1個各構成要素，但亦可具備複數個相同之構成要素。又，於圖20中，顯示1台電腦裝置700，但亦可將軟體安裝於複數台電腦裝置。 亦可藉由該複數個電腦裝置各者執行軟體之不同部分之處理，而產生處理結果。即，運轉計畫擬訂裝置100、100A亦可作為系統而構成。 處理器701係包含電腦之控制裝置及運算裝置之電子電路。 處理器701基於自電腦裝置700之內部構成之各裝置等輸入之資料及程式而進行運算處理，且將運算結果及控制信號輸出至各裝置等。具體而言，處理器701執行電腦裝置700之OS(Operating System：作業系統)、及應用程式等，而控制構成電腦裝置700之各裝置。 處理器701只要可進行上述處理則並非特別限定。處理器701例如可為泛用目的處理器、中央處理裝置(CPU)、微處理器、數位信號處理器(DSP：Digital Signal Processor)、控制器、微控制器、運轉狀態機等。又，處理器701亦可為專供特定用途之積體電路、場可程式化閘陣列(FPGA：Field Programmable Gate Array)、可程式邏輯電路(PLD：Programmable logic device)等。又，處理器701亦可由複數個處理裝置構成。例如，可為DSP及微處理器之組合，亦可為與DSP核心協動之1個以上之微處理器。 主記憶裝置702係記憶處理器701所執行之命令及各種資料等之記憶裝置，且藉由處理器701直接讀取記憶於主記憶裝置702之資訊。輔助記憶裝置703係主記憶裝置702以外之記憶裝置。另，記憶裝置意指可儲存電子資訊之任意電子零件。作為主記憶裝置702，主要使用RAM(Random Access Memory：隨機存取記憶體)、DRAM(Dynamic Random Access Memory：動態隨機存取記憶體)、SRAM(Static Random Access Memory：靜態隨機存取記憶體)等用於暫時保存資訊之揮發性記憶體，但於本發明之實施形態中，主記憶裝置702並非限於該等揮發性記憶體。用作主記憶裝置702及輔助記憶裝置703之記憶裝置可為揮發性記憶體，亦可為非揮發性記憶體。非揮發性記憶體有可程式化唯讀記憶體(PROM：Programmable Read Only Memory)、可抹除可程式化唯讀記憶體(EPROM：Electronically Programmable Read Only Memory)、電子可抹除PROM(EEPROM：Electronically Erasable and Programmable Read Only Memory)、非揮發性隨機存取記憶體(NVRAM：Non-Volatile Random Access Memory)、快閃記憶體、MRAM(Magnetoresistive Random Access Memory：磁阻式隨機存取記憶體)等。又，作為輔助記憶裝置703亦可使用磁性或光學之資料儲存器。作為資料儲存器，可使用硬碟等磁碟、DVD(Digital Versatile Disk：數位多功能光碟)等光碟、USB(Universal Serial Bus：通用串列匯流排)記憶體等快閃記憶體、及磁帶等。 另，若處理器701對主記憶裝置702或輔助記憶裝置703直接或間接讀取、寫入資訊，或進行該等兩者，則可以說記憶裝置與處理器電性通信。另，主記憶裝置702亦可整合於處理器。該情形時，亦可以說主記憶裝置702與處理器電性通信。 網路介面704係藉由無線或有線而用以連接於通信網路800之介面。網路介面704使用適合現存之通信規格者即可。亦可藉由網路介面704，將輸出結果等發送至經由通信網路800而通信連接之外部裝置900。外部裝置900可為外部記憶媒體，亦可為顯示裝置，又可為資料庫等儲存器。 器件介面705係連接於記錄輸出結果等之外部記憶媒體之USB記憶體等之介面。外部記憶媒體可為HDD(Hard Disk Drive：硬磁碟驅動器)､CD-R(Compact Disk-Recordable：可錄式光碟)､CD-RW(Compact Disk-Rewritable：可抹寫式光碟)､DVD-RAM(Digital Versatile Disk-Random Access Memory：數位多功能光碟-隨機存取記憶體)､DVD-R(Digital Versatile Disc-Recordable：可錄式數位多功能光碟)､BD-ROM(Blu-ray Disc-Random Access Memory：藍光光碟-隨機存取記憶體)､BD-R(Blu-ray Disc-Recordable：可錄式藍光光碟)､BD-RE(Blu-ray Disc-Rewritable：可抹寫式藍光光碟)､SAN(Storage area network：儲存器區域網路)､DAT(Digital Audio Tape：數位錄音帶)等任意記錄媒體。亦可經由器件介面705，與儲存器等連接。 又，電腦裝置700之一部分或全部、即運轉計畫擬訂裝置100、100A之一部分或全部亦可由安裝有處理器701等之半導體積體電路等專用之電子電路(即硬體)構成。專用之硬體亦可由與RAM、ROM(Read Only Memory：唯讀記憶體)等記憶裝置組合而構成。 另，於圖20中顯示1台電腦裝置，但亦可將軟體安裝於複數台電腦裝置。亦可藉由該複數台電腦裝置各者執行軟體之不同部分之處理，而算出處理結果。 雖然上文已描述特定實施例，但是該等實施例僅為示例，而非意欲制約本發明之範疇。實際上，上文所述之新穎方法與系統得以其他各種形式加以具體化，且只要不脫離本發明之精神，得省略、替代及變更上文所述之方法及系統的形式。該等形式或修改皆視為屬於本發明之範疇內，且包含在下列申請專利範圍及其等效物之範疇內。Hereinafter, the operation plan preparation device of the embodiment of the present invention will be described with reference to the drawings. (First Embodiment) The operation plan preparation device of the first embodiment of the present invention will be described. Fig. 1 is a block diagram showing an example of the schematic configuration of an operation plan preparation system including the operation plan preparation device of the first embodiment. As shown in Figure 1, the operation plan preparation system includes an operation plan preparation device 100, an input/output device 200, a generator characteristic data management device 300, a periodic inspection control data management device 400, an operation control data management device 500, and power demand Forecasting device 600. The operation plan drawing device 100 draws up an implementation plan for regular inspections of power generating units. In addition, the power generation unit includes a generator and its peripheral devices (turbines, boilers, etc.). In addition, the generator of the power generation unit is, for example, a thermal generator, but it is not limited to this, and may also be a generator using water power, nuclear power, or renewable energy. In this embodiment, the operation plan preparation device 100 targets a plurality of power generation units, and prepares a regular inspection plan as part of the operation plan. The regular inspection plan is a plan for regular inspection of the power generation unit. The details of the operation plan preparation device 100 will be described later. The input and output device 200 specifies the data required for the operation plan preparation device 100 to prepare an operation plan. In addition, the data required to formulate an operation plan includes periodic inspection control data, generator characteristic data, power demand data, and operation control data. Here, the outline of each document is explained. The regular inspection control data is the data indicating the characteristics of the regular inspection. The periodic inspection restriction data includes the type of periodic inspection (type of periodic inspection) and detailed information about the periodic inspection. The detailed information of the periodic inspection includes the end date of the last periodic inspection, the allowable interval, the required period (the number of days required), and the cost (total cost) required to implement the periodic inspection, etc. Here, the allowable interval is the allowable time interval between regular inspections, for example, the upper limit (ie, the maximum period) of the regular inspection interval prescribed by law. For example, if the allowable interval is 4 years, the next periodic inspection must be implemented within 4 years from the end of the previous periodic inspection. The periodic inspection restriction data is used for the formulation of the restriction conditions and objective function of the optimization problem described later. The regular inspection restriction data may also include information about the equipment, parts, and labor (man-days) required for the implementation of the regular inspection. The information related to the equipment includes, for example, the type, quantity, and number of equipment required. In addition, the information related to the parts includes, for example, the type, quantity, inventory number, and warehousing schedule of the required parts. The generator characteristic data is data representing the characteristics of the generator. The data includes the output power of the generator and the calculated value based on the output power. For example, it may also include the minimum, maximum, average, calorific value, operating cost per unit of calorific value, and calorific value per unit of time. The generator characteristic data is used to formulate the constraints and objective functions of the optimization problem described later. The power demand data is data representing the power generation (power demand) required by the power generation unit or power generation unit group. Here, the power generation unit group refers to a group composed of a plurality of power generation units. In addition, one power generation unit may belong to a plurality of power generation unit groups. The power demand data is used as a constraint for the optimization problem described later. The operation restriction data refers to data indicating the restriction on the operation of the power generation unit. For example, the time required to restart the power generation unit after it stops (refer to the stop period in FIG. 16) is one of the operation constraints. In addition, the operation restriction imposed on the power generation unit group is imposed on all power generation units belonging to the power generation unit group. The operation restriction data is used as the restriction condition for the optimization problem described later. The generator characteristic data management device 300, the regular inspection restriction data management device 400, the operation restriction data management device 500, and the power demand forecasting device 600 are communicably connected to the operation plan preparation device 100 and the input/output device 200. The generator characteristic data management device 300 is a device for managing generator characteristic data. The regular inspection control data management device 400 is a device for managing regular inspection control data. The operation restriction data management device 500 is a device for managing operation restriction data. The power demand prediction device 600 predicts power demand and generates power demand data. Next, referring to Figs. 2 to 10, specific examples of the database storing the test control data will be described. Figure 2 shows an example of a database that associates the type of scheduled inspection and the end date of the last scheduled inspection with the ID of the generating unit. For example, the data with the login ID=1 means that the last regular inspection of the type A of the power generation unit with the power generation unit ID of "1" ended on October 7, 2010. Figure 3 shows an example of a database that memorizes the allowable interval for each type of regular inspection. Figure 4 shows an example of a database that associates the type of periodic inspection, the required period, and the total cost with the ID of the power generation unit. Figure 5 shows an example of a database that memorizes the information of machine materials required for regular inspection. In this example, for the power generation unit whose power generation unit ID is "1", the type and quantity of the machine materials required for each elapsed day of the periodic inspection type A are memorized. In addition, this type of database can also be set up for other power generation units and other types of regular inspections. Fig. 6 shows an example of a database of the number of holdings of each type of memory equipment, that is, the number of holdings currently. Figure 7 shows an example of a database storing information of parts required for regular inspection. In this example, for the power generation unit whose power generation unit ID is "1", the type and quantity of the parts required for each elapsed day of the periodic inspection type A are memorized. Fig. 8 shows an example of a database that memorizes the inventory number of each category of parts. Fig. 9 shows an example of a database of the storage date (arrival date) and the storage quantity of each type of memory parts. Figure 10 shows an example of a database that associates the manual information required for regular inspections with the ID of the power generation unit and the type of regular inspection and remembers them. In this example, the memory has the labor required for each elapsed number of days. In addition, the manual type can be set for each skill required for regular inspection, and the required manual labor can be memorized in the database according to the manual type. <Operation plan preparation device 100> Next, the detailed configuration of the operation plan preparation device 100 will be described. As shown in FIG. 1, the operation plan preparation device 100 includes an input unit 101, a storage unit 102, an output unit 103, and a regular inspection plan preparation unit 104. The input unit 101 obtains the data required for the preparation and processing of the operation plan from the generator characteristic data management device 300, the periodic inspection restriction data management device 400, the operation restriction data management device 500, and the power demand prediction device 600, and stores the acquired data in memory Department 102. The data required for formulating the operation plan is obtained from an external device or system (not shown) designated by the input/output device 200. In this embodiment, the input unit 101 acquires generator characteristic data from the generator characteristic data management device 300, acquires the periodic inspection restriction data from the periodic inspection restriction data management device 400, and acquires the operation restriction data from the operation restriction data management device 500, The power demand forecasting device 600 obtains power demand data. In addition, the input unit 101 may also receive information other than data required for the operation plan preparation process, such as instructions for each component element of the operation plan preparation device 100. In this case, the input unit 101 sends the received information to the constituent elements that require the information. For example, when the input unit 101 receives a scheduled inspection plan preparation instruction from the outside, the instruction is sent from the input unit 101 to the scheduled inspection plan preparation unit 104. The storage unit 102 stores various data received by the input unit 101. Manage the memorized data as a database (DB). In the present embodiment, as shown in FIG. 1, the memory unit 102 has a database DB1 that stores data about regular inspection control, a database DB2 that stores generator characteristic data, a database DB3 that stores power demand data, and a database that stores operation control data The database DB4. In addition, the storage unit 102 can also distinguish storage destinations based on the stored information. The storage unit 102 can also store the processing results of each component of the operation plan drawing device 100, for example, by the periodic inspection plan drawn by the periodic inspection plan drawing unit 104. The memory unit 102 is composed of a memory such as a flash memory, or a storage such as a hard disk. In addition, the memory unit 102 can be composed of one memory or one storage, or can be composed of a plurality of memories or a plurality of storages, or can be composed of a combination of a memory and a storage. The output unit 103 outputs an operation plan such as a scheduled inspection plan. In this embodiment, the output unit 103 uses the input/output device 200 as the output destination of information. In addition, the output unit 103 may also output information to devices other than the input/output device 200. Furthermore, the output unit 103 may also output information other than the operation plan. For example, it is also possible to output the data used to prepare the operation plan, or the intermediate processing results until the operation plan is prepared. In addition, the output unit 103 can obtain the information to be output from an information processing unit such as the calibration test plan drawing unit 104 or the storage unit 102. The output form of the information output by the output unit 103 is not particularly limited. For example, the output unit 103 can output information such as a scheduled inspection plan as image information to be displayed on an external display, or can also be output as file information to be stored in an external device. The scheduled inspection plan preparation department 104 prepares the scheduled inspection plan for the power generation unit. In more detail, the periodic inspection plan formulation unit 104 at least draws up a periodic plan for each power generation unit that indicates when and when to implement which type of periodic inspection. The details will be described later. The regular inspection plan formulation unit 104 draws up a regular inspection plan by solving the optimization problem based on the constraints and the objective function. For example, drawing up a plan for periodic inspections per unit period. Here, the unit period of regular inspection refers to the minimum period when the planning period of the regular inspection plan is divided into multiple periods. The unit period is, for example, one day (24 hours). As shown in FIG. 1, the verification plan preparation unit 104 has an objective function setting unit 1041, a restriction condition setting unit 1042, and an optimization problem solving unit 1043. Each component is explained in detail below. The objective function setting unit 1041 defines the objective function of the optimization problem. In more detail, the objective function setting unit 1041 sets an objective function indicating the cost associated with the periodic inspection. The objective function of this embodiment is a function that represents the sum of the operating costs and periodic inspection costs of a plurality of power generation units (refer to the equation (1) described later). Operating cost is the cost required for the operation of the power generation unit. The operating cost includes, for example, the cost of goods, manpower, and services required for the operation of the power generation unit. Items required for the operation of the power generation unit include, for example, the power source (fuel, etc.) of the power generation unit, and other than the power source (cooling water, catalyst, consumables, medicine, etc.). The type of power source is not particularly limited. For example, it may be fossil fuel, wood fuel, nuclear fuel, pumping water stored in dams, etc., and chemical substances such as methylcyclohexane used in hydrogen power generation. In addition, the operating cost may also include the costs associated with the operation of the power generation unit. For example, the operating cost may also include the cost of limestone and liquid ammonia used to remove chemical substances contained in the exhaust gas generated by power generation. The cost of regular inspection is the cost required to implement the regular inspection of the generating unit. The cost of regular inspection includes, for example, the cost of machinery (trucks, cranes, etc.), parts (replacement parts, consumable parts, etc.), manpower, services, and other items required for regular inspection of the power generation unit. The following formula shows an example of an objective function representing the sum of operating cost and regular inspection cost. The first term on the right side of formula (1) represents the sum of the operating costs of multiple power generating units, and the second term on the right represents the sum of the regular inspection costs of multiple power generating units. Here, u: power generation unit, U: set of power generation units for which the verification plan is drawn up , d: day, D: set of days, α u_d : operating cost, U u_d : power generation unit start flag, k: regular inspection Type, K: set of types of regular inspection, β u_k : cost of regular inspection, n: number of regular inspections, N: number of regular inspections of objects, I u_k_n_d : implementation flag of regular inspection. The operating cost α u_d represents the operating cost of the generating unit u on day d. In addition, α u_d may be a fixed value for the variables u and d, or may be a value determined based on the output power of the power generation unit or the unit price of power generation. The cost of periodic inspection β u_k represents the cost required for the periodic inspection of the periodic inspection type k of the generating unit u. In addition, β u_k may be, for example, a fixed value determined according to each type of periodic inspection, or may be a value determined based on parameters such as the implementation period of the periodic inspection. If β u_k is a fixed value, for example, the total cost in Figure 4 can also be used as the cost of regular inspection. The number of periodic inspections n represents the serial number of the periodic inspections. If it is the first periodic inspection, the value is "1", and if it is the second periodic inspection, the value is "2". The power generation unit start flag U u_d indicates that the power generation unit u starts or stops on day d. If the power generation unit is in the starting state, U u_d takes the value of "1", if it is in the stopped state, it takes the value of "0". The periodic inspection implementation flag I u_k_n_d indicates whether to implement the nth periodic inspection of the periodic inspection type k of the power generation unit u on day d. If you want to implement periodic inspection, I u_k_n_d takes the value "1", if you do not implement the periodic inspection, it takes the value "0". In addition, the purpose function of formula (1) is an example, and it is not limited to this. For example, the objective function can also be only the second item of formula (1) (the cost of regular inspection). In addition, the objective function may target a plurality of power generation units as described above, or may target only one power generation unit. The constraint condition setting unit 1042 formulates the constraint condition of the optimization problem. More specifically, the restriction condition setting unit 1042 sets restriction conditions based on at least the periodic inspection restriction data, generator characteristic data, and power demand data. In addition, the restriction condition setting unit 1042 may use the operation restriction data to set restriction conditions. The following shows a specific example of the constraints of the formalization. Equations (2) to (5) represent the constraints that should be met on the starting date of the periodic inspection. The formula (2) expresses the restriction conditions of Su_k_ 1 on the starting date of the initial periodic inspection. Equation (3) expresses the restriction conditions for the start date Su_k_n (n=2, 3,...) of the periodic inspection after the second time. FIG example, the formula (3) to be described later after the display 14, the second periodic inspection must begin on day S u_k_2 between the restriction condition at time T 2 and time T 1. In addition, the formula (3) is set using the regular inspection control data stored in the database DB1. For example, use the periodic inspection control data shown in Figures 2 to 4. Here, DAY first is the earliest day of the candidate starting date for the first periodic inspection, and DAY end is the latest day of the candidate starting date for the first periodic inspection. Here, Su_k_n is the starting date of the nth periodic inspection of the periodic inspection type k of the generating unit u. LT u_k is the period (periodical inspection period, required period) required for the periodic inspection of the periodic inspection type k of the generating unit u. W u_k refers to the periodical inspection of the periodic inspection type k of the power generating unit u, from the end of the last periodic inspection to the minimum guaranteed period (the period during which the periodic inspection cannot be implemented). CT u_k is the allowable interval for periodic inspection of the regular inspection type k of the generating unit u. Equation (4) indicates that Su_k_n , the starting date of the periodic inspection, is the restriction condition used on the day when the periodic inspection is implemented. The formula (5) expresses the restriction condition for taking any of the days belonging to the set D as the day of the scheduled inspection. Equation (6) expresses the restriction conditions for the power generation unit subject to periodic inspection to be in a stopped state on the implementation day of the periodic inspection. Here, D'is the collection on the day when the periodic inspection is implemented. Equation (7) is expressed as the constraint condition to meet the power demand. In addition, formula (7) is set using the power demand data stored in the database DB3. Here, X u_d is the output power of the power generation unit u on day d. DMD d is the power demand of the power generating units contained in the set U on day d. DMD d is, for example, the maximum power demand. Equation (8) represents the restriction condition used when the output power of the power generating unit becomes below the upper limit of output. In addition, formula (8) is set using generator characteristic data stored in the database DB2. Here, PMPMU u_d is the upper limit value of the output power of the power generating unit u on day d. Equation (9) expresses the restriction condition for the output power of the power generating unit to be above the lower limit of output. In addition, formula (9) is set using generator characteristic data stored in the database DB2. Here, PMPML u_d is the lower limit of the output power of the power generating unit u on day d. By using the above formulas (2) to (9) to solve the optimization problem of the objective function, determine the starting date and implementation period of the periodic inspection. Furthermore, as shown below, the equipment, parts, and labor required for periodic inspections can also be considered. <Consideration of the equipment required for the periodic inspection> The restriction setting unit 1042 may also consider the equipment required for the periodic inspection and set restriction conditions. Equations (10) and (11) express the constraints that can be used to implement periodic inspections. In addition, formula (10) is set using the scheduled inspection restriction data shown in FIG. 5, and formula (11) is set using the scheduled inspection restriction data shown in FIG. Here, M u_ k _ n _ t _ x d is the total number of machine materials t required for the nth periodic inspection of the periodic inspection type k of the power generation unit u. xd is the day d on which formula (5) is satisfied, and represents the day when the regular inspection is implemented. t is the type of machine material. m u_ k _ n _d_ t is the quantity of machine material t required on day d when the nth periodic inspection of the regular inspection type k of the power generation unit u is carried out. Here, Machine t is the holding number of machine t. <Consideration of parts required for periodic inspection> The restriction condition setting unit 1042 may also consider parts required for periodic inspection and set restriction conditions. Equations (12) and (13) express the constraints that can be used to implement the parts required for periodic inspections. In addition, equation (12) is set using the periodic inspection restriction data shown in FIG. 7, and equation (13) is set using the periodic inspection restriction data shown in FIGS. 7, 8 and 9. Here, P u_ k _ n _c_ x d Number of lines required for the respect to the embodiment of the power generation unit u of a given n-th sample type k of periodic inspection of the part c. xd is the day d on which formula (5) is satisfied, and represents the day when the regular inspection is implemented. c is the type of parts. p u_ k _ n _d_ c is the number of parts c required on day d when the nth periodic inspection of the regular inspection type k of the power generation unit u is carried out. Here, Parts c_d is the inventory quantity of the part c on day d, and R c_d is the quantity of the part c that was put into the warehouse on day d. The first item on the right side of the formula (13) represents the inventory quantity of the part c on the previous day, the second item on the right represents the usage (consumption) of the part c on the day, and the third item on the right represents the quantity of the part c that was put into the warehouse on that day. The value of Parts c_d obtained by formula (13) must be 0 or more. <Consideration of labor required for periodic inspection> The restriction condition setting unit 1042 may also consider labor required for periodic inspection and set restriction conditions. Equations (14) and (15) express the constraints that can ensure the labor required to implement periodic inspections. In addition, formula (14) is set using the periodic inspection restriction data shown in FIG. 10. Equation (14) represents the number of labor required on the implementation day of the periodic inspection, and Equation (15) represents the constraint that the number of labor required for the periodic inspection performed on day d is less than the number of labor that can be guaranteed on day d condition. Here, MH u_ k _ n _ x d is the total number of labor required for the nth periodic inspection of the periodic inspection type k of the generating unit u. xd is the day d on which formula (5) is satisfied, and represents the day when the regular inspection is implemented. mh u_ k _ n _d is the number of manpower required on day d when performing the nth periodic inspection of the regular inspection type k of the generating unit u. Here, ManHours d is the number of labor that can be mobilized on day d. In the foregoing, an example of the constraint condition formulae formulated by the constraint condition setting unit 1042 has been described. The above constraint condition formula is an example, and the constraint condition setting unit 1042 may also use a known method to set the constraint condition. In addition, the set constraints can be constraints on the power generation unit alone, or can be constraints on the power generation unit group. The restriction on the power generation unit group may be the restriction on the entire power generation unit group, such as the power generation or fuel usage of the entire power generation unit group. Or, the constraints on the power generation unit group may also be constraints on each power generation unit belonging to the power generation unit group. Next, the optimization problem solving unit 1043 will be described. The optimization problem solving unit 1043 solves an optimization problem based on the goal function set by the goal function setting unit 1041 and the restriction conditions set by the restriction condition setting unit 1042. That is, the optimization problem solving unit 1043 is configured to solve the optimization problem of the objective function set by the objective function setting unit 1041 under the constraint conditions set by the constraint condition setting unit 1042. For example, the optimization problem solving unit 1043 solves the optimization problem that minimizes the value of the objective function of formula (1). As a solution method, for example, well-known optimization problems such as the secondary planning method and the linear planning method can be used. In addition, for solving processing, a dedicated program can be used, or a well-known solver can be used. In addition, the optimization problem solving unit 1043 can solve the optimization problem by making the running cost or the calibration cost a specific value, or can solve the optimization problem by minimizing the cost of the calibration. The optimization problem solving unit 1043 draws up a plan for regular inspection (the type of regular inspection, start date and period, etc.) by solving the above optimization problem. In this embodiment, the optimization problem solving unit 1043 solves the optimization problem in such a way that the sum of the operating cost and the regular inspection cost of the plurality of power generation units included in the set U is minimized. In this way, for each of the power generation units included in the set U, the type of regular inspection, the date and period of the start of the regular inspection, etc. are obtained. Figures 13 and 14 are examples of regular inspections prepared by the scheduled inspection plan drawing section 104. Fig. 13 shows the implementation schedule of each inspection type A, B, C for the power generating units with IDs 1 and 2. In Figure 13, the horizontally extending bars indicate regular inspections. The length of the article indicates the period of implementation of the periodic inspection. In addition, the horizontal axis in FIG. 13 represents time, but information such as date can also be displayed near the horizontal axis. Figure 14 shows the schedule of regular inspections of a certain type of regular inspection of a certain power generation unit. In Figure 14, the period of regular inspection means the period from the start date to the end date of the regular inspection. In addition, the periodic inspection interval is the allowable interval mentioned above, which means the maximum period from the end of the previous periodic inspection to the execution of the next periodic inspection. The power generation unit must undergo the next periodic inspection from the end of the periodic inspection to the expiration of the periodic inspection interval. In the embodiment in FIG. 14, from the first time (n = 1) before the start to the end of periodic inspection 2nd (n = 2) during a periodic check to ensure that a given embodiment is not the subject of W u_ k. The period during which regular inspections cannot be performed is the minimum period that determines the implementation interval of regular inspections. In addition, in FIG. 14, time T 1 is the earliest time sequence for the second periodic inspection, and time T 2 is the latest time sequence for the second periodic inspection. Figure 15 shows an example of a regular inspection plan that takes into account the machine materials, parts and labor required for regular inspections. As shown in Figure 15, from the start date to the end date of the regular inspection, the types and quantities of machines and materials required for the regular inspections, the types and quantities of parts, and labor are displayed on each implementation date. <Regular inspection plan preparation processing> With reference to Fig. 12, the routine inspection plan preparation section 104 will explain the processing flow of the scheduled inspection plan preparation processing. In addition, the data required in the following processing flow (ie, regular inspection control data, generator characteristic data, power demand data, and operation control data) are obtained in advance by the input unit 101 and stored in the memory unit 102. First, the objective function setting unit 1041 sets the objective function of the optimization problem (step S101). In this embodiment, the cost function shown in the above formula (1) is formulated as an objective function. Next, the constraint condition setting unit 1042 sets constraint conditions for the optimization problem (step S102). In this embodiment, the above-mentioned formulas (2) to (9) are defined as formulas expressing constraints. In addition, in this step, the restriction condition setting unit 1042 may generate formulas (10) to (15) as needed. For example, if you consider the machinery and materials required for regular inspections, you can generate equations (10) and (11). If you consider the parts required to implement regular inspections, produce equations (12) and (13). If you consider the labor required to implement periodic inspections, produce equations (14) and (15). Next, the optimization problem solving unit 1043 solves the optimization problem of the objective function set in step S101 (step S103) under the constraints set in step S102, thereby drawing up a verification plan. In this embodiment, the optimization problem solving unit 1043 solves the optimization problem that minimizes the value of the objective function of formula (1), and establishes that the total cost such as operating cost and regular inspection cost is the smallest Regular inspection plan. After that, the output unit 103 outputs the planned inspection plan to an external device. As described above, in the first embodiment, the objective function setting unit 1041 sets the objective function indicating the cost associated with the periodic inspection, and the restriction condition setting unit 1042 is based on at least periodic inspection restriction data, generator characteristic data, and power demand data. The restriction conditions are set, and the optimization problem solving unit 1043 solves the optimization problem of the goal function set by the goal function setting unit 1041 under the restriction conditions set by the goal function setting unit 1041. Thus, according to the first embodiment, it is possible to formulate a regular inspection plan that not only considers the generator characteristic data and power demand data, but also the regular inspection restriction data. As a result, a highly accurate and optimized regular inspection plan can be drawn up. In the description of the above embodiment, the scheduled inspection plan for a plurality of power generation units is drawn from the state where there is no scheduled inspection plan at all, but it is not limited to this, the operation plan preparation device 100 can also perform the planned inspection The plan changes, and a new regular inspection plan is drawn up. The operation plan drafting device 100 can also change the regular inspection plan of one or more power generating units (hereinafter, referred to as "designated power generating units") belonging to the set U of the planned inspection plan. As a condition for changing the scheduled inspection plan, for example, consider the situation where the first power generation unit fails and the second power generation unit cannot be regularly inspected to meet the power demand. For example, to formulate a scheduled inspection plan for the designated power generation unit in such a way that the time movement amount of the scheduled inspection plan for the designated power generation unit (ie, the migration amount on the start date of the scheduled inspection implementation) is the smallest. In addition, it is also possible to formulate a scheduled inspection plan for a designated power generation unit with the start date of the scheduled inspection plan for the designated power generation unit being the desired day. In the case of optimizing the time movement amount of the scheduled inspection plan for the specified power generation unit, the objective function becomes a function representing the time movement cost of the scheduled inspection plan. The time movement cost is a cost that changes based on the amount of migration since the scheduled inspection implementation start date originally planned, and the cost includes, for example, the scheduled inspection cost, operating cost, etc. The optimization problem solving unit 1043 solves the optimization problem in a way that the objective function is minimized, that is, in a way that the displacement amount of the planned verification plan is minimized. In this way, it is possible to change the scheduled inspection plan of the designated power generation unit without affecting the overall cost of the planned inspection plan as much as possible. In addition, when changing a part of the planned inspection plan for multiple power generation units, the objective function is not limited to the time movement cost, and can also be a function that represents operating costs, inspection costs and other expenses. For example, the operation plan preparation device 100 (optimization problem solving unit 1043) can also target power generation units other than the designated power generation unit to meet the constraints of the scheduled inspection plan (machinery, parts, labor, etc.), In addition, the total cost required for regular inspections of power generating units is the smallest way to formulate a regular inspection plan for designated power generating units. For example, for the planned inspection plan of 10 power generating units, the planned inspection plan of 2 of the designated power generating units is changed, and the planning device 100 is operated to meet the requirements of the 10 power generating units without changing the inspection plan With the constraints of the regular inspection plan for 8 power generating units, and the total cost required for regular inspection of 10 power generating units is the smallest method, a regular inspection plan for 2 designated power generating units is drawn up. (Second Embodiment) The operation plan preparation device of the second embodiment of the present invention will be described. Fig. 17 is a block diagram showing an example of the schematic configuration of the operation plan preparation system including the operation plan preparation device of the second embodiment. In FIG. 17, the same reference numerals are given to components having the same functions as those in FIG. 1. The difference between the second embodiment and the first embodiment is that the operation plan preparation device of the second embodiment further includes an operation state determination unit and an output power determination unit, and is based on the scheduled inspection prepared by the scheduled inspection plan preparation unit Plan and draft the operation plan of the power generation unit. Before describing the operation plan preparation device of the second embodiment, referring to FIG. 16, the characteristics of the output power of the power generation unit will be described. Figure 16 is a diagram for explaining the characteristics of the output power of the power generating unit. The power generation unit is stopped and started according to the proposed operation plan. It takes time from receiving the stop instruction to the actual stop of the power generation unit. Similarly, it takes time from receiving the start instruction until the output of the power generating unit actually reaches a specific value. The actual disconnection of the power generation unit that received the stop instruction from the power system is called "disconnecting the parallel connection". In addition, the connection of the power generation unit to the power system by receiving the start instruction is referred to as "parallel connection". The time to release the parallel connection to the parallel connection is called the "stop period" of the generating unit. The state during the stop of the power generation unit is called the "stop state", and the operating state other than the stopped state is called the "start state". In this way, in this embodiment, there are two types of operating states of the power generating unit: a start state and a stop state. As shown in Fig. 16, when a stop instruction is received, the output power of the power generation unit continues to decrease from a certain output value to zero. The part where the output level drops is called the stop curve. In addition, after parallel connection, the output power of the power generation unit gradually rises and reaches a certain output value. The part where the output level rises is called the startup curve. <Operation plan preparation device 100A> Next, the operation plan preparation device 100A of the second embodiment will be described. As shown in FIG. 17, the operation plan preparation device 100A includes an input unit 101, a storage unit 102, an output unit 103, a regular inspection plan preparation unit 104, an operation state determination unit 105, and an output power determination unit 106. Since the components other than the operating state determination unit 105 and the output power determination unit 106 are the same as in the first embodiment, detailed descriptions are omitted. The operation state determination unit 105 performs an operation state determination process for determining the operation state (start state or stop state) of the power generation unit. The operation state determination unit 105 determines the operation state of the power generation unit based on the generator characteristic data, operation restriction data, power demand data, and the periodic inspection plan drawn up by the periodic inspection plan preparation unit 104. Specifically, the operating state determination unit 105 determines the timing of canceling the parallel connection and the parallel connection by solving the optimization problem based on the constraint condition and the objective function. And, based on the determined time sequence of the de-parallel and parallel connection of the generating unit, the operating state of the generating unit is determined. The output power determination unit 106 performs output power determination processing for determining the value of the output power per unit period of the power generation unit. Here, the "unit period" refers to the minimum period when the planning period of the operation plan is divided into a plurality of periods. The unit period is also called a grid. The output power determination unit 106 determines the output power of the power generation unit based on generator characteristic data, operation restriction data, power demand data, and the operating state of the power generation unit determined by the operation state determination unit 105. Specifically, the output power determination unit 106 determines the output power of each power generation unit by solving the optimization problem based on the constraint conditions and the objective function. Next, referring to FIG. 18, the processing flow of the operation state determination unit 105 of the operation plan preparation device 100A will be described. The operating state determination unit 105 obtains necessary data from the storage unit 102 (S201). In more detail, in addition to generator characteristic data, power demand data, and operation restriction data, the operating state determination unit 105 also obtains data from the memory unit 102 that displays the scheduled inspection plan prepared by the scheduled inspection plan preparation unit 104. After obtaining the necessary data, the operating state determination unit 105 executes the objective function setting process (S202). In this objective function setting process, the objective function of the optimization problem is formulated in the same way as the process of the objective function setting unit 1041 described in the first embodiment. A well-known method may be used for the purpose function setting process, and the purpose function can be determined arbitrarily. For example, it is possible to minimize the operating cost of one power generating unit or the operating cost of a power generating unit group including a plurality of power generating units. Or it can be the goal to make the operating cost close to an arbitrarily determined target value. After the objective function setting processing is performed, the operating state determination unit 105 executes the restriction setting processing (S203). In the constraint condition setting process, the constraint condition of the optimization problem is formulated in the same manner as the process of the constraint condition setting unit 1042 described in the first embodiment. The restriction condition setting processing can use well-known methods. The proposed restriction condition can be a restriction condition for one power generation unit or a restriction condition for a group of power generation units. In addition, the restriction on the power generation unit group may be a restriction on the entire power generation unit group such as the power generation or fuel usage of the entire power generation unit group, or may be a restriction on each power generation unit belonging to the power generation unit group. Next, the operating state determination unit 105 solves the optimization problem based on the objective function set in the objective function setting process of step S202 and the constraint condition set in the constraint condition setting process of step S203. As a solution method, similar to the processing of the optimization problem solving unit 1043 described in the first embodiment, well-known optimization problem solving methods such as the secondary planning method and the linear planning method can be used. In addition, for solving processing, a dedicated program can be used, or a well-known solver can be used. According to the above-mentioned processing flow, the sequence of disconnecting and paralleling the power generation unit is determined. In addition, the operating state determining unit 105 draws up information that displays the operating state of the power generating unit every hour based on the calculated time sequence for canceling the parallel connection and the parallel connection. For example, it is planned to display the information of the operation status according to the determined multiple intervals during the planning period. Next, referring to FIG. 19, the processing flow of the output power determination unit 106 of the operation plan preparation device 100A will be described. The output power determination unit 106 obtains necessary data from the memory unit 102 (S301). In more detail, in addition to generator characteristic data, power demand data, and operation restriction data, the output power determining unit 106 also obtains data representing the operating status of each power generation unit determined by the operating status determining unit 105 from the memory unit 102. In addition, the data indicating the operating state of the power generation unit determined by the operating state determining unit 105 is different from the operating restriction data obtained in this step, but it is not limited to this, and may be included in the operating restriction data. Next, the output power determination unit 106 executes the purpose function setting process (S302). In this objective function setting process, the objective function of the optimization problem is formulated in the same manner as the process of the operating state determination unit 105. A well-known method may be used for the purpose function setting process, and the purpose function can be determined arbitrarily. Next, the output power determination unit 106 executes restriction condition setting processing (S303). In the constraint condition setting process, similarly to the process of the operating state determination unit 105, the constraint condition of the optimization problem is formulated. The restriction condition setting processing can use well-known methods. The proposed restriction condition can be a restriction condition for one power generation unit or a restriction condition for a group of power generation units. In addition, the restriction on the power generation unit group may be a restriction on the entire power generation unit group such as the power generation or fuel usage of the entire power generation unit group, or may be a restriction on each power generation unit belonging to the power generation unit group. Next, the output power determination unit 106 solves the optimization problem based on the objective function set in the objective function setting process of step S302 and the operation restriction set in the restriction condition setting process of step S303. As a solution method, similarly to the processing of the operating state determination unit 105, well-known optimization problem solving methods such as the secondary planning method and the linear planning method can be used. In addition, for solving processing, a dedicated program can be used, or a well-known solver can be used. According to the above processing flow, determine the output power of each power generation unit during the project period. As described above, in the second embodiment, the operation state determination unit 105 determines the operation state of each power generation unit based on the scheduled inspection plan drawn up by the inspection plan preparation unit 104. In addition, the output power determining unit 106 determines the output power of each power generating unit based on the operating state of each power generating unit determined by the operating state determining unit 105. As a result, according to the second embodiment, an operation plan that takes into account the regular inspection plan of the power generation unit can be drawn up. Therefore, for example, it is possible to formulate an operation plan to maximize the profit of the power supplier. In addition, the processing flow described in the above embodiment is only an example, as long as the required processing result can be obtained, other processing can be performed, or the processing order can be changed. In addition, the output method of the processing result is not particularly limited. For example, for each of the periodic inspection plan preparation unit 104, the operating state determination unit 105, and the output power determination unit 106, the output unit 103 may output the processing results one by one after completing each processing. That is, the output unit 103 can transmit the processing result to an external device (the input/output device 200, etc.) every time it receives a processing result from each component. Alternatively, the processing results of each constituent element may be sequentially stored in the storage unit 102. Moreover, the output unit 103 may also refer to the memory unit 102 to obtain the requested information when the input unit 101 receives a request from an external device. The above-mentioned embodiment is only an example, and part of the constituent elements of the embodiment may be located in an external device. For example, the operation plan preparation device 100A of the second embodiment has the scheduled inspection plan preparation unit 104, but the scheduled inspection plan preparation unit 104 may be provided in an external device. In this case, the input unit 101 obtains the scheduled inspection plan from the external device having the scheduled inspection plan preparation unit 104 and sends it to the operating state determination unit 105. In addition, it can also be assumed that the constituent elements of the operation plan preparation device are installed in different devices. For example, the first device having the calibration plan preparation unit 104, the second device having the operating state determining unit 105, and the third device having the output power determining unit 106 may be communicably connected to each other and connected to the operation plan. The drawing drawing device 100A also functions. The processing of the first and second embodiments described above can be realized by software (program). Therefore, each of the above-mentioned embodiments can be realized by using a general-purpose computer device as the basic hardware, and allowing a processor such as a central processing unit (CPU: Central Processing Unit) installed in the computer device to execute a program. FIG. 20 is a block diagram showing an example of the hardware configuration of the operation plan preparation device 100, 100A. The operation plan preparation device 100, 100A is equipped with a processor 701, a main memory device 702, an auxiliary memory device 703, a network interface 704, and a device interface 705, and can be implemented as a computer device 700 connected to these via a bus 706 . In addition, the operation plan preparation device 100, 100A may be provided with a general-purpose input device and output device as the input and output device 200. The operation plan preparation device 100, 100A can be realized by pre-installing the program executed by each device in the computer device 700, or by storing the program in a CD-ROM (Compact Disc-Read Only Memory) Such a storage medium, or distribute via the Internet, and install it in the computer device 700 appropriately. In addition, in FIG. 20, the computer device 700 includes one of each component, but it may include a plurality of the same components. In addition, in FIG. 20, one computer device 700 is shown, but the software can be installed on a plurality of computer devices. It is also possible to generate processing results by executing the processing of different parts of the software by each of the plurality of computer devices. That is, the operation plan preparation apparatuses 100 and 100A may be configured as a system. The processor 701 is an electronic circuit including a computer control device and a computing device. The processor 701 performs arithmetic processing based on the data and programs input from each device and the like of the internal structure of the computer device 700, and outputs the calculation result and control signal to each device and the like. Specifically, the processor 701 executes the OS (Operating System) of the computer device 700, application programs, etc., and controls each device that constitutes the computer device 700. The processor 701 is not particularly limited as long as it can perform the aforementioned processing. The processor 701 may be, for example, a general purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP: Digital Signal Processor), a controller, a microcontroller, an operating state machine, etc. In addition, the processor 701 may also be an integrated circuit, a field programmable gate array (FPGA: Field Programmable Gate Array), a programmable logic circuit (PLD: Programmable logic device), etc. for specific purposes. In addition, the processor 701 may be composed of a plurality of processing devices. For example, it can be a combination of a DSP and a microprocessor, or more than one microprocessor cooperating with the DSP core. The main memory device 702 is a memory device that stores commands and various data executed by the processor 701, and the information stored in the main memory device 702 is directly read by the processor 701. The auxiliary memory device 703 is a memory device other than the main memory device 702. In addition, the memory device refers to any electronic component that can store electronic information. As the main memory device 702, RAM (Random Access Memory: random access memory), DRAM (Dynamic Random Access Memory: dynamic random access memory), SRAM (Static Random Access Memory: static random access memory) are mainly used Such as volatile memories used to temporarily store information, but in the embodiment of the present invention, the main memory device 702 is not limited to these volatile memories. The memory devices used as the main memory device 702 and the auxiliary memory device 703 may be volatile memory or non-volatile memory. Non-volatile memory includes programmable read-only memory (PROM: Programmable Read Only Memory), erasable programmable read-only memory (EPROM: Electrically Programmable Read Only Memory), and electronically erasable PROM (EEPROM: Electronically Erasable and Programmable Read Only Memory), non-volatile random access memory (NVRAM: Non-Volatile Random Access Memory), flash memory, MRAM (Magnetoresistive Random Access Memory: magnetoresistive random access memory), etc. . In addition, as the auxiliary memory device 703, a magnetic or optical data storage device can also be used. As the data storage, it is possible to use disks such as hard disks, optical disks such as DVD (Digital Versatile Disk), flash memory such as USB (Universal Serial Bus) memory, and tapes, etc. . In addition, if the processor 701 directly or indirectly reads and writes information to the main memory device 702 or the auxiliary memory device 703, or performs both, it can be said that the memory device and the processor are in electrical communication. In addition, the main memory device 702 can also be integrated into the processor. In this case, it can also be said that the main memory device 702 is in electrical communication with the processor. The network interface 704 is an interface for connecting to the communication network 800 by wireless or wired. The network interface 704 can be adapted to the existing communication specifications. The network interface 704 can also be used to send the output result to an external device 900 that is communicatively connected via the communication network 800. The external device 900 may be an external storage medium, a display device, or a storage such as a database. The device interface 705 is an interface connected to an external storage medium such as USB memory for recording output results. The external storage medium can be HDD (Hard Disk Drive: Hard Disk Drive), CD-R (Compact Disk-Recordable: Recordable Disc), CD-RW (Compact Disk-Rewritable: Rewritable Disc), DVD- RAM (Digital Versatile Disk-Random Access Memory: Digital Versatile Disk-Random Access Memory), DVD-R (Digital Versatile Disc-Recordable: Recordable Digital Versatile Disc), BD-ROM (Blu-ray Disc- Random Access Memory: Blu-ray Disc-Random Access Memory), BD-R (Blu-ray Disc-Recordable: Recordable Blu-ray Disc), BD-RE (Blu-ray Disc-Rewritable: Rewritable Blu-ray Disc) ､SAN (Storage area network: storage area network), DAT (Digital Audio Tape: digital audio tape) and other arbitrary recording media. It can also be connected to a storage device via the device interface 705. In addition, part or all of the computer device 700, that is, part or all of the operation plan drawing devices 100, 100A, may also be composed of dedicated electronic circuits (ie, hardware) such as semiconductor integrated circuits with the processor 701 and the like installed. Dedicated hardware can also be combined with memory devices such as RAM and ROM (Read Only Memory). In addition, one computer device is shown in FIG. 20, but the software can also be installed on multiple computer devices. It is also possible to calculate the processing result by executing the processing of different parts of the software by each of the plurality of computer devices. Although specific embodiments have been described above, these embodiments are only examples and are not intended to limit the scope of the present invention. In fact, the novel methods and systems described above can be embodied in various other forms, and as long as they do not deviate from the spirit of the present invention, the forms of the methods and systems described above may be omitted, substituted, and changed. These forms or modifications are deemed to belong to the scope of the present invention, and are included in the scope of the following patent applications and their equivalents.